diff --git a/models/BitcoinPricePrediction/Bitcoin Price Prediction LSTM-checkpoint.ipynb b/models/BitcoinPricePrediction/Bitcoin Price Prediction LSTM-checkpoint.ipynb
new file mode 100644
index 00000000..864a471d
--- /dev/null
+++ b/models/BitcoinPricePrediction/Bitcoin Price Prediction LSTM-checkpoint.ipynb	
@@ -0,0 +1,816 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5c848f3c",
+   "metadata": {},
+   "source": [
+    "# Implementation of LSTM on Bitcoin Dataset "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "02a26815",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, LSTM, Dropout\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error,r2_score\n",
+    "import warnings\n",
+    "import seaborn as sns\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "414d0275",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0ca0fe1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the data\n",
+    "BTC = pd.read_csv(\"BTC-USD.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "485c0c74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(BTC.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bb6dd29f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert the 'Date' column to datetime and set it as the index\n",
+    "BTC['Date'] = pd.to_datetime(BTC['Date'])\n",
+    "BTC.set_index('Date', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "917dccb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-09-17</th>\n",
+       "      <td>465.864014</td>\n",
+       "      <td>468.174011</td>\n",
+       "      <td>452.421997</td>\n",
+       "      <td>457.334015</td>\n",
+       "      <td>457.334015</td>\n",
+       "      <td>21056800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-18</th>\n",
+       "      <td>456.859985</td>\n",
+       "      <td>456.859985</td>\n",
+       "      <td>413.104004</td>\n",
+       "      <td>424.440002</td>\n",
+       "      <td>424.440002</td>\n",
+       "      <td>34483200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-19</th>\n",
+       "      <td>424.102997</td>\n",
+       "      <td>427.834991</td>\n",
+       "      <td>384.532013</td>\n",
+       "      <td>394.795990</td>\n",
+       "      <td>394.795990</td>\n",
+       "      <td>37919700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-20</th>\n",
+       "      <td>394.673004</td>\n",
+       "      <td>423.295990</td>\n",
+       "      <td>389.882996</td>\n",
+       "      <td>408.903992</td>\n",
+       "      <td>408.903992</td>\n",
+       "      <td>36863600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-21</th>\n",
+       "      <td>408.084991</td>\n",
+       "      <td>412.425995</td>\n",
+       "      <td>393.181000</td>\n",
+       "      <td>398.821014</td>\n",
+       "      <td>398.821014</td>\n",
+       "      <td>26580100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Open        High         Low       Close   Adj Close  \\\n",
+       "Date                                                                     \n",
+       "2014-09-17  465.864014  468.174011  452.421997  457.334015  457.334015   \n",
+       "2014-09-18  456.859985  456.859985  413.104004  424.440002  424.440002   \n",
+       "2014-09-19  424.102997  427.834991  384.532013  394.795990  394.795990   \n",
+       "2014-09-20  394.673004  423.295990  389.882996  408.903992  408.903992   \n",
+       "2014-09-21  408.084991  412.425995  393.181000  398.821014  398.821014   \n",
+       "\n",
+       "              Volume  \n",
+       "Date                  \n",
+       "2014-09-17  21056800  \n",
+       "2014-09-18  34483200  \n",
+       "2014-09-19  37919700  \n",
+       "2014-09-20  36863600  \n",
+       "2014-09-21  26580100  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "BTC.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8c36ea2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Open         0\n",
+      "High         0\n",
+      "Low          0\n",
+      "Close        0\n",
+      "Adj Close    0\n",
+      "Volume       0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Checking null value\n",
+    "print(BTC.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "04975018",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the closing prices\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(BTC['Close'], label='BTC Close')\n",
+    "plt.title('BTC Closing Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Closing Price')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "305deb21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Time Series Scatter Plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.scatterplot(x=BTC.index, y=nifty_50_df['Close'])\n",
+    "plt.title('Time Series Scatter Plot of BTC Closing Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Closing Price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3c9100e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare the data for modeling\n",
+    "data = BTC[['Close']].values\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "scaled_data = scaler.fit_transform(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4f5237f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "train_size = int(len(scaled_data) * 0.7)\n",
+    "train_data, test_data = scaled_data[:train_size], scaled_data[train_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0d58b738",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a function to create datasets for training and testing\n",
+    "def create_dataset(dataset, time_step):\n",
+    "    X, Y = [], []\n",
+    "    for i in range(len(dataset) - time_step - 1):\n",
+    "        a = dataset[i:(i + time_step), 0]\n",
+    "        X.append(a)\n",
+    "        Y.append(dataset[i + time_step, 0])\n",
+    "    return np.array(X), np.array(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "878eb0b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the training and testing datasets\n",
+    "time_step = 50\n",
+    "X_train, y_train = create_dataset(train_data, time_step)\n",
+    "X_test, y_test = create_dataset(test_data, time_step)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "54590d29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reshape the data for GRU layers\n",
+    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
+    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0eef6959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the GRU model\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(200, return_sequences=True, input_shape=(time_step, 1)))\n",
+    "model.add(Dropout(0.4))\n",
+    "model.add(LSTM(160, return_sequences=False))\n",
+    "model.add(Dense(50))\n",
+    "model.add(Dense(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9f2baf93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=[tf.keras.metrics.MeanAbsolutePercentageError()])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "840e276b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 75ms/step - loss: 9.3456e-04 - mean_absolute_percentage_error: 6334.2710 - val_loss: 0.0029 - val_mean_absolute_percentage_error: 7.5628\n",
+      "Epoch 2/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 1.0591e-04 - mean_absolute_percentage_error: 1018.6110 - val_loss: 0.0030 - val_mean_absolute_percentage_error: 7.6941\n",
+      "Epoch 3/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 70ms/step - loss: 1.1633e-04 - mean_absolute_percentage_error: 7816.4395 - val_loss: 0.0024 - val_mean_absolute_percentage_error: 7.0491\n",
+      "Epoch 4/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 8.3075e-05 - mean_absolute_percentage_error: 504.8027 - val_loss: 0.0014 - val_mean_absolute_percentage_error: 5.2843\n",
+      "Epoch 5/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 70ms/step - loss: 6.1255e-05 - mean_absolute_percentage_error: 8174.3931 - val_loss: 0.0013 - val_mean_absolute_percentage_error: 5.4285\n",
+      "Epoch 6/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 74ms/step - loss: 6.9984e-05 - mean_absolute_percentage_error: 99.3050 - val_loss: 0.0025 - val_mean_absolute_percentage_error: 6.8182\n",
+      "Epoch 7/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 5.6695e-05 - mean_absolute_percentage_error: 1054.3582 - val_loss: 0.0010 - val_mean_absolute_percentage_error: 4.5428\n",
+      "Epoch 8/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 5.8794e-05 - mean_absolute_percentage_error: 187.0351 - val_loss: 0.0025 - val_mean_absolute_percentage_error: 6.4947\n",
+      "Epoch 9/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 74ms/step - loss: 5.5804e-05 - mean_absolute_percentage_error: 1814.9207 - val_loss: 0.0040 - val_mean_absolute_percentage_error: 8.9693\n",
+      "Epoch 10/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 73ms/step - loss: 6.5772e-05 - mean_absolute_percentage_error: 4588.8706 - val_loss: 0.0029 - val_mean_absolute_percentage_error: 7.1847\n",
+      "Epoch 11/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 74ms/step - loss: 4.9648e-05 - mean_absolute_percentage_error: 674.5043 - val_loss: 7.7237e-04 - val_mean_absolute_percentage_error: 4.6697\n",
+      "Epoch 12/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 78ms/step - loss: 4.9525e-05 - mean_absolute_percentage_error: 1800.6107 - val_loss: 0.0041 - val_mean_absolute_percentage_error: 8.4830\n",
+      "Epoch 13/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 74ms/step - loss: 6.5563e-05 - mean_absolute_percentage_error: 4040.1660 - val_loss: 0.0047 - val_mean_absolute_percentage_error: 10.6343\n",
+      "Epoch 14/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 71ms/step - loss: 7.2244e-05 - mean_absolute_percentage_error: 758.6786 - val_loss: 0.0031 - val_mean_absolute_percentage_error: 7.8176\n",
+      "Epoch 15/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 7.4023e-05 - mean_absolute_percentage_error: 1087.8573 - val_loss: 0.0022 - val_mean_absolute_percentage_error: 5.9852\n",
+      "Epoch 16/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 3.9633e-05 - mean_absolute_percentage_error: 2644.4453 - val_loss: 0.0035 - val_mean_absolute_percentage_error: 8.2766\n",
+      "Epoch 17/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 4.6292e-05 - mean_absolute_percentage_error: 297.4688 - val_loss: 0.0022 - val_mean_absolute_percentage_error: 5.7070\n",
+      "Epoch 18/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 4.2596e-05 - mean_absolute_percentage_error: 764.3101 - val_loss: 9.8909e-04 - val_mean_absolute_percentage_error: 4.9153\n",
+      "Epoch 19/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 4.9494e-05 - mean_absolute_percentage_error: 7113.5874 - val_loss: 0.0012 - val_mean_absolute_percentage_error: 4.6490\n",
+      "Epoch 20/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 5.1483e-05 - mean_absolute_percentage_error: 248.3509 - val_loss: 0.0016 - val_mean_absolute_percentage_error: 4.8916\n",
+      "Epoch 21/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 5.4170e-05 - mean_absolute_percentage_error: 2772.0061 - val_loss: 0.0055 - val_mean_absolute_percentage_error: 11.0825\n",
+      "Epoch 22/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 8.3230e-05 - mean_absolute_percentage_error: 1567.4910 - val_loss: 0.0044 - val_mean_absolute_percentage_error: 8.1850\n",
+      "Epoch 23/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 5.4889e-05 - mean_absolute_percentage_error: 5557.3311 - val_loss: 7.5806e-04 - val_mean_absolute_percentage_error: 4.9037\n",
+      "Epoch 24/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 5.3994e-05 - mean_absolute_percentage_error: 988.3615 - val_loss: 7.0361e-04 - val_mean_absolute_percentage_error: 4.0240\n",
+      "Epoch 25/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 3.7349e-05 - mean_absolute_percentage_error: 292.9031 - val_loss: 0.0011 - val_mean_absolute_percentage_error: 4.1635\n",
+      "Epoch 26/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 6.6912e-05 - mean_absolute_percentage_error: 614.2693 - val_loss: 0.0016 - val_mean_absolute_percentage_error: 4.9561\n",
+      "Epoch 27/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 5.3310e-05 - mean_absolute_percentage_error: 382.2147 - val_loss: 6.1011e-04 - val_mean_absolute_percentage_error: 4.9062\n",
+      "Epoch 28/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 4.4019e-05 - mean_absolute_percentage_error: 1867.5616 - val_loss: 0.0015 - val_mean_absolute_percentage_error: 4.7577\n",
+      "Epoch 29/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 4.7360e-05 - mean_absolute_percentage_error: 2654.7888 - val_loss: 0.0012 - val_mean_absolute_percentage_error: 4.4042\n",
+      "Epoch 30/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 5.0328e-05 - mean_absolute_percentage_error: 10450.4170 - val_loss: 0.0032 - val_mean_absolute_percentage_error: 6.9876\n",
+      "Epoch 31/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 4.0789e-05 - mean_absolute_percentage_error: 1524.9989 - val_loss: 0.0026 - val_mean_absolute_percentage_error: 6.0299\n",
+      "Epoch 32/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 3.6128e-05 - mean_absolute_percentage_error: 95.4812 - val_loss: 0.0044 - val_mean_absolute_percentage_error: 8.5315\n",
+      "Epoch 33/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 3.5721e-05 - mean_absolute_percentage_error: 2680.2915 - val_loss: 0.0019 - val_mean_absolute_percentage_error: 5.2643\n",
+      "Epoch 34/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 4.8169e-05 - mean_absolute_percentage_error: 1601.8258 - val_loss: 0.0024 - val_mean_absolute_percentage_error: 6.3191\n",
+      "Epoch 35/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 4.1726e-05 - mean_absolute_percentage_error: 357.6598 - val_loss: 0.0039 - val_mean_absolute_percentage_error: 8.9009\n",
+      "Epoch 36/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 72ms/step - loss: 4.9945e-05 - mean_absolute_percentage_error: 1982.7183 - val_loss: 0.0031 - val_mean_absolute_percentage_error: 7.0954\n",
+      "Epoch 37/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 71ms/step - loss: 4.3548e-05 - mean_absolute_percentage_error: 3072.9231 - val_loss: 0.0029 - val_mean_absolute_percentage_error: 6.7503\n",
+      "Epoch 38/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 74ms/step - loss: 4.1372e-05 - mean_absolute_percentage_error: 369.3521 - val_loss: 0.0016 - val_mean_absolute_percentage_error: 4.8071\n",
+      "Epoch 39/50\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 71ms/step - loss: 3.9119e-05 - mean_absolute_percentage_error: 2531.5281 - val_loss: 8.4789e-04 - val_mean_absolute_percentage_error: 4.1872\n",
+      "Epoch 40/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 70ms/step - loss: 4.1736e-05 - mean_absolute_percentage_error: 186.6775 - val_loss: 0.0013 - val_mean_absolute_percentage_error: 4.3535\n",
+      "Epoch 41/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 4.0967e-05 - mean_absolute_percentage_error: 3119.4128 - val_loss: 0.0011 - val_mean_absolute_percentage_error: 4.1578\n",
+      "Epoch 42/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 4.3203e-05 - mean_absolute_percentage_error: 286.2504 - val_loss: 0.0013 - val_mean_absolute_percentage_error: 4.9916\n",
+      "Epoch 43/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 4.9291e-05 - mean_absolute_percentage_error: 880.4064 - val_loss: 0.0017 - val_mean_absolute_percentage_error: 4.7630\n",
+      "Epoch 44/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 3.2202e-05 - mean_absolute_percentage_error: 149.4473 - val_loss: 0.0042 - val_mean_absolute_percentage_error: 8.9403\n",
+      "Epoch 45/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 4.3330e-05 - mean_absolute_percentage_error: 1935.5453 - val_loss: 0.0029 - val_mean_absolute_percentage_error: 6.2180\n",
+      "Epoch 46/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 5.0304e-05 - mean_absolute_percentage_error: 1176.3553 - val_loss: 0.0036 - val_mean_absolute_percentage_error: 7.7830\n",
+      "Epoch 47/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 5.1335e-05 - mean_absolute_percentage_error: 967.2071 - val_loss: 0.0012 - val_mean_absolute_percentage_error: 4.3959\n",
+      "Epoch 48/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 5.0895e-05 - mean_absolute_percentage_error: 887.9686 - val_loss: 0.0037 - val_mean_absolute_percentage_error: 7.0067\n",
+      "Epoch 49/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 4.0970e-05 - mean_absolute_percentage_error: 256.8599 - val_loss: 0.0017 - val_mean_absolute_percentage_error: 4.8695\n",
+      "Epoch 50/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 70ms/step - loss: 4.2376e-05 - mean_absolute_percentage_error: 977.0376 - val_loss: 0.0040 - val_mean_absolute_percentage_error: 8.2062\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "history = model.fit(X_train, y_train, batch_size=32, epochs=50, validation_data=(X_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "99bc5a47",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step\n",
+      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions\n",
+    "train_predict = model.predict(X_train)\n",
+    "test_predict = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "17402eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inverse transform the predictions\n",
+    "train_predict = scaler.inverse_transform(train_predict)\n",
+    "test_predict = scaler.inverse_transform(test_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "be3b26cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inverse transform the original values\n",
+    "original_y_train = scaler.inverse_transform([y_train])\n",
+    "original_y_test = scaler.inverse_transform([y_test])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7b02d176",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create plots for the predicted values\n",
+    "train_predict_plot = np.empty_like(scaled_data)\n",
+    "train_predict_plot[:, :] = np.nan\n",
+    "train_predict_plot[time_step:len(train_predict) + time_step, :] = train_predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "04edb8f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_predict_plot = np.empty_like(scaled_data)\n",
+    "test_predict_plot[:, :] = np.nan\n",
+    "test_predict_plot[len(train_predict) + (time_step * 2) + 1:len(scaled_data) - 1, :] = test_predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c16c4bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(scaler.inverse_transform(scaled_data), label='Actual')\n",
+    "plt.plot(train_predict_plot, label='Train Predict')\n",
+    "plt.plot(test_predict_plot, label='Test Predict')\n",
+    "plt.title('Actual vs Predicted Values')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Closing Price')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "40ce439f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the model loss and MAPE over epochs\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Plot Loss\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(history.history['loss'], label='Training Loss')\n",
+    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+    "plt.title('Model Loss (MSE) Over Epochs')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Loss (MSE)')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Plot MAPE\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(history.history['mean_absolute_percentage_error'], label='Training MAPE')\n",
+    "plt.plot(history.history['val_mean_absolute_percentage_error'], label='Validation MAPE')\n",
+    "plt.title('Model Accuracy (MAPE) Over Epochs')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('MAPE (%)')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "4345590a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot true vs predicted residuals\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(scaler.inverse_transform(scaled_data), label=\"True\")\n",
+    "plt.plot(test_predict_plot, label=\"Test Predicted\")\n",
+    "plt.title(\"True vs Predicted BTC   Close Prices\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "20c82c28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train R²: 0.9908917580432313\n",
+      "Test R²: 0.9505946512099303\n",
+      "Training RMSE:  379.70474936296483\n",
+      "Testing RMSE:  4269.376224219396\n",
+      "Training MAE:  209.93181744027748\n",
+      "Testing MAE:  3096.8610011811516\n",
+      "Training MSE:  144175.69668879194\n",
+      "Testing MSE:  18227573.34392987\n",
+      "Training MATE:  80.32775864843757\n",
+      "Testing MATE:  2231.859374874999\n",
+      "Training SMATE:  0.10018216309141781\n",
+      "Testing SMATE:  0.13996355131035762\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate R² score, rmse, mae, mse, mate, smate for training and testing sets\n",
+    "train_r2 = r2_score(original_y_train[0], train_predict[:, 0])\n",
+    "test_r2 = r2_score(original_y_test[0], test_predict[:, 0])\n",
+    "\n",
+    "train_rmse = np.sqrt(mean_squared_error(original_y_train[0], train_predict[:, 0]))\n",
+    "test_rmse = np.sqrt(mean_squared_error(original_y_test[0], test_predict[:, 0]))\n",
+    "\n",
+    "train_mae = mean_absolute_error(original_y_train[0], train_predict[:, 0])\n",
+    "test_mae = mean_absolute_error(original_y_test[0], test_predict[:, 0])\n",
+    "\n",
+    "train_mse = mean_squared_error(original_y_train[0], train_predict[:, 0])\n",
+    "test_mse = mean_squared_error(original_y_test[0], test_predict[:, 0])\n",
+    "\n",
+    "train_mate = median_absolute_error(original_y_train[0], train_predict[:, 0])\n",
+    "test_mate = median_absolute_error(original_y_test[0], test_predict[:, 0])\n",
+    "\n",
+    "train_smate = np.sqrt(mean_squared_error(original_y_train[0], train_predict[:, 0])) / np.mean(original_y_train)\n",
+    "test_smate = np.sqrt(mean_squared_error(original_y_test[0], test_predict[:, 0])) / np.mean(original_y_test)\n",
+    "\n",
+    "print(f'Train R²: {train_r2}')\n",
+    "print(f'Test R²: {test_r2}')\n",
+    "\n",
+    "print(\"Training RMSE: \", train_rmse)\n",
+    "print(\"Testing RMSE: \", test_rmse)\n",
+    "\n",
+    "print(\"Training MAE: \", train_mae)\n",
+    "print(\"Testing MAE: \", test_mae)\n",
+    "\n",
+    "print(\"Training MSE: \", train_mse)\n",
+    "print(\"Testing MSE: \", test_mse)\n",
+    "\n",
+    "print(\"Training MATE: \", train_mate)\n",
+    "print(\"Testing MATE: \", test_mate)\n",
+    "\n",
+    "print(\"Training SMATE: \", train_smate)\n",
+    "print(\"Testing SMATE: \", test_smate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "226800da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+----------+-------------+----------------+\n",
+      "| Metric   |    Training |        Testing |\n",
+      "+==========+=============+================+\n",
+      "| R² Score |      0.9909 |    0.9506      |\n",
+      "+----------+-------------+----------------+\n",
+      "| RMSE     |    379.705  | 4269.38        |\n",
+      "+----------+-------------+----------------+\n",
+      "| MSE      | 144176      |    1.82276e+07 |\n",
+      "+----------+-------------+----------------+\n",
+      "| MAE      |    209.932  | 3096.86        |\n",
+      "+----------+-------------+----------------+\n",
+      "| MATE     |     80.3278 | 2231.86        |\n",
+      "+----------+-------------+----------------+\n",
+      "| SMATE    |      0.1002 |    0.14        |\n",
+      "+----------+-------------+----------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tabulate import tabulate\n",
+    "import numpy as np\n",
+    "# Create a table\n",
+    "table = [\n",
+    "    [\"Metric\", \"Training\", \"Testing\"],\n",
+    "    [\"R² Score\", f\"{train_r2:.4f}\", f\"{test_r2:.4f}\"],\n",
+    "    [\"RMSE\", f\"{train_rmse:.4f}\", f\"{test_rmse:.4f}\"],\n",
+    "    [\"MSE\", f\"{train_mse:.4f}\", f\"{test_mse:.4f}\"],\n",
+    "    [\"MAE\", f\"{train_mae:.4f}\", f\"{test_mae:.4f}\"],\n",
+    "    [\"MATE\", f\"{train_mate:.4f}\", f\"{test_mate:.4f}\"],\n",
+    "    [\"SMATE\", f\"{train_smate:.4f}\", f\"{test_smate:.4f}\"]\n",
+    "]\n",
+    "\n",
+    "print(tabulate(table, headers=\"firstrow\", tablefmt=\"grid\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45ae943f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f734340",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/models/BitcoinPricePrediction/Bitcoin Price Prediction LSTM.ipynb b/models/BitcoinPricePrediction/Bitcoin Price Prediction LSTM.ipynb
new file mode 100644
index 00000000..864a471d
--- /dev/null
+++ b/models/BitcoinPricePrediction/Bitcoin Price Prediction LSTM.ipynb	
@@ -0,0 +1,816 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5c848f3c",
+   "metadata": {},
+   "source": [
+    "# Implementation of LSTM on Bitcoin Dataset "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "02a26815",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, LSTM, Dropout\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error,r2_score\n",
+    "import warnings\n",
+    "import seaborn as sns\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "414d0275",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0ca0fe1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the data\n",
+    "BTC = pd.read_csv(\"BTC-USD.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "485c0c74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(BTC.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bb6dd29f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert the 'Date' column to datetime and set it as the index\n",
+    "BTC['Date'] = pd.to_datetime(BTC['Date'])\n",
+    "BTC.set_index('Date', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "917dccb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-09-17</th>\n",
+       "      <td>465.864014</td>\n",
+       "      <td>468.174011</td>\n",
+       "      <td>452.421997</td>\n",
+       "      <td>457.334015</td>\n",
+       "      <td>457.334015</td>\n",
+       "      <td>21056800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-18</th>\n",
+       "      <td>456.859985</td>\n",
+       "      <td>456.859985</td>\n",
+       "      <td>413.104004</td>\n",
+       "      <td>424.440002</td>\n",
+       "      <td>424.440002</td>\n",
+       "      <td>34483200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-19</th>\n",
+       "      <td>424.102997</td>\n",
+       "      <td>427.834991</td>\n",
+       "      <td>384.532013</td>\n",
+       "      <td>394.795990</td>\n",
+       "      <td>394.795990</td>\n",
+       "      <td>37919700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-20</th>\n",
+       "      <td>394.673004</td>\n",
+       "      <td>423.295990</td>\n",
+       "      <td>389.882996</td>\n",
+       "      <td>408.903992</td>\n",
+       "      <td>408.903992</td>\n",
+       "      <td>36863600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-21</th>\n",
+       "      <td>408.084991</td>\n",
+       "      <td>412.425995</td>\n",
+       "      <td>393.181000</td>\n",
+       "      <td>398.821014</td>\n",
+       "      <td>398.821014</td>\n",
+       "      <td>26580100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Open        High         Low       Close   Adj Close  \\\n",
+       "Date                                                                     \n",
+       "2014-09-17  465.864014  468.174011  452.421997  457.334015  457.334015   \n",
+       "2014-09-18  456.859985  456.859985  413.104004  424.440002  424.440002   \n",
+       "2014-09-19  424.102997  427.834991  384.532013  394.795990  394.795990   \n",
+       "2014-09-20  394.673004  423.295990  389.882996  408.903992  408.903992   \n",
+       "2014-09-21  408.084991  412.425995  393.181000  398.821014  398.821014   \n",
+       "\n",
+       "              Volume  \n",
+       "Date                  \n",
+       "2014-09-17  21056800  \n",
+       "2014-09-18  34483200  \n",
+       "2014-09-19  37919700  \n",
+       "2014-09-20  36863600  \n",
+       "2014-09-21  26580100  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "BTC.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8c36ea2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Open         0\n",
+      "High         0\n",
+      "Low          0\n",
+      "Close        0\n",
+      "Adj Close    0\n",
+      "Volume       0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Checking null value\n",
+    "print(BTC.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "04975018",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the closing prices\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(BTC['Close'], label='BTC Close')\n",
+    "plt.title('BTC Closing Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Closing Price')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "305deb21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Time Series Scatter Plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.scatterplot(x=BTC.index, y=nifty_50_df['Close'])\n",
+    "plt.title('Time Series Scatter Plot of BTC Closing Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Closing Price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3c9100e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare the data for modeling\n",
+    "data = BTC[['Close']].values\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "scaled_data = scaler.fit_transform(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4f5237f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "train_size = int(len(scaled_data) * 0.7)\n",
+    "train_data, test_data = scaled_data[:train_size], scaled_data[train_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0d58b738",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a function to create datasets for training and testing\n",
+    "def create_dataset(dataset, time_step):\n",
+    "    X, Y = [], []\n",
+    "    for i in range(len(dataset) - time_step - 1):\n",
+    "        a = dataset[i:(i + time_step), 0]\n",
+    "        X.append(a)\n",
+    "        Y.append(dataset[i + time_step, 0])\n",
+    "    return np.array(X), np.array(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "878eb0b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the training and testing datasets\n",
+    "time_step = 50\n",
+    "X_train, y_train = create_dataset(train_data, time_step)\n",
+    "X_test, y_test = create_dataset(test_data, time_step)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "54590d29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reshape the data for GRU layers\n",
+    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
+    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0eef6959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the GRU model\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(200, return_sequences=True, input_shape=(time_step, 1)))\n",
+    "model.add(Dropout(0.4))\n",
+    "model.add(LSTM(160, return_sequences=False))\n",
+    "model.add(Dense(50))\n",
+    "model.add(Dense(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9f2baf93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=[tf.keras.metrics.MeanAbsolutePercentageError()])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "840e276b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 75ms/step - loss: 9.3456e-04 - mean_absolute_percentage_error: 6334.2710 - val_loss: 0.0029 - val_mean_absolute_percentage_error: 7.5628\n",
+      "Epoch 2/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 1.0591e-04 - mean_absolute_percentage_error: 1018.6110 - val_loss: 0.0030 - val_mean_absolute_percentage_error: 7.6941\n",
+      "Epoch 3/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 70ms/step - loss: 1.1633e-04 - mean_absolute_percentage_error: 7816.4395 - val_loss: 0.0024 - val_mean_absolute_percentage_error: 7.0491\n",
+      "Epoch 4/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 8.3075e-05 - mean_absolute_percentage_error: 504.8027 - val_loss: 0.0014 - val_mean_absolute_percentage_error: 5.2843\n",
+      "Epoch 5/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 70ms/step - loss: 6.1255e-05 - mean_absolute_percentage_error: 8174.3931 - val_loss: 0.0013 - val_mean_absolute_percentage_error: 5.4285\n",
+      "Epoch 6/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 74ms/step - loss: 6.9984e-05 - mean_absolute_percentage_error: 99.3050 - val_loss: 0.0025 - val_mean_absolute_percentage_error: 6.8182\n",
+      "Epoch 7/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 5.6695e-05 - mean_absolute_percentage_error: 1054.3582 - val_loss: 0.0010 - val_mean_absolute_percentage_error: 4.5428\n",
+      "Epoch 8/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 5.8794e-05 - mean_absolute_percentage_error: 187.0351 - val_loss: 0.0025 - val_mean_absolute_percentage_error: 6.4947\n",
+      "Epoch 9/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 74ms/step - loss: 5.5804e-05 - mean_absolute_percentage_error: 1814.9207 - val_loss: 0.0040 - val_mean_absolute_percentage_error: 8.9693\n",
+      "Epoch 10/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 73ms/step - loss: 6.5772e-05 - mean_absolute_percentage_error: 4588.8706 - val_loss: 0.0029 - val_mean_absolute_percentage_error: 7.1847\n",
+      "Epoch 11/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 74ms/step - loss: 4.9648e-05 - mean_absolute_percentage_error: 674.5043 - val_loss: 7.7237e-04 - val_mean_absolute_percentage_error: 4.6697\n",
+      "Epoch 12/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 78ms/step - loss: 4.9525e-05 - mean_absolute_percentage_error: 1800.6107 - val_loss: 0.0041 - val_mean_absolute_percentage_error: 8.4830\n",
+      "Epoch 13/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 74ms/step - loss: 6.5563e-05 - mean_absolute_percentage_error: 4040.1660 - val_loss: 0.0047 - val_mean_absolute_percentage_error: 10.6343\n",
+      "Epoch 14/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 71ms/step - loss: 7.2244e-05 - mean_absolute_percentage_error: 758.6786 - val_loss: 0.0031 - val_mean_absolute_percentage_error: 7.8176\n",
+      "Epoch 15/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 7.4023e-05 - mean_absolute_percentage_error: 1087.8573 - val_loss: 0.0022 - val_mean_absolute_percentage_error: 5.9852\n",
+      "Epoch 16/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 3.9633e-05 - mean_absolute_percentage_error: 2644.4453 - val_loss: 0.0035 - val_mean_absolute_percentage_error: 8.2766\n",
+      "Epoch 17/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 4.6292e-05 - mean_absolute_percentage_error: 297.4688 - val_loss: 0.0022 - val_mean_absolute_percentage_error: 5.7070\n",
+      "Epoch 18/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 4.2596e-05 - mean_absolute_percentage_error: 764.3101 - val_loss: 9.8909e-04 - val_mean_absolute_percentage_error: 4.9153\n",
+      "Epoch 19/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 4.9494e-05 - mean_absolute_percentage_error: 7113.5874 - val_loss: 0.0012 - val_mean_absolute_percentage_error: 4.6490\n",
+      "Epoch 20/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 5.1483e-05 - mean_absolute_percentage_error: 248.3509 - val_loss: 0.0016 - val_mean_absolute_percentage_error: 4.8916\n",
+      "Epoch 21/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 5.4170e-05 - mean_absolute_percentage_error: 2772.0061 - val_loss: 0.0055 - val_mean_absolute_percentage_error: 11.0825\n",
+      "Epoch 22/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 8.3230e-05 - mean_absolute_percentage_error: 1567.4910 - val_loss: 0.0044 - val_mean_absolute_percentage_error: 8.1850\n",
+      "Epoch 23/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 5.4889e-05 - mean_absolute_percentage_error: 5557.3311 - val_loss: 7.5806e-04 - val_mean_absolute_percentage_error: 4.9037\n",
+      "Epoch 24/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 5.3994e-05 - mean_absolute_percentage_error: 988.3615 - val_loss: 7.0361e-04 - val_mean_absolute_percentage_error: 4.0240\n",
+      "Epoch 25/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 3.7349e-05 - mean_absolute_percentage_error: 292.9031 - val_loss: 0.0011 - val_mean_absolute_percentage_error: 4.1635\n",
+      "Epoch 26/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 6.6912e-05 - mean_absolute_percentage_error: 614.2693 - val_loss: 0.0016 - val_mean_absolute_percentage_error: 4.9561\n",
+      "Epoch 27/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 5.3310e-05 - mean_absolute_percentage_error: 382.2147 - val_loss: 6.1011e-04 - val_mean_absolute_percentage_error: 4.9062\n",
+      "Epoch 28/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 4.4019e-05 - mean_absolute_percentage_error: 1867.5616 - val_loss: 0.0015 - val_mean_absolute_percentage_error: 4.7577\n",
+      "Epoch 29/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 4.7360e-05 - mean_absolute_percentage_error: 2654.7888 - val_loss: 0.0012 - val_mean_absolute_percentage_error: 4.4042\n",
+      "Epoch 30/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 66ms/step - loss: 5.0328e-05 - mean_absolute_percentage_error: 10450.4170 - val_loss: 0.0032 - val_mean_absolute_percentage_error: 6.9876\n",
+      "Epoch 31/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 4.0789e-05 - mean_absolute_percentage_error: 1524.9989 - val_loss: 0.0026 - val_mean_absolute_percentage_error: 6.0299\n",
+      "Epoch 32/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 3.6128e-05 - mean_absolute_percentage_error: 95.4812 - val_loss: 0.0044 - val_mean_absolute_percentage_error: 8.5315\n",
+      "Epoch 33/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 3.5721e-05 - mean_absolute_percentage_error: 2680.2915 - val_loss: 0.0019 - val_mean_absolute_percentage_error: 5.2643\n",
+      "Epoch 34/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 4.8169e-05 - mean_absolute_percentage_error: 1601.8258 - val_loss: 0.0024 - val_mean_absolute_percentage_error: 6.3191\n",
+      "Epoch 35/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 67ms/step - loss: 4.1726e-05 - mean_absolute_percentage_error: 357.6598 - val_loss: 0.0039 - val_mean_absolute_percentage_error: 8.9009\n",
+      "Epoch 36/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 72ms/step - loss: 4.9945e-05 - mean_absolute_percentage_error: 1982.7183 - val_loss: 0.0031 - val_mean_absolute_percentage_error: 7.0954\n",
+      "Epoch 37/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 71ms/step - loss: 4.3548e-05 - mean_absolute_percentage_error: 3072.9231 - val_loss: 0.0029 - val_mean_absolute_percentage_error: 6.7503\n",
+      "Epoch 38/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 74ms/step - loss: 4.1372e-05 - mean_absolute_percentage_error: 369.3521 - val_loss: 0.0016 - val_mean_absolute_percentage_error: 4.8071\n",
+      "Epoch 39/50\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 71ms/step - loss: 3.9119e-05 - mean_absolute_percentage_error: 2531.5281 - val_loss: 8.4789e-04 - val_mean_absolute_percentage_error: 4.1872\n",
+      "Epoch 40/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 70ms/step - loss: 4.1736e-05 - mean_absolute_percentage_error: 186.6775 - val_loss: 0.0013 - val_mean_absolute_percentage_error: 4.3535\n",
+      "Epoch 41/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 4.0967e-05 - mean_absolute_percentage_error: 3119.4128 - val_loss: 0.0011 - val_mean_absolute_percentage_error: 4.1578\n",
+      "Epoch 42/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 4.3203e-05 - mean_absolute_percentage_error: 286.2504 - val_loss: 0.0013 - val_mean_absolute_percentage_error: 4.9916\n",
+      "Epoch 43/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 4.9291e-05 - mean_absolute_percentage_error: 880.4064 - val_loss: 0.0017 - val_mean_absolute_percentage_error: 4.7630\n",
+      "Epoch 44/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step - loss: 3.2202e-05 - mean_absolute_percentage_error: 149.4473 - val_loss: 0.0042 - val_mean_absolute_percentage_error: 8.9403\n",
+      "Epoch 45/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 4.3330e-05 - mean_absolute_percentage_error: 1935.5453 - val_loss: 0.0029 - val_mean_absolute_percentage_error: 6.2180\n",
+      "Epoch 46/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 5.0304e-05 - mean_absolute_percentage_error: 1176.3553 - val_loss: 0.0036 - val_mean_absolute_percentage_error: 7.7830\n",
+      "Epoch 47/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 5.1335e-05 - mean_absolute_percentage_error: 967.2071 - val_loss: 0.0012 - val_mean_absolute_percentage_error: 4.3959\n",
+      "Epoch 48/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 5.0895e-05 - mean_absolute_percentage_error: 887.9686 - val_loss: 0.0037 - val_mean_absolute_percentage_error: 7.0067\n",
+      "Epoch 49/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - loss: 4.0970e-05 - mean_absolute_percentage_error: 256.8599 - val_loss: 0.0017 - val_mean_absolute_percentage_error: 4.8695\n",
+      "Epoch 50/50\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 70ms/step - loss: 4.2376e-05 - mean_absolute_percentage_error: 977.0376 - val_loss: 0.0040 - val_mean_absolute_percentage_error: 8.2062\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "history = model.fit(X_train, y_train, batch_size=32, epochs=50, validation_data=(X_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "99bc5a47",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step\n",
+      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions\n",
+    "train_predict = model.predict(X_train)\n",
+    "test_predict = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "17402eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inverse transform the predictions\n",
+    "train_predict = scaler.inverse_transform(train_predict)\n",
+    "test_predict = scaler.inverse_transform(test_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "be3b26cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inverse transform the original values\n",
+    "original_y_train = scaler.inverse_transform([y_train])\n",
+    "original_y_test = scaler.inverse_transform([y_test])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7b02d176",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create plots for the predicted values\n",
+    "train_predict_plot = np.empty_like(scaled_data)\n",
+    "train_predict_plot[:, :] = np.nan\n",
+    "train_predict_plot[time_step:len(train_predict) + time_step, :] = train_predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "04edb8f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_predict_plot = np.empty_like(scaled_data)\n",
+    "test_predict_plot[:, :] = np.nan\n",
+    "test_predict_plot[len(train_predict) + (time_step * 2) + 1:len(scaled_data) - 1, :] = test_predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c16c4bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(scaler.inverse_transform(scaled_data), label='Actual')\n",
+    "plt.plot(train_predict_plot, label='Train Predict')\n",
+    "plt.plot(test_predict_plot, label='Test Predict')\n",
+    "plt.title('Actual vs Predicted Values')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Closing Price')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "40ce439f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the model loss and MAPE over epochs\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Plot Loss\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(history.history['loss'], label='Training Loss')\n",
+    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+    "plt.title('Model Loss (MSE) Over Epochs')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Loss (MSE)')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Plot MAPE\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(history.history['mean_absolute_percentage_error'], label='Training MAPE')\n",
+    "plt.plot(history.history['val_mean_absolute_percentage_error'], label='Validation MAPE')\n",
+    "plt.title('Model Accuracy (MAPE) Over Epochs')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('MAPE (%)')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "4345590a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot true vs predicted residuals\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(scaler.inverse_transform(scaled_data), label=\"True\")\n",
+    "plt.plot(test_predict_plot, label=\"Test Predicted\")\n",
+    "plt.title(\"True vs Predicted BTC   Close Prices\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "20c82c28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train R²: 0.9908917580432313\n",
+      "Test R²: 0.9505946512099303\n",
+      "Training RMSE:  379.70474936296483\n",
+      "Testing RMSE:  4269.376224219396\n",
+      "Training MAE:  209.93181744027748\n",
+      "Testing MAE:  3096.8610011811516\n",
+      "Training MSE:  144175.69668879194\n",
+      "Testing MSE:  18227573.34392987\n",
+      "Training MATE:  80.32775864843757\n",
+      "Testing MATE:  2231.859374874999\n",
+      "Training SMATE:  0.10018216309141781\n",
+      "Testing SMATE:  0.13996355131035762\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate R² score, rmse, mae, mse, mate, smate for training and testing sets\n",
+    "train_r2 = r2_score(original_y_train[0], train_predict[:, 0])\n",
+    "test_r2 = r2_score(original_y_test[0], test_predict[:, 0])\n",
+    "\n",
+    "train_rmse = np.sqrt(mean_squared_error(original_y_train[0], train_predict[:, 0]))\n",
+    "test_rmse = np.sqrt(mean_squared_error(original_y_test[0], test_predict[:, 0]))\n",
+    "\n",
+    "train_mae = mean_absolute_error(original_y_train[0], train_predict[:, 0])\n",
+    "test_mae = mean_absolute_error(original_y_test[0], test_predict[:, 0])\n",
+    "\n",
+    "train_mse = mean_squared_error(original_y_train[0], train_predict[:, 0])\n",
+    "test_mse = mean_squared_error(original_y_test[0], test_predict[:, 0])\n",
+    "\n",
+    "train_mate = median_absolute_error(original_y_train[0], train_predict[:, 0])\n",
+    "test_mate = median_absolute_error(original_y_test[0], test_predict[:, 0])\n",
+    "\n",
+    "train_smate = np.sqrt(mean_squared_error(original_y_train[0], train_predict[:, 0])) / np.mean(original_y_train)\n",
+    "test_smate = np.sqrt(mean_squared_error(original_y_test[0], test_predict[:, 0])) / np.mean(original_y_test)\n",
+    "\n",
+    "print(f'Train R²: {train_r2}')\n",
+    "print(f'Test R²: {test_r2}')\n",
+    "\n",
+    "print(\"Training RMSE: \", train_rmse)\n",
+    "print(\"Testing RMSE: \", test_rmse)\n",
+    "\n",
+    "print(\"Training MAE: \", train_mae)\n",
+    "print(\"Testing MAE: \", test_mae)\n",
+    "\n",
+    "print(\"Training MSE: \", train_mse)\n",
+    "print(\"Testing MSE: \", test_mse)\n",
+    "\n",
+    "print(\"Training MATE: \", train_mate)\n",
+    "print(\"Testing MATE: \", test_mate)\n",
+    "\n",
+    "print(\"Training SMATE: \", train_smate)\n",
+    "print(\"Testing SMATE: \", test_smate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "226800da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+----------+-------------+----------------+\n",
+      "| Metric   |    Training |        Testing |\n",
+      "+==========+=============+================+\n",
+      "| R² Score |      0.9909 |    0.9506      |\n",
+      "+----------+-------------+----------------+\n",
+      "| RMSE     |    379.705  | 4269.38        |\n",
+      "+----------+-------------+----------------+\n",
+      "| MSE      | 144176      |    1.82276e+07 |\n",
+      "+----------+-------------+----------------+\n",
+      "| MAE      |    209.932  | 3096.86        |\n",
+      "+----------+-------------+----------------+\n",
+      "| MATE     |     80.3278 | 2231.86        |\n",
+      "+----------+-------------+----------------+\n",
+      "| SMATE    |      0.1002 |    0.14        |\n",
+      "+----------+-------------+----------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tabulate import tabulate\n",
+    "import numpy as np\n",
+    "# Create a table\n",
+    "table = [\n",
+    "    [\"Metric\", \"Training\", \"Testing\"],\n",
+    "    [\"R² Score\", f\"{train_r2:.4f}\", f\"{test_r2:.4f}\"],\n",
+    "    [\"RMSE\", f\"{train_rmse:.4f}\", f\"{test_rmse:.4f}\"],\n",
+    "    [\"MSE\", f\"{train_mse:.4f}\", f\"{test_mse:.4f}\"],\n",
+    "    [\"MAE\", f\"{train_mae:.4f}\", f\"{test_mae:.4f}\"],\n",
+    "    [\"MATE\", f\"{train_mate:.4f}\", f\"{test_mate:.4f}\"],\n",
+    "    [\"SMATE\", f\"{train_smate:.4f}\", f\"{test_smate:.4f}\"]\n",
+    "]\n",
+    "\n",
+    "print(tabulate(table, headers=\"firstrow\", tablefmt=\"grid\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45ae943f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f734340",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/models/BitcoinPricePrediction/README.md b/models/BitcoinPricePrediction/README.md
new file mode 100644
index 00000000..30873810
--- /dev/null
+++ b/models/BitcoinPricePrediction/README.md
@@ -0,0 +1,38 @@
+## Bitcoin Price Prediction using LSTM
+This repository contains an implementation of a Long Short-Term Memory (LSTM) model for predicting Bitcoin prices using historical data. The model is built using Keras and TensorFlow. It predicts the closing price of Bitcoin by training on past data and evaluating its performance on a test set. The results are evaluated using common regression metrics such as R², RMSE, MAE, and others.
+
+## **Dataset**
+The dataset used in this project is historical Bitcoin price data downloaded from a public source. The file BTC-USD.csv contains columns such as Date, Open, High, Low, Close, Adj Close, and Volume. The prediction is based solely on the Close price of Bitcoin.
+
+**Dataset Preprocessing:**
+* Missing values are handled.
+* The Date column is converted into datetime format and set as the index.
+* The closing prices are normalized using MinMaxScaler for better model performance.
+  
+## Model Architecture
+The implemented model is a multi-layer LSTM neural network that includes dropout layers to reduce overfitting. Here's an overview of the model:
+
+**Input Layer:** Time series data reshaped to 3D for LSTM layers.
+**LSTM Layers:** Two LSTM layers with 200 and 160 units, respectively, with return_sequences enabled in the first layer.
+**Dropout:** Added after each LSTM layer to prevent overfitting.
+**Dense Layers:** Two Dense layers; the final layer has 1 neuron for regression output (predicted closing price).
+
+## Model Hyperparameters
+**Batch Size:** 32
+**Epochs:** 50
+**Loss Function:** Mean Squared Error (MSE)
+**Optimizer:** Adam
+**Metrics:** Mean Absolute Percentage Error (MAPE)
+
+## Evaluation Metrics
+The model is evaluated using several regression metrics, including:
+
+* **R² Score:** Measures the proportion of variance in the dependent variable that is predictable.
+* **RMSE:** Root Mean Squared Error, used to measure the differences between predicted and observed values.
+* **MSE:** Mean Squared Error, similar to RMSE but without square rooting.
+* **MAE:** Mean Absolute Error, the average of the absolute errors between actual and predicted values.
+* **MATE:** Median Absolute Error, a robust measure of error.
+* **SMATE:** Scaled Mean Absolute Error, normalized version of MAE.
+
+  A detailed comparison of the training and testing set metrics is included in the project.
+
diff --git a/models/BitcoinPricePrediction/data/BTC-USD.csv b/models/BitcoinPricePrediction/data/BTC-USD.csv
new file mode 100644
index 00000000..f6bb1449
--- /dev/null
+++ b/models/BitcoinPricePrediction/data/BTC-USD.csv
@@ -0,0 +1,2714 @@
+Date,Open,High,Low,Close,Adj Close,Volume
+2014-09-17,465.864014,468.174011,452.421997,457.334015,457.334015,21056800
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+2014-09-23,402.092010,441.557007,396.196991,435.790985,435.790985,45099500
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+2014-09-25,423.156006,423.519989,409.467987,411.574005,411.574005,26814400
+2014-09-26,411.428986,414.937988,400.009003,404.424988,404.424988,21460800
+2014-09-27,403.556000,406.622986,397.372009,399.519989,399.519989,15029300
+2014-09-28,399.471008,401.016998,374.332001,377.181000,377.181000,23613300
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+2022-01-17,43118.121094,43179.390625,41680.320313,42250.550781,42250.550781,21690904261
+2022-01-18,42250.074219,42534.402344,41392.214844,42375.632813,42375.632813,22417209227
+2022-01-19,42374.039063,42478.304688,41242.914063,41744.328125,41744.328125,23091543258
+2022-01-20,41744.027344,43413.023438,40672.824219,40680.417969,40680.417969,20382033940
+2022-01-21,40699.605469,41060.527344,35791.425781,36457.316406,36457.316406,43011992031
+2022-01-22,36471.589844,36688.812500,34349.250000,35030.250000,35030.250000,39714385405
+2022-01-23,35047.359375,36433.312500,34784.968750,36276.804688,36276.804688,26017975951
+2022-01-24,36275.734375,37247.519531,33184.058594,36654.328125,36654.328125,41856658597
+2022-01-25,36654.804688,37444.570313,35779.429688,36954.003906,36954.003906,26428189594
+2022-01-26,36950.515625,38825.410156,36374.906250,36852.121094,36852.121094,31324598034
+2022-01-27,36841.878906,37148.324219,35629.281250,37138.234375,37138.234375,25041426629
+2022-01-28,37128.445313,37952.878906,36211.109375,37784.332031,37784.332031,22238830523
+2022-01-29,37780.714844,38576.261719,37406.472656,38138.179688,38138.179688,17194183075
+2022-01-30,38151.917969,38266.339844,37437.710938,37917.601563,37917.601563,14643548444
+2022-01-31,37920.281250,38647.261719,36733.574219,38483.125000,38483.125000,20734730465
+2022-02-01,38481.765625,39115.132813,38113.664063,38743.273438,38743.273438,20288500328
+2022-02-02,38743.714844,38834.617188,36832.730469,36952.984375,36952.984375,19155189416
+2022-02-03,36944.804688,37154.601563,36375.539063,37154.601563,37154.601563,18591534769
+2022-02-04,37149.265625,41527.785156,37093.628906,41500.875000,41500.875000,29412210792
+2022-02-05,41501.480469,41847.164063,41038.097656,41441.164063,41441.164063,19652846215
+2022-02-06,41441.121094,42500.785156,41244.906250,42412.433594,42412.433594,16142097334
+2022-02-07,42406.781250,44401.863281,41748.156250,43840.285156,43840.285156,28641855926
+2022-02-08,43854.652344,45293.867188,42807.835938,44118.445313,44118.445313,33079398868
+2022-02-09,44096.703125,44727.800781,43232.968750,44338.796875,44338.796875,23245887300
+2022-02-10,44347.800781,45661.171875,43402.808594,43565.113281,43565.113281,32142048537
+2022-02-11,43571.128906,43810.832031,42114.539063,42407.937500,42407.937500,26954925781
+2022-02-12,42412.300781,42992.550781,41852.574219,42244.468750,42244.468750,18152390304
+2022-02-13,42236.566406,42693.054688,41950.941406,42197.515625,42197.515625,14741589015
+2022-02-14,42157.398438,42775.777344,41681.957031,42586.917969,42586.917969,20827783012
+2022-02-15,42586.464844,44667.218750,42491.035156,44575.203125,44575.203125,22721659051
+2022-02-16,44578.277344,44578.277344,43456.691406,43961.859375,43961.859375,19792547657
+2022-02-17,43937.070313,44132.972656,40249.371094,40538.011719,40538.011719,26246662813
+2022-02-18,40552.132813,40929.152344,39637.617188,40030.976563,40030.976563,23310007704
+2022-02-19,40022.132813,40246.027344,40010.867188,40126.429688,40126.429688,22263900160
\ No newline at end of file
diff --git a/models/Pre Owned Car Price Predictor/Cleaned_Car_data.csv b/models/Pre Owned Car Price Predictor/Cleaned_Car_data.csv
new file mode 100644
index 00000000..4eadd267
--- /dev/null
+++ b/models/Pre Owned Car Price Predictor/Cleaned_Car_data.csv	
@@ -0,0 +1,817 @@
+,name,company,year,Price,kms_driven,fuel_type
+0,Hyundai Santro Xing,Hyundai,2007,80000,45000,Petrol
+1,Mahindra Jeep CL550,Mahindra,2006,425000,40,Diesel
+2,Hyundai Grand i10,Hyundai,2014,325000,28000,Petrol
+3,Ford EcoSport Titanium,Ford,2014,575000,36000,Diesel
+4,Ford Figo,Ford,2012,175000,41000,Diesel
+5,Hyundai Eon,Hyundai,2013,190000,25000,Petrol
+6,Ford EcoSport Ambiente,Ford,2016,830000,24530,Diesel
+7,Maruti Suzuki Alto,Maruti,2015,250000,60000,Petrol
+8,Skoda Fabia Classic,Skoda,2010,182000,60000,Petrol
+9,Maruti Suzuki Stingray,Maruti,2015,315000,30000,Petrol
+10,Hyundai Elite i20,Hyundai,2014,415000,32000,Petrol
+11,Mahindra Scorpio SLE,Mahindra,2015,320000,48660,Diesel
+12,Hyundai Santro Xing,Hyundai,2007,80000,45000,Petrol
+13,Mahindra Jeep CL550,Mahindra,2006,425000,40,Diesel
+14,Audi A8,Audi,2017,1000000,4000,Petrol
+15,Audi Q7,Audi,2014,500000,16934,Diesel
+16,Mahindra Scorpio S10,Mahindra,2016,350000,43000,Diesel
+17,Maruti Suzuki Alto,Maruti,2014,160000,35550,Petrol
+18,Mahindra Scorpio S10,Mahindra,2016,350000,43000,Diesel
+19,Mahindra Scorpio S10,Mahindra,2016,310000,39522,Diesel
+20,Maruti Suzuki Alto,Maruti,2015,75000,39000,Petrol
+21,Hyundai i20 Sportz,Hyundai,2012,100000,55000,Petrol
+22,Hyundai i20 Sportz,Hyundai,2012,100000,55000,Petrol
+23,Hyundai i20 Sportz,Hyundai,2012,100000,55000,Petrol
+24,Maruti Suzuki Alto,Maruti,2017,190000,72000,Petrol
+25,Maruti Suzuki Vitara,Maruti,2016,290000,15975,Diesel
+26,Maruti Suzuki Alto,Maruti,2008,95000,70000,Petrol
+27,Mahindra Bolero DI,Mahindra,2017,180000,23452,Diesel
+28,Maruti Suzuki Swift,Maruti,2014,385000,35522,Diesel
+29,Mahindra Scorpio S10,Mahindra,2015,250000,48508,Diesel
+30,Maruti Suzuki Swift,Maruti,2017,180000,15487,Petrol
+31,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+32,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+33,Toyota Innova 2.0,Toyota,2012,650000,82000,Diesel
+34,Renault Lodgy 85,Renault,2018,689999,20000,Diesel
+35,Skoda Yeti Ambition,Skoda,2012,448000,68000,Diesel
+36,Maruti Suzuki Baleno,Maruti,2017,549000,32000,Diesel
+37,Renault Duster 110,Renault,2012,501000,38000,Diesel
+38,Renault Duster 85,Renault,2013,489999,27000,Diesel
+39,Honda City 1.5,Honda,2011,280000,33000,Petrol
+40,Maruti Suzuki Alto,Maruti,2015,250000,60000,Petrol
+41,Maruti Suzuki Dzire,Maruti,2013,349999,46000,Diesel
+42,Honda Amaze,Honda,2013,284999,46000,Diesel
+43,Honda Amaze 1.5,Honda,2015,345000,36000,Diesel
+44,Honda City,Honda,2015,499999,55000,Petrol
+45,Datsun Redi GO,Datsun,2017,235000,16000,Petrol
+46,Maruti Suzuki SX4,Maruti,2010,249999,36000,Petrol
+47,Mitsubishi Pajero Sport,Mitsubishi,2015,1475000,47000,Diesel
+48,Mahindra Bolero DI,Mahindra,2017,180000,23452,Diesel
+49,Maruti Suzuki Swift,Maruti,2014,385000,35522,Diesel
+50,Mahindra Scorpio S10,Mahindra,2015,250000,48508,Diesel
+51,Maruti Suzuki Swift,Maruti,2017,180000,15487,Petrol
+52,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+53,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+54,Mahindra Scorpio S10,Mahindra,2015,395000,35000,Diesel
+55,Maruti Suzuki Swift,Maruti,2017,220000,30874,Petrol
+56,Honda City ZX,Honda,2017,170000,15000,Diesel
+57,Maruti Suzuki Wagon,Maruti,2013,85000,29685,Petrol
+58,Ford Figo,Ford,2012,175000,41000,Diesel
+59,Hyundai Eon,Hyundai,2013,190000,25000,Petrol
+60,Tata Indigo eCS,Tata,2017,200000,130000,Diesel
+61,Ford EcoSport Ambiente,Ford,2016,830000,24530,Diesel
+62,Tata Indigo eCS,Tata,2017,200000,130000,Diesel
+63,Mahindra Scorpio SLE,Mahindra,2012,570000,19000,Diesel
+64,Volkswagen Polo Highline,Volkswagen,2014,315000,60000,Petrol
+65,Skoda Fabia Classic,Skoda,2010,182000,60000,Petrol
+66,Maruti Suzuki Stingray,Maruti,2015,315000,30000,Petrol
+67,Chevrolet Spark LS,Chevrolet,2010,110000,41000,Petrol
+68,Renault Duster 110PS,Renault,2012,501000,35000,Diesel
+69,Honda City,Honda,2015,448999,54000,Petrol
+70,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+71,Datsun Redi GO,Datsun,2017,235000,16000,Petrol
+72,Skoda Fabia 1.2L,Skoda,2011,159500,38200,Diesel
+73,Honda Amaze,Honda,2015,344999,22000,Petrol
+74,Honda Amaze,Honda,2015,344999,22000,Petrol
+75,Renault Duster,Renault,2014,449999,50000,Diesel
+76,Mini Cooper S,Mini,2013,1891111,13500,Petrol
+77,Mahindra Scorpio S4,Mahindra,2015,865000,30000,Diesel
+78,Mahindra Scorpio VLX,Mahindra,2014,699000,50000,Diesel
+79,Mahindra Quanto C8,Mahindra,2013,375000,20000,Diesel
+80,Ford EcoSport,Ford,2017,489999,39000,Petrol
+81,Honda Brio,Honda,2012,224999,30000,Petrol
+82,Volkswagen Vento Highline,Volkswagen,2019,1200000,3600,Diesel
+83,Hyundai i20 Magna,Hyundai,2009,195000,32000,Petrol
+84,Toyota Corolla Altis,Toyota,2010,351000,38000,Diesel
+85,Hyundai Verna Transform,Hyundai,2008,160000,45000,Petrol
+86,Toyota Corolla Altis,Toyota,2009,240000,35000,Petrol
+87,Honda City 1.5,Honda,2005,90000,50000,Petrol
+88,Hyundai Elite i20,Hyundai,2014,415000,32000,Petrol
+89,Skoda Fabia 1.2L,Skoda,2011,155000,45863,Diesel
+90,BMW 3 Series,BMW,2011,600000,60500,Petrol
+91,Maruti Suzuki A,Maruti,2011,189500,12500,Petrol
+92,Toyota Etios GD,Toyota,2013,350000,60000,Diesel
+93,Ford Figo Diesel,Ford,2012,210000,35000,Diesel
+94,Maruti Suzuki Swift,Maruti,2014,390000,35000,Petrol
+95,Chevrolet Beat LT,Chevrolet,2012,135000,45000,Diesel
+96,BMW 7 Series,BMW,2009,1600000,35000,Petrol
+97,Mahindra XUV500 W8,Mahindra,2013,701000,38000,Diesel
+98,Hyundai i10 Magna,Hyundai,2014,265000,18000,Petrol
+99,Hyundai Verna Fluidic,Hyundai,2015,525000,35000,Diesel
+100,Maruti Suzuki Swift,Maruti,2013,372000,13349,Petrol
+101,Maruti Suzuki Ertiga,Maruti,2016,635000,29000,Petrol
+102,Ford EcoSport Titanium,Ford,2014,550000,44000,Diesel
+103,Maruti Suzuki Ertiga,Maruti,2016,575000,29000,Petrol
+104,Maruti Suzuki Ertiga,Maruti,2013,485000,42000,Diesel
+105,Maruti Suzuki Alto,Maruti,2012,155000,14000,Petrol
+106,Hyundai Grand i10,Hyundai,2014,345000,49000,Diesel
+107,Honda Amaze 1.2,Honda,2014,325000,42000,Petrol
+108,Hyundai i20 Asta,Hyundai,2012,329500,36200,Diesel
+109,Ford Figo Diesel,Ford,2014,195000,50000,Diesel
+110,Maruti Suzuki Eeco,Maruti,2015,251111,55000,Petrol
+111,Maruti Suzuki Ertiga,Maruti,2014,569999,45000,Petrol
+112,Maruti Suzuki Esteem,Maruti,2007,69999,51000,Petrol
+113,Maruti Suzuki Ritz,Maruti,2014,299999,19000,Petrol
+114,Maruti Suzuki Dzire,Maruti,2009,220000,46000,Petrol
+115,Maruti Suzuki Ritz,Maruti,2013,399999,33000,Diesel
+116,Maruti Suzuki Swift,Maruti,2013,372000,13349,Petrol
+117,Maruti Suzuki Dzire,Maruti,2015,450000,104000,Diesel
+118,Toyota Etios Liva,Toyota,2014,270000,55000,Petrol
+119,Hyundai i20 Sportz,Hyundai,2011,350000,33333,Diesel
+120,Chevrolet Spark,Chevrolet,2012,158400,33600,Petrol
+121,Maruti Suzuki Alto,Maruti,2017,350000,5600,Petrol
+122,Nissan Micra XV,Nissan,2011,179000,41000,Petrol
+123,Maruti Suzuki Swift,Maruti,2007,125000,70000,Petrol
+124,Maruti Suzuki Alto,Maruti,2018,200000,7500,Petrol
+125,Honda Amaze 1.5,Honda,2013,299000,45000,Diesel
+126,Maruti Suzuki Alto,Maruti,2015,220000,38000,Petrol
+127,Chevrolet Beat,Chevrolet,2015,150000,30000,Petrol
+128,Honda City 1.5,Honda,2010,285000,35000,Petrol
+129,Ford EcoSport Trend,Ford,2016,830000,24330,Diesel
+130,Hyundai i20 Asta,Hyundai,2009,210000,65480,Petrol
+131,Maruti Suzuki Swift,Maruti,2013,340000,41000,Petrol
+132,Tata Indica V2,Tata,2006,90000,20000,Petrol
+133,Hindustan Motors Ambassador,Hindustan,2000,70000,200000,Diesel
+134,Toyota Corolla Altis,Toyota,2010,289999,70000,Petrol
+135,Toyota Corolla Altis,Toyota,2012,349999,59000,Petrol
+136,Toyota Innova 2.5,Toyota,2012,849999,99000,Diesel
+137,Volkswagen Jetta Highline,Volkswagen,2014,749999,46000,Diesel
+138,Volkswagen Polo Comfortline,Volkswagen,2015,399999,2800,Petrol
+139,Volkswagen Polo,Volkswagen,2014,274999,32000,Petrol
+140,Mahindra Scorpio,Mahindra,2015,984999,22000,Diesel
+141,Renault Duster,Renault,2014,449999,50000,Diesel
+142,Honda Amaze,Honda,2015,344999,22000,Petrol
+143,Nissan Sunny,Nissan,2012,224999,45000,Petrol
+144,Hyundai Elite i20,Hyundai,2018,599999,21000,Petrol
+145,Renault Kwid,Renault,2016,244999,11000,Petrol
+146,Renault Duster,Renault,2013,399999,41000,Diesel
+147,Ford EcoSport,Ford,2017,489999,39000,Petrol
+148,Renault Duster,Renault,2014,474999,50000,Diesel
+149,Mahindra Scorpio VLX,Mahindra,2011,499999,66000,Diesel
+150,Maruti Suzuki Alto,Maruti,2018,310000,3000,Petrol
+151,Chevrolet Spark LT,Chevrolet,2010,85000,45000,Petrol
+152,Datsun Redi GO,Datsun,2016,245000,7000,Petrol
+153,Maruti Suzuki Swift,Maruti,2010,189500,38500,Diesel
+154,Fiat Punto Emotion,Fiat,2012,169500,37200,Diesel
+155,Maruti Suzuki Swift,Maruti,2010,159500,43200,Diesel
+156,Toyota Etios GD,Toyota,2013,275000,24800,Petrol
+157,Hyundai i20 Sportz,Hyundai,2014,370000,60000,Diesel
+158,Hyundai i10 Sportz,Hyundai,2010,168000,45872,Petrol
+159,Chevrolet Beat LT,Chevrolet,2011,150000,40000,Diesel
+160,Chevrolet Beat LS,Chevrolet,2011,145000,45000,Diesel
+161,Chevrolet Beat LT,Chevrolet,2012,98500,38000,Diesel
+162,Mahindra Scorpio VLX,Mahindra,2014,699000,50000,Diesel
+163,Tata Indigo CS,Tata,2011,85000,11400,Diesel
+164,Toyota Corolla Altis,Toyota,2015,575000,42000,Petrol
+165,Honda City 1.5,Honda,2014,549000,39000,Petrol
+166,Maruti Suzuki Swift,Maruti,2011,209000,47000,Diesel
+167,Hyundai Eon Era,Hyundai,2013,185000,27000,Petrol
+168,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+169,Mahindra XUV500,Mahindra,2014,699999,52000,Diesel
+170,Honda Brio,Honda,2012,224999,30000,Petrol
+171,Ford Fiesta,Ford,2011,274999,55000,Diesel
+172,Honda Amaze,Honda,2013,284999,46000,Diesel
+173,Honda City,Honda,2015,599999,30000,Diesel
+174,Maruti Suzuki Wagon,Maruti,2012,199999,44000,Petrol
+175,Honda City,Honda,2014,544999,45000,Diesel
+176,Hyundai i20,Hyundai,2009,199000,31000,Petrol
+177,Tata Indigo eCS,Tata,2016,320000,175430,Diesel
+178,Hyundai Fluidic Verna,Hyundai,2015,540000,38000,Diesel
+179,Mahindra Quanto C8,Mahindra,2013,340000,37000,Diesel
+180,Fiat Petra ELX,Fiat,2008,75000,65000,Petrol
+181,Skoda Fabia 1.2L,Skoda,2011,159500,38200,Diesel
+182,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+183,Hyundai Santro Xing,Hyundai,2005,49000,7500,Petrol
+184,Maruti Suzuki Ciaz,Maruti,2016,700000,3350,Petrol
+185,Maruti Suzuki Zen,Maruti,2000,55000,60000,Petrol
+186,Honda City,Honda,2015,448999,54000,Petrol
+187,Hyundai Creta 1.6,Hyundai,2017,895000,32000,Petrol
+188,Mahindra Scorpio SLX,Mahindra,2007,355000,75000,Diesel
+189,Mahindra Scorpio SLE,Mahindra,2012,565000,62000,Diesel
+190,Toyota Innova 2.5,Toyota,2006,365000,73000,Diesel
+191,Maruti Suzuki Alto,Maruti,2011,145000,41000,Petrol
+192,Maruti Suzuki Wagon,Maruti,2011,210000,35000,Petrol
+193,Tata Nano Cx,Tata,2013,40000,2200,Petrol
+194,Maruti Suzuki Alto,Maruti,2013,125000,39000,Petrol
+195,Maruti Suzuki Wagon,Maruti,2009,135000,45000,Petrol
+196,Maruti Suzuki Swift,Maruti,2006,135000,45000,Petrol
+197,Tata Sumo Victa,Tata,2012,285000,65000,Diesel
+198,Maruti Suzuki Wagon,Maruti,2010,145000,54870,Petrol
+199,Maruti Suzuki Alto,Maruti,2010,135000,34580,Petrol
+200,Volkswagen Passat Diesel,Volkswagen,2009,450000,97000,Diesel
+201,Renault Scala RxL,Renault,2015,375000,25000,Diesel
+202,Mahindra Quanto C8,Mahindra,2013,375000,20000,Diesel
+203,Hyundai Grand i10,Hyundai,2014,365000,20000,Petrol
+204,Hyundai i20 Active,Hyundai,2015,500000,18000,Petrol
+205,Mahindra Xylo E4,Mahindra,2012,400000,35000,Diesel
+206,Mahindra Jeep MM,Mahindra,2019,390000,60,Diesel
+207,Renault Duster 110PS,Renault,2012,501000,35000,Diesel
+208,Mahindra Bolero SLE,Mahindra,2013,330000,80200,Diesel
+209,Force Motors Force,Force,2015,580000,3200,Diesel
+210,Maruti Suzuki SX4,Maruti,2012,265000,46000,Diesel
+211,Mahindra Jeep CL550,Mahindra,2019,379000,0,Diesel
+212,Maruti Suzuki Alto,Maruti,2015,219000,5000,Petrol
+213,Mahindra Jeep CL550,Mahindra,2018,385000,588,Diesel
+214,Toyota Etios,Toyota,2011,275000,36000,Diesel
+215,Volkswagen Polo,Volkswagen,2015,330000,38000,Diesel
+216,Honda City ZX,Honda,2008,110000,45000,Petrol
+217,Maruti Suzuki Wagon,Maruti,2006,80000,71200,Petrol
+218,Honda City VX,Honda,2016,519000,52000,Diesel
+219,Mahindra Thar CRDe,Mahindra,2016,730000,29000,Diesel
+220,Mitsubishi Pajero Sport,Mitsubishi,2015,1475000,47000,Diesel
+221,Audi A4 1.8,Audi,2009,699000,47000,Petrol
+222,Mercedes Benz GLA,Mercedes,2015,2000000,20000,Diesel
+223,Land Rover Freelander,Land,2015,2100000,30000,Diesel
+224,Renault Kwid RXT,Renault,2017,340000,5000,Petrol
+225,Tata Aria Pleasure,Tata,2014,390000,35000,Diesel
+226,Mercedes Benz B,Mercedes,2014,1400000,31000,Petrol
+227,Datsun GO T,Datsun,2016,245000,7000,Petrol
+228,Tata Indigo eCS,Tata,2016,320000,175430,Diesel
+229,Tata Indigo eCS,Tata,2016,320000,175400,Diesel
+230,Honda Jazz VX,Honda,2016,450000,41000,Petrol
+231,Honda Amaze 1.2,Honda,2014,311000,33000,Petrol
+232,Honda Amaze,Honda,2013,284999,46000,Diesel
+233,Honda City,Honda,2012,399999,45000,Petrol
+234,Honda City,Honda,2015,599999,39000,Diesel
+235,Honda Amaze,Honda,2015,344999,22000,Petrol
+236,Audi A4 1.8,Audi,2009,699000,47000,Petrol
+237,Force Motors Force,Force,2015,580000,3200,Diesel
+238,Mahindra Scorpio S4,Mahindra,2015,855000,30000,Diesel
+239,Hyundai i20 Active,Hyundai,2015,535000,37000,Diesel
+240,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+241,Maruti Suzuki Ciaz,Maruti,2017,699000,14000,Petrol
+242,Chevrolet Tavera Neo,Chevrolet,2013,375000,55000,Diesel
+243,Honda Amaze,Honda,2013,284999,46000,Diesel
+244,Hyundai Eon Sportz,Hyundai,2012,178000,30000,Petrol
+245,Tata Sumo Gold,Tata,2013,300000,50000,Diesel
+246,Maruti Suzuki Wagon,Maruti,2003,90000,45000,Petrol
+247,Maruti Suzuki Esteem,Maruti,2006,95000,45000,Petrol
+248,Maruti Suzuki Eeco,Maruti,2015,255000,9300,Petrol
+249,Chevrolet Enjoy 1.4,Chevrolet,2013,245000,55000,Diesel
+250,Hyundai i20 Asta,Hyundai,2012,329500,36200,Diesel
+251,Ford Figo Diesel,Ford,2014,195000,50000,Diesel
+252,Maruti Suzuki Eeco,Maruti,2015,251111,55000,Petrol
+253,Maruti Suzuki Ertiga,Maruti,2014,569999,45000,Petrol
+254,Maruti Suzuki Esteem,Maruti,2007,69999,51000,Petrol
+255,Maruti Suzuki Ritz,Maruti,2014,299999,19000,Petrol
+256,Maruti Suzuki Dzire,Maruti,2009,220000,46000,Petrol
+257,Maruti Suzuki Ritz,Maruti,2013,399999,33000,Diesel
+258,Maruti Suzuki SX4,Maruti,2010,249999,36000,Petrol
+259,Maruti Suzuki Wagon,Maruti,2015,289999,22000,Petrol
+260,Mini Cooper S,Mini,2013,1891111,13500,Petrol
+261,Nissan Terrano XL,Nissan,2015,499999,60000,Diesel
+262,Renault Duster 85,Renault,2013,489999,27000,Diesel
+263,Renault Duster 85,Renault,2014,489999,59000,Diesel
+264,Renault Duster 85,Renault,2015,549999,19000,Diesel
+265,Maruti Suzuki Dzire,Maruti,2013,380000,30000,Petrol
+266,Renault Kwid RXT,Renault,2018,325000,15000,Petrol
+267,Maruti Suzuki Maruti,Maruti,2003,57000,56758,Petrol
+268,Renault Kwid 1.0,Renault,2018,349999,10000,Petrol
+269,Renault Lodgy 85,Renault,2018,689999,20000,Diesel
+270,Renault Scala RxL,Renault,2014,349999,49000,Diesel
+271,Hyundai Grand i10,Hyundai,2014,410000,41000,Petrol
+272,Maruti Suzuki Swift,Maruti,2011,225000,45000,Petrol
+273,Chevrolet Beat LS,Chevrolet,2010,120000,43000,Petrol
+274,Tata Indigo eCS,Tata,2016,320000,175430,Diesel
+275,Hyundai Santro Xing,Hyundai,2000,59000,56450,Petrol
+276,Hyundai Fluidic Verna,Hyundai,2015,540000,38000,Diesel
+277,Chevrolet Beat LS,Chevrolet,2010,80000,56000,Petrol
+278,Mahindra Quanto C8,Mahindra,2013,340000,37000,Diesel
+279,Fiat Petra ELX,Fiat,2008,75000,65000,Petrol
+280,Chevrolet Beat LS,Chevrolet,2015,220000,32700,Petrol
+281,Skoda Fabia 1.2L,Skoda,2011,159500,38200,Diesel
+282,Ford EcoSport Titanium,Ford,2016,599000,30000,Diesel
+283,Hyundai Accent GLX,Hyundai,2006,80000,56000,Petrol
+284,Mahindra TUV300 T4,Mahindra,2016,675000,9000,Diesel
+285,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+286,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+287,Tata Indica V2,Tata,2008,150000,11000,Petrol
+288,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+289,Tata Indigo CS,Tata,2009,72500,46000,Diesel
+290,Maruti Suzuki Swift,Maruti,2019,610000,73,Petrol
+291,Mahindra Scorpio VLX,Mahindra,2004,230000,160000,Diesel
+292,Honda Accord,Honda,2009,175000,58559,Petrol
+293,Mahindra Scorpio S4,Mahindra,2015,855000,30000,Diesel
+294,Chevrolet Tavera Neo,Chevrolet,2013,375000,55000,Diesel
+295,Ford EcoSport Titanium,Ford,2014,520000,57000,Diesel
+296,Maruti Suzuki Ertiga,Maruti,2015,524999,50000,Diesel
+297,Honda Amaze,Honda,2014,299999,37000,Petrol
+298,Maruti Suzuki Dzire,Maruti,2012,299999,40000,Petrol
+299,Honda City,Honda,2011,284999,55000,Petrol
+300,Mahindra Scorpio 2.6,Mahindra,2007,220000,170000,Diesel
+301,Maruti Suzuki Dzire,Maruti,2014,424999,55000,Diesel
+302,Honda City,Honda,2015,644999,39000,Petrol
+303,Honda Mobilio,Honda,2014,399999,44000,Petrol
+304,Toyota Corolla Altis,Toyota,2009,199999,65000,Petrol
+305,Honda City,Honda,2014,584999,39000,Petrol
+306,Skoda Laura,Skoda,2012,349999,44000,Diesel
+307,Renault Duster,Renault,2015,449999,49000,Diesel
+308,Maruti Suzuki Ertiga,Maruti,2018,799999,9000,Diesel
+309,Maruti Suzuki Dzire,Maruti,2015,444999,45000,Diesel
+310,Mahindra XUV500,Mahindra,2014,649999,47000,Diesel
+311,Hyundai Verna Fluidic,Hyundai,2012,444999,40000,Diesel
+312,Maruti Suzuki Vitara,Maruti,2016,689999,29000,Diesel
+313,Maruti Suzuki Wagon,Maruti,2016,344999,15000,Petrol
+314,Mahindra Scorpio,Mahindra,2015,944999,45000,Diesel
+315,Honda Amaze,Honda,2014,274999,35000,Petrol
+316,Mahindra XUV500,Mahindra,2013,689999,80000,Diesel
+317,Mahindra Scorpio,Mahindra,2013,574999,68000,Diesel
+318,Skoda Laura,Skoda,2013,374999,50000,Diesel
+319,Volkswagen Polo,Volkswagen,2010,199999,60000,Diesel
+320,Hyundai Elite i20,Hyundai,2016,549999,9000,Petrol
+321,Tata Manza Aura,Tata,2012,130000,72000,Diesel
+322,Chevrolet Sail UVA,Chevrolet,2013,210000,60000,Petrol
+323,Renault Duster 110,Renault,2012,501000,38000,Diesel
+324,Hyundai Verna Fluidic,Hyundai,2013,401000,45000,Diesel
+325,Audi A4 2.0,Audi,2012,1350000,40000,Diesel
+326,Hyundai Elantra SX,Hyundai,2013,600000,20000,Petrol
+327,Mahindra Scorpio VLX,Mahindra,2013,610000,35000,Diesel
+328,Mahindra KUV100 K8,Mahindra,2016,400000,20000,Diesel
+329,Renault Scala RxL,Renault,2015,375000,25000,Diesel
+330,Mahindra Quanto C8,Mahindra,2013,375000,20000,Diesel
+331,Hyundai Grand i10,Hyundai,2014,365000,20000,Petrol
+332,Hyundai i20 Active,Hyundai,2015,500000,18000,Petrol
+333,Mahindra Xylo E4,Mahindra,2012,400000,35000,Diesel
+334,Hyundai Grand i10,Hyundai,2017,524999,6821,Petrol
+335,Hyundai i20,Hyundai,2014,449999,23000,Petrol
+336,Hyundai Eon,Hyundai,2014,174999,14000,Petrol
+337,Hyundai i10,Hyundai,2012,244999,38000,Petrol
+338,Hyundai i20 Active,Hyundai,2015,574999,35000,Diesel
+339,Datsun Redi GO,Datsun,2017,244999,22000,Petrol
+340,Toyota Etios Liva,Toyota,2011,239999,41000,Petrol
+341,Hyundai Accent,Hyundai,2010,99999,45000,Petrol
+342,Hyundai Verna,Hyundai,2014,489999,44000,Diesel
+343,Maruti Suzuki Swift,Maruti,2013,324999,45000,Diesel
+344,Toyota Fortuner,Toyota,2011,1074999,52000,Diesel
+345,Hyundai i10 Sportz,Hyundai,2012,230000,34000,Petrol
+346,Mahindra Bolero Power,Mahindra,2018,699000,1800,Diesel
+347,Mahindra XUV500,Mahindra,2015,1000000,15000,Diesel
+348,Honda City 1.5,Honda,2010,240000,400000,Petrol
+349,Chevrolet Spark LT,Chevrolet,2009,110000,44000,Petrol
+350,Mahindra Jeep MM,Mahindra,2019,390000,60,Diesel
+351,Renault Duster 110PS,Renault,2012,501000,35000,Diesel
+352,Mahindra XUV500,Mahindra,2016,1130000,72000,Diesel
+353,Tata Indigo eCS,Tata,2014,250000,40000,Diesel
+354,Mahindra Bolero SLE,Mahindra,2013,330000,80200,Diesel
+355,Force Motors Force,Force,2015,580000,3200,Diesel
+356,Skoda Rapid Elegance,Skoda,2013,340000,48000,Diesel
+357,Tata Vista Quadrajet,Tata,2011,120000,90000,Diesel
+358,Maruti Suzuki Alto,Maruti,2015,265000,12000,Petrol
+359,Maruti Suzuki SX4,Maruti,2012,265000,46000,Diesel
+360,Maruti Suzuki Zen,Maruti,2003,85000,69900,Petrol
+361,Mahindra Jeep CL550,Mahindra,2019,379000,0,Diesel
+362,Hyundai i10 Magna,Hyundai,2011,175000,45000,Petrol
+363,Maruti Suzuki Alto,Maruti,2015,219000,5000,Petrol
+364,Maruti Suzuki Swift,Maruti,2016,350000,166000,Diesel
+365,Honda City ZX,Honda,2008,149000,42000,Petrol
+366,Mahindra Jeep CL550,Mahindra,2018,385000,588,Diesel
+367,Mahindra Jeep MM,Mahindra,2006,425000,122,Diesel
+368,Chevrolet Beat Diesel,Chevrolet,2017,150000,62000,Diesel
+369,Honda City 1.5,Honda,2010,225000,70000,Petrol
+370,Hyundai Verna 1.4,Hyundai,2014,375000,36000,Petrol
+371,Toyota Innova 2.5,Toyota,2012,770000,0,Diesel
+372,Maruti Suzuki Maruti,Maruti,1995,30000,55000,Petrol
+373,Toyota Etios,Toyota,2011,275000,36000,Diesel
+374,Volkswagen Polo,Volkswagen,2015,330000,38000,Diesel
+375,Maruti Suzuki Swift,Maruti,2014,335000,55000,Diesel
+376,Hyundai Elite i20,Hyundai,2015,450000,20000,Diesel
+377,Maruti Suzuki Swift,Maruti,2012,225000,40000,Petrol
+378,Maruti Suzuki Versa,Maruti,2004,80000,50000,Petrol
+379,Tata Indigo LX,Tata,2016,130000,104000,Diesel
+380,Volkswagen Vento Konekt,Volkswagen,2011,245000,65000,Diesel
+381,Mercedes Benz C,Mercedes,2002,399000,41000,Petrol
+382,Maruti Suzuki Ertiga,Maruti,2013,450000,90000,Diesel
+383,Honda City,Honda,2000,65000,80000,Petrol
+384,Hyundai Santro Xing,Hyundai,2006,75000,46000,Petrol
+385,Maruti Suzuki Omni,Maruti,2001,70000,70000,Petrol
+386,Hyundai Sonata Transform,Hyundai,2017,190000,36469,Diesel
+387,Hyundai Elite i20,Hyundai,2018,600000,7800,Petrol
+388,Volkswagen Vento Konekt,Volkswagen,2011,245000,65000,Diesel
+389,Maruti Suzuki Alto,Maruti,2017,240000,60000,Petrol
+390,Maruti Suzuki Alto,Maruti,2011,155000,32000,Petrol
+391,Honda Jazz S,Honda,2009,169999,24695,Petrol
+392,Hyundai Grand i10,Hyundai,2017,450000,15141,Petrol
+393,Maruti Suzuki Zen,Maruti,2001,40000,40000,Petrol
+394,Mahindra Scorpio W,Mahindra,2012,165000,65000,Diesel
+395,Maruti Suzuki Alto,Maruti,2014,270000,22000,Petrol
+396,Hyundai Grand i10,Hyundai,2016,280000,59910,Diesel
+397,Mahindra XUV500 W8,Mahindra,2012,560000,100000,Diesel
+398,Hyundai Creta 1.6,Hyundai,2016,950000,25000,Petrol
+399,Hyundai i20 Magna,Hyundai,2013,310000,35000,Petrol
+400,Renault Duster 85,Renault,2015,715000,65000,Diesel
+401,Hyundai Grand i10,Hyundai,2014,340000,35000,Petrol
+402,Honda Brio V,Honda,2012,235000,33000,Petrol
+403,Mahindra TUV300 T4,Mahindra,2017,610000,68000,Diesel
+404,Chevrolet Spark LS,Chevrolet,2010,95000,23000,Petrol
+405,Mahindra TUV300 T8,Mahindra,2018,1000000,4500,Diesel
+406,Maruti Suzuki Swift,Maruti,2015,220000,129000,Diesel
+407,Nissan X Trail,Nissan,2019,1200000,300,Diesel
+408,Maruti Suzuki Alto,Maruti,2015,230000,5000,Petrol
+409,Ford Ikon 1.3,Ford,2001,45000,65000,Petrol
+410,Toyota Fortuner 3.0,Toyota,2010,940000,131000,Diesel
+411,Tata Manza ELAN,Tata,2010,155555,111111,Petrol
+412,Mercedes Benz A,Mercedes,2013,1500000,14000,Petrol
+413,Chevrolet Beat LS,Chevrolet,2016,210000,22000,Diesel
+414,Ford EcoSport Trend,Ford,2013,495000,38000,Diesel
+415,Tata Indigo LS,Tata,2016,125000,70000,Diesel
+416,Hyundai i20 Magna,Hyundai,2010,195000,36000,Petrol
+417,Volkswagen Vento Highline,Volkswagen,2015,550000,34000,Diesel
+418,Renault Kwid RXT,Renault,2015,270000,43000,Petrol
+419,Ford EcoSport Titanium,Ford,2014,500000,40000,Diesel
+420,Honda Amaze 1.5,Honda,2016,240000,160000,Diesel
+421,Hyundai Verna 1.6,Hyundai,2017,800000,12000,Petrol
+422,BMW 5 Series,BMW,2011,1299000,49000,Diesel
+423,Skoda Superb 1.8,Skoda,2011,530000,68000,Petrol
+424,Audi Q3 2.0,Audi,2013,1499000,37000,Diesel
+425,Mahindra Bolero DI,Mahindra,2012,220000,59466,Diesel
+426,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+427,Ford Figo Duratorq,Ford,2012,250000,99000,Diesel
+428,Maruti Suzuki Wagon,Maruti,2018,395000,25500,Petrol
+429,Mahindra Logan Diesel,Mahindra,2009,130000,66000,Petrol
+430,Tata Nano GenX,Tata,2010,32000,44005,Petrol
+431,Mahindra TUV300 T4,Mahindra,2016,540000,35000,Diesel
+432,Mahindra TUV300 T4,Mahindra,2016,540000,35000,Diesel
+433,Hyundai Elite i20,Hyundai,2015,405000,28000,Petrol
+434,Hyundai Elite i20,Hyundai,2015,400000,30000,Petrol
+435,Honda City SV,Honda,2017,760000,4000,Petrol
+436,Maruti Suzuki Baleno,Maruti,2016,500000,28000,Petrol
+437,Ford Figo Petrol,Ford,2011,175000,75000,Petrol
+438,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+439,Honda City,Honda,2017,750000,3000,Petrol
+440,Hyundai Elite i20,Hyundai,2015,419000,20000,Petrol
+441,Maruti Suzuki Versa,Maruti,2004,90000,50000,Petrol
+442,Hyundai Eon Era,Hyundai,2018,140000,2110,Petrol
+443,Mitsubishi Pajero Sport,Mitsubishi,2015,1540000,43222,Petrol
+444,Hyundai i10 Magna,Hyundai,2008,275000,100200,Petrol
+445,Toyota Corolla H2,Toyota,2003,150000,100000,Petrol
+446,Maruti Suzuki Swift,Maruti,2011,230000,65,Petrol
+447,Tata Indigo CS,Tata,2015,123000,100000,Diesel
+448,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+449,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+450,Hyundai Xcent Base,Hyundai,2016,300000,140000,Diesel
+451,Honda City,Honda,2015,499999,55000,Petrol
+452,Hyundai Accent Executive,Hyundai,2009,165000,48000,Petrol
+453,Maruti Suzuki Baleno,Maruti,2016,498000,22000,Petrol
+454,Tata Zest XE,Tata,2018,480000,103553,Diesel
+455,Maruti Suzuki Dzire,Maruti,2017,488000,80000,Diesel
+456,Tata Sumo Gold,Tata,2014,250000,99000,Diesel
+457,Toyota Corolla Altis,Toyota,2010,220000,58000,Petrol
+458,Maruti Suzuki Eeco,Maruti,2013,290000,70000,LPG
+459,Toyota Fortuner 3.0,Toyota,2015,1525000,120000,Diesel
+460,Mahindra XUV500 W6,Mahindra,2013,548900,49800,Diesel
+461,Tata Tigor Revotron,Tata,2019,650000,100,Diesel
+462,Maruti Suzuki 800,Maruti,2001,55000,81876,Petrol
+463,Maruti Suzuki Ertiga,Maruti,2015,550000,75000,Petrol
+464,Maruti Suzuki Versa,Maruti,2004,90000,50000,Petrol
+465,Honda Mobilio S,Honda,2014,399000,44000,Diesel
+466,Maruti Suzuki Ertiga,Maruti,2016,730000,55000,Diesel
+467,Maruti Suzuki Vitara,Maruti,2017,725000,36000,Diesel
+468,Hyundai Verna 1.6,Hyundai,2016,195000,56000,Diesel
+469,Maruti Suzuki Swift,Maruti,2007,130000,62000,Petrol
+470,Toyota Fortuner 3.0,Toyota,2015,1525000,120000,Diesel
+471,Maruti Suzuki Omni,Maruti,2014,190000,6020,Petrol
+472,Honda Amaze,Honda,2013,250000,55700,Diesel
+473,Tata Indica,Tata,2005,80000,42000,Petrol
+474,Hyundai Santro Xing,Hyundai,2003,120000,50000,Petrol
+475,Maruti Suzuki Zen,Maruti,2010,149000,35000,Petrol
+476,Maruti Suzuki Wagon,Maruti,2014,250000,18500,Petrol
+477,Maruti Suzuki Wagon,Maruti,2007,120000,7000,Petrol
+478,Honda Brio VX,Honda,2017,450000,11000,Petrol
+479,Maruti Suzuki Zen,Maruti,2003,99999,53000,Petrol
+480,Maruti Suzuki Zen,Maruti,2008,135000,23000,Petrol
+481,Maruti Suzuki Wagon,Maruti,2016,225000,35500,Diesel
+482,Maruti Suzuki Alto,Maruti,2010,99000,22134,Petrol
+483,Renault Kwid RXT,Renault,2019,370000,1000,Petrol
+484,Tata Nano Lx,Tata,2010,52000,9000,Petrol
+485,Jaguar XE XE,Jaguar,2016,2800000,8500,Petrol
+486,Hyundai Eon Magna,Hyundai,2014,190000,35000,Petrol
+487,Honda City 1.5,Honda,2014,499000,22000,Petrol
+488,Hindustan Motors Ambassador,Hindustan,2002,90000,25000,Diesel
+489,Maruti Suzuki Ritz,Maruti,2010,149000,40000,Petrol
+490,Hyundai Grand i10,Hyundai,2017,400000,20000,Petrol
+491,Hyundai Eon D,Hyundai,2016,120000,87000,Petrol
+492,Maruti Suzuki Swift,Maruti,2015,250000,55000,Petrol
+493,Maruti Suzuki Wagon,Maruti,2017,375000,23000,Petrol
+494,Honda Amaze 1.2,Honda,2014,381000,6000,Petrol
+495,Maruti Suzuki Estilo,Maruti,2013,180000,65000,Petrol
+496,Maruti Suzuki Vitara,Maruti,2016,580000,25000,Diesel
+497,Maruti Suzuki Eeco,Maruti,2015,278000,39000,Petrol
+498,Hyundai Creta 1.6,Hyundai,2016,1000000,8000,Petrol
+499,Mahindra Scorpio Vlx,Mahindra,2013,690000,75000,Diesel
+500,Maruti Suzuki Ertiga,Maruti,2012,480000,51000,Diesel
+501,Mitsubishi Lancer 1.8,Mitsubishi,2006,85000,50000,Petrol
+502,Maruti Suzuki Maruti,Maruti,2001,40000,75000,Petrol
+503,Maruti Suzuki Alto,Maruti,2015,90000,55800,Petrol
+504,Hyundai Grand i10,Hyundai,2015,340000,53000,Petrol
+505,Hyundai Eon D,Hyundai,2018,260000,25000,Petrol
+506,Ford Fiesta SXi,Ford,2009,250000,56400,Petrol
+507,Maruti Suzuki Ritz,Maruti,2010,180000,72160,Diesel
+508,Hyundai Verna Fluidic,Hyundai,2012,350000,10000,Diesel
+509,Maruti Suzuki Wagon,Maruti,2006,90001,48000,Petrol
+510,Maruti Suzuki Estilo,Maruti,2007,115000,36000,Petrol
+511,Audi A6 2.0,Audi,2012,1599000,11500,Diesel
+512,Maruti Suzuki Wagon,Maruti,2003,130000,133000,Petrol
+513,Maruti Suzuki Wagon,Maruti,2009,159000,27000,Petrol
+514,Maruti Suzuki Wagon,Maruti,2009,160000,35000,Petrol
+515,Maruti Suzuki Alto,Maruti,2010,110000,55000,Petrol
+516,Maruti Suzuki Baleno,Maruti,2016,425000,40000,Petrol
+517,Hyundai Verna 1.6,Hyundai,2019,900000,2000,Petrol
+518,Maruti Suzuki Swift,Maruti,2009,150000,45000,Petrol
+519,Hyundai Getz Prime,Hyundai,2009,110000,20000,Petrol
+520,Hyundai Santro,Hyundai,2000,51999,88000,Petrol
+521,Hyundai Getz Prime,Hyundai,2009,115000,20000,Petrol
+522,Chevrolet Beat PS,Chevrolet,2012,215000,65422,Diesel
+523,Ford EcoSport Trend,Ford,2017,580000,10000,Petrol
+524,Maruti Suzuki Dzire,Maruti,2013,380000,35000,Petrol
+525,Hyundai Fluidic Verna,Hyundai,2013,350000,117000,Diesel
+526,Tata Indica V2,Tata,2005,35000,150000,Diesel
+527,BMW X1 xDrive20d,BMW,2011,1150000,72000,Diesel
+528,Hyundai i20 Asta,Hyundai,2010,300000,10750,Petrol
+529,Honda City 1.5,Honda,2009,269000,55000,Petrol
+530,Tata Nano,Tata,2013,60000,6800,Petrol
+531,Chevrolet Cruze LTZ,Chevrolet,2014,400000,41000,Diesel
+532,Hyundai Verna Fluidic,Hyundai,2015,430000,73000,Diesel
+533,Maruti Suzuki Swift,Maruti,2011,140000,65000,Diesel
+534,Mahindra XUV500 W6,Mahindra,2014,8500003,45000,Diesel
+535,Mahindra XUV500 W10,Mahindra,2018,1299000,40000,Diesel
+536,Maruti Suzuki Alto,Maruti,2014,199000,37000,Petrol
+537,Hyundai Accent GLE,Hyundai,2006,90000,55000,Petrol
+538,Force Motors One,Force,2013,550000,140000,Diesel
+539,Maruti Suzuki Alto,Maruti,2019,265000,9800,Petrol
+540,Chevrolet Spark 1.0,Chevrolet,2011,100000,27000,Petrol
+541,Hyundai i10,Hyundai,2009,215000,27000,Petrol
+542,Toyota Etios Liva,Toyota,2012,380000,20000,Diesel
+543,Renault Duster 85PS,Renault,2013,401919,57923,Diesel
+544,Chevrolet Enjoy,Chevrolet,2014,490000,30201,Diesel
+545,Maruti Suzuki Alto,Maruti,2017,280000,6200,Petrol
+546,BMW 5 Series,BMW,2009,650000,37518,Petrol
+547,Toyota Etios Liva,Toyota,2014,160000,24652,Petrol
+548,Mahindra Jeep MM,Mahindra,2004,424000,383,Diesel
+549,Chevrolet Beat LS,Chevrolet,2016,225000,95000,Diesel
+550,Chevrolet Cruze LTZ,Chevrolet,2011,350000,35000,Diesel
+551,Jeep Wrangler Unlimited,Jeep,2015,950000,3528,Diesel
+552,Maruti Suzuki Ertiga,Maruti,2013,485000,52500,Diesel
+553,Hyundai Verna VGT,Hyundai,2010,205000,47900,Diesel
+554,Maruti Suzuki Omni,Maruti,2012,160000,14000,Petrol
+555,Maruti Suzuki Celerio,Maruti,2018,310000,37000,Petrol
+556,Tata Zest Quadrajet,Tata,2017,180000,90000,Diesel
+557,Mahindra XUV500 W6,Mahindra,2013,549900,52800,Diesel
+558,Tata Indigo CS,Tata,2016,150000,104000,Diesel
+559,Hyundai i10 Era,Hyundai,2011,175000,30000,Petrol
+560,Tata Indigo eCS,Tata,2014,95000,195000,Diesel
+561,Tata Indigo LX,Tata,2016,230000,104000,Diesel
+562,Tata Indigo eCS,Tata,2016,230000,104000,Diesel
+563,Tata Indigo Marina,Tata,2004,180000,70000,Diesel
+564,Hyundai Xcent SX,Hyundai,2015,400000,43000,Diesel
+565,Hyundai Eon Magna,Hyundai,2013,185000,23000,Petrol
+566,Renault Duster 85,Renault,2015,385000,51000,Diesel
+567,Maruti Suzuki Alto,Maruti,2009,90000,62000,Petrol
+568,Tata Nano LX,Tata,2010,32000,48008,Petrol
+569,Renault Duster 110,Renault,2013,435000,39000,Diesel
+570,Maruti Suzuki Wagon,Maruti,2010,225000,40000,Petrol
+571,Maruti Suzuki Swift,Maruti,2006,189700,48247,Petrol
+572,Maruti Suzuki Ertiga,Maruti,2012,389700,39000,Diesel
+573,Maruti Suzuki Swift,Maruti,2014,365000,23000,Petrol
+574,Maruti Suzuki Alto,Maruti,2017,360000,9400,Petrol
+575,Hyundai i20 Magna,Hyundai,2010,210000,50000,Petrol
+576,Hyundai i10 Magna,Hyundai,2009,170000,75000,Petrol
+577,Tata Zest XE,Tata,2017,380000,70000,Diesel
+578,Mahindra Xylo E8,Mahindra,2009,295000,64000,Diesel
+579,Toyota Corolla Altis,Toyota,2010,185000,55000,Petrol
+580,Tata Manza Aqua,Tata,2014,160000,200000,Diesel
+581,Renault Kwid 1.0,Renault,2018,290000,2137,Petrol
+582,Tata Venture EX,Tata,2013,100000,30000,Diesel
+583,Maruti Suzuki Swift,Maruti,2014,315000,44000,Petrol
+584,Skoda Octavia Classic,Skoda,2006,114990,65000,Diesel
+585,Maruti Suzuki Omni,Maruti,2012,120000,160000,LPG
+586,Chevrolet Beat Diesel,Chevrolet,2011,125000,56000,Diesel
+587,Tata Sumo Gold,Tata,2012,210000,75000,Diesel
+588,Hyundai Verna 1.6,Hyundai,2018,855000,42000,Diesel
+589,Tata Sumo Gold,Tata,2012,210000,75000,Diesel
+590,Mahindra Scorpio 2.6,Mahindra,2007,260000,56000,Diesel
+591,Maruti Suzuki Zen,Maruti,2002,95000,10544,Petrol
+592,Maruti Suzuki Swift,Maruti,2011,255000,64000,Petrol
+593,Mahindra Scorpio SLX,Mahindra,2008,300000,70000,Diesel
+594,Hyundai Grand i10,Hyundai,2014,340000,25000,Petrol
+595,Hyundai Elite i20,Hyundai,2017,550000,15000,Petrol
+596,Ford Ikon 1.6,Ford,2003,60000,50000,Petrol
+597,Toyota Innova 2.5,Toyota,2011,750000,147000,Diesel
+598,Nissan Sunny XL,Nissan,2011,230000,52000,Petrol
+599,Chevrolet Beat LT,Chevrolet,2012,130000,90001,Diesel
+600,Maruti Suzuki Alto,Maruti,2017,270000,21000,Petrol
+601,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel
+602,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel
+603,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel
+604,Toyota Innova 2.0,Toyota,2012,600000,80000,Diesel
+605,Maruti Suzuki Swift,Maruti,2010,190000,74000,Diesel
+606,Hyundai Elite i20,Hyundai,2015,500000,22000,Petrol
+607,Mahindra XUV500 W10,Mahindra,2016,1065000,41000,Diesel
+608,Volkswagen Polo Trendline,Volkswagen,2015,350000,25000,Diesel
+609,Toyota Etios Liva,Toyota,2012,350000,85000,Diesel
+610,Mahindra TUV300 T4,Mahindra,2016,540000,29500,Diesel
+611,Hyundai Elite i20,Hyundai,2015,470000,30000,Petrol
+612,Hyundai Santro Xing,Hyundai,2014,179000,57000,Petrol
+613,Maruti Suzuki Zen,Maruti,2003,48000,60000,Petrol
+614,Maruti Suzuki Ciaz,Maruti,2016,650000,50000,Petrol
+615,Hyundai Eon Era,Hyundai,2013,190000,39700,Petrol
+616,Hyundai Elantra 1.8,Hyundai,2012,500000,65000,Petrol
+617,Maruti Suzuki Swift,Maruti,2010,270000,67000,Diesel
+618,Maruti Suzuki Zen,Maruti,2008,125000,46000,Petrol
+619,Hyundai Eon Era,Hyundai,2012,188000,38000,Petrol
+620,Hyundai Grand i10,Hyundai,2016,380000,27000,Petrol
+621,Hyundai Verna Fluidic,Hyundai,2011,365000,43000,Diesel
+622,Ford EcoSport Trend,Ford,2014,465000,47000,Petrol
+623,Hyundai i20 Magna,Hyundai,2011,240000,42000,Petrol
+624,Chevrolet Beat Diesel,Chevrolet,2016,179999,19336,Diesel
+625,Tata Indica eV2,Tata,2015,140000,60105,Diesel
+626,Jaguar XF 2.2,Jaguar,2013,2190000,29000,Diesel
+627,Audi Q5 2.0,Audi,2014,2390000,34000,Diesel
+628,BMW 3 Series,BMW,2011,1075000,35000,Diesel
+629,Maruti Suzuki Swift,Maruti,2015,475000,22000,Petrol
+630,BMW X1 sDrive20d,BMW,2012,1025000,41000,Diesel
+631,Maruti Suzuki S,Maruti,2016,615000,21000,Diesel
+632,Maruti Suzuki Ertiga,Maruti,2013,475000,48000,Diesel
+633,Maruti Suzuki Alto,Maruti,2016,270000,38000,Petrol
+634,Honda City SV,Honda,2014,475000,34000,Diesel
+635,Volkswagen Vento Comfortline,Volkswagen,2011,240000,45933,Petrol
+636,Honda City 1.5,Honda,2005,120000,68000,Petrol
+637,Audi A4 2.0,Audi,2016,1900000,44000,Diesel
+638,Mahindra KUV100,Mahindra,2017,360000,35000,Diesel
+639,Tata Zest XE,Tata,2018,450000,102563,Diesel
+640,Mahindra XUV500 W8,Mahindra,2015,900000,28600,Diesel
+641,Maruti Suzuki Swift,Maruti,2017,650000,41800,Diesel
+642,Tata Sumo Gold,Tata,2014,275000,116000,Diesel
+643,Maruti Suzuki Swift,Maruti,2009,210000,59000,Petrol
+644,Mahindra Scorpio 2.6,Mahindra,2004,175000,58000,Diesel
+645,Maruti Suzuki Omni,Maruti,2009,85000,45000,Petrol
+646,Mitsubishi Pajero Sport,Mitsubishi,2015,1490000,42590,Diesel
+647,Renault Duster,Renault,2014,800000,7400,Diesel
+648,Volkswagen Jetta Comfortline,Volkswagen,2009,450000,54500,Diesel
+649,Maruti Suzuki Ertiga,Maruti,2012,1000000,200000,Diesel
+650,Audi A4 2.0,Audi,2013,1510000,27000,Diesel
+651,Volvo S80 Summum,Volvo,2015,1850000,42000,Diesel
+652,Toyota Corolla Altis,Toyota,2014,790000,29000,Petrol
+653,Mitsubishi Pajero Sport,Mitsubishi,2015,1725000,37000,Diesel
+654,Chevrolet Beat LT,Chevrolet,2012,135000,36000,Petrol
+655,BMW X1,BMW,2011,1000000,34000,Diesel
+656,Datsun Redi GO,Datsun,2018,299999,7000,Petrol
+657,Mercedes Benz C,Mercedes,2009,1225000,76000,Diesel
+658,Mahindra Scorpio SLX,Mahindra,2004,175000,60000,Diesel
+659,Volkswagen Vento Comfortline,Volkswagen,2011,200000,95000,Diesel
+660,Tata Indigo CS,Tata,2017,270000,50000,Diesel
+661,Ford Figo Petrol,Ford,2019,525000,0,Petrol
+662,Honda City ZX,Honda,2006,180000,50000,Petrol
+663,Maruti Suzuki Wagon,Maruti,2008,140000,68000,Petrol
+664,Ford EcoSport Trend,Ford,2014,400000,16000,Petrol
+665,Maruti Suzuki Swift,Maruti,2016,499000,51000,Diesel
+666,Maruti Suzuki Omni,Maruti,2009,85000,56000,Petrol
+667,Maruti Suzuki Zen,Maruti,2004,70000,100000,Petrol
+668,Renault Duster RxL,Renault,2015,550000,36000,Petrol
+669,Maruti Suzuki Swift,Maruti,2014,370000,11523,Petrol
+670,Maruti Suzuki Baleno,Maruti,2018,690000,1000,Petrol
+671,Honda WR V,Honda,2009,250000,60000,Petrol
+672,Tata Indigo CS,Tata,2016,110000,85000,Diesel
+673,Renault Duster 110,Renault,2013,490000,38600,Diesel
+674,Mahindra Scorpio LX,Mahindra,2009,320000,95500,Diesel
+675,Maruti Suzuki Zen,Maruti,2004,68000,56000,Petrol
+676,Maruti Suzuki Wagon,Maruti,2014,130000,37458,Petrol
+677,Maruti Suzuki SX4,Maruti,2016,970000,85960,Diesel
+678,Audi A3 Cabriolet,Audi,2015,3100000,12516,Petrol
+679,Hyundai Eon D,Hyundai,2018,280000,35000,Petrol
+680,Maruti Suzuki Zen,Maruti,2009,125000,0,Petrol
+681,Mahindra Scorpio SLX,Mahindra,2008,285000,80000,Diesel
+682,Hyundai Santro AE,Hyundai,2011,165000,45000,Petrol
+683,Maruti Suzuki Swift,Maruti,2009,250000,51000,Diesel
+684,Mahindra Scorpio S4,Mahindra,2015,865000,30000,Diesel
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+686,Hyundai Santro,Hyundai,2003,60000,51000,Petrol
+687,Chevrolet Beat LT,Chevrolet,2015,215000,90000,Diesel
+688,Maruti Suzuki Swift,Maruti,2015,475000,43000,Diesel
+689,Mahindra XUV500 W8,Mahindra,2015,899000,53000,Diesel
+690,Toyota Fortuner 3.0,Toyota,2013,1499000,97000,Diesel
+691,Maruti Suzuki Alto,Maruti,2013,240000,20000,Petrol
+692,Hyundai Getz GLE,Hyundai,2007,99000,55000,Petrol
+693,Maruti Suzuki Swift,Maruti,2014,260000,120000,Diesel
+694,Hyundai Creta 1.6,Hyundai,2019,1200000,0,Petrol
+695,Hyundai Santro Xing,Hyundai,2007,115000,46000,Petrol
+696,Hyundai Santro Xing,Hyundai,2009,88000,43200,Petrol
+697,Mahindra Xylo D2,Mahindra,2011,390000,56000,Diesel
+698,Hyundai Santro Xing,Hyundai,2007,135000,42000,Petrol
+699,Tata Indica V2,Tata,2009,90000,30600,Diesel
+700,Hyundai i10 Sportz,Hyundai,2011,220000,38000,Petrol
+701,Hyundai Grand i10,Hyundai,2017,424999,2550,Petrol
+702,Hyundai Santro Xing,Hyundai,2007,135000,47000,Petrol
+703,Honda City 1.5,Honda,2005,95000,41000,Petrol
+704,Nissan Micra XL,Nissan,2017,430000,62500,Diesel
+705,Honda City 1.5,Honda,2005,115000,68000,Petrol
+706,Maruti Suzuki Alto,Maruti,2015,215000,50000,Petrol
+707,Maruti Suzuki Wagon,Maruti,2004,53000,69000,Petrol
+708,Maruti Suzuki Ertiga,Maruti,2012,500000,48000,Diesel
+709,Tata Indica eV2,Tata,2012,85000,55000,Diesel
+710,Maruti Suzuki Omni,Maruti,2013,165000,25000,Petrol
+711,Hyundai Eon Era,Hyundai,2014,200000,28400,Petrol
+712,Hyundai Eon,Hyundai,2014,200000,28000,Petrol
+713,Maruti Suzuki Swift,Maruti,2015,425000,42000,Diesel
+714,Hyundai Verna 1.6,Hyundai,2012,600000,29000,Diesel
+715,Chevrolet Tavera LS,Chevrolet,2005,130000,68485,Diesel
+716,Tata Tiago Revotron,Tata,2018,430000,3500,Petrol
+717,Tata Tiago Revotorq,Tata,2019,568500,0,Petrol
+718,Maruti Suzuki Zen,Maruti,2006,71000,32000,Petrol
+719,Mahindra KUV100 K8,Mahindra,2018,560000,8000,Diesel
+720,Ford EcoSport Titanium,Ford,2014,590000,34000,Diesel
+721,Hindustan Motors Ambassador,Hindustan,1995,750000,37000,Petrol
+722,Ford Fusion 1.4,Ford,2007,125000,85455,Diesel
+723,Hyundai Santro Xing,Hyundai,2007,135000,46000,Petrol
+724,Hyundai Santro,Hyundai,2002,60000,47000,Petrol
+725,Fiat Linea Emotion,Fiat,2009,120000,64000,Petrol
+726,Ford Ikon 1.3,Ford,2008,95000,46000,Petrol
+727,Maruti Suzuki Omni,Maruti,2017,240000,8000,Petrol
+728,Tata Indica V2,Tata,2012,115000,64000,Diesel
+729,Mahindra Scorpio S4,Mahindra,2015,795000,63000,Diesel
+730,Hyundai Santro Xing,Hyundai,2007,55000,65000,Petrol
+731,Mahindra Xylo D2,Mahindra,2009,300000,62000,Diesel
+732,Hyundai Grand i10,Hyundai,2014,320000,41000,Petrol
+733,Maruti Suzuki Alto,Maruti,2015,265000,14000,Petrol
+734,Toyota Corolla,Toyota,2006,160000,40000,Petrol
+735,Hyundai Eon Magna,Hyundai,2017,300000,1600,Petrol
+736,Tata Sumo Grande,Tata,2010,130000,90000,Diesel
+737,Maruti Suzuki Swift,Maruti,2011,250000,58000,Diesel
+738,Volkswagen Polo Highline1.2L,Volkswagen,2013,380000,27000,Petrol
+739,Maruti Suzuki Alto,Maruti,2003,42000,60000,Petrol
+740,Tata Tiago Revotron,Tata,2017,400000,31000,Petrol
+741,Maruti Suzuki Swift,Maruti,2009,120000,90000,Diesel
+742,Maruti Suzuki Swift,Maruti,2009,120000,90000,Diesel
+743,Tata Indigo eCS,Tata,2016,130000,150000,Diesel
+744,Chevrolet Beat LS,Chevrolet,2014,189000,31000,Diesel
+745,Mahindra Xylo E8,Mahindra,2011,365000,43000,Diesel
+746,Hyundai Eon D,Hyundai,2013,170000,20000,Petrol
+747,Tata Sumo Gold,Tata,2013,215000,100000,Petrol
+748,Tata Nano,Tata,2013,60000,7000,Petrol
+749,Hyundai Elite i20,Hyundai,2017,599999,31000,Petrol
+750,Hyundai i10 Magna,Hyundai,2009,400000,33000,Petrol
+751,Hyundai Creta,Hyundai,2016,900000,60000,Diesel
+752,Volkswagen Polo,Volkswagen,2013,299999,48000,Diesel
+753,Maruti Suzuki Dzire,Maruti,2014,374999,33000,Petrol
+754,Tata Bolt XM,Tata,2015,600000,15000,Petrol
+755,Maruti Suzuki Alto,Maruti,2005,70000,47000,Petrol
+756,Maruti Suzuki Alto,Maruti,2005,100000,40000,Petrol
+757,Maruti Suzuki Ritz,Maruti,2010,150000,38000,Diesel
+758,Maruti Suzuki Alto,Maruti,2017,225000,12500,Petrol
+759,Maruti Suzuki Dzire,Maruti,2009,210000,42000,Petrol
+760,Hyundai i20 Asta,Hyundai,2014,425000,31000,Petrol
+761,Maruti Suzuki Swift,Maruti,2008,162000,60000,Diesel
+762,Tata Indica V2,Tata,2005,60000,80000,Diesel
+763,Mahindra Scorpio VLX,Mahindra,2014,650000,77000,Diesel
+764,Toyota Innova 2.5,Toyota,2012,750000,75000,Diesel
+765,Mahindra Xylo E8,Mahindra,2010,375000,40000,Diesel
+766,Hyundai i20 Magna,Hyundai,2011,230000,47000,Petrol
+767,Maruti Suzuki Omni,Maruti,2000,35999,60000,Petrol
+768,Mahindra KUV100,Mahindra,2016,380000,26500,Petrol
+769,Mahindra KUV100 K8,Mahindra,2019,560000,2875,Petrol
+770,Datsun Go Plus,Datsun,2016,285000,13900,Petrol
+771,Ford Endeavor 4x4,Ford,2019,2900000,9000,Diesel
+772,Tata Indica V2,Tata,2005,39999,80000,Diesel
+773,Hyundai Santro Xing,Hyundai,2006,85000,60000,Petrol
+774,Maruti Suzuki Wagon,Maruti,2016,395000,20000,Petrol
+775,Maruti Suzuki Swift,Maruti,2008,175000,58000,Diesel
+776,Maruti Suzuki Alto,Maruti,2019,400000,1500,Petrol
+777,Toyota Innova 2.5,Toyota,2011,750000,75000,Diesel
+778,Maruti Suzuki Alto,Maruti,2016,250000,2450,Petrol
+779,Maruti Suzuki Alto,Maruti,2019,425000,1625,Petrol
+780,Volkswagen Polo Highline1.2L,Volkswagen,2017,525000,45000,Petrol
+781,Mahindra Logan,Mahindra,2009,130000,65000,Diesel
+782,Maruti Suzuki 800,Maruti,2000,30000,33400,Petrol
+783,Mahindra Scorpio,Mahindra,2011,475000,60123,Diesel
+784,Chevrolet Sail 1.2,Chevrolet,2013,300000,28000,Petrol
+785,Hyundai Santro AE,Hyundai,2003,60000,70000,Petrol
+786,Maruti Suzuki Wagon,Maruti,2006,100000,7000,Petrol
+787,Hyundai Eon,Hyundai,2018,260000,25000,Petrol
+788,Tata Manza,Tata,2015,100000,100000,Diesel
+789,Toyota Etios G,Toyota,2013,265000,42000,Petrol
+790,Hyundai Getz Prime,Hyundai,2009,115000,20000,Petrol
+791,Toyota Qualis,Toyota,2003,180000,100000,Diesel
+792,Hyundai Santro Xing,Hyundai,2004,45000,137495,Petrol
+793,Tata Indica eV2,Tata,2016,50500,91200,Diesel
+794,Honda City 1.5,Honda,2009,270000,55000,Petrol
+795,Tata Zest XE,Tata,2017,290000,120000,Diesel
+796,Mahindra Quanto C4,Mahindra,2013,325000,63000,Diesel
+797,Tata Indigo eCS,Tata,2016,160000,104000,Diesel
+798,Maruti Suzuki Swift,Maruti,2016,350000,146000,Diesel
+799,Hyundai Elite i20,Hyundai,2011,290000,40000,Petrol
+800,Hyundai i20 Select,Hyundai,2011,290000,40000,Petrol
+801,Chevrolet Tavera Neo,Chevrolet,2007,465000,100800,Diesel
+802,Maruti Suzuki Dzire,Maruti,2016,325000,150000,Diesel
+803,Hyundai Elite i20,Hyundai,2018,510000,2100,Petrol
+804,Honda City VX,Honda,2016,860000,95000,Petrol
+805,Maruti Suzuki Dzire,Maruti,2016,450000,2500,Diesel
+806,Hyundai Getz,Hyundai,2006,125000,80000,Petrol
+807,Mercedes Benz C,Mercedes,2006,500001,15000,Petrol
+808,Maruti Suzuki Alto,Maruti,2005,95000,65000,Petrol
+809,Maruti Suzuki Swift,Maruti,2009,250000,51000,Diesel
+810,Skoda Fabia,Skoda,2009,110000,45000,Petrol
+811,Maruti Suzuki Ritz,Maruti,2011,270000,50000,Petrol
+812,Tata Indica V2,Tata,2009,110000,30000,Diesel
+813,Toyota Corolla Altis,Toyota,2009,300000,132000,Petrol
+814,Tata Zest XM,Tata,2018,260000,27000,Diesel
+815,Mahindra Quanto C8,Mahindra,2013,390000,40000,Diesel
diff --git a/models/Pre Owned Car Price Predictor/LinearRegressionModel.pkl b/models/Pre Owned Car Price Predictor/LinearRegressionModel.pkl
new file mode 100644
index 00000000..51b3524f
Binary files /dev/null and b/models/Pre Owned Car Price Predictor/LinearRegressionModel.pkl differ
diff --git a/models/Pre Owned Car Price Predictor/README.md b/models/Pre Owned Car Price Predictor/README.md
new file mode 100644
index 00000000..b5107abb
--- /dev/null
+++ b/models/Pre Owned Car Price Predictor/README.md	
@@ -0,0 +1,63 @@
+# Pre-Owned Car Price Predictor
+
+## Overview
+The **Pre-Owned Car Price Predictor** is a machine learning project aimed at predicting the prices of pre-owned cars based on various features such as make, model, year, mileage, and fuel type. This tool helps potential buyers and sellers get an accurate price estimation for used cars, enabling informed decision-making in the car resale market.
+
+## Features
+- **Comprehensive Dataset Analysis**: The project uses a dataset with essential car details, including make, model, year, mileage, fuel type, and price, for model training.
+- **Model Performance Evaluation**: The model's performance is measured using Mean Absolute Error (MAE) and R-squared metrics to ensure accurate predictions.
+
+## Technologies Used
+- **Python**: The primary programming language used in the project.
+- **Machine Learning**: Predictive modeling to estimate car prices based on user inputs.
+- **Scikit-Learn**: Used for model building and evaluation.
+- **Pandas and NumPy**: Data manipulation and preprocessing.
+- **Matplotlib and Seaborn**: Data visualization for analyzing relationships and trends within the data.
+
+## Skills Demonstrated
+1. **Data Preprocessing**: Cleaning and transforming raw data for analysis and model training.
+2. **Data Manipulation**: Extracting meaningful features and organizing data for the model.
+3. **Data Visualization**: Using plots and charts to understand data distributions and correlations.
+4. **Data Cleaning**: Handling missing values and removing outliers to improve model accuracy.
+5. **Statistical Analysis**: Assessing relationships between features and the target variable.
+6. **Machine Learning Modeling**: Building and evaluating predictive models.
+7. **Problem Solving**: Developing a solution for price prediction in a real-world context.
+
+## Data Sources
+ 
+- **Data Preprocessing**: Extensive preprocessing was required to clean the data, handle missing values, and engineer features for analysis.
+
+## Model Training and Evaluation
+- The machine learning model was trained using a **Linear Regression** algorithm, achieving an accuracy of **86%** on the test set.
+- **Model Pipeline**: A robust pipeline was built to automate preprocessing and model training steps, making the solution scalable.
+- **Hyperparameter Tuning**: Parameters were optimized to enhance the model's accuracy and generalization.
+
+## Getting Started
+
+### Prerequisites
+- Python 3.x
+- Required packages listed in `requirements.txt`
+
+### Installation
+1. Clone the repository:
+    ```bash
+    git clone https://github.com/yashasvini121/predictive-calc
+    ```
+2. Navigate to the project directory:
+    ```bash
+    cd car-price-predictor
+    ```
+3. Install the required packages:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+## Project Structure
+- `LinearRegressionModel.pkl`: Serialized machine learning model.
+- `Cleaned_Car_data.csv`: The cleaned dataset used for training.
+
+
+## Acknowledgments
+
+- **Libraries and Tools**: Python,  Scikit-Learn, Pandas, NumPy, Matplotlib, and Seaborn for their support in building this project.
+
diff --git a/models/Pre Owned Car Price Predictor/car_price_predictor.ipynb b/models/Pre Owned Car Price Predictor/car_price_predictor.ipynb
new file mode 100644
index 00000000..42ef73c4
--- /dev/null
+++ b/models/Pre Owned Car Price Predictor/car_price_predictor.ipynb	
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{"id":"l_ga3NT4ATXk"},"source":["# Car Price Predictor"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":1318,"status":"ok","timestamp":1708073867329,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"7YLJBi5zASXp"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import matplotlib as mpl\n","%matplotlib inline\n","mpl.style.use('ggplot')"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":694,"status":"ok","timestamp":1708073869573,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"njjlg8EwASXt"},"outputs":[],"source":["car=pd.read_csv('/content/drive/MyDrive/Data Sets/car.csv')"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1708073870976,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"fp6N0MyRASXu","outputId":"d306fe5d-158d-4ec7-c4f3-bf635ef99c4d"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"car\",\n  \"rows\": 892,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Maruti Suzuki Ritz GENUS VXI\",\n          \"Toyota Innova 2.0 G4\",\n          \"Hyundai Eon\"\n        ],\n        \"num_unique_values\": 525,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"URJENT\",\n          \"7\",\n          \"selling\"\n        ],\n        \"num_unique_values\": 48,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"2007\",\n          \"2012\",\n          \"n...\"\n        ],\n        \"num_unique_values\": 61,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"4,89,999\",\n          \"2,39,999\",\n          \"1,40,000\"\n        ],\n        \"num_unique_values\": 274,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kms_driven\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"24,330 kms\",\n          \"50,000 kms\",\n          \"60,000 kms\"\n        ],\n        \"num_unique_values\": 258,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fuel_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Petrol\",\n          \"Diesel\",\n          \"LPG\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}","type":"dataframe","variable_name":"car"},"text/html":["\n","  <div id=\"df-b4fc5e91-7545-45e9-a930-7ce9de6e9806\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr 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"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["car.head()"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":583,"status":"ok","timestamp":1708073874288,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"fHrl8CLxASXv","outputId":"5fd7196b-27f2-4d94-85de-36cbbc23d3dd"},"outputs":[{"data":{"text/plain":["(892, 6)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["car.shape"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1708073875018,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"-JgokO5SASXv","outputId":"c7e171f9-fdff-4e79-a3ae-e335a9507609"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 892 entries, 0 to 891\n","Data columns (total 6 columns):\n"," #   Column      Non-Null Count  Dtype \n","---  ------      --------------  ----- \n"," 0   name        892 non-null    object\n"," 1   company     892 non-null    object\n"," 2   year        892 non-null    object\n"," 3   Price       892 non-null    object\n"," 4   kms_driven  840 non-null    object\n"," 5   fuel_type   837 non-null    object\n","dtypes: object(6)\n","memory usage: 41.9+ KB\n"]}],"source":["car.info()"]},{"cell_type":"markdown","metadata":{"id":"Fm8hcXzjASXv"},"source":["##### Creating backup copy"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":461,"status":"ok","timestamp":1708073877771,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"GBBqPchyASXx"},"outputs":[],"source":["backup=car.copy()"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":502,"status":"ok","timestamp":1708073879568,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"QeJ2TzR4CG0v","outputId":"ed1f6f1b-0c71-4737-cc95-a7edd60c18ff"},"outputs":[{"data":{"text/plain":["array(['2007', '2006', '2018', '2014', '2015', '2012', '2013', '2016',\n","       '2010', '2017', '2008', '2011', '2019', '2009', '2005', '2000',\n","       '...', '150k', 'TOUR', '2003', 'r 15', '2004', 'Zest', '/-Rs',\n","       'sale', '1995', 'ara)', '2002', 'SELL', '2001', 'tion', 'odel',\n","       '2 bs', 'arry', 'Eon', 'o...', 'ture', 'emi', 'car', 'able', 'no.',\n","       'd...', 'SALE', 'digo', 'sell', 'd Ex', 'n...', 'e...', 'D...',\n","       ', Ac', 'go .', 'k...', 'o c4', 'zire', 'cent', 'Sumo', 'cab',\n","       't xe', 'EV2', 'r...', 'zest'], dtype=object)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["car['year'].unique()"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1708073880921,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"DxXGLpdYCWxz","outputId":"94f1b6d0-bd9c-4119-cadd-f3f893996ce3"},"outputs":[{"data":{"text/plain":["array(['80,000', '4,25,000', 'Ask For Price', '3,25,000', '5,75,000',\n","       '1,75,000', '1,90,000', '8,30,000', '2,50,000', '1,82,000',\n","       '3,15,000', '4,15,000', '3,20,000', '10,00,000', '5,00,000',\n","       '3,50,000', '1,60,000', '3,10,000', '75,000', '1,00,000',\n","       '2,90,000', '95,000', '1,80,000', '3,85,000', '1,05,000',\n","       '6,50,000', '6,89,999', '4,48,000', '5,49,000', '5,01,000',\n","       '4,89,999', 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'3,70,000', '1,68,000', '1,45,000', '98,500',\n","       '2,09,000', '1,85,000', '9,00,000', '6,99,999', '1,99,999',\n","       '5,44,999', '1,99,000', '5,40,000', '49,000', '7,00,000', '55,000',\n","       '8,95,000', '3,55,000', '5,65,000', '3,65,000', '40,000',\n","       '4,00,000', '3,30,000', '5,80,000', '3,79,000', '2,19,000',\n","       '5,19,000', '7,30,000', '20,00,000', '21,00,000', '14,00,000',\n","       '3,11,000', '8,55,000', '5,35,000', '1,78,000', '3,00,000',\n","       '2,55,000', '5,49,999', '3,80,000', '57,000', '4,10,000',\n","       '2,25,000', '1,20,000', '59,000', '5,99,000', '6,75,000', '72,500',\n","       '6,10,000', '2,30,000', '5,20,000', '5,24,999', '4,24,999',\n","       '6,44,999', '5,84,999', '7,99,999', '4,44,999', '6,49,999',\n","       '9,44,999', '5,74,999', '3,74,999', '1,30,000', '4,01,000',\n","       '13,50,000', '1,74,999', '2,39,999', '99,999', '3,24,999',\n","       '10,74,999', '11,30,000', '1,49,000', '7,70,000', '30,000',\n","       '3,35,000', '3,99,000', '65,000', '1,69,999', '1,65,000',\n","       '5,60,000', '9,50,000', '7,15,000', '45,000', '9,40,000',\n","       '1,55,555', '15,00,000', '4,95,000', '8,00,000', '12,99,000',\n","       '5,30,000', '14,99,000', '32,000', '4,05,000', '7,60,000',\n","       '7,50,000', '4,19,000', '1,40,000', '15,40,000', '1,23,000',\n","       '4,98,000', '4,80,000', '4,88,000', '15,25,000', '5,48,900',\n","       '7,25,000', '99,000', '52,000', '28,00,000', '4,99,000',\n","       '3,81,000', '2,78,000', '6,90,000', '2,60,000', '90,001',\n","       '1,15,000', '15,99,000', '1,59,000', '51,999', '2,15,000',\n","       '35,000', '11,50,000', '2,69,000', '60,000', '4,30,000',\n","       '85,00,003', '4,01,919', '4,90,000', '4,24,000', '2,05,000',\n","       '5,49,900', '3,71,500', '4,35,000', '1,89,700', '3,89,700',\n","       '3,60,000', '2,95,000', '1,14,990', '10,65,000', '4,70,000',\n","       '48,000', '1,88,000', '4,65,000', '1,79,999', '21,90,000',\n","       '23,90,000', '10,75,000', '4,75,000', '10,25,000', '6,15,000',\n","       '19,00,000', '14,90,000', '15,10,000', '18,50,000', '7,90,000',\n","       '17,25,000', '12,25,000', '68,000', '9,70,000', '31,00,000',\n","       '8,99,000', '88,000', '53,000', '5,68,500', '71,000', '5,90,000',\n","       '7,95,000', '42,000', '1,89,000', '1,62,000', '35,999',\n","       '29,00,000', '39,999', '50,500', '5,10,000', '8,60,000',\n","       '5,00,001'], dtype=object)"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["car['Price'].unique()"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1708073884075,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"F5LwVG2zCe2V","outputId":"b2ffe9fa-09ff-4789-c7c3-554ecf72cee7"},"outputs":[{"data":{"text/plain":["array(['45,000 kms', '40 kms', '22,000 kms', '28,000 kms', '36,000 kms',\n","       '59,000 kms', '41,000 kms', '25,000 kms', '24,530 kms',\n","       '60,000 kms', '30,000 kms', '32,000 kms', '48,660 kms',\n","       '4,000 kms', '16,934 kms', '43,000 kms', '35,550 kms',\n","       '39,522 kms', '39,000 kms', '55,000 kms', '72,000 kms',\n","       '15,975 kms', '70,000 kms', '23,452 kms', '35,522 kms',\n","       '48,508 kms', '15,487 kms', '82,000 kms', '20,000 kms',\n","       '68,000 kms', '38,000 kms', '27,000 kms', '33,000 kms',\n","       '46,000 kms', '16,000 kms', '47,000 kms', '35,000 kms',\n","       '30,874 kms', '15,000 kms', '29,685 kms', '1,30,000 kms',\n","       '19,000 kms', nan, '54,000 kms', '13,000 kms', '38,200 kms',\n","       '50,000 kms', '13,500 kms', '3,600 kms', '45,863 kms',\n","       '60,500 kms', '12,500 kms', '18,000 kms', '13,349 kms',\n","       '29,000 kms', '44,000 kms', '42,000 kms', '14,000 kms',\n","       '49,000 kms', '36,200 kms', '51,000 kms', '1,04,000 kms',\n","       '33,333 kms', '33,600 kms', '5,600 kms', '7,500 kms', '26,000 kms',\n","       '24,330 kms', '65,480 kms', '28,028 kms', '2,00,000 kms',\n","       '99,000 kms', '2,800 kms', '21,000 kms', '11,000 kms',\n","       '66,000 kms', '3,000 kms', '7,000 kms', '38,500 kms', '37,200 kms',\n","       '43,200 kms', '24,800 kms', '45,872 kms', '40,000 kms',\n","       '11,400 kms', '97,200 kms', '52,000 kms', '31,000 kms',\n","       '1,75,430 kms', '37,000 kms', '65,000 kms', '3,350 kms',\n","       '75,000 kms', '62,000 kms', '73,000 kms', '2,200 kms',\n","       '54,870 kms', '34,580 kms', '97,000 kms', '60 kms', '80,200 kms',\n","       '3,200 kms', '0,000 kms', '5,000 kms', '588 kms', '71,200 kms',\n","       '1,75,400 kms', '9,300 kms', '56,758 kms', '10,000 kms',\n","       '56,450 kms', '56,000 kms', '32,700 kms', '9,000 kms', '73 kms',\n","       '1,60,000 kms', '84,000 kms', '58,559 kms', '57,000 kms',\n","       '1,70,000 kms', '80,000 kms', '6,821 kms', '23,000 kms',\n","       '34,000 kms', '1,800 kms', '4,00,000 kms', '48,000 kms',\n","       '90,000 kms', '12,000 kms', '69,900 kms', '1,66,000 kms',\n","       '122 kms', '0 kms', '24,000 kms', '36,469 kms', '7,800 kms',\n","       '24,695 kms', '15,141 kms', '59,910 kms', '1,00,000 kms',\n","       '4,500 kms', '1,29,000 kms', '300 kms', '1,31,000 kms',\n","       '1,11,111 kms', '59,466 kms', '25,500 kms', '44,005 kms',\n","       '2,110 kms', '43,222 kms', '1,00,200 kms', '65 kms',\n","       '1,40,000 kms', '1,03,553 kms', '58,000 kms', '1,20,000 kms',\n","       '49,800 kms', '100 kms', '81,876 kms', '6,020 kms', '55,700 kms',\n","       '18,500 kms', '1,80,000 kms', '53,000 kms', '35,500 kms',\n","       '22,134 kms', '1,000 kms', '8,500 kms', '87,000 kms', '6,000 kms',\n","       '15,574 kms', '8,000 kms', '55,800 kms', '56,400 kms',\n","       '72,160 kms', '11,500 kms', '1,33,000 kms', '2,000 kms',\n","       '88,000 kms', '65,422 kms', '1,17,000 kms', '1,50,000 kms',\n","       '10,750 kms', '6,800 kms', '5 kms', '9,800 kms', '57,923 kms',\n","       '30,201 kms', '6,200 kms', '37,518 kms', '24,652 kms', '383 kms',\n","       '95,000 kms', '3,528 kms', '52,500 kms', '47,900 kms',\n","       '52,800 kms', '1,95,000 kms', '48,008 kms', '48,247 kms',\n","       '9,400 kms', '64,000 kms', '2,137 kms', '10,544 kms', '49,500 kms',\n","       '1,47,000 kms', '90,001 kms', '48,006 kms', '74,000 kms',\n","       '85,000 kms', '29,500 kms', '39,700 kms', '67,000 kms',\n","       '19,336 kms', '60,105 kms', '45,933 kms', '1,02,563 kms',\n","       '28,600 kms', '41,800 kms', '1,16,000 kms', '42,590 kms',\n","       '7,400 kms', '54,500 kms', '76,000 kms', '00 kms', '11,523 kms',\n","       '38,600 kms', '95,500 kms', '37,458 kms', '85,960 kms',\n","       '12,516 kms', '30,600 kms', '2,550 kms', '62,500 kms',\n","       '69,000 kms', '28,400 kms', '68,485 kms', '3,500 kms',\n","       '85,455 kms', '63,000 kms', '1,600 kms', '77,000 kms',\n","       '26,500 kms', '2,875 kms', '13,900 kms', '1,500 kms', '2,450 kms',\n","       '1,625 kms', '33,400 kms', '60,123 kms', '38,900 kms',\n","       '1,37,495 kms', '91,200 kms', '1,46,000 kms', '1,00,800 kms',\n","       '2,100 kms', '2,500 kms', '1,32,000 kms', 'Petrol'], dtype=object)"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["car['kms_driven'].unique()"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":628,"status":"ok","timestamp":1708073887692,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"YL1wb4MbCva-","outputId":"b3d31841-e623-4833-dfdd-4519ed98ed93"},"outputs":[{"data":{"text/plain":["array(['Petrol', 'Diesel', nan, 'LPG'], dtype=object)"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["car['fuel_type'].unique()"]},{"cell_type":"markdown","metadata":{"id":"6hoLXf-qASXy"},"source":["## Quality\n","\n","- names are pretty inconsistent\n","- names have company names attached to it\n","- some names are spam like 'Maruti Ertiga showroom condition with' and 'Well mentained Tata Sumo'\n","- company: many of the names are not of any company like 'Used', 'URJENT', and so on.\n","- year has many non-year values\n","- year is in object. Change to integer\n","- Price has Ask for Price\n","- Price has commas in its prices and is in object\n","- kms_driven has object values with kms at last.\n","- It has nan values and two rows have 'Petrol' in them\n","- fuel_type has nan values"]},{"cell_type":"markdown","metadata":{"id":"2ZqnNqAEASXy"},"source":["## Cleaning Data"]},{"cell_type":"markdown","metadata":{"id":"YkDLdGwzASXz"},"source":["#### year has many non-year values"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1708073890227,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"Cr4ss20kASXz"},"outputs":[],"source":["car=car[car['year'].str.isnumeric()]"]},{"cell_type":"markdown","metadata":{"id":"mxZzHsevASX0"},"source":["#### year is in object. Change to integer"]},{"cell_type":"code","execution_count":12,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073891497,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"fW4hzWXvASX1"},"outputs":[],"source":["car['year']=car['year'].astype(int)"]},{"cell_type":"markdown","metadata":{"id":"1-Vurw_FASX1"},"source":["#### Price has Ask for Price"]},{"cell_type":"code","execution_count":13,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073893318,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"BTQ0N36hASX1"},"outputs":[],"source":["car=car[car['Price']!='Ask For Price']"]},{"cell_type":"markdown","metadata":{"id":"24MOBFFMASX1"},"source":["#### Price has commas in its prices and is in object"]},{"cell_type":"code","execution_count":14,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073893800,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"PVKKKJuAASX2"},"outputs":[],"source":["car['Price']=car['Price'].str.replace(',','').astype(int)"]},{"cell_type":"markdown","metadata":{"id":"3lq9k6N6ASX2"},"source":["####  kms_driven has object values with kms at last."]},{"cell_type":"code","execution_count":15,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073895228,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"9VbO2ntuASX2"},"outputs":[],"source":["car['kms_driven']=car['kms_driven'].str.split().str.get(0).str.replace(',','')"]},{"cell_type":"markdown","metadata":{"id":"Bl9AvzVxASX2"},"source":["#### It has nan values and two rows have 'Petrol' in them"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1708073896782,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"gjlIcL7hASX2"},"outputs":[],"source":["car=car[car['kms_driven'].str.isnumeric()]"]},{"cell_type":"code","execution_count":17,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073898249,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"e29DzkWYASX2"},"outputs":[],"source":["car['kms_driven']=car['kms_driven'].astype(int)"]},{"cell_type":"markdown","metadata":{"id":"4CnnSYJxASX2"},"source":["#### fuel_type has nan values"]},{"cell_type":"code","execution_count":18,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073900698,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"r71rsddhASX2"},"outputs":[],"source":["car=car[~car['fuel_type'].isna()]"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1708073902297,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"J8-TLlOpASX2","outputId":"0194971a-87d0-4461-c45f-2a243d9fc7a5"},"outputs":[{"data":{"text/plain":["(816, 6)"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["car.shape"]},{"cell_type":"markdown","metadata":{"id":"1uOE4esxASX2"},"source":["### name and company had spammed data...but with the previous cleaning, those rows got removed."]},{"cell_type":"markdown","metadata":{"id":"z2tmQj0fASX3"},"source":["#### Company does not need any cleaning now. Changing car names. Keeping only the first three words"]},{"cell_type":"code","execution_count":20,"metadata":{"executionInfo":{"elapsed":511,"status":"ok","timestamp":1708073908962,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"4hnWM22_ASX3"},"outputs":[],"source":["car['name']=car['name'].str.split().str.slice(start=0,stop=3).str.join(' ')"]},{"cell_type":"markdown","metadata":{"id":"7NGZVZLDASX3"},"source":["#### Resetting the index of the final cleaned data"]},{"cell_type":"code","execution_count":21,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1708073910818,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"Ds2lZi52ASX3"},"outputs":[],"source":["car=car.reset_index(drop=True)"]},{"cell_type":"markdown","metadata":{"id":"iZVf6J-AASX3"},"source":["## Cleaned Data"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1708073913653,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"TVS_0yNVASX3","outputId":"452afd40-1710-4925-a93f-d597e9170694"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"car\",\n  \"rows\": 816,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Tata Nano\",\n          \"Ford EcoSport Ambiente\",\n          \"Renault Kwid\"\n        ],\n        \"num_unique_values\": 254,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Honda\",\n          \"Nissan\",\n          \"Hyundai\"\n        ],\n        \"num_unique_values\": 25,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1995,\n        \"max\": 2019,\n        \"samples\": [\n          2007,\n          2004,\n          2000\n        ],\n        \"num_unique_values\": 21,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 475184,\n        \"min\": 30000,\n        \"max\": 8500003,\n        \"samples\": [\n          280000,\n          355000,\n          450000\n        ],\n        \"num_unique_values\": 272,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kms_driven\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34297,\n        \"min\": 0,\n        \"max\": 400000,\n        \"samples\": [\n          47000,\n          24530,\n          24652\n        ],\n        \"num_unique_values\": 246,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fuel_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Petrol\",\n          \"Diesel\",\n          \"LPG\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}","type":"dataframe","variable_name":"car"},"text/html":["\n","  <div id=\"df-656e7f33-43b5-4b2b-ac5c-d1b9e069edab\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>name</th>\n","      <th>company</th>\n","      <th>year</th>\n","      <th>Price</th>\n","      <th>kms_driven</th>\n","      <th>fuel_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Hyundai Santro Xing</td>\n","      <td>Hyundai</td>\n","      <td>2007</td>\n","      <td>80000</td>\n","      <td>45000</td>\n","      <td>Petrol</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Mahindra Jeep CL550</td>\n","      <td>Mahindra</td>\n","      <td>2006</td>\n","      <td>425000</td>\n","      <td>40</td>\n","      <td>Diesel</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Hyundai Grand i10</td>\n","      <td>Hyundai</td>\n","      <td>2014</td>\n","      <td>325000</td>\n","      <td>28000</td>\n","      <td>Petrol</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford EcoSport Titanium</td>\n","      <td>Ford</td>\n","      <td>2014</td>\n","      <td>575000</td>\n","      <td>36000</td>\n","      <td>Diesel</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Ford Figo</td>\n","      <td>Ford</td>\n","      <td>2012</td>\n","      <td>175000</td>\n","      <td>41000</td>\n","      <td>Diesel</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>811</th>\n","      <td>Maruti Suzuki Ritz</td>\n","      <td>Maruti</td>\n","      <td>2011</td>\n","      <td>270000</td>\n","      <td>50000</td>\n","      <td>Petrol</td>\n","    </tr>\n","    <tr>\n","      <th>812</th>\n","      <td>Tata Indica V2</td>\n","      <td>Tata</td>\n","      <td>2009</td>\n","      <td>110000</td>\n","      <td>30000</td>\n","      <td>Diesel</td>\n","    </tr>\n","    <tr>\n","      <th>813</th>\n","      <td>Toyota Corolla Altis</td>\n","      <td>Toyota</td>\n","      <td>2009</td>\n","      <td>300000</td>\n","      <td>132000</td>\n","      <td>Petrol</td>\n","    </tr>\n","    <tr>\n","      <th>814</th>\n","      <td>Tata Zest XM</td>\n","      <td>Tata</td>\n","      <td>2018</td>\n","      <td>260000</td>\n","      <td>27000</td>\n","      <td>Diesel</td>\n","    </tr>\n","    <tr>\n","      <th>815</th>\n","      <td>Mahindra Quanto C8</td>\n","      <td>Mahindra</td>\n","      <td>2013</td>\n","      <td>390000</td>\n","      <td>40000</td>\n","      <td>Diesel</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>816 rows × 6 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-656e7f33-43b5-4b2b-ac5c-d1b9e069edab')\"\n","            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  Petrol\n","814            Tata Zest XM      Tata  2018  260000       27000    Diesel\n","815      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n","\n","[816 rows x 6 columns]"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["car"]},{"cell_type":"code","execution_count":23,"metadata":{"executionInfo":{"elapsed":462,"status":"ok","timestamp":1708073917810,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"j9BagzInASX3"},"outputs":[],"source":["car.to_csv('Cleaned_Car_data.csv')"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":574,"status":"ok","timestamp":1708073920952,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"pBQp4NZCASX3","outputId":"67e98916-40a9-4a76-d398-3c8905ad0e02"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 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2015.000000  4.912500e+05   56818.500000   \n","max                     NaN     NaN  2019.000000  8.500003e+06  400000.000000   \n","\n","       fuel_type  \n","count        816  \n","unique         3  \n","top       Petrol  \n","freq         428  \n","mean         NaN  \n","std          NaN  \n","min          NaN  \n","25%          NaN  \n","50%          NaN  \n","75%          NaN  \n","max          NaN  "]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["car.describe(include='all')"]},{"cell_type":"markdown","metadata":{"id":"QioNtToOASX4"},"source":["### Checking relationship of Company with Price"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":908,"status":"ok","timestamp":1708073930252,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"B80WbvwDASX4","outputId":"90c67240-51f8-44ee-ff44-766abba4b3e0"},"outputs":[{"data":{"text/plain":["array(['Hyundai', 'Mahindra', 'Ford', 'Maruti', 'Skoda', 'Audi', 'Toyota',\n","       'Renault', 'Honda', 'Datsun', 'Mitsubishi', 'Tata', 'Volkswagen',\n","       'Chevrolet', 'Mini', 'BMW', 'Nissan', 'Hindustan', 'Fiat', 'Force',\n","       'Mercedes', 'Land', 'Jaguar', 'Jeep', 'Volvo'], dtype=object)"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["car['company'].unique()"]},{"cell_type":"code","execution_count":27,"metadata":{"executionInfo":{"elapsed":871,"status":"ok","timestamp":1708073943708,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"R8Ux3EYdASX-"},"outputs":[],"source":["import seaborn as sns"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":722},"executionInfo":{"elapsed":1190,"status":"ok","timestamp":1708073946413,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"S-sSXIWKASX-","outputId":"ef77542f-2f00-4432-c844-a849adaa72d6"},"outputs":[{"name":"stderr","output_type":"stream","text":["<ipython-input-28-985898158040>:3: UserWarning: FixedFormatter should only be used together with FixedLocator\n","  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","text/plain":["<Figure size 1500x700 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.subplots(figsize=(15,7))\n","ax=sns.boxplot(x='company',y='Price',data=car)\n","ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"hm7zPqDPASX-"},"source":["### Checking relationship of Year with Price"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":4837,"status":"ok","timestamp":1708073970789,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"HEjWfxs5ASX-","outputId":"18ea26dd-5feb-4446-cde1-0603f5795bfc"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 15.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 30.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 31.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 36.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 25.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 42.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 37.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 37.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 32.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 42.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 39.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 16.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 20.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","<ipython-input-30-cf5aa8fae272>:3: UserWarning: FixedFormatter should only be used together with FixedLocator\n","  ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 22.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 26.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 12.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 38.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 34.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 35.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 29.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 35.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 37.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 37.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 13.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 17.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n","  warnings.warn(msg, UserWarning)\n"]},{"data":{"image/png":"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size 2000x1000 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.subplots(figsize=(20,10))\n","ax=sns.swarmplot(x='year',y='Price',data=car)\n","ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"Y_2jYadGASX_"},"source":["### Checking relationship of kms_driven with Price"]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":724},"executionInfo":{"elapsed":820,"status":"ok","timestamp":1708073981338,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"saL8tgy5ASX_","outputId":"8e27fa37-905f-4cb4-d304-a7e303890a81"},"outputs":[{"data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 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","text/plain":["<Figure size 1050x700 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.relplot(x='kms_driven',y='Price',data=car,height=7,aspect=1.5)"]},{"cell_type":"markdown","metadata":{"id":"w-c5nrmhASX_"},"source":["### Checking relationship of Fuel Type with Price"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":655},"executionInfo":{"elapsed":580,"status":"ok","timestamp":1708073985715,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"Re3TgViuASX_","outputId":"8d1c5f0d-1e62-4275-9b55-f6442f5c79f4"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='fuel_type', ylabel='Price'>"]},"execution_count":32,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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size 1400x700 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.subplots(figsize=(14,7))\n","sns.boxplot(x='fuel_type',y='Price',data=car)"]},{"cell_type":"markdown","metadata":{"id":"Qd_F4KkMASX_"},"source":["### Relationship of Price with FuelType, Year and Company mixed"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":727},"executionInfo":{"elapsed":1978,"status":"ok","timestamp":1708073991202,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"AsdyyHGVASX_","outputId":"090364bd-d495-4982-ec78-2ef2121bc174","scrolled":true},"outputs":[{"data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 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size 1514.25x700 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["ax=sns.relplot(x='company',y='Price',data=car,hue='fuel_type',size='year',height=7,aspect=2)\n","ax.set_xticklabels(rotation=40,ha='right')"]},{"cell_type":"markdown","metadata":{"id":"8oZSIWeBASX_"},"source":["### Extracting Training Data"]},{"cell_type":"code","execution_count":34,"metadata":{"executionInfo":{"elapsed":489,"status":"ok","timestamp":1708074007636,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"HHPXWP7_ASX_"},"outputs":[],"source":["X=car[['name','company','year','kms_driven','fuel_type']]\n","y=car['Price']"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":655,"status":"ok","timestamp":1708074015394,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"lU6Pi6sHASX_","outputId":"b6766d19-b232-4888-ddd7-251097c8b17c"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"X\",\n  \"rows\": 816,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Tata Nano\",\n          \"Ford EcoSport Ambiente\",\n          \"Renault Kwid\"\n        ],\n        \"num_unique_values\": 254,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Honda\",\n          \"Nissan\",\n          \"Hyundai\"\n        ],\n        \"num_unique_values\": 25,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1995,\n        \"max\": 2019,\n        \"samples\": [\n          2007,\n          2004,\n          2000\n        ],\n        \"num_unique_values\": 21,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kms_driven\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34297,\n        \"min\": 0,\n        \"max\": 400000,\n        \"samples\": [\n          47000,\n          24530,\n          24652\n        ],\n        \"num_unique_values\": 246,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fuel_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Petrol\",\n          \"Diesel\",\n          \"LPG\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}","type":"dataframe","variable_name":"X"},"text/html":["\n","  <div id=\"df-2768751c-db15-4a21-bb6e-e72ed18374cc\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>name</th>\n","      <th>company</th>\n","      <th>year</th>\n","      <th>kms_driven</th>\n","      <th>fuel_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Hyundai Santro Xing</td>\n","      <td>Hyundai</td>\n","      <td>2007</td>\n","      <td>45000</td>\n","      <td>Petrol</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Mahindra Jeep CL550</td>\n","      <td>Mahindra</td>\n","      <td>2006</td>\n","      <td>40</td>\n","      <td>Diesel</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Hyundai Grand i10</td>\n","      <td>Hyundai</td>\n","      <td>2014</td>\n","      <td>28000</td>\n","      <td>Petrol</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford EcoSport Titanium</td>\n","      <td>Ford</td>\n","      <td>2014</td>\n","      <td>36000</td>\n","      <td>Diesel</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Ford Figo</td>\n","      <td>Ford</td>\n","      <td>2012</td>\n","      <td>41000</td>\n","      <td>Diesel</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>811</th>\n","      <td>Maruti Suzuki Ritz</td>\n","      <td>Maruti</td>\n","      <td>2011</td>\n","      <td>50000</td>\n","      <td>Petrol</td>\n","    </tr>\n","    <tr>\n","      <th>812</th>\n","      <td>Tata Indica V2</td>\n","      <td>Tata</td>\n","      <td>2009</td>\n","      <td>30000</td>\n","      <td>Diesel</td>\n","    </tr>\n","    <tr>\n","      <th>813</th>\n","      <td>Toyota Corolla Altis</td>\n","      <td>Toyota</td>\n","      <td>2009</td>\n","      <td>132000</td>\n","      <td>Petrol</td>\n","    </tr>\n","    <tr>\n","      <th>814</th>\n","      <td>Tata Zest XM</td>\n","      <td>Tata</td>\n","      <td>2018</td>\n","      <td>27000</td>\n","      <td>Diesel</td>\n","    </tr>\n","    <tr>\n","      <th>815</th>\n","      <td>Mahindra Quanto C8</td>\n","      <td>Mahindra</td>\n","      <td>2013</td>\n","      <td>40000</td>\n","      <td>Diesel</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>816 rows × 5 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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Diesel\n","815      Mahindra Quanto C8  Mahindra  2013       40000    Diesel\n","\n","[816 rows x 5 columns]"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["X"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1708074020081,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"UzuQCTGUASYA","outputId":"7c6258c1-a847-43c8-dce6-b229431a0779"},"outputs":[{"data":{"text/plain":["(816,)"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["y.shape"]},{"cell_type":"markdown","metadata":{"id":"4K_H0rJlASYA"},"source":["### Applying Train Test Split"]},{"cell_type":"code","execution_count":37,"metadata":{"executionInfo":{"elapsed":493,"status":"ok","timestamp":1708074030497,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"TZs0yQQ-ASYA"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"]},{"cell_type":"code","execution_count":38,"metadata":{"executionInfo":{"elapsed":463,"status":"ok","timestamp":1708074041194,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"pDNZaOEEASYA"},"outputs":[],"source":["from sklearn.linear_model import LinearRegression"]},{"cell_type":"code","execution_count":39,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1708074042724,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"LsGjTL94ASYA"},"outputs":[],"source":["from sklearn.preprocessing import OneHotEncoder\n","from sklearn.compose import make_column_transformer\n","from sklearn.pipeline import make_pipeline\n","from sklearn.metrics import r2_score"]},{"cell_type":"markdown","metadata":{"id":"diicWbHHASYA"},"source":["#### Creating an OneHotEncoder object to contain all the possible categories"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"executionInfo":{"elapsed":535,"status":"ok","timestamp":1708074050282,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"QXeFmYSfASYA","outputId":"39d81469-9754-49ea-9da3-1039e5b8c3c6"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div>"],"text/plain":["OneHotEncoder()"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["ohe=OneHotEncoder()\n","ohe.fit(X[['name','company','fuel_type']])"]},{"cell_type":"markdown","metadata":{"id":"XEaQLySCASYB"},"source":["#### Creating a column transformer to transform categorical columns"]},{"cell_type":"code","execution_count":41,"metadata":{"executionInfo":{"elapsed":710,"status":"ok","timestamp":1708074142733,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"LmRY2gStASYB"},"outputs":[],"source":["column_trans=make_column_transformer((OneHotEncoder(categories=ohe.categories_),['name','company','fuel_type']),\n","                                    remainder='passthrough')"]},{"cell_type":"markdown","metadata":{"id":"VpSa2b10ASYB"},"source":["#### Linear Regression Model"]},{"cell_type":"code","execution_count":42,"metadata":{"executionInfo":{"elapsed":491,"status":"ok","timestamp":1708074146127,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"rvgPblK7ASYB"},"outputs":[],"source":["lr=LinearRegression()"]},{"cell_type":"markdown","metadata":{"id":"jPvhGhNiASYB"},"source":["#### Making a pipeline"]},{"cell_type":"code","execution_count":43,"metadata":{"executionInfo":{"elapsed":891,"status":"ok","timestamp":1708074151905,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"QubEsgltASYB"},"outputs":[],"source":["pipe=make_pipeline(column_trans,lr)"]},{"cell_type":"markdown","metadata":{"id":"HacmTQ7DASYB"},"source":["#### Fitting the  model"]},{"cell_type":"code","execution_count":44,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":636},"executionInfo":{"elapsed":818,"status":"ok","timestamp":1708074189375,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"Oo-g6rY5ASYB","outputId":"43ec7301-3e74-48fb-cf2d-90cee57bf41d","scrolled":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n","                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n","                                   transformers=[(&#x27;onehotencoder&#x27;,\n","                                                  OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n","       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n","       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n","       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat...\n","                                                                            array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n","       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n","       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n","       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n","      dtype=object),\n","                                                                            array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n","                                                  [&#x27;name&#x27;, &#x27;company&#x27;,\n","                                                   &#x27;fuel_type&#x27;])])),\n","                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n","                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n","                                   transformers=[(&#x27;onehotencoder&#x27;,\n","                                                  OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n","       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n","       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n","       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat...\n","                                                                            array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n","       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n","       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n","       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n","      dtype=object),\n","                                                                            array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n","                                                  [&#x27;name&#x27;, &#x27;company&#x27;,\n","                                                   &#x27;fuel_type&#x27;])])),\n","                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n","                  transformers=[(&#x27;onehotencoder&#x27;,\n","                                 OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n","       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n","       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n","       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat Diesel&#x27;,\n","       &#x27;Chevrolet Beat LS&#x27;, &#x27;Chevrolet B...\n","       &#x27;Volkswagen Vento Konekt&#x27;, &#x27;Volvo S80 Summum&#x27;], dtype=object),\n","                                                           array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n","       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n","       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n","       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n","      dtype=object),\n","                                                           array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n","                                 [&#x27;name&#x27;, &#x27;company&#x27;, &#x27;fuel_type&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;name&#x27;, &#x27;company&#x27;, &#x27;fuel_type&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n","       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n","       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n","       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat Diesel&#x27;,\n","       &#x27;Chevrolet Beat LS&#x27;, &#x27;Chevrolet Beat LT&#x27;, &#x27;Chevrolet Beat PS&#x27;,\n","       &#x27;Chevrolet Cruze LTZ&#x27;, &#x27;Chevrolet Enjoy&#x27;, &#x27;Chevrolet E...\n","       &#x27;Volkswagen Vento Comfortline&#x27;, &#x27;Volkswagen Vento Highline&#x27;,\n","       &#x27;Volkswagen Vento Konekt&#x27;, &#x27;Volvo S80 Summum&#x27;], dtype=object),\n","                          array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n","       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n","       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n","       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n","      dtype=object),\n","                          array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;year&#x27;, &#x27;kms_driven&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"],"text/plain":["Pipeline(steps=[('columntransformer',\n","                 ColumnTransformer(remainder='passthrough',\n","                                   transformers=[('onehotencoder',\n","                                                  OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n","       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n","       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n","       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n","                                                                            array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n","       'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n","       'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n","       'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n","      dtype=object),\n","                                                                            array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n","                                                  ['name', 'company',\n","                                                   'fuel_type'])])),\n","                ('linearregression', LinearRegression())])"]},"execution_count":44,"metadata":{},"output_type":"execute_result"}],"source":["pipe.fit(X_train,y_train)"]},{"cell_type":"code","execution_count":45,"metadata":{"executionInfo":{"elapsed":507,"status":"ok","timestamp":1708074258512,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"qimpYCLQASYB"},"outputs":[],"source":["y_pred=pipe.predict(X_test)"]},{"cell_type":"markdown","metadata":{"id":"IEssz0HwASYB"},"source":["#### Checking R2 Score"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":599,"status":"ok","timestamp":1708074278028,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"RhlIxudNASYC","outputId":"55bf9355-e8d6-4947-8273-dde8c81a72a1"},"outputs":[{"data":{"text/plain":["-0.15149763480164546"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["r2_score(y_test,y_pred)"]},{"cell_type":"code","execution_count":47,"metadata":{"executionInfo":{"elapsed":23523,"status":"ok","timestamp":1708074338093,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"LtwzxwYdASYC"},"outputs":[],"source":["scores=[]\n","for i in range(1000):\n","    X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=i)\n","    lr=LinearRegression()\n","    pipe=make_pipeline(column_trans,lr)\n","    pipe.fit(X_train,y_train)\n","    y_pred=pipe.predict(X_test)\n","    scores.append(r2_score(y_test,y_pred))"]},{"cell_type":"code","execution_count":48,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":485,"status":"ok","timestamp":1708074346361,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"n_fehP7hASYC","outputId":"56782bf1-7b53-4f5c-cde0-f2fccdfffe7f"},"outputs":[{"data":{"text/plain":["247"]},"execution_count":48,"metadata":{},"output_type":"execute_result"}],"source":["np.argmax(scores)"]},{"cell_type":"code","execution_count":49,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":636,"status":"ok","timestamp":1708074350322,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"2QGHXKrkASYC","outputId":"1423ff3c-a707-4e5e-cc25-d9d40321dbdd"},"outputs":[{"data":{"text/plain":["0.8604602644312209"]},"execution_count":49,"metadata":{},"output_type":"execute_result"}],"source":["scores[np.argmax(scores)]"]},{"cell_type":"code","execution_count":50,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":643,"status":"ok","timestamp":1708074446306,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"fpKTj-BXASYC","outputId":"ce88e413-3a96-4f85-be6c-14c0e049c604"},"outputs":[{"data":{"text/plain":["array([453164.82282671])"]},"execution_count":50,"metadata":{},"output_type":"execute_result"}],"source":["pipe.predict(pd.DataFrame(columns=X_test.columns,data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))"]},{"cell_type":"markdown","metadata":{"id":"DRnBh9hnASYC"},"source":["#### The best model is found at a certain random state"]},{"cell_type":"code","execution_count":51,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":486,"status":"ok","timestamp":1708074449831,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"8UE1OEdyASYC","outputId":"abb46b6c-91de-4ec5-85d1-f4644a450784"},"outputs":[{"data":{"text/plain":["0.8604602644312209"]},"execution_count":51,"metadata":{},"output_type":"execute_result"}],"source":["X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=np.argmax(scores))\n","lr=LinearRegression()\n","pipe=make_pipeline(column_trans,lr)\n","pipe.fit(X_train,y_train)\n","y_pred=pipe.predict(X_test)\n","r2_score(y_test,y_pred)"]},{"cell_type":"code","execution_count":52,"metadata":{"executionInfo":{"elapsed":467,"status":"ok","timestamp":1708074455968,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"q0BV9fJxASYC"},"outputs":[],"source":["import pickle"]},{"cell_type":"code","execution_count":53,"metadata":{"executionInfo":{"elapsed":573,"status":"ok","timestamp":1708074478602,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"AYew5982ASYD"},"outputs":[],"source":["pickle.dump(pipe,open('LinearRegressionModel.pkl','wb'))"]},{"cell_type":"code","execution_count":54,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":485,"status":"ok","timestamp":1708074501651,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"o3OFBISaASYD","outputId":"ada68292-051d-4a68-f9ab-46584517b52a"},"outputs":[{"data":{"text/plain":["array([455413.55294819])"]},"execution_count":54,"metadata":{},"output_type":"execute_result"}],"source":["pipe.predict(pd.DataFrame(columns=['name','company','year','kms_driven','fuel_type'],data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))"]},{"cell_type":"code","execution_count":55,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":490,"status":"ok","timestamp":1708074568157,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"YwEs-8nEASYD","outputId":"3ece31cd-2afa-40a0-e623-e2c01c81309d"},"outputs":[{"data":{"text/plain":["array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n","       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n","       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n","       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat Diesel',\n","       'Chevrolet Beat LS', 'Chevrolet Beat LT', 'Chevrolet Beat PS',\n","       'Chevrolet Cruze LTZ', 'Chevrolet Enjoy', 'Chevrolet Enjoy 1.4',\n","       'Chevrolet Sail 1.2', 'Chevrolet Sail UVA', 'Chevrolet Spark',\n","       'Chevrolet Spark 1.0', 'Chevrolet Spark LS', 'Chevrolet Spark LT',\n","       'Chevrolet Tavera LS', 'Chevrolet Tavera Neo', 'Datsun GO T',\n","       'Datsun Go Plus', 'Datsun Redi GO', 'Fiat Linea Emotion',\n","       'Fiat Petra ELX', 'Fiat Punto Emotion', 'Force Motors Force',\n","       'Force Motors One', 'Ford EcoSport', 'Ford EcoSport Ambiente',\n","       'Ford EcoSport Titanium', 'Ford EcoSport Trend',\n","       'Ford Endeavor 4x4', 'Ford Fiesta', 'Ford Fiesta SXi', 'Ford Figo',\n","       'Ford Figo Diesel', 'Ford Figo Duratorq', 'Ford Figo Petrol',\n","       'Ford Fusion 1.4', 'Ford Ikon 1.3', 'Ford Ikon 1.6',\n","       'Hindustan Motors Ambassador', 'Honda Accord', 'Honda Amaze',\n","       'Honda Amaze 1.2', 'Honda Amaze 1.5', 'Honda Brio', 'Honda Brio V',\n","       'Honda Brio VX', 'Honda City', 'Honda City 1.5', 'Honda City SV',\n","       'Honda City VX', 'Honda City ZX', 'Honda Jazz S', 'Honda Jazz VX',\n","       'Honda Mobilio', 'Honda Mobilio S', 'Honda WR V', 'Hyundai Accent',\n","       'Hyundai Accent Executive', 'Hyundai Accent GLE',\n","       'Hyundai Accent GLX', 'Hyundai Creta', 'Hyundai Creta 1.6',\n","       'Hyundai Elantra 1.8', 'Hyundai Elantra SX', 'Hyundai Elite i20',\n","       'Hyundai Eon', 'Hyundai Eon D', 'Hyundai Eon Era',\n","       'Hyundai Eon Magna', 'Hyundai Eon Sportz', 'Hyundai Fluidic Verna',\n","       'Hyundai Getz', 'Hyundai Getz GLE', 'Hyundai Getz Prime',\n","       'Hyundai Grand i10', 'Hyundai Santro', 'Hyundai Santro AE',\n","       'Hyundai Santro Xing', 'Hyundai Sonata Transform', 'Hyundai Verna',\n","       'Hyundai Verna 1.4', 'Hyundai Verna 1.6', 'Hyundai Verna Fluidic',\n","       'Hyundai Verna Transform', 'Hyundai Verna VGT',\n","       'Hyundai Xcent Base', 'Hyundai Xcent SX', 'Hyundai i10',\n","       'Hyundai i10 Era', 'Hyundai i10 Magna', 'Hyundai i10 Sportz',\n","       'Hyundai i20', 'Hyundai i20 Active', 'Hyundai i20 Asta',\n","       'Hyundai i20 Magna', 'Hyundai i20 Select', 'Hyundai i20 Sportz',\n","       'Jaguar XE XE', 'Jaguar XF 2.2', 'Jeep Wrangler Unlimited',\n","       'Land Rover Freelander', 'Mahindra Bolero DI',\n","       'Mahindra Bolero Power', 'Mahindra Bolero SLE',\n","       'Mahindra Jeep CL550', 'Mahindra Jeep MM', 'Mahindra KUV100',\n","       'Mahindra KUV100 K8', 'Mahindra Logan', 'Mahindra Logan Diesel',\n","       'Mahindra Quanto C4', 'Mahindra Quanto C8', 'Mahindra Scorpio',\n","       'Mahindra Scorpio 2.6', 'Mahindra Scorpio LX',\n","       'Mahindra Scorpio S10', 'Mahindra Scorpio S4',\n","       'Mahindra Scorpio SLE', 'Mahindra Scorpio SLX',\n","       'Mahindra Scorpio VLX', 'Mahindra Scorpio Vlx',\n","       'Mahindra Scorpio W', 'Mahindra TUV300 T4', 'Mahindra TUV300 T8',\n","       'Mahindra Thar CRDe', 'Mahindra XUV500', 'Mahindra XUV500 W10',\n","       'Mahindra XUV500 W6', 'Mahindra XUV500 W8', 'Mahindra Xylo D2',\n","       'Mahindra Xylo E4', 'Mahindra Xylo E8', 'Maruti Suzuki 800',\n","       'Maruti Suzuki A', 'Maruti Suzuki Alto', 'Maruti Suzuki Baleno',\n","       'Maruti Suzuki Celerio', 'Maruti Suzuki Ciaz',\n","       'Maruti Suzuki Dzire', 'Maruti Suzuki Eeco',\n","       'Maruti Suzuki Ertiga', 'Maruti Suzuki Esteem',\n","       'Maruti Suzuki Estilo', 'Maruti Suzuki Maruti',\n","       'Maruti Suzuki Omni', 'Maruti Suzuki Ritz', 'Maruti Suzuki S',\n","       'Maruti Suzuki SX4', 'Maruti Suzuki Stingray',\n","       'Maruti Suzuki Swift', 'Maruti Suzuki Versa',\n","       'Maruti Suzuki Vitara', 'Maruti Suzuki Wagon', 'Maruti Suzuki Zen',\n","       'Mercedes Benz A', 'Mercedes Benz B', 'Mercedes Benz C',\n","       'Mercedes Benz GLA', 'Mini Cooper S', 'Mitsubishi Lancer 1.8',\n","       'Mitsubishi Pajero Sport', 'Nissan Micra XL', 'Nissan Micra XV',\n","       'Nissan Sunny', 'Nissan Sunny XL', 'Nissan Terrano XL',\n","       'Nissan X Trail', 'Renault Duster', 'Renault Duster 110',\n","       'Renault Duster 110PS', 'Renault Duster 85', 'Renault Duster 85PS',\n","       'Renault Duster RxL', 'Renault Kwid', 'Renault Kwid 1.0',\n","       'Renault Kwid RXT', 'Renault Lodgy 85', 'Renault Scala RxL',\n","       'Skoda Fabia', 'Skoda Fabia 1.2L', 'Skoda Fabia Classic',\n","       'Skoda Laura', 'Skoda Octavia Classic', 'Skoda Rapid Elegance',\n","       'Skoda Superb 1.8', 'Skoda Yeti Ambition', 'Tata Aria Pleasure',\n","       'Tata Bolt XM', 'Tata Indica', 'Tata Indica V2', 'Tata Indica eV2',\n","       'Tata Indigo CS', 'Tata Indigo LS', 'Tata Indigo LX',\n","       'Tata Indigo Marina', 'Tata Indigo eCS', 'Tata Manza',\n","       'Tata Manza Aqua', 'Tata Manza Aura', 'Tata Manza ELAN',\n","       'Tata Nano', 'Tata Nano Cx', 'Tata Nano GenX', 'Tata Nano LX',\n","       'Tata Nano Lx', 'Tata Sumo Gold', 'Tata Sumo Grande',\n","       'Tata Sumo Victa', 'Tata Tiago Revotorq', 'Tata Tiago Revotron',\n","       'Tata Tigor Revotron', 'Tata Venture EX', 'Tata Vista Quadrajet',\n","       'Tata Zest Quadrajet', 'Tata Zest XE', 'Tata Zest XM',\n","       'Toyota Corolla', 'Toyota Corolla Altis', 'Toyota Corolla H2',\n","       'Toyota Etios', 'Toyota Etios G', 'Toyota Etios GD',\n","       'Toyota Etios Liva', 'Toyota Fortuner', 'Toyota Fortuner 3.0',\n","       'Toyota Innova 2.0', 'Toyota Innova 2.5', 'Toyota Qualis',\n","       'Volkswagen Jetta Comfortline', 'Volkswagen Jetta Highline',\n","       'Volkswagen Passat Diesel', 'Volkswagen Polo',\n","       'Volkswagen Polo Comfortline', 'Volkswagen Polo Highline',\n","       'Volkswagen Polo Highline1.2L', 'Volkswagen Polo Trendline',\n","       'Volkswagen Vento Comfortline', 'Volkswagen Vento Highline',\n","       'Volkswagen Vento Konekt', 'Volvo S80 Summum'], dtype=object)"]},"execution_count":55,"metadata":{},"output_type":"execute_result"}],"source":["pipe.steps[0][1].transformers[0][1].categories[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qGp9xSicASYD"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.3"}},"nbformat":4,"nbformat_minor":0}
diff --git a/models/Pre Owned Car Price Predictor/raw_car_data.csv b/models/Pre Owned Car Price Predictor/raw_car_data.csv
new file mode 100644
index 00000000..76ad6211
--- /dev/null
+++ b/models/Pre Owned Car Price Predictor/raw_car_data.csv	
@@ -0,0 +1,893 @@
+name,company,year,Price,kms_driven,fuel_type
+Hyundai Santro Xing XO eRLX Euro III,Hyundai,2007,"80,000","45,000 kms",Petrol
+Mahindra Jeep CL550 MDI,Mahindra,2006,"4,25,000",40 kms,Diesel
+Maruti Suzuki Alto 800 Vxi,Maruti,2018,Ask For Price,"22,000 kms",Petrol
+Hyundai Grand i10 Magna 1.2 Kappa VTVT,Hyundai,2014,"3,25,000","28,000 kms",Petrol
+Ford EcoSport Titanium 1.5L TDCi,Ford,2014,"5,75,000","36,000 kms",Diesel
+Ford EcoSport Titanium 1.5L TDCi,Ford,2015,Ask For Price,"59,000 kms",Diesel
+Ford Figo,Ford,2012,"1,75,000","41,000 kms",Diesel
+Hyundai Eon,Hyundai,2013,"1,90,000","25,000 kms",Petrol
+Ford EcoSport Ambiente 1.5L TDCi,Ford,2016,"8,30,000","24,530 kms",Diesel
+Maruti Suzuki Alto K10 VXi AMT,Maruti,2015,"2,50,000","60,000 kms",Petrol
+Skoda Fabia Classic 1.2 MPI,Skoda,2010,"1,82,000","60,000 kms",Petrol
+Maruti Suzuki Stingray VXi,Maruti,2015,"3,15,000","30,000 kms",Petrol
+Hyundai Elite i20 Magna 1.2,Hyundai,2014,"4,15,000","32,000 kms",Petrol
+Mahindra Scorpio SLE BS IV,Mahindra,2015,"3,20,000","48,660 kms",Diesel
+Hyundai Santro Xing XO eRLX Euro III,Hyundai,2007,"80,000","45,000 kms",Petrol
+Mahindra Jeep CL550 MDI,Mahindra,2006,"4,25,000",40 kms,Diesel
+Audi A8,Audi,2017,"10,00,000","4,000 kms",Petrol
+Audi Q7,Audi,2014,"5,00,000","16,934 kms",Diesel
+Mahindra Scorpio S10,Mahindra,2016,"3,50,000","43,000 kms",Diesel
+Maruti Suzuki Alto 800,Maruti,2014,"1,60,000","35,550 kms",Petrol
+Mahindra Scorpio S10,Mahindra,2016,"3,50,000","43,000 kms",Diesel
+Mahindra Scorpio S10,Mahindra,2016,"3,10,000","39,522 kms",Diesel
+Maruti Suzuki Alto 800 Vxi,Maruti,2015,"75,000","39,000 kms",Petrol
+Hyundai i20 Sportz 1.2,Hyundai,2012,"1,00,000","55,000 kms",Petrol
+Hyundai i20 Sportz 1.2,Hyundai,2012,"1,00,000","55,000 kms",Petrol
+Hyundai i20 Sportz 1.2,Hyundai,2012,"1,00,000","55,000 kms",Petrol
+Maruti Suzuki Alto 800 Lx,Maruti,2017,"1,90,000","72,000 kms",Petrol
+Maruti Suzuki Vitara Brezza ZDi,Maruti,2016,"2,90,000","15,975 kms",Diesel
+Maruti Suzuki Alto LX,Maruti,2008,"95,000","70,000 kms",Petrol
+Mahindra Bolero DI,Mahindra,2017,"1,80,000","23,452 kms",Diesel
+Maruti Suzuki Swift Dzire ZDi,Maruti,2014,"3,85,000","35,522 kms",Diesel
+Mahindra Scorpio S10 4WD,Mahindra,2015,"2,50,000","48,508 kms",Diesel
+Maruti Suzuki Swift Vdi BSIII,Maruti,2017,"1,80,000","15,487 kms",Petrol
+Maruti Suzuki Wagon R VXi BS III,Maruti,2013,"1,05,000","39,000 kms",Petrol
+Maruti Suzuki Wagon R VXi Minor,Maruti,2013,"1,05,000","39,000 kms",Petrol
+Toyota Innova 2.0 G 8 STR BS IV,Toyota,2012,"6,50,000","82,000 kms",Diesel
+Renault Lodgy 85 PS RXL,Renault,2018,"6,89,999","20,000 kms",Diesel
+Skoda Yeti Ambition 2.0 TDI CR 4x2,Skoda,2012,"4,48,000","68,000 kms",Diesel
+Maruti Suzuki Baleno Delta 1.2,Maruti,2017,"5,49,000","32,000 kms",Diesel
+Renault Duster 110 PS RxZ Diesel Plus,Renault,2012,"5,01,000","38,000 kms",Diesel
+Renault Duster 85 PS RxE Diesel,Renault,2013,"4,89,999","27,000 kms",Diesel
+Honda City 1.5 S MT,Honda,2011,"2,80,000","33,000 kms",Petrol
+Maruti Suzuki Alto K10 VXi AMT,Maruti,2015,"2,50,000","60,000 kms",Petrol
+Maruti Suzuki Dzire,Maruti,2013,"3,49,999","46,000 kms",Diesel
+Honda Amaze,Honda,2013,"2,84,999","46,000 kms",Diesel
+Honda Amaze 1.5 SX i DTEC,Honda,2015,"3,45,000","36,000 kms",Diesel
+Honda City,Honda,2015,"4,99,999","55,000 kms",Petrol
+Datsun Redi GO S,Datsun,2017,"2,35,000","16,000 kms",Petrol
+Maruti Suzuki SX4 ZXI MT,Maruti,2010,"2,49,999","36,000 kms",Petrol
+Mitsubishi Pajero Sport Limited Edition,Mitsubishi,2015,"14,75,000","47,000 kms",Diesel
+Mahindra Bolero DI,Mahindra,2017,"1,80,000","23,452 kms",Diesel
+Maruti Suzuki Swift Dzire ZDi,Maruti,2014,"3,85,000","35,522 kms",Diesel
+Mahindra Scorpio S10 4WD,Mahindra,2015,"2,50,000","48,508 kms",Diesel
+Maruti Suzuki Swift Vdi BSIII,Maruti,2017,"1,80,000","15,487 kms",Petrol
+Maruti Suzuki Wagon R VXi BS III,Maruti,2013,"1,05,000","39,000 kms",Petrol
+Maruti Suzuki Wagon R VXi Minor,Maruti,2013,"1,05,000","39,000 kms",Petrol
+Mahindra Scorpio S10,Mahindra,2015,"3,95,000","35,000 kms",Diesel
+Maruti Suzuki Swift VXi 1.2 ABS BS IV,Maruti,2017,"2,20,000","30,874 kms",Petrol
+Honda City ZX CVT,Honda,2017,"1,70,000","15,000 kms",Diesel
+Maruti Suzuki Wagon R LX BS IV,Maruti,2013,"85,000","29,685 kms",Petrol
+Ford Figo,Ford,2012,"1,75,000","41,000 kms",Diesel
+Hyundai Eon,Hyundai,2013,"1,90,000","25,000 kms",Petrol
+Tata Indigo eCS LS CR4 BS IV,Tata,2017,"2,00,000","1,30,000 kms",Diesel
+Ford EcoSport Ambiente 1.5L TDCi,Ford,2016,"8,30,000","24,530 kms",Diesel
+Tata Indigo eCS LS CR4 BS IV,Tata,2017,"2,00,000","1,30,000 kms",Diesel
+Mahindra Scorpio SLE BS IV,Mahindra,2012,"5,70,000","19,000 kms",Diesel
+Volkswagen Polo Highline Exquisite P,Volkswagen,2014,"3,15,000","60,000 kms",Petrol
+Skoda Fabia Classic 1.2 MPI,Skoda,2010,"1,82,000","60,000 kms",Petrol
+Maruti Suzuki Stingray VXi,Maruti,2015,"3,15,000","30,000 kms",Petrol
+I want to sell my car Tata Zest,I,2017,Ask For Price,,
+Chevrolet Spark LS 1.0,Chevrolet,2010,"1,10,000","41,000 kms",Petrol
+Renault Duster 110PS Diesel RxZ,Renault,2012,"5,01,000","35,000 kms",Diesel
+Honda City,Honda,2015,"4,48,999","54,000 kms",Petrol
+Mini Cooper S 1.6,Mini,2013,"18,91,111","13,000 kms",Petrol
+Datsun Redi GO S,Datsun,2017,"2,35,000","16,000 kms",Petrol
+Skoda Fabia 1.2L Diesel Ambiente,Skoda,2011,"1,59,500","38,200 kms",Diesel
+Honda Amaze,Honda,2015,"3,44,999","22,000 kms",Petrol
+Honda Amaze,Honda,2015,"3,44,999","22,000 kms",Petrol
+Renault Duster,Renault,2014,"4,49,999","50,000 kms",Diesel
+Mini Cooper S 1.6,Mini,2013,"18,91,111","13,500 kms",Petrol
+Mahindra Scorpio S4,Mahindra,2015,"8,65,000","30,000 kms",Diesel
+Mahindra Scorpio VLX 2WD BS IV,Mahindra,2014,"6,99,000","50,000 kms",Diesel
+Mahindra Quanto C8,Mahindra,2013,"3,75,000","20,000 kms",Diesel
+Ford EcoSport,Ford,2017,"4,89,999","39,000 kms",Petrol
+Honda Brio,Honda,2012,"2,24,999","30,000 kms",Petrol
+I want to sell my car Tata Zest,I,2017,Ask For Price,,
+Volkswagen Vento Highline Plus 1.5 Diesel AT,Volkswagen,2019,"12,00,000","3,600 kms",Diesel
+Hyundai i20 Magna,Hyundai,2009,"1,95,000","32,000 kms",Petrol
+Toyota Corolla Altis Diesel D4DG,Toyota,2010,"3,51,000","38,000 kms",Diesel
+Hyundai Verna Transform SX VTVT,Hyundai,2008,"1,60,000","45,000 kms",Petrol
+Toyota Corolla Altis Petrol Ltd,Toyota,2009,"2,40,000","35,000 kms",Petrol
+Honda City 1.5 EXi New,Honda,2005,"90,000","50,000 kms",Petrol
+Hyundai Elite i20 Magna 1.2,Hyundai,2014,"4,15,000","32,000 kms",Petrol
+Skoda Fabia 1.2L Diesel Elegance,Skoda,2011,"1,55,000","45,863 kms",Diesel
+BMW 3 Series 320i,BMW,2011,"6,00,000","60,500 kms",Petrol
+Maruti Suzuki A Star Lxi,Maruti,2011,"1,89,500","12,500 kms",Petrol
+Toyota Etios GD,Toyota,2013,"3,50,000","60,000 kms",Diesel
+Ford Figo Diesel EXI Option,Ford,2012,"2,10,000","35,000 kms",Diesel
+Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2014,"3,90,000","35,000 kms",Petrol
+Chevrolet Beat LT Diesel,Chevrolet,2012,"1,35,000","45,000 kms",Diesel
+BMW 7 Series 740Li Sedan,BMW,2009,"16,00,000","35,000 kms",Petrol
+Mahindra XUV500 W8 AWD 2013,Mahindra,2013,"7,01,000","38,000 kms",Diesel
+Hyundai i10 Magna 1.2,Hyundai,2014,"2,65,000","18,000 kms",Petrol
+Hyundai Verna Fluidic New,Hyundai,2015,"5,25,000","35,000 kms",Diesel
+Maruti Suzuki Swift VXi 1.2 BS IV,Maruti,2013,"3,72,000","13,349 kms",Petrol
+Maruti Suzuki Ertiga ZXI Plus,Maruti,2016,"6,35,000","29,000 kms",Petrol
+Ford EcoSport Titanium 1.5L TDCi,Ford,2014,"5,50,000","44,000 kms",Diesel
+Maruti Suzuki Ertiga Vxi,Maruti,2016,"5,75,000","29,000 kms",Petrol
+Maruti Suzuki Ertiga VDi,Maruti,2013,"4,85,000","42,000 kms",Diesel
+Maruti Suzuki Alto LXi BS III,Maruti,2012,"1,55,000","14,000 kms",Petrol
+Hyundai Grand i10 Asta 1.1 CRDi,Hyundai,2014,"3,45,000","49,000 kms",Diesel
+Honda Amaze 1.2 S i VTEC,Honda,2014,"3,25,000","42,000 kms",Petrol
+Hyundai i20 Asta 1.4 CRDI 6 Speed,Hyundai,2012,"3,29,500","36,200 kms",Diesel
+Ford Figo Diesel EXI,Ford,2014,"1,95,000","50,000 kms",Diesel
+Maruti Suzuki Eeco 5 STR WITH AC HTR,Maruti,2015,"2,51,111","55,000 kms",Petrol
+Maruti Suzuki Ertiga ZXi,Maruti,2014,"5,69,999","45,000 kms",Petrol
+Maruti Suzuki Esteem LXi BS III,Maruti,2007,"69,999","51,000 kms",Petrol
+Maruti Suzuki Ritz VXI,Maruti,2014,"2,99,999","19,000 kms",Petrol
+Maruti Suzuki Dzire,Maruti,2009,"2,20,000","46,000 kms",Petrol
+Maruti Suzuki Ritz LDi,Maruti,2013,"3,99,999","33,000 kms",Diesel
+Maruti Suzuki Swift VXi 1.2 BS IV,Maruti,2013,"3,72,000","13,349 kms",Petrol
+Maruti Suzuki Dzire VDI,Maruti,2015,"4,50,000","1,04,000 kms",Diesel
+Toyota Etios Liva G,Toyota,2014,"2,70,000","55,000 kms",Petrol
+Hyundai i20 Sportz 1.4 CRDI,Hyundai,2011,"3,50,000","33,333 kms",Diesel
+Chevrolet Spark,Chevrolet,2012,"1,58,400","33,600 kms",Petrol
+Maruti Suzuki Alto K10 VXi AMT,Maruti,2017,"3,50,000","5,600 kms",Petrol
+Nissan Micra XV,Nissan,2011,"1,79,000","41,000 kms",Petrol
+Maruti Suzuki Swift,Maruti,2007,"1,25,000","70,000 kms",Petrol
+Maruti Suzuki Alto 800,Maruti,2018,"2,00,000","7,500 kms",Petrol
+Honda Amaze 1.5 S i DTEC,Honda,2013,"2,99,000","45,000 kms",Diesel
+Maruti Suzuki Alto 800 Vxi,Maruti,2015,"2,20,000","38,000 kms",Petrol
+Chevrolet Beat,Chevrolet,2015,"1,50,000","30,000 kms",Petrol
+Toyota Corolla,Toyota,2009,"2,75,000","26,000 kms",
+Honda City 1.5 V MT,Honda,2010,"2,85,000","35,000 kms",Petrol
+Ford EcoSport Trend 1.5L TDCi,Ford,2016,"8,30,000","24,330 kms",Diesel
+Hyundai i20 Asta 1.2,Hyundai,2009,"2,10,000","65,480 kms",Petrol
+Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2013,"3,40,000","41,000 kms",Petrol
+Tata Indica V2 eLS,Tata,2006,"90,000","20,000 kms",Petrol
+Maruti Suzuki Alto 800 Lxi,Maruti,2018,Ask For Price,"28,028 kms",Petrol
+Hindustan Motors Ambassador,Hindustan,2000,"70,000","2,00,000 kms",Diesel
+Toyota Corolla Altis 1.8 GL,Toyota,2010,"2,89,999","70,000 kms",Petrol
+Toyota Corolla Altis 1.8 J,Toyota,2012,"3,49,999","59,000 kms",Petrol
+Toyota Innova 2.5 GX BS IV 7 STR,Toyota,2012,"8,49,999","99,000 kms",Diesel
+Volkswagen Jetta Highline TDI AT,Volkswagen,2014,"7,49,999","46,000 kms",Diesel
+Volkswagen Polo Comfortline 1.2L P,Volkswagen,2015,"3,99,999","2,800 kms",Petrol
+Volkswagen Polo,Volkswagen,2014,"2,74,999","32,000 kms",Petrol
+Mahindra Scorpio,Mahindra,2015,"9,84,999","22,000 kms",Diesel
+Renault Duster,Renault,2014,"4,49,999","50,000 kms",Diesel
+Honda Amaze,Honda,2015,"3,44,999","22,000 kms",Petrol
+Nissan Sunny,Nissan,2012,"2,24,999","45,000 kms",Petrol
+Hyundai Elite i20,Hyundai,2018,"5,99,999","21,000 kms",Petrol
+Renault Kwid,Renault,2016,"2,44,999","11,000 kms",Petrol
+Renault Duster,Renault,2013,"3,99,999","41,000 kms",Diesel
+Ford EcoSport,Ford,2017,"4,89,999","39,000 kms",Petrol
+Renault Duster,Renault,2014,"4,74,999","50,000 kms",Diesel
+Mahindra Scorpio VLX Airbag,Mahindra,2011,"4,99,999","66,000 kms",Diesel
+Maruti Suzuki Alto 800 Lxi,Maruti,2018,"3,10,000","3,000 kms",Petrol
+Chevrolet Spark LT 1.0,Chevrolet,2010,"85,000","45,000 kms",Petrol
+Datsun Redi GO T O,Datsun,2016,"2,45,000","7,000 kms",Petrol
+Maruti Suzuki Swift RS VDI,Maruti,2010,"1,89,500","38,500 kms",Diesel
+Fiat Punto Emotion 1.2,Fiat,2012,"1,69,500","37,200 kms",Diesel
+Maruti Suzuki Swift RS VDI,Maruti,2010,"1,59,500","43,200 kms",Diesel
+Toyota Etios GD,Toyota,2013,"2,75,000","24,800 kms",Petrol
+Hyundai i20 Sportz 1.4 CRDI,Hyundai,2014,"3,70,000","60,000 kms",Diesel
+Hyundai i10 Sportz 1.2,Hyundai,2010,"1,68,000","45,872 kms",Petrol
+Chevrolet Beat LT Opt Diesel,Chevrolet,2011,"1,50,000","40,000 kms",Diesel
+Chevrolet Beat LS Diesel,Chevrolet,2011,"1,45,000","45,000 kms",Diesel
+Chevrolet Beat LT Diesel,Chevrolet,2012,"98,500","38,000 kms",Diesel
+Mahindra Scorpio VLX 2WD BS IV,Mahindra,2014,"6,99,000","50,000 kms",Diesel
+Tata Indigo CS,Tata,2011,"85,000","11,400 kms",Diesel
+Toyota Corolla Altis 1.8 J,Toyota,2015,"5,75,000","42,000 kms",Petrol
+Honda City 1.5 V MT,Honda,2014,"5,49,000","39,000 kms",Petrol
+Maruti Suzuki Swift VDi,Maruti,2011,"2,09,000","47,000 kms",Diesel
+Hyundai Eon Era Plus,Hyundai,2013,"1,85,000","27,000 kms",Petrol
+Mahindra Scorpio S10,Mahindra,2015,"9,00,000","97,200 kms",Diesel
+Mahindra XUV500,Mahindra,2014,"6,99,999","52,000 kms",Diesel
+Honda Brio,Honda,2012,"2,24,999","30,000 kms",Petrol
+Ford Fiesta,Ford,2011,"2,74,999","55,000 kms",Diesel
+Honda Amaze,Honda,2013,"2,84,999","46,000 kms",Diesel
+Honda City,Honda,2015,"5,99,999","30,000 kms",Diesel
+Maruti Suzuki Wagon R,Maruti,2012,"1,99,999","44,000 kms",Petrol
+Honda City,Honda,2014,"5,44,999","45,000 kms",Diesel
+Hyundai i20,Hyundai,2009,"1,99,000","31,000 kms",Petrol
+Tata Indigo eCS LX TDI BS III,Tata,2016,"3,20,000","1,75,430 kms",Diesel
+Hyundai Fluidic Verna 1.6 CRDi SX,Hyundai,2015,"5,40,000","38,000 kms",Diesel
+"Commercial , DZire LDI, 2016, for sale",Commercial,...,Ask For Price,,
+Mahindra Quanto C8,Mahindra,2013,"3,40,000","37,000 kms",Diesel
+Fiat Petra ELX 1.2 PS,Fiat,2008,"75,000","65,000 kms",Petrol
+Skoda Fabia 1.2L Diesel Ambiente,Skoda,2011,"1,59,500","38,200 kms",Diesel
+Mini Cooper S 1.6,Mini,2013,"18,91,111","13,000 kms",Petrol
+Hyundai Santro Xing XS,Hyundai,2005,"49,000","7,500 kms",Petrol
+Maruti Suzuki Ciaz VXi Plus,Maruti,2016,"7,00,000","3,350 kms",Petrol
+Maruti Suzuki Zen VX,Maruti,2000,"55,000","60,000 kms",Petrol
+Honda City,Honda,2015,"4,48,999","54,000 kms",Petrol
+Hyundai Creta 1.6 SX Plus Petrol,Hyundai,2017,"8,95,000","32,000 kms",Petrol
+"Tata indigo ecs LX, 201",Tata,150k,"1,50,000",,
+Mahindra Scorpio SLX,Mahindra,2007,"3,55,000","75,000 kms",Diesel
+Mahindra Scorpio SLE BS IV,Mahindra,2012,"5,65,000","62,000 kms",Diesel
+Toyota Innova 2.5 G BS III 8 STR,Toyota,2006,"3,65,000","73,000 kms",Diesel
+Maruti Suzuki Alto K10 VXi AMT,Maruti,2011,"1,45,000","41,000 kms",Petrol
+Maruti Suzuki Wagon R LXI BS IV,Maruti,2011,"2,10,000","35,000 kms",Petrol
+Tata Nano Cx BSIV,Tata,2013,"40,000","2,200 kms",Petrol
+Maruti Suzuki Alto Std BS IV,Maruti,2013,"1,25,000","39,000 kms",Petrol
+Maruti Suzuki Wagon R LXi BS III,Maruti,2009,"1,35,000","45,000 kms",Petrol
+Maruti Suzuki Swift VXI BSIII,Maruti,2006,"1,35,000","45,000 kms",Petrol
+Tata Sumo Victa EX 10 by 7 Str BSIII,Tata,2012,"2,85,000","65,000 kms",Diesel
+MARUTI SUZUKI DESI,MARUTI,TOUR,"4,00,000",,
+Maruti Suzuki Wagon R LXi BS III,Maruti,2010,"1,45,000","54,870 kms",Petrol
+Maruti Suzuki Alto LXi BS III,Maruti,2010,"1,35,000","34,580 kms",Petrol
+Volkswagen Passat Diesel Comfortline AT,Volkswagen,2009,"4,50,000","97,000 kms",Diesel
+Renault Scala RxL Diesel Travelogue,Renault,2015,"3,75,000","25,000 kms",Diesel
+Mahindra Quanto C8,Mahindra,2013,"3,75,000","20,000 kms",Diesel
+Hyundai Grand i10 Sportz O 1.2 Kappa VTVT,Hyundai,2014,"3,65,000","20,000 kms",Petrol
+Hyundai i20 Active 1.2 SX,Hyundai,2015,"5,00,000","18,000 kms",Petrol
+Mahindra Xylo E4,Mahindra,2012,"4,00,000","35,000 kms",Diesel
+Mahindra Jeep MM 550 XDB,Mahindra,2019,"3,90,000",60 kms,Diesel
+Renault Duster 110PS Diesel RxZ,Renault,2012,"5,01,000","35,000 kms",Diesel
+Mahindra Bolero SLE BS IV,Mahindra,2013,"3,30,000","80,200 kms",Diesel
+Force Motors Force One LX ABS 7 STR,Force,2015,"5,80,000","3,200 kms",Diesel
+Maruti Suzuki SX4,Maruti,2012,"2,65,000","46,000 kms",Diesel
+Mahindra Jeep CL550 MDI,Mahindra,2019,"3,79,000","0,000 kms",Diesel
+Maruti Suzuki Alto 800,Maruti,2015,"2,19,000","5,000 kms",Petrol
+Mahindra Jeep CL550 MDI,Mahindra,2018,"3,85,000",588 kms,Diesel
+Toyota Etios,Toyota,2011,"2,75,000","36,000 kms",Diesel
+Volkswagen Polo,Volkswagen,2015,"3,30,000","38,000 kms",Diesel
+Honda City ZX VTEC,Honda,2008,"1,10,000","45,000 kms",Petrol
+Maruti Suzuki Wagon R LX BS III,Maruti,2006,"80,000","71,200 kms",Petrol
+Honda City VX O MT Diesel,Honda,2016,"5,19,000","52,000 kms",Diesel
+Mahindra Thar CRDe 4x4 AC,Mahindra,2016,"7,30,000","29,000 kms",Diesel
+Mitsubishi Pajero Sport Limited Edition,Mitsubishi,2015,"14,75,000","47,000 kms",Diesel
+Audi A4 1.8 TFSI Multitronic Premium Plus,Audi,2009,"6,99,000","47,000 kms",Petrol
+Mercedes Benz GLA Class 200 CDI Sport,Mercedes,2015,"20,00,000","20,000 kms",Diesel
+Land Rover Freelander 2 SE,Land,2015,"21,00,000","30,000 kms",Diesel
+Renault Kwid RXT,Renault,2017,"3,40,000","5,000 kms",Petrol
+Tata Aria Pleasure 4X2,Tata,2014,"3,90,000","35,000 kms",Diesel
+Mercedes Benz B Class B180 Sports,Mercedes,2014,"14,00,000","31,000 kms",Petrol
+Datsun GO T O,Datsun,2016,"2,45,000","7,000 kms",Petrol
+Tata Indigo eCS LX TDI BS III,Tata,2016,"3,20,000","1,75,430 kms",Diesel
+Tata Indigo eCS LX TDI BS III,Tata,2016,"3,20,000","1,75,400 kms",Diesel
+Honda Jazz VX MT,Honda,2016,"4,50,000","41,000 kms",Petrol
+Honda Amaze 1.2 S i VTEC,Honda,2014,"3,11,000","33,000 kms",Petrol
+Honda Amaze,Honda,2013,"2,84,999","46,000 kms",Diesel
+Honda City,Honda,2012,"3,99,999","45,000 kms",Petrol
+Honda City,Honda,2015,"5,99,999","39,000 kms",Diesel
+Honda Amaze,Honda,2015,"3,44,999","22,000 kms",Petrol
+Audi A4 1.8 TFSI Multitronic Premium Plus,Audi,2009,"6,99,000","47,000 kms",Petrol
+Force Motors Force One LX ABS 7 STR,Force,2015,"5,80,000","3,200 kms",Diesel
+Mahindra Scorpio S4,Mahindra,2015,"8,55,000","30,000 kms",Diesel
+Hyundai i20 Active 1.4L SX O,Hyundai,2015,"5,35,000","37,000 kms",Diesel
+Mini Cooper S,Mini,2013,"18,91,111","13,000 kms",Petrol
+Maruti Suzuki Ciaz ZXI Plus,Maruti,2017,"6,99,000","14,000 kms",Petrol
+Chevrolet Tavera Neo,Chevrolet,2013,"3,75,000","55,000 kms",Diesel
+Honda Amaze,Honda,2013,"2,84,999","46,000 kms",Diesel
+Hyundai Eon Sportz,Hyundai,2012,"1,78,000","30,000 kms",Petrol
+Tata Sumo Gold Select Variant,Tata,2013,"3,00,000","50,000 kms",Diesel
+Maruti Suzuki Wagon R 1.0,Maruti,2003,"90,000","45,000 kms",Petrol
+Maruti Suzuki Esteem VXi BS III,Maruti,2006,"95,000","45,000 kms",Petrol
+Maruti Suzuki Eeco 5 STR WITH AC HTR,Maruti,2015,"2,55,000","9,300 kms",Petrol
+Chevrolet Enjoy 1.4 LS 8 STR,Chevrolet,2013,"2,45,000","55,000 kms",Diesel
+Hyundai i20 Asta 1.4 CRDI 6 Speed,Hyundai,2012,"3,29,500","36,200 kms",Diesel
+Ford Figo Diesel EXI,Ford,2014,"1,95,000","50,000 kms",Diesel
+Maruti Suzuki Eeco 5 STR WITH AC HTR,Maruti,2015,"2,51,111","55,000 kms",Petrol
+Maruti Suzuki Ertiga ZXi,Maruti,2014,"5,69,999","45,000 kms",Petrol
+Maruti Suzuki Esteem LXi BS III,Maruti,2007,"69,999","51,000 kms",Petrol
+Maruti Suzuki Ritz VXI,Maruti,2014,"2,99,999","19,000 kms",Petrol
+Maruti Suzuki Dzire,Maruti,2009,"2,20,000","46,000 kms",Petrol
+Maruti Suzuki Ritz LDi,Maruti,2013,"3,99,999","33,000 kms",Diesel
+Maruti Suzuki SX4 ZXI MT,Maruti,2010,"2,49,999","36,000 kms",Petrol
+Maruti Suzuki Wagon R 1.0 VXi,Maruti,2015,"2,89,999","22,000 kms",Petrol
+Mini Cooper S 1.6,Mini,2013,"18,91,111","13,500 kms",Petrol
+Nissan Terrano XL D Plus,Nissan,2015,"4,99,999","60,000 kms",Diesel
+Renault Duster 85 PS RxE Diesel,Renault,2013,"4,89,999","27,000 kms",Diesel
+Renault Duster 85 PS RxE Diesel,Renault,2014,"4,89,999","59,000 kms",Diesel
+Renault Duster 85 PS RxL Diesel,Renault,2015,"5,49,999","19,000 kms",Diesel
+Maruti Suzuki Dzire ZXI,Maruti,2013,"3,80,000","30,000 kms",Petrol
+Renault Kwid RXT Opt,Renault,2018,"3,25,000","15,000 kms",Petrol
+Maruti Suzuki Maruti 800 Std,Maruti,2003,"57,000","56,758 kms",Petrol
+Renault Kwid 1.0 RXT AMT,Renault,2018,"3,49,999","10,000 kms",Petrol
+Renault Lodgy 85 PS RXL,Renault,2018,"6,89,999","20,000 kms",Diesel
+Renault Scala RxL Diesel,Renault,2014,"3,49,999","49,000 kms",Diesel
+Hyundai Grand i10 Asta 1.2 Kappa VTVT O,Hyundai,2014,"4,10,000","41,000 kms",Petrol
+Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2011,"2,25,000","45,000 kms",Petrol
+Chevrolet Beat LS Petrol,Chevrolet,2010,"1,20,000","43,000 kms",Petrol
+Tata Indigo eCS LX TDI BS III,Tata,2016,"3,20,000","1,75,430 kms",Diesel
+Hyundai Santro Xing XO eRLX Euro III,Hyundai,2000,"59,000","56,450 kms",Petrol
+Hyundai Fluidic Verna 1.6 CRDi SX,Hyundai,2015,"5,40,000","38,000 kms",Diesel
+"Commercial , DZire LDI, 2016, for sale",Commercial,...,Ask For Price,,
+Chevrolet Beat LS Petrol,Chevrolet,2010,"80,000","56,000 kms",Petrol
+Mahindra Quanto C8,Mahindra,2013,"3,40,000","37,000 kms",Diesel
+Fiat Petra ELX 1.2 PS,Fiat,2008,"75,000","65,000 kms",Petrol
+Chevrolet Beat LS Petrol,Chevrolet,2015,"2,20,000","32,700 kms",Petrol
+Skoda Fabia 1.2L Diesel Ambiente,Skoda,2011,"1,59,500","38,200 kms",Diesel
+Ford EcoSport Titanium 1.5L TDCi,Ford,2016,"5,99,000","30,000 kms",Diesel
+Hyundai Accent GLX,Hyundai,2006,"80,000","56,000 kms",Petrol
+Yama,Yamaha,r 15,"55,000",,
+Maruti Suzuki Swift LDi,Maruti,2010,Ask For Price,"52,000 kms",Diesel
+Mahindra TUV300 T4 Plus,Mahindra,2016,"6,75,000","9,000 kms",Diesel
+Mini Cooper S 1.6,Mini,2013,"18,91,111","13,000 kms",Petrol
+Mini Cooper S 1.6,Mini,2013,"18,91,111","13,000 kms",Petrol
+Tata Indica V2 Xeta e GLE,Tata,2008,"1,50,000","11,000 kms",Petrol
+Mini Cooper S,Mini,2013,"18,91,111","13,000 kms",Petrol
+Tata Indigo CS LS DiCOR,Tata,2009,"72,500","46,000 kms",Diesel
+Maruti Suzuki Swift VXi 1.2 ABS BS IV,Maruti,2019,"6,10,000",73 kms,Petrol
+Mahindra Scorpio VLX Special Edition BS III,Mahindra,2004,"2,30,000","1,60,000 kms",Diesel
+Tata Indica eV2 LS,Tata,2017,Ask For Price,"84,000 kms",Diesel
+Honda Accord,Honda,2009,"1,75,000","58,559 kms",Petrol
+Mahindra Scorpio S4,Mahindra,2015,"8,55,000","30,000 kms",Diesel
+Chevrolet Tavera Neo,Chevrolet,2013,"3,75,000","55,000 kms",Diesel
+Ford EcoSport Titanium 1.5 TDCi,Ford,2014,"5,20,000","57,000 kms",Diesel
+Maruti Suzuki Ertiga,Maruti,2015,"5,24,999","50,000 kms",Diesel
+Honda Amaze,Honda,2014,"2,99,999","37,000 kms",Petrol
+Maruti Suzuki Dzire,Maruti,2012,"2,99,999","40,000 kms",Petrol
+Honda City,Honda,2011,"2,84,999","55,000 kms",Petrol
+Mahindra Scorpio 2.6 CRDe,Mahindra,2007,"2,20,000","1,70,000 kms",Diesel
+Maruti Suzuki Dzire,Maruti,2014,"4,24,999","55,000 kms",Diesel
+Honda City,Honda,2015,"6,44,999","39,000 kms",Petrol
+Honda Mobilio,Honda,2014,"3,99,999","44,000 kms",Petrol
+Toyota Corolla Altis,Toyota,2009,"1,99,999","65,000 kms",Petrol
+Honda City,Honda,2014,"5,84,999","39,000 kms",Petrol
+Skoda Laura,Skoda,2012,"3,49,999","44,000 kms",Diesel
+Renault Duster,Renault,2015,"4,49,999","49,000 kms",Diesel
+Maruti Suzuki Ertiga,Maruti,2018,"7,99,999","9,000 kms",Diesel
+Maruti Suzuki Dzire,Maruti,2015,"4,44,999","45,000 kms",Diesel
+Mahindra XUV500,Mahindra,2014,"6,49,999","47,000 kms",Diesel
+Hyundai Verna Fluidic,Hyundai,2012,"4,44,999","40,000 kms",Diesel
+Maruti Suzuki Vitara Brezza,Maruti,2016,"6,89,999","29,000 kms",Diesel
+Maruti Suzuki Wagon R,Maruti,2016,"3,44,999","15,000 kms",Petrol
+Mahindra Scorpio,Mahindra,2015,"9,44,999","45,000 kms",Diesel
+Honda Amaze,Honda,2014,"2,74,999","35,000 kms",Petrol
+Mahindra XUV500,Mahindra,2013,"6,89,999","80,000 kms",Diesel
+Mahindra Scorpio,Mahindra,2013,"5,74,999","68,000 kms",Diesel
+Skoda Laura,Skoda,2013,"3,74,999","50,000 kms",Diesel
+Volkswagen Polo,Volkswagen,2010,"1,99,999","60,000 kms",Diesel
+Hyundai Elite i20,Hyundai,2016,"5,49,999","9,000 kms",Petrol
+Tata Manza Aura Quadrajet,Tata,2012,"1,30,000","72,000 kms",Diesel
+Chevrolet Sail UVA Petrol LT ABS,Chevrolet,2013,"2,10,000","60,000 kms",Petrol
+Renault Duster 110 PS RxZ Diesel Plus,Renault,2012,"5,01,000","38,000 kms",Diesel
+Hyundai Verna Fluidic 1.6 VTVT SX,Hyundai,2013,"4,01,000","45,000 kms",Diesel
+Audi A4 2.0 TDI 177bhp Premium,Audi,2012,"13,50,000","40,000 kms",Diesel
+Hyundai Elantra SX,Hyundai,2013,"6,00,000","20,000 kms",Petrol
+Mahindra Scorpio VLX 4WD Airbag,Mahindra,2013,"6,10,000","35,000 kms",Diesel
+Mahindra KUV100 K8 D 6 STR,Mahindra,2016,"4,00,000","20,000 kms",Diesel
+Renault Scala RxL Diesel Travelogue,Renault,2015,"3,75,000","25,000 kms",Diesel
+Mahindra Quanto C8,Mahindra,2013,"3,75,000","20,000 kms",Diesel
+Hyundai Grand i10 Sportz O 1.2 Kappa VTVT,Hyundai,2014,"3,65,000","20,000 kms",Petrol
+Hyundai i20 Active 1.2 SX,Hyundai,2015,"5,00,000","18,000 kms",Petrol
+Mahindra Xylo E4,Mahindra,2012,"4,00,000","35,000 kms",Diesel
+Hyundai Grand i10,Hyundai,2017,"5,24,999","6,821 kms",Petrol
+Hyundai i20,Hyundai,2014,"4,49,999","23,000 kms",Petrol
+Hyundai Eon,Hyundai,2014,"1,74,999","14,000 kms",Petrol
+Hyundai i10,Hyundai,2012,"2,44,999","38,000 kms",Petrol
+Hyundai i20 Active,Hyundai,2015,"5,74,999","35,000 kms",Diesel
+Datsun Redi GO,Datsun,2017,"2,44,999","22,000 kms",Petrol
+Toyota Etios Liva,Toyota,2011,"2,39,999","41,000 kms",Petrol
+Hyundai Accent,Hyundai,2010,"99,999","45,000 kms",Petrol
+Hyundai Verna,Hyundai,2014,"4,89,999","44,000 kms",Diesel
+Maruti Suzuki Swift,Maruti,2013,"3,24,999","45,000 kms",Diesel
+Toyota Fortuner,Toyota,2011,"10,74,999","52,000 kms",Diesel
+Hyundai i10 Sportz,Hyundai,2012,"2,30,000","34,000 kms",Petrol
+Mahindra Bolero Power Plus SLE,Mahindra,2018,"6,99,000","1,800 kms",Diesel
+selling car Ta,selling,Zest,Ask For Price,,
+Mahindra XUV500,Mahindra,2015,"10,00,000","15,000 kms",Diesel
+Honda City 1.5 V MT Exclusive,Honda,2010,"2,40,000","4,00,000 kms",Petrol
+Chevrolet Spark LT 1.0 Airbag,Chevrolet,2009,"1,10,000","44,000 kms",Petrol
+Mahindra Jeep MM 550 XDB,Mahindra,2019,"3,90,000",60 kms,Diesel
+Renault Duster 110PS Diesel RxZ,Renault,2012,"5,01,000","35,000 kms",Diesel
+Mahindra XUV500,Mahindra,2016,"11,30,000","72,000 kms",Diesel
+Tata Indigo eCS VX CR4 BS IV,Tata,2014,"2,50,000","40,000 kms",Diesel
+Tata Zest 90,Tata,/-Rs,Ask For Price,,
+Mahindra Bolero SLE BS IV,Mahindra,2013,"3,30,000","80,200 kms",Diesel
+Force Motors Force One LX ABS 7 STR,Force,2015,"5,80,000","3,200 kms",Diesel
+Skoda Rapid Elegance 1.6 TDI CR MT,Skoda,2013,"3,40,000","48,000 kms",Diesel
+Tata Vista Quadrajet VX,Tata,2011,"1,20,000","90,000 kms",Diesel
+Maruti Suzuki Alto K10 VXi AT,Maruti,2015,"2,65,000","12,000 kms",Petrol
+Maruti Suzuki SX4,Maruti,2012,"2,65,000","46,000 kms",Diesel
+Maruti Suzuki Zen LXi BS III,Maruti,2003,"85,000","69,900 kms",Petrol
+Mahindra Jeep CL550 MDI,Mahindra,2019,"3,79,000","0,000 kms",Diesel
+Hyundai i10 Magna 1.2,Hyundai,2011,"1,75,000","45,000 kms",Petrol
+Maruti Suzuki Alto 800,Maruti,2015,"2,19,000","5,000 kms",Petrol
+Maruti Suzuki Swift Dzire Tour LDi,Maruti,2016,"3,50,000","1,66,000 kms",Diesel
+Honda City ZX EXi,Honda,2008,"1,49,000","42,000 kms",Petrol
+Mahindra Jeep CL550 MDI,Mahindra,2018,"3,85,000",588 kms,Diesel
+Mahindra Jeep MM 550 XDB,Mahindra,2006,"4,25,000",122 kms,Diesel
+Chevrolet Beat Diesel,Chevrolet,2017,"1,50,000","62,000 kms",Diesel
+Honda City 1.5 S MT,Honda,2010,"2,25,000","70,000 kms",Petrol
+Maruti Suzuki Swift Dzire car,Maruti,sale,"3,00,000",,
+Hyundai Verna 1.4 VTVT,Hyundai,2014,"3,75,000","36,000 kms",Petrol
+Toyota Innova 2.5 E MS 7 STR BS IV,Toyota,2012,"7,70,000",0 kms,Diesel
+Maruti Suzuki Alto 800 Lxi,Maruti,2018,Ask For Price,"24,000 kms",Petrol
+Maruti Suzuki Maruti 800 Std – Befo,Maruti,1995,"30,000","55,000 kms",Petrol
+Toyota Etios,Toyota,2011,"2,75,000","36,000 kms",Diesel
+Volkswagen Polo,Volkswagen,2015,"3,30,000","38,000 kms",Diesel
+Maruti Suzuki Swift,Maruti,2014,"3,35,000","55,000 kms",Diesel
+Hyundai Elite i20 Asta 1.4 CRDI,Hyundai,2015,"4,50,000","20,000 kms",Diesel
+Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2012,"2,25,000","40,000 kms",Petrol
+Maruti Suzuki Swift Dzire Tour (Gat,Maruti,ara),"3,00,000",,
+Maruti Suzuki Versa DX2 8 SEATER BSIII,Maruti,2004,"80,000","50,000 kms",Petrol
+Tata Indigo LX TDI BS III,Tata,2016,"1,30,000","1,04,000 kms",Diesel
+Volkswagen Vento Konekt Diesel Highline,Volkswagen,2011,"2,45,000","65,000 kms",Diesel
+Mercedes Benz C Class 200 CDI Classic,Mercedes,2002,"3,99,000","41,000 kms",Petrol
+Maruti Suzuki Ertiga VDi,Maruti,2013,"4,50,000","90,000 kms",Diesel
+URJE,URJENT,SELL,"1,80,000",,
+Honda City,Honda,2000,"65,000","80,000 kms",Petrol
+Hyundai Santro Xing GLS,Hyundai,2006,"75,000","46,000 kms",Petrol
+Maruti Suzuki Omni Limited Edition,Maruti,2001,"70,000","70,000 kms",Petrol
+Hyundai Sonata Transform 2.4 GDi MT,Hyundai,2017,"1,90,000","36,469 kms",Diesel
+Hyundai Elite i20 Sportz 1.2,Hyundai,2018,"6,00,000","7,800 kms",Petrol
+Volkswagen Vento Konekt Diesel Highline,Volkswagen,2011,"2,45,000","65,000 kms",Diesel
+Maruti Suzuki Alto 800 Lxi,Maruti,2017,"2,40,000","60,000 kms",Petrol
+Maruti Suzuki Alto LXi BS III,Maruti,2011,"1,55,000","32,000 kms",Petrol
+Honda Jazz S MT,Honda,2009,"1,69,999","24,695 kms",Petrol
+Hyundai Grand i10 Sportz 1.2 Kappa VTVT,Hyundai,2017,"4,50,000","15,141 kms",Petrol
+Maruti Suzuki Zen LXi BSII,Maruti,2001,"40,000","40,000 kms",Petrol
+Mahindra Scorpio W Turbo 2.6DX 9 Seater,Mahindra,2012,"1,65,000","65,000 kms",Diesel
+Swift Dzire Tour 27 Dec 2016 Regis,Swift,tion,"3,70,000",,
+Maruti Suzuki Alto K10 VXi,Maruti,2014,"2,70,000","22,000 kms",Petrol
+Hyundai Grand i10 Asta 1.2 Kappa VTVT,Hyundai,2016,"2,80,000","59,910 kms",Diesel
+Mahindra XUV500 W8,Mahindra,2012,"5,60,000","1,00,000 kms",Diesel
+Hyundai Creta 1.6 SX Plus Petrol,Hyundai,2016,"9,50,000","25,000 kms",Petrol
+Hyundai i20 Magna O 1.2,Hyundai,2013,"3,10,000","35,000 kms",Petrol
+Renault Duster 85 PS RxL Explore LE,Renault,2015,"7,15,000","65,000 kms",Diesel
+Hyundai Grand i10 Sportz 1.2 Kappa VTVT,Hyundai,2014,"3,40,000","35,000 kms",Petrol
+Honda Brio V MT,Honda,2012,"2,35,000","33,000 kms",Petrol
+Mahindra TUV300 T4 Plus,Mahindra,2017,"6,10,000","68,000 kms",Diesel
+Chevrolet Spark LS 1.0,Chevrolet,2010,"95,000","23,000 kms",Petrol
+Mahindra TUV300 T8,Mahindra,2018,"10,00,000","4,500 kms",Diesel
+Maruti Suzuki Swift Dzire Tour LDi,Maruti,2015,"2,20,000","1,29,000 kms",Diesel
+Nissan X Trail Select Variant,Nissan,2019,"12,00,000",300 kms,Diesel
+Maruti Suzuki Alto 800 Vxi,Maruti,2015,"2,30,000","5,000 kms",Petrol
+Ford Ikon 1.3 CLXi NXt Finesse,Ford,2001,"45,000","65,000 kms",Petrol
+Toyota Fortuner 3.0 4x4 MT,Toyota,2010,"9,40,000","1,31,000 kms",Diesel
+Tata Manza ELAN Quadrajet,Tata,2010,"1,55,555","1,11,111 kms",Petrol
+Tata zest x,Tata,odel,"3,20,000",,
+Mahindra xyl,Mahindra,2 bs,"3,50,000",,
+Mercedes Benz A Class A 180 Sport Petrol,Mercedes,2013,"15,00,000","14,000 kms",Petrol
+Chevrolet Beat LS Diesel,Chevrolet,2016,"2,10,000","22,000 kms",Diesel
+Ford EcoSport Trend 1.5L TDCi,Ford,2013,"4,95,000","38,000 kms",Diesel
+Tata Indigo LS,Tata,2016,"1,25,000","70,000 kms",Diesel
+Hyundai i20 Magna 1.2,Hyundai,2010,"1,95,000","36,000 kms",Petrol
+Volkswagen Vento Highline Plus 1.5 Diesel AT,Volkswagen,2015,"5,50,000","34,000 kms",Diesel
+Renault Kwid RXT,Renault,2015,"2,70,000","43,000 kms",Petrol
+Used Commercial Maruti Omn,Used,arry,"1,50,000",,
+Ford EcoSport Titanium 1.5L TDCi,Ford,2014,"5,00,000","40,000 kms",Diesel
+Honda Amaze 1.5 E i DTEC,Honda,2016,"2,40,000","1,60,000 kms",Diesel
+Hyundai Verna 1.6 EX VTVT,Hyundai,2017,"8,00,000","12,000 kms",Petrol
+BMW 5 Series 520d Sedan,BMW,2011,"12,99,000","49,000 kms",Diesel
+Skoda Superb 1.8 TFSI AT,Skoda,2011,"5,30,000","68,000 kms",Petrol
+Audi Q3 2.0 TDI quattro Premium,Audi,2013,"14,99,000","37,000 kms",Diesel
+Mahindra Bolero DI BSII,Mahindra,2012,"2,20,000","59,466 kms",Diesel
+Maruti Suzuki Zen Estilo LXI Green CNG,Maruti,2011,Ask For Price,"16,000 kms",Petrol
+Mahindra Scorpio S10,Mahindra,2015,"9,00,000","97,200 kms",Diesel
+Ford Figo Duratorq Diesel Titanium 1.4,Ford,2012,"2,50,000","99,000 kms",Diesel
+Maruti Suzuki Wagon R VXI BS IV,Maruti,2018,"3,95,000","25,500 kms",Petrol
+Mahindra Logan Diesel 1.5 DLS,Mahindra,2009,"1,30,000","66,000 kms",Petrol
+Tata Nano GenX XMA,Tata,2010,"32,000","44,005 kms",Petrol
+Mahindra TUV300 T4 Plus,Mahindra,2016,"5,40,000","35,000 kms",Diesel
+Mahindra TUV300 T4 Plus,Mahindra,2016,"5,40,000","35,000 kms",Diesel
+Hyundai Elite i20 Magna 1.2,Hyundai,2015,"4,05,000","28,000 kms",Petrol
+Hyundai Elite i20 Magna 1.2,Hyundai,2015,"4,00,000","30,000 kms",Petrol
+Honda City SV,Honda,2017,"7,60,000","4,000 kms",Petrol
+Maruti Suzuki Baleno Delta 1.2,Maruti,2016,"5,00,000","28,000 kms",Petrol
+Ford Figo Petrol LXI,Ford,2011,"1,75,000","75,000 kms",Petrol
+Mahindra Scorpio S10,Mahindra,2015,"9,00,000","97,200 kms",Diesel
+Honda City,Honda,2017,"7,50,000","3,000 kms",Petrol
+Hyundai Elite i20 Magna 1.2,Hyundai,2015,"4,19,000","20,000 kms",Petrol
+Maruti Suzuki Versa DX2 8 SEATER BSIII,Maruti,2004,"90,000","50,000 kms",Petrol
+Hyundai Eon Era Plus,Hyundai,2018,"1,40,000","2,110 kms",Petrol
+Mitsubishi Pajero Sport Limited Edition,Mitsubishi,2015,"15,40,000","43,222 kms",Petrol
+Hyundai i10 Magna 1.2 Kappa2,Hyundai,2008,"2,75,000","1,00,200 kms",Petrol
+Toyota Corolla H2,Toyota,2003,"1,50,000","1,00,000 kms",Petrol
+Maruti Suzuki Swift Dzire Tour VXi,Maruti,2011,"2,30,000",65 kms,Petrol
+Tata Indigo CS eLS BS IV,Tata,2015,"1,23,000","1,00,000 kms",Diesel
+Mahindra Scorpio S10,Mahindra,2015,"9,00,000","97,200 kms",Diesel
+Mahindra Scorpio S10,Mahindra,2015,"9,00,000","97,200 kms",Diesel
+Hyundai Xcent Base 1.1 CRDi,Hyundai,2016,"3,00,000","1,40,000 kms",Diesel
+Honda City,Honda,2015,"4,99,999","55,000 kms",Petrol
+Hyundai Accent Executive Edition,Hyundai,2009,"1,65,000","48,000 kms",Petrol
+Maruti Suzuki Baleno Delta 1.2,Maruti,2016,"4,98,000","22,000 kms",Petrol
+Tata Zest XE 75 PS Diesel,Tata,2018,"4,80,000","1,03,553 kms",Diesel
+Maruti Suzuki Dzire LDI,Maruti,2017,"4,88,000","80,000 kms",Diesel
+Tata Sumo Gold LX BS IV,Tata,2014,"2,50,000","99,000 kms",Diesel
+Toyota Corolla Altis GL Petrol,Toyota,2010,"2,20,000","58,000 kms",Petrol
+Maruti Suzuki Eeco 7 STR,Maruti,2013,"2,90,000","70,000 kms",LPG
+Toyota Fortuner 3.0 4x2 MT,Toyota,2015,"15,25,000","1,20,000 kms",Diesel
+Mahindra XUV500 W6,Mahindra,2013,"5,48,900","49,800 kms",Diesel
+Tata Tigor Revotron XZ,Tata,2019,"6,50,000",100 kms,Diesel
+Maruti Suzuki 800,Maruti,2001,"55,000","81,876 kms",Petrol
+Maruti Suzuki Ertiga Vxi,Maruti,2015,"5,50,000","75,000 kms",Petrol
+Maruti Suzuki Versa DX2 8 SEATER BSIII,Maruti,2004,"90,000","50,000 kms",Petrol
+Honda Mobilio S i DTEC,Honda,2014,"3,99,000","44,000 kms",Diesel
+Maruti Suzuki Ertiga,Maruti,2016,"7,30,000","55,000 kms",Diesel
+Maruti Suzuki Vitara Brezza,Maruti,2017,"7,25,000","36,000 kms",Diesel
+Hyundai Verna 1.6 CRDI E,Hyundai,2016,"1,95,000","56,000 kms",Diesel
+Maruti Suzuki Swift VXI BSIII,Maruti,2007,"1,30,000","62,000 kms",Petrol
+Toyota Fortuner 3.0 4x2 MT,Toyota,2015,"15,25,000","1,20,000 kms",Diesel
+Maruti Suzuki Omni Select Variant,Maruti,2014,"1,90,000","6,020 kms",Petrol
+Honda Amaze,Honda,2013,"2,50,000","55,700 kms",Diesel
+Tata Indica,Tata,2005,"80,000","42,000 kms",Petrol
+Hyundai Santro Xing,Hyundai,2003,"1,20,000","50,000 kms",Petrol
+Maruti Suzuki Zen Estilo,Maruti,2010,"1,49,000","35,000 kms",Petrol
+Maruti Suzuki Wagon R LXI BS IV,Maruti,2014,"2,50,000","18,500 kms",Petrol
+Maruti Suzuki Wagon R,Maruti,2007,"1,20,000","7,000 kms",Petrol
+Honda Brio VX AT,Honda,2017,"4,50,000","11,000 kms",Petrol
+Hyundai Xcent Base 1.1 CRDi,Hyundai,2015,Ask For Price,"1,80,000 kms",Diesel
+Maruti Suzuki Zen LXi BSII,Maruti,2003,"99,999","53,000 kms",Petrol
+Maruti Suzuki Zen Estilo LXI Green CNG,Maruti,2008,"1,35,000","23,000 kms",Petrol
+Maruti Suzuki Wagon R Select Variant,Maruti,2016,"2,25,000","35,500 kms",Diesel
+Maruti Suzuki Alto LXi BS III,Maruti,2010,"99,000","22,134 kms",Petrol
+Renault Kwid RXT,Renault,2019,"3,70,000","1,000 kms",Petrol
+Tata Nano Lx BSIV,Tata,2010,"52,000","9,000 kms",Petrol
+Jaguar XE XE Portfolio,Jaguar,2016,"28,00,000","8,500 kms",Petrol
+Hyundai Xcent S 1.2,Hyundai,2015,Ask For Price,"35,000 kms",Petrol
+Hyundai Eon Magna Plus,Hyundai,2014,"1,90,000","35,000 kms",Petrol
+Honda City 1.5 S MT,Honda,2014,"4,99,000","22,000 kms",Petrol
+Hindustan Motors Ambassador,Hindustan,2002,"90,000","25,000 kms",Diesel
+Maruti Suzuki Ritz GENUS VXI,Maruti,2010,"1,49,000","40,000 kms",Petrol
+Hyundai Grand i10 Magna AT 1.2 Kappa VTVT,Hyundai,2017,"4,00,000","20,000 kms",Petrol
+Hyundai Eon D Lite Plus,Hyundai,2016,"1,20,000","87,000 kms",Petrol
+Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2015,"2,50,000","55,000 kms",Petrol
+Maruti Suzuki Wagon R VXI BS IV,Maruti,2017,"3,75,000","23,000 kms",Petrol
+Honda Amaze 1.2 VX i VTEC,Honda,2014,"3,81,000","6,000 kms",Petrol
+Maruti Suzuki Estilo VXi ABS BS IV,Maruti,2013,"1,80,000","65,000 kms",Petrol
+Maruti Suzuki Vitara Brezza LDi O,Maruti,2016,"5,80,000","25,000 kms",Diesel
+Maruti Suzuki Eeco 5 STR WITH AC HTR,Maruti,2015,"2,78,000","39,000 kms",Petrol
+Toyota Innova 2.0 V,Toyota,2009,Ask For Price,"15,574 kms",Diesel
+Hyundai Creta 1.6 SX Plus Petrol AT,Hyundai,2016,"10,00,000","8,000 kms",Petrol
+Mahindra Scorpio Vlx BSIV,Mahindra,2013,"6,90,000","75,000 kms",Diesel
+Maruti Suzuki Ertiga VDi,Maruti,2012,"4,80,000","51,000 kms",Diesel
+Mitsubishi Lancer 1.8 LXi,Mitsubishi,2006,"85,000","50,000 kms",Petrol
+Maruti Suzuki Maruti 800 AC,Maruti,2001,"40,000","75,000 kms",Petrol
+Maruti Suzuki Alto 800 LXI CNG O,Maruti,2015,"90,000","55,800 kms",Petrol
+Hyundai Grand i10 Magna 1.2 Kappa VTVT,Hyundai,2015,"3,40,000","53,000 kms",Petrol
+Hyundai Eon D Lite Plus,Hyundai,2018,"2,60,000","25,000 kms",Petrol
+Ford Fiesta SXi 1.6 ABS,Ford,2009,"2,50,000","56,400 kms",Petrol
+Maruti Suzuki Ritz VDi,Maruti,2010,"1,80,000","72,160 kms",Diesel
+Hyundai Verna Fluidic New,Hyundai,2012,"3,50,000","10,000 kms",Diesel
+Maruti Suzuki Wagon R LXi BS III,Maruti,2006,"90,001","48,000 kms",Petrol
+Maruti Suzuki Estilo LX BS IV,Maruti,2007,"1,15,000","36,000 kms",Petrol
+Audi A6 2.0 TDI Premium,Audi,2012,"15,99,000","11,500 kms",Diesel
+Maruti Suzuki Wagon R LXi BS III,Maruti,2003,"1,30,000","1,33,000 kms",Petrol
+Maruti Suzuki Wagon R,Maruti,2009,"1,59,000","27,000 kms",Petrol
+Maruti Suzuki Wagon R,Maruti,2009,"1,60,000","35,000 kms",Petrol
+Maruti Suzuki Alto,Maruti,2010,"1,10,000","55,000 kms",Petrol
+Maruti Suzuki Baleno Sigma 1.2,Maruti,2016,"4,25,000","40,000 kms",Petrol
+Hyundai Verna 1.6 SX VTVT AT,Hyundai,2019,"9,00,000","2,000 kms",Petrol
+Maruti Suzuki Swift GLAM,Maruti,2009,"1,50,000","45,000 kms",Petrol
+Hyundai Getz Prime 1.3 GVS,Hyundai,2009,"1,10,000","20,000 kms",Petrol
+Hyundai Santro,Hyundai,2000,"51,999","88,000 kms",Petrol
+Hyundai Getz Prime 1.3 GLX,Hyundai,2009,"1,15,000","20,000 kms",Petrol
+Chevrolet Beat PS Diesel,Chevrolet,2012,"2,15,000","65,422 kms",Diesel
+Ford EcoSport Trend 1.5 Ti VCT,Ford,2017,"5,80,000","10,000 kms",Petrol
+Maruti Suzuki Dzire ZXI,Maruti,2013,"3,80,000","35,000 kms",Petrol
+Hyundai Fluidic Verna 1.6 CRDi SX,Hyundai,2013,"3,50,000","1,17,000 kms",Diesel
+Tata Indica V2 DLG,Tata,2005,"35,000","1,50,000 kms",Diesel
+BMW X1 xDrive20d xLine,BMW,2011,"11,50,000","72,000 kms",Diesel
+Hyundai i20 Asta 1.2,Hyundai,2010,"3,00,000","10,750 kms",Petrol
+Honda City 1.5 V AT,Honda,2009,"2,69,000","55,000 kms",Petrol
+Tata Nano,Tata,2013,"60,000","6,800 kms",Petrol
+Chevrolet Cruze LTZ AT,Chevrolet,2014,"4,00,000","41,000 kms",Diesel
+Hyundai Verna Fluidic New,Hyundai,2015,"4,30,000","73,000 kms",Diesel
+Hyun,Hyundai,Eon,Ask For Price,,
+Maruti Suzuki Swift Dzire VDi,Maruti,2011,"1,40,000","65,000 kms",Diesel
+Mahindra XUV500 W6,Mahindra,2014,"85,00,003","45,000 kms",Diesel
+Mahindra XUV500 W10,Mahindra,2018,"12,99,000","40,000 kms",Diesel
+Maruti Suzuki Alto K10 LXi CNG,Maruti,2014,"1,99,000","37,000 kms",Petrol
+Hyundai Accent GLE,Hyundai,2006,"90,000","55,000 kms",Petrol
+Force Motors One SUV,Force,2013,"5,50,000","1,40,000 kms",Diesel
+Datsun Go Plus T O,Datsun,2016,Ask For Price,5 kms,Petrol
+Maruti Suzuki Alto,Maruti,2019,"2,65,000","9,800 kms",Petrol
+Chevrolet Spark 1.0 LT,Chevrolet,2011,"1,00,000","27,000 kms",Petrol
+Hyundai i10,Hyundai,2009,"2,15,000","27,000 kms",Petrol
+Toyota Etios Liva GD,Toyota,2012,"3,80,000","20,000 kms",Diesel
+Renault Duster 85PS Diesel RxL Optional with Nav,Renault,2013,"4,01,919","57,923 kms",Diesel
+Chevrolet Enjoy,Chevrolet,2014,"4,90,000","30,201 kms",Diesel
+Maruti Suzuki Alto 800 Lxi,Maruti,2017,"2,80,000","6,200 kms",Petrol
+BMW 5 Series 530i,BMW,2009,"6,50,000","37,518 kms",Petrol
+Toyota Etios Liva G,Toyota,2014,"1,60,000","24,652 kms",Petrol
+Mahindra Jeep MM 550 XDB,Mahindra,2004,"4,24,000",383 kms,Diesel
+Chevrolet Beat LS Diesel,Chevrolet,2016,"2,25,000","95,000 kms",Diesel
+Chevrolet Cruze LTZ,Chevrolet,2011,"3,50,000","35,000 kms",Diesel
+Jeep Wrangler Unlimited 4x4 Diesel,Jeep,2015,"9,50,000","3,528 kms",Diesel
+Maruti Suzuki Ertiga VDi,Maruti,2013,"4,85,000","52,500 kms",Diesel
+Hyundai Verna VGT CRDi SX ABS,Hyundai,2010,"2,05,000","47,900 kms",Diesel
+Maruti Suzuki Omni,Maruti,2012,"1,60,000","14,000 kms",Petrol
+Maruti Suzuki Celerio VDi,Maruti,2018,"3,10,000","37,000 kms",Petrol
+Tata Zest Quadrajet 1.3,Tata,2017,"1,80,000","90,000 kms",Diesel
+Mahindra XUV500 W6,Mahindra,2013,"5,49,900","52,800 kms",Diesel
+Tata Indigo CS eLX BS IV,Tata,2016,"1,50,000","1,04,000 kms",Diesel
+Hyundai i10 Era,Hyundai,2011,"1,75,000","30,000 kms",Petrol
+Tata Indigo eCS LX TDI BS III,Tata,2014,"95,000","1,95,000 kms",Diesel
+Tata Indigo LX TDI BS III,Tata,2016,"2,30,000","1,04,000 kms",Diesel
+Tata Indigo eCS LX CR4 BS IV,Tata,2016,"2,30,000","1,04,000 kms",Diesel
+Tata Indigo Marina LS,Tata,2004,"1,80,000","70,000 kms",Diesel
+Commercial Chevrolet Sail Hatchback ca,Commercial,o...,"2,25,000",,
+Hyundai Xcent SX 1.2,Hyundai,2015,"4,00,000","43,000 kms",Diesel
+Hyundai Eon Magna Plus,Hyundai,2013,"1,85,000","23,000 kms",Petrol
+Renault Duster 85 PS RxL Diesel,Renault,2015,"3,85,000","51,000 kms",Diesel
+Maruti Suzuki Alto K10 LXi CNG,Maruti,2009,"90,000","62,000 kms",Petrol
+Tata Nano LX Special Edition,Tata,2010,"32,000","48,008 kms",Petrol
+Commercial Car Ta,Commercial,Zest,"3,71,500",,
+Renault Duster 110 PS RxZ Diesel,Renault,2013,"4,35,000","39,000 kms",Diesel
+Maruti Suzuki Wagon R AX BSIV,Maruti,2010,"2,25,000","40,000 kms",Petrol
+Maruti Suzuki Swift,Maruti,2006,"1,89,700","48,247 kms",Petrol
+Maruti Suzuki Ertiga,Maruti,2012,"3,89,700","39,000 kms",Diesel
+Maruti Suzuki Swift VXi 1.2 ABS BS IV,Maruti,2014,"3,65,000","23,000 kms",Petrol
+Maruti Suzuki Alto K10 New,Maruti,2017,"3,60,000","9,400 kms",Petrol
+Hyundai i20 Magna,Hyundai,2010,"2,10,000","50,000 kms",Petrol
+Hyundai i10 Magna 1.2,Hyundai,2009,"1,70,000","75,000 kms",Petrol
+tata Indica,tata,sale,"1,30,000",,
+Tata Zest XE 75 PS Diesel,Tata,2017,"3,80,000","70,000 kms",Diesel
+Mahindra Xylo E8,Mahindra,2009,"2,95,000","64,000 kms",Diesel
+Toyota Corolla Altis GL Petrol,Toyota,2010,"1,85,000","55,000 kms",Petrol
+Tata Manza Aqua Quadrajet,Tata,2014,"1,60,000","2,00,000 kms",Diesel
+Mahindra KUV100 K8 D 6 STR,Mahindra,2018,Ask For Price,"7,500 kms",Diesel
+Used bt new conditions ta,Used,Zest,"2,55,000",,
+Renault Kwid 1.0,Renault,2018,"2,90,000","2,137 kms",Petrol
+Sale tata,Sale,ture,"1,00,000",,
+Tata Venture EX 8 STR,Tata,2013,"1,00,000","30,000 kms",Diesel
+Maruti Suzuki Swift Dzire Tour LXi,Maruti,2014,"3,15,000","44,000 kms",Petrol
+Maruti Suzuki Alto LX BSII,Maruti,2002,Ask For Price,"56,000 kms",Petrol
+Skoda Octavia Classic 1.9 TDI MT,Skoda,2006,"1,14,990","65,000 kms",Diesel
+Maruti Suzuki Omni LPG BS IV,Maruti,2012,"1,20,000","1,60,000 kms",LPG
+Chevrolet Beat Diesel,Chevrolet,2011,"1,25,000","56,000 kms",Diesel
+Tata Sumo Gold EX BS IV,Tata,2012,"2,10,000","75,000 kms",Diesel
+Tata indigo 2017 top model..,Tata,emi,"1,70,000",,
+Hyundai Verna 1.6 CRDI SX,Hyundai,2018,"8,55,000","42,000 kms",Diesel
+Tata Sumo Gold EX BS IV,Tata,2012,"2,10,000","75,000 kms",Diesel
+Mahindra Scorpio 2.6 CRDe,Mahindra,2007,"2,60,000","56,000 kms",Diesel
+Maruti Suzuki Zen LXi BS III,Maruti,2002,"95,000","10,544 kms",Petrol
+Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2011,"2,55,000","64,000 kms",Petrol
+Mahindra Scorpio SLX 2.6 Turbo 8 Str,Mahindra,2008,"3,00,000","70,000 kms",Diesel
+Hyundai Grand i10 Sportz 1.2 Kappa VTVT,Hyundai,2014,"3,40,000","25,000 kms",Petrol
+Hyundai Elite i20 Sportz 1.2,Hyundai,2017,"5,50,000","15,000 kms",Petrol
+Ford Ikon 1.6 Nxt,Ford,2003,"60,000","50,000 kms",Petrol
+Hyundai Elite i20 Sportz 1.2,Hyundai,2015,Ask For Price,"49,500 kms",Petrol
+Tata indigo,Tata,car,"1,50,000",,
+Toyota Innova 2.5 V 7 STR,Toyota,2011,"7,50,000","1,47,000 kms",Diesel
+Nissan Sunny XL,Nissan,2011,"2,30,000","52,000 kms",Petrol
+Chevrolet Beat LT Diesel,Chevrolet,2012,"1,30,000","90,001 kms",Diesel
+Maruti Suzuki Alto 800 Lxi,Maruti,2017,"2,70,000","21,000 kms",Petrol
+Maruti Suzuki Swift VDi BS IV,Maruti,2012,"2,80,000","48,006 kms",Diesel
+Maruti Suzuki Swift VDi BS IV,Maruti,2012,"2,80,000","48,006 kms",Diesel
+Maruti Suzuki Swift,Maruti,2012,"2,80,000","48,006 kms",Diesel
+very good condition tata bolts are av,very,able,"2,00,000",,
+Toyota Innova 2.0 G4,Toyota,2012,"6,00,000","80,000 kms",Diesel
+Sale Hyundai xcent commerc,Sale,no.,Ask For Price,,
+Maruti Suzuki Swift VDi ABS,Maruti,2010,"1,90,000","74,000 kms",Diesel
+Hyundai Elite i20 Asta 1.2,Hyundai,2015,"5,00,000","22,000 kms",Petrol
+Mahindra XUV500 W10,Mahindra,2016,"10,65,000","41,000 kms",Diesel
+Volkswagen Polo Trendline 1.5L D,Volkswagen,2015,"3,50,000","25,000 kms",Diesel
+Toyota Etios Liva Diesel,Toyota,2012,"3,50,000","85,000 kms",Diesel
+Mahindra TUV300 T4 Plus,Mahindra,2016,"5,40,000","29,500 kms",Diesel
+Hyundai Elite i20 Asta 1.2,Hyundai,2015,"4,70,000","30,000 kms",Petrol
+Hyundai Santro Xing GLS,Hyundai,2014,"1,79,000","57,000 kms",Petrol
+Maruti Suzuki Zen LXi BS III,Maruti,2003,"48,000","60,000 kms",Petrol
+Maruti Suzuki Ciaz ZXi Plus RS,Maruti,2016,"6,50,000","50,000 kms",Petrol
+Hyundai Eon Era Plus,Hyundai,2013,"1,90,000","39,700 kms",Petrol
+Hyundai Elantra 1.8 S,Hyundai,2012,"5,00,000","65,000 kms",Petrol
+Maruti Suzuki Swift VDi,Maruti,2010,"2,70,000","67,000 kms",Diesel
+Maruti Suzuki Zen Estilo LXI Green CNG,Maruti,2008,"1,25,000","46,000 kms",Petrol
+Hyundai Eon Era Plus,Hyundai,2012,"1,88,000","38,000 kms",Petrol
+Hyundai Grand i10 Magna 1.2 Kappa VTVT,Hyundai,2016,"3,80,000","27,000 kms",Petrol
+Hyundai Verna Fluidic New,Hyundai,2011,"3,65,000","43,000 kms",Diesel
+Ford EcoSport Trend 1.5L Ti VCT,Ford,2014,"4,65,000","47,000 kms",Petrol
+Hyundai i20 Magna,Hyundai,2011,"2,40,000","42,000 kms",Petrol
+Chevrolet Beat Diesel,Chevrolet,2016,"1,79,999","19,336 kms",Diesel
+Tata Indica eV2 LS,Tata,2015,"1,40,000","60,105 kms",Diesel
+Jaguar XF 2.2 Diesel Luxury,Jaguar,2013,"21,90,000","29,000 kms",Diesel
+Audi Q5 2.0 TDI quattro Premium Plus,Audi,2014,"23,90,000","34,000 kms",Diesel
+BMW 3 Series 320d Sedan,BMW,2011,"10,75,000","35,000 kms",Diesel
+Maruti Suzuki Swift ZXi 1.2 BS IV,Maruti,2015,"4,75,000","22,000 kms",Petrol
+BMW X1 sDrive20d,BMW,2012,"10,25,000","41,000 kms",Diesel
+Maruti Suzuki S Cross Sigma 1.3,Maruti,2016,"6,15,000","21,000 kms",Diesel
+Maruti Suzuki Ertiga LDi,Maruti,2013,"4,75,000","48,000 kms",Diesel
+Maruti Suzuki Alto K10 VXi AMT,Maruti,2016,"2,70,000","38,000 kms",Petrol
+Honda City SV,Honda,2014,"4,75,000","34,000 kms",Diesel
+Volkswagen Vento Comfortline Petrol,Volkswagen,2011,"2,40,000","45,933 kms",Petrol
+Honda City 1.5 EXi New,Honda,2005,"1,20,000","68,000 kms",Petrol
+Audi A4 2.0 TDI 177bhp Premium,Audi,2016,"19,00,000","44,000 kms",Diesel
+Mahindra KUV100,Mahindra,2017,"3,60,000","35,000 kms",Diesel
+Tata Zest XE 75 PS Diesel,Tata,2018,"4,50,000","1,02,563 kms",Diesel
+Mahindra XUV500 W8,Mahindra,2015,"9,00,000","28,600 kms",Diesel
+Maruti Suzuki Swift Dzire Tour VDi,Maruti,2017,"6,50,000","41,800 kms",Diesel
+Tata Sumo Gold LX BS IV,Tata,2014,"2,75,000","1,16,000 kms",Diesel
+Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2009,"2,10,000","59,000 kms",Petrol
+Mahindra Scorpio 2.6 SLX,Mahindra,2004,"1,75,000","58,000 kms",Diesel
+Maruti Suzuki Omni 8 STR BS III,Maruti,2009,"85,000","45,000 kms",Petrol
+Mitsubishi Pajero Sport Limited Edition,Mitsubishi,2015,"14,90,000","42,590 kms",Diesel
+Renault Duster,Renault,2014,"8,00,000","7,400 kms",Diesel
+Volkswagen Jetta Comfortline 1.9 TDI AT,Volkswagen,2009,"4,50,000","54,500 kms",Diesel
+Maruti Suzuki Ertiga Vxi,Maruti,2012,"10,00,000","2,00,000 kms",Diesel
+Audi A4 2.0 TDI 177bhp Premium,Audi,2013,"15,10,000","27,000 kms",Diesel
+Volvo S80 Summum D4,Volvo,2015,"18,50,000","42,000 kms",Diesel
+Toyota Corolla Altis VL AT Petrol,Toyota,2014,"7,90,000","29,000 kms",Petrol
+Mitsubishi Pajero Sport 2.5 AT,Mitsubishi,2015,"17,25,000","37,000 kms",Diesel
+Chevrolet Beat LT Petrol,Chevrolet,2012,"1,35,000","36,000 kms",Petrol
+BMW X1,BMW,2011,"10,00,000","34,000 kms",Diesel
+Datsun Redi GO S,Datsun,2018,"2,99,999","7,000 kms",Petrol
+Mercedes Benz C Class C 220 CDI Avantgarde,Mercedes,2009,"12,25,000","76,000 kms",Diesel
+Mahindra Scorpio SLX,Mahindra,2004,"1,75,000","60,000 kms",Diesel
+Volkswagen Vento Comfortline Diesel,Volkswagen,2011,"2,00,000","95,000 kms",Diesel
+Tata Indigo CS GLS,Tata,2017,"2,70,000","50,000 kms",Diesel
+Ford Figo Petrol Titanium,Ford,2019,"5,25,000",00 kms,Petrol
+Honda City ZX GXi,Honda,2006,"1,80,000","50,000 kms",Petrol
+Maruti Suzuki Wagon R Duo Lxi,Maruti,2008,"1,40,000","68,000 kms",Petrol
+Ford EcoSport Trend 1.5L TDCi,Ford,2014,"4,00,000","16,000 kms",Petrol
+Maruti Suzuki Swift Dzire VDi,Maruti,2016,"4,99,000","51,000 kms",Diesel
+Maruti Suzuki Omni 8 STR BS III,Maruti,2009,"85,000","56,000 kms",Petrol
+Maruti Suzuki Zen LX BSII,Maruti,2004,"70,000","1,00,000 kms",Petrol
+Renault Duster RxL Petrol,Renault,2015,"5,50,000","36,000 kms",Petrol
+Maruti Suzuki Swift VXi 1.2 BS IV,Maruti,2014,"3,70,000","11,523 kms",Petrol
+Maruti Suzuki Baleno Zeta 1.2,Maruti,2018,"6,90,000","1,000 kms",Petrol
+Honda WR V S MT Petrol,Honda,2009,"2,50,000","60,000 kms",Petrol
+Tata Indigo CS eLX BS IV,Tata,2016,"1,10,000","85,000 kms",Diesel
+Renault Duster 110 PS RxL Diesel,Renault,2013,"4,90,000","38,600 kms",Diesel
+Mahindra Scorpio LX BS III,Mahindra,2009,"3,20,000","95,500 kms",Diesel
+Maruti Suzuki Zen LXi BS III,Maruti,2004,"68,000","56,000 kms",Petrol
+Maruti Suzuki Wagon R LXi BS III,Maruti,2014,"1,30,000","37,458 kms",Petrol
+Maruti Suzuki SX4 Celebration Diesel,Maruti,2016,"9,70,000","85,960 kms",Diesel
+Audi A3 Cabriolet 40 TFSI,Audi,2015,"31,00,000","12,516 kms",Petrol
+Hyundai Eon D Lite Plus,Hyundai,2018,"2,80,000","35,000 kms",Petrol
+Maruti Suzuki Zen Estilo LXI Green CNG,Maruti,2009,"1,25,000",00 kms,Petrol
+Mahindra Scorpio SLX,Mahindra,2008,"2,85,000","80,000 kms",Diesel
+I want to sell my commercial car due t,I,o...,"4,75,000",,
+Hyundai Santro AE GLS Audio,Hyundai,2011,"1,65,000","45,000 kms",Petrol
+i want sale my car.no emi....uber atta,i,d...,"3,20,000",,
+Maruti Suzuki Swift Dzire Tour VDi,Maruti,2009,"2,50,000","51,000 kms",Diesel
+Mahindra Scorpio S4,Mahindra,2015,"8,65,000","30,000 kms",Diesel
+Tata ZEST 6 month old,Tata,car,"3,70,000",,
+Mahindra Xylo D2 BS IV,Mahindra,2011,"3,90,000","48,000 kms",Diesel
+Hyundai Santro,Hyundai,2003,"60,000","51,000 kms",Petrol
+Chevrolet Beat LT Diesel,Chevrolet,2015,"2,15,000","90,000 kms",Diesel
+Maruti Suzuki Swift Dzire VDi,Maruti,2015,"4,75,000","43,000 kms",Diesel
+Mahindra XUV500 W8,Mahindra,2015,"8,99,000","53,000 kms",Diesel
+Toyota Fortuner 3.0 4x4 MT,Toyota,2013,"14,99,000","97,000 kms",Diesel
+Maruti Suzuki Alto K10 VXi,Maruti,2013,"2,40,000","20,000 kms",Petrol
+Hyundai Getz GLE,Hyundai,2007,"99,000","55,000 kms",Petrol
+Maruti Suzuki Swift Dzire Tour LDi,Maruti,2014,"2,60,000","1,20,000 kms",Diesel
+Hyundai Creta 1.6 SX,Hyundai,2019,"12,00,000",0 kms,Petrol
+Hyundai Santro Xing XL AT eRLX Euro III,Hyundai,2007,"1,15,000","46,000 kms",Petrol
+Hyundai Santro Xing XL eRLX Euro III,Hyundai,2009,"88,000","43,200 kms",Petrol
+Mahindra Xylo D2 BS IV,Mahindra,2011,"3,90,000","56,000 kms",Diesel
+Hyundai Santro Xing XL eRLX Euro III,Hyundai,2007,"1,35,000","42,000 kms",Petrol
+Tata Indica V2 DLS BS III,Tata,2009,"90,000","30,600 kms",Diesel
+Hyundai i10 Sportz 1.2,Hyundai,2011,"2,20,000","38,000 kms",Petrol
+Hyundai Grand i10 Magna 1.2 Kappa VTVT,Hyundai,2017,"4,24,999","2,550 kms",Petrol
+Hyundai Santro Xing XL AT eRLX Euro III,Hyundai,2007,"1,35,000","47,000 kms",Petrol
+Honda City 1.5 E MT,Honda,2005,"95,000","41,000 kms",Petrol
+Nissan Micra XL,Nissan,2017,"4,30,000","62,500 kms",Diesel
+Honda City 1.5 S Inspire,Honda,2005,"1,15,000","68,000 kms",Petrol
+Maruti Suzuki Alto 800 Lxi,Maruti,2015,"2,15,000","50,000 kms",Petrol
+Maruti Suzuki Wagon R LX BS III,Maruti,2004,"53,000","69,000 kms",Petrol
+Maruti Suzuki Ertiga VDi,Maruti,2012,"5,00,000","48,000 kms",Diesel
+Tata Indica eV2 eXeta eGLX,Tata,2012,"85,000","55,000 kms",Diesel
+Maruti Suzuki Omni E 8 STR BS IV,Maruti,2013,"1,65,000","25,000 kms",Petrol
+Hyundai Eon Era Plus,Hyundai,2014,"2,00,000","28,400 kms",Petrol
+Hyundai Eon,Hyundai,2014,"2,00,000","28,000 kms",Petrol
+Maruti Suzuki Swift LDi,Maruti,2015,"4,25,000","42,000 kms",Diesel
+MARUTI SUZUKI ERTIGA F,MARUTI,SALE,"6,50,000",,
+Hyundai Verna 1.6 CRDI SX Plus AT,Hyundai,2012,"6,00,000","29,000 kms",Diesel
+Chevrolet Tavera LS B3 10 Seats BSII,Chevrolet,2005,"1,30,000","68,485 kms",Diesel
+Tata Tiago Revotron XM,Tata,2018,"4,30,000","3,500 kms",Petrol
+Tata Tiago Revotorq XZ,Tata,2019,"5,68,500",0 kms,Petrol
+Tata Nexon,Tata,2019,Ask For Price,0 kms,Petrol
+Tata,Tata,digo,Ask For Price,,
+Maruti Suzuki Zen LXi BS III,Maruti,2006,"71,000","32,000 kms",Petrol
+Mahindra KUV100 K8 D 6 STR,Mahindra,2018,"5,60,000","8,000 kms",Diesel
+Ford EcoSport Titanium 1.5 TDCi,Ford,2014,"5,90,000","34,000 kms",Diesel
+Hindustan Motors Ambassador Classic Mark 4 – Befo,Hindustan,1995,"7,50,000","37,000 kms",Petrol
+Ford Fusion 1.4 TDCi Diesel,Ford,2007,"1,25,000","85,455 kms",Diesel
+Hyundai Santro Xing XL AT eRLX Euro III,Hyundai,2007,"1,35,000","46,000 kms",Petrol
+Hyundai Santro,Hyundai,2002,"60,000","47,000 kms",Petrol
+Fiat Linea Emotion 1.4 L T Jet Petrol,Fiat,2009,"1,20,000","64,000 kms",Petrol
+Ford Ikon 1.3 Flair Josh 100,Ford,2008,"95,000","46,000 kms",Petrol
+Maruti Suzuki Omni E 8 STR BS IV,Maruti,2017,"2,40,000","8,000 kms",Petrol
+Tata Indica V2 LS,Tata,2012,"1,15,000","64,000 kms",Diesel
+Mahindra Scorpio S4,Mahindra,2015,"7,95,000","63,000 kms",Diesel
+Hyundai Santro Xing XL eRLX Euro III,Hyundai,2007,"55,000","65,000 kms",Petrol
+Mahindra Xylo D2,Mahindra,2009,"3,00,000","62,000 kms",Diesel
+Hyundai Grand i10 Asta 1.2 Kappa VTVT,Hyundai,2014,"3,20,000","41,000 kms",Petrol
+Maruti Suzuki Alto 800 Lxi,Maruti,2015,"2,65,000","14,000 kms",Petrol
+Toyota Corolla,Toyota,2006,"1,60,000","40,000 kms",Petrol
+Hyundai Eon Magna,Hyundai,2017,"3,00,000","1,600 kms",Petrol
+Tata Sumo Grande MKII GX,Tata,2010,"1,30,000","90,000 kms",Diesel
+Maruti Suzuki Swift VDi,Maruti,2011,"2,50,000","58,000 kms",Diesel
+Volkswagen Polo Highline1.2L P,Volkswagen,2013,"3,80,000","27,000 kms",Petrol
+Maruti Suzuki Alto 800 Lx,Maruti,2003,"42,000","60,000 kms",Petrol
+Tata Tiago Revotron XZ,Tata,2017,"4,00,000","31,000 kms",Petrol
+Maruti Suzuki Swift LDi,Maruti,2009,"1,20,000","90,000 kms",Diesel
+Maruti Suzuki Swift VDi,Maruti,2009,"1,20,000","90,000 kms",Diesel
+Tata Indigo eCS,Tata,2016,"1,30,000","1,50,000 kms",Diesel
+Chevrolet Beat LS Diesel,Chevrolet,2014,"1,89,000","31,000 kms",Diesel
+2012 Tata Sumo Gold f,2012,sell,"2,50,000",,
+Mahindra Xylo E8 BS IV,Mahindra,2011,"3,65,000","43,000 kms",Diesel
+Hyundai Eon D Lite Plus,Hyundai,2013,"1,70,000","20,000 kms",Petrol
+Well mentained Tata Sumo,Well,d Ex,"3,80,000",,
+all paper updated tata indica v2 and u,all,n...,"1,45,000",,
+Maruti Ertiga showroom condition with,Maruti,e...,"4,80,000",,
+7 SEATER MAHINDRA BOLERO IN VERY GOOD,7,D...,Ask For Price,,
+9 SEATER MAHINDRA BOL,9,", Ac",Ask For Price,,
+scratch less Tata I,scratch,go .,"1,40,000",,
+Maruti Suzuki swift dzire for sale in,Maruti,d...,"3,60,000",,
+Commercial Chevrolet beat for sale in,Commercial,k...,"1,80,000",,
+urgent sell my Mahindra qu,urgent,o c4,"3,50,000",,
+Tata Sumo Gold FX BSIII,Tata,2013,"2,15,000","1,00,000 kms",Petrol
+sell my car Maruti Suzuki Swif,sell,zire,"3,00,000",,
+Maruti Suzuki Swift Dzire good car fo,Maruti,o...,"3,10,000",,
+Hyunda,Hyundai,cent,Ask For Price,,
+Commercial Maruti Suzuki Alto Lxi 800,Commercial,...,Ask For Price,,
+urgent sale Ta,urgent,Sumo,"2,20,000",,
+Maruti Suzuki Alto vxi t,Maruti,cab,"95,000",,
+tata,tata,t xe,Ask For Price,,
+TATA INDI,TATA,EV2,"1,10,000",,
+Tata Nano,Tata,2013,"60,000","7,000 kms",Petrol
+Hyundai Elite i20,Hyundai,2017,"5,99,999","31,000 kms",Petrol
+Hyundai i10 Magna 1.2 Kappa2,Hyundai,2009,"4,00,000","33,000 kms",Petrol
+Hyundai Creta,Hyundai,2016,"9,00,000","60,000 kms",Diesel
+Volkswagen Polo,Volkswagen,2013,"2,99,999","48,000 kms",Diesel
+Maruti Suzuki Dzire,Maruti,2014,"3,74,999","33,000 kms",Petrol
+Tata Bolt XM Petrol,Tata,2015,"6,00,000","15,000 kms",Petrol
+Maruti Suzuki Alto 800 Lx,Maruti,2005,"70,000","47,000 kms",Petrol
+Maruti Suzuki Alto,Maruti,2005,"1,00,000","40,000 kms",Petrol
+Hyundai Venue,Hyundai,2019,Ask For Price,"7,000 kms",Diesel
+Maruti Suzuki Ritz,Maruti,2010,"1,50,000","38,000 kms",Diesel
+Maruti Suzuki Alto 800 Lxi,Maruti,2017,"2,25,000","12,500 kms",Petrol
+Maruti Suzuki Dzire,Maruti,2009,"2,10,000","42,000 kms",Petrol
+Renault Lodgy,Renault,2016,Ask For Price,"20,000 kms",Diesel
+Hyundai i20 Asta,Hyundai,2014,"4,25,000","31,000 kms",Petrol
+Maruti Suzuki Swift Select Variant,Maruti,2008,"1,62,000","60,000 kms",Diesel
+Tata Indica V2 DLX BS III,Tata,2005,"60,000","80,000 kms",Diesel
+Mahindra Scorpio VLX 2.2 mHawk Airbag BSIV,Mahindra,2014,"6,50,000","77,000 kms",Diesel
+Toyota Innova 2.5 E 8 STR,Toyota,2012,"7,50,000","75,000 kms",Diesel
+Mahindra Xylo E8,Mahindra,2010,"3,75,000","40,000 kms",Diesel
+Hyundai i20 Magna 1.2,Hyundai,2011,"2,30,000","47,000 kms",Petrol
+Maruti Suzuki Omni,Maruti,2000,"35,999","60,000 kms",Petrol
+Mahindra KUV100,Mahindra,2016,"3,80,000","26,500 kms",Petrol
+Mahindra KUV100 K8 6 STR,Mahindra,2019,"5,60,000","2,875 kms",Petrol
+Datsun Go Plus,Datsun,2016,"2,85,000","13,900 kms",Petrol
+Ford Endeavor 4x4 Thunder Plus,Ford,2019,"29,00,000","9,000 kms",Diesel
+Tata Indica V2,Tata,2005,"39,999","80,000 kms",Diesel
+Hyundai Santro Xing GL,Hyundai,2006,"85,000","60,000 kms",Petrol
+Maruti Suzuki Wagon R 1.0 VXi,Maruti,2016,"3,95,000","20,000 kms",Petrol
+Maruti Suzuki Swift Select Variant,Maruti,2008,"1,75,000","58,000 kms",Diesel
+Maruti Suzuki Alto 800 Lx,Maruti,2019,"4,00,000","1,500 kms",Petrol
+Toyota Innova 2.5 Z Diesel 7 Seater,Toyota,2011,"7,50,000","75,000 kms",Diesel
+Any type car avaiabel hare...comercica,Any,r...,"1,70,000",,
+Maruti Suzuki Alto 800,Maruti,2016,"2,50,000","2,450 kms",Petrol
+Maruti Suzuki Alto AX,Maruti,2019,"4,25,000","1,625 kms",Petrol
+Maruti Suzuki Alto 800 Lx,Maruti,2019,Ask For Price,"1,500 kms",Petrol
+Volkswagen Polo Highline1.2L P,Volkswagen,2017,"5,25,000","45,000 kms",Petrol
+Mahindra Logan,Mahindra,2009,"1,30,000","65,000 kms",Diesel
+Maruti Suzuki 800 Std BS III,Maruti,2000,"30,000","33,400 kms",Petrol
+Mahindra Scorpio,Mahindra,2011,"4,75,000","60,123 kms",Diesel
+Chevrolet Sail 1.2 LS,Chevrolet,2013,"3,00,000","28,000 kms",Petrol
+Volkswagen Vento Highline Plus 1.5 Diesel,Volkswagen,2015,Ask For Price,"38,900 kms",Diesel
+Hyundai Santro AE GLS Audio,Hyundai,2003,"60,000","70,000 kms",Petrol
+Maruti Suzuki Wagon R VXi Minor,Maruti,2006,"1,00,000","7,000 kms",Petrol
+Hyundai Eon,Hyundai,2018,"2,60,000","25,000 kms",Petrol
+Tata Manza,Tata,2015,"1,00,000","1,00,000 kms",Diesel
+Toyota Innova 2.0 G1 Petrol 8seater,Toyota,2019,Ask For Price,"4,000 kms",Petrol
+Toyota Etios G,Toyota,2013,"2,65,000","42,000 kms",Petrol
+Hyundai Getz Prime 1.3 GLX,Hyundai,2009,"1,15,000","20,000 kms",Petrol
+Toyota Qualis,Toyota,2003,"1,80,000","1,00,000 kms",Diesel
+Hyundai Santro Xing,Hyundai,2004,"45,000","1,37,495 kms",Petrol
+Tata Indica eV2 LS,Tata,2016,"50,500","91,200 kms",Diesel
+Honda City 1.5 S MT,Honda,2009,"2,70,000","55,000 kms",Petrol
+Tata Zest XE 75 PS Diesel,Tata,2017,"2,90,000","1,20,000 kms",Diesel
+Mahindra Quanto C4,Mahindra,2013,"3,25,000","63,000 kms",Diesel
+Tata Indigo eCS LX CR4 BS IV,Tata,2016,"1,60,000","1,04,000 kms",Diesel
+Maruti Suzuki Swift Dzire,Maruti,2016,"3,50,000","1,46,000 kms",Diesel
+Hyundai Elite i20,Hyundai,2011,"2,90,000","40,000 kms",Petrol
+Hyundai i20 Select Variant,Hyundai,2011,"2,90,000","40,000 kms",Petrol
+Chevrolet Tavera Neo,Chevrolet,2007,"4,65,000","1,00,800 kms",Diesel
+Maruti Suzuki Dzire,Maruti,2016,"3,25,000","1,50,000 kms",Diesel
+Hyundai Elite i20,Hyundai,2018,"5,10,000","2,100 kms",Petrol
+Honda City VX Petrol,Honda,2016,"8,60,000","95,000 kms",Petrol
+Maruti Suzuki Dzire,Maruti,2016,"4,50,000","2,500 kms",Diesel
+Hyundai Getz,Hyundai,2006,"1,25,000","80,000 kms",Petrol
+Mercedes Benz C Class 200 K MT,Mercedes,2006,"5,00,001","15,000 kms",Petrol
+Maruti Suzuki Alto LXi BS III,Maruti,2005,"95,000","65,000 kms",Petrol
+Maruti Suzuki Swift Dzire Tour VDi,Maruti,2009,"2,50,000","51,000 kms",Diesel
+Skoda Fabia,Skoda,2009,"1,10,000","45,000 kms",Petrol
+Maruti Suzuki Alto 800 Select Variant,Maruti,2015,Ask For Price,"70,000 kms",Petrol
+Maruti Suzuki Ritz VXI ABS,Maruti,2011,"2,70,000","50,000 kms",Petrol
+tata zest 2017 f,tata,sale,"4,50,000",,
+Tata Indica V2 DLE BS III,Tata,2009,"1,10,000","30,000 kms",Diesel
+Toyota Corolla Altis,Toyota,2009,"3,00,000","1,32,000 kms",Petrol
+Ta,Tara,zest,"3,10,000",,
+Tata Zest XM Diesel,Tata,2018,"2,60,000","27,000 kms",Diesel
+Mahindra Quanto C8,Mahindra,2013,"3,90,000","40,000 kms",Diesel
+Honda Amaze 1.2 E i VTEC,Honda,2014,"1,80,000",Petrol,
+Chevrolet Sail 1.2 LT ABS,Chevrolet,2014,"1,60,000",Petrol,
\ No newline at end of file
diff --git a/models/Pre Owned Car Price Predictor/requirements.txt b/models/Pre Owned Car Price Predictor/requirements.txt
new file mode 100644
index 00000000..4db9eeb1
--- /dev/null
+++ b/models/Pre Owned Car Price Predictor/requirements.txt	
@@ -0,0 +1,19 @@
+click==7.1.2
+Flask==1.1.2
+Flask-Cors==3.0.8
+gunicorn==20.0.4
+itsdangerous==1.1.0
+Jinja2==2.11.2
+joblib==0.15.1
+MarkupSafe==1.1.1
+numpy==1.18.5
+pandas==1.0.4
+pickle-mixin==1.0.2
+python-dateutil==2.8.1
+pytz==2020.1
+scikit-learn==0.22.2
+scipy==1.4.1
+six==1.15.0
+sklearn==0.0
+threadpoolctl==2.1.0
+Werkzeug==1.0.1
\ No newline at end of file
diff --git a/pages/car_price_predictor.py b/pages/car_price_predictor.py
new file mode 100644
index 00000000..8e453659
--- /dev/null
+++ b/pages/car_price_predictor.py
@@ -0,0 +1,222 @@
+# -*- coding: utf-8 -*-
+"""car_price_predictor.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1rnMmmgkB7PlOcx__mXoXdJ9HYTBxE_TP
+
+# Car Price Predictor
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+# %matplotlib inline
+mpl.style.use('ggplot')
+
+car=pd.read_csv('/content/drive/MyDrive/Data Sets/car.csv')
+
+car.head()
+
+car.shape
+
+car.info()
+
+"""##### Creating backup copy"""
+
+backup=car.copy()
+
+car['year'].unique()
+
+car['Price'].unique()
+
+car['kms_driven'].unique()
+
+car['fuel_type'].unique()
+
+"""## Quality
+
+- names are pretty inconsistent
+- names have company names attached to it
+- some names are spam like 'Maruti Ertiga showroom condition with' and 'Well mentained Tata Sumo'
+- company: many of the names are not of any company like 'Used', 'URJENT', and so on.
+- year has many non-year values
+- year is in object. Change to integer
+- Price has Ask for Price
+- Price has commas in its prices and is in object
+- kms_driven has object values with kms at last.
+- It has nan values and two rows have 'Petrol' in them
+- fuel_type has nan values
+
+## Cleaning Data
+
+#### year has many non-year values
+"""
+
+car=car[car['year'].str.isnumeric()]
+
+"""#### year is in object. Change to integer"""
+
+car['year']=car['year'].astype(int)
+
+"""#### Price has Ask for Price"""
+
+car=car[car['Price']!='Ask For Price']
+
+"""#### Price has commas in its prices and is in object"""
+
+car['Price']=car['Price'].str.replace(',','').astype(int)
+
+"""####  kms_driven has object values with kms at last."""
+
+car['kms_driven']=car['kms_driven'].str.split().str.get(0).str.replace(',','')
+
+"""#### It has nan values and two rows have 'Petrol' in them"""
+
+car=car[car['kms_driven'].str.isnumeric()]
+
+car['kms_driven']=car['kms_driven'].astype(int)
+
+"""#### fuel_type has nan values"""
+
+car=car[~car['fuel_type'].isna()]
+
+car.shape
+
+"""### name and company had spammed data...but with the previous cleaning, those rows got removed.
+
+#### Company does not need any cleaning now. Changing car names. Keeping only the first three words
+"""
+
+car['name']=car['name'].str.split().str.slice(start=0,stop=3).str.join(' ')
+
+"""#### Resetting the index of the final cleaned data"""
+
+car=car.reset_index(drop=True)
+
+"""## Cleaned Data"""
+
+car
+
+car.to_csv('Cleaned_Car_data.csv')
+
+car.info()
+
+car.describe(include='all')
+
+"""### Checking relationship of Company with Price"""
+
+car['company'].unique()
+
+import seaborn as sns
+
+plt.subplots(figsize=(15,7))
+ax=sns.boxplot(x='company',y='Price',data=car)
+ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')
+plt.show()
+
+"""### Checking relationship of Year with Price"""
+
+plt.subplots(figsize=(20,10))
+ax=sns.swarmplot(x='year',y='Price',data=car)
+ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')
+plt.show()
+
+"""### Checking relationship of kms_driven with Price"""
+
+sns.relplot(x='kms_driven',y='Price',data=car,height=7,aspect=1.5)
+
+"""### Checking relationship of Fuel Type with Price"""
+
+plt.subplots(figsize=(14,7))
+sns.boxplot(x='fuel_type',y='Price',data=car)
+
+"""### Relationship of Price with FuelType, Year and Company mixed"""
+
+ax=sns.relplot(x='company',y='Price',data=car,hue='fuel_type',size='year',height=7,aspect=2)
+ax.set_xticklabels(rotation=40,ha='right')
+
+"""### Extracting Training Data"""
+
+X=car[['name','company','year','kms_driven','fuel_type']]
+y=car['Price']
+
+X
+
+y.shape
+
+"""### Applying Train Test Split"""
+
+from sklearn.model_selection import train_test_split
+X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)
+
+from sklearn.linear_model import LinearRegression
+
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import make_column_transformer
+from sklearn.pipeline import make_pipeline
+from sklearn.metrics import r2_score
+
+"""#### Creating an OneHotEncoder object to contain all the possible categories"""
+
+ohe=OneHotEncoder()
+ohe.fit(X[['name','company','fuel_type']])
+
+"""#### Creating a column transformer to transform categorical columns"""
+
+column_trans=make_column_transformer((OneHotEncoder(categories=ohe.categories_),['name','company','fuel_type']),
+                                    remainder='passthrough')
+
+"""#### Linear Regression Model"""
+
+lr=LinearRegression()
+
+"""#### Making a pipeline"""
+
+pipe=make_pipeline(column_trans,lr)
+
+"""#### Fitting the  model"""
+
+pipe.fit(X_train,y_train)
+
+y_pred=pipe.predict(X_test)
+
+"""#### Checking R2 Score"""
+
+r2_score(y_test,y_pred)
+
+scores=[]
+for i in range(1000):
+    X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=i)
+    lr=LinearRegression()
+    pipe=make_pipeline(column_trans,lr)
+    pipe.fit(X_train,y_train)
+    y_pred=pipe.predict(X_test)
+    scores.append(r2_score(y_test,y_pred))
+
+np.argmax(scores)
+
+scores[np.argmax(scores)]
+
+pipe.predict(pd.DataFrame(columns=X_test.columns,data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))
+
+"""#### The best model is found at a certain random state"""
+
+X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=np.argmax(scores))
+lr=LinearRegression()
+pipe=make_pipeline(column_trans,lr)
+pipe.fit(X_train,y_train)
+y_pred=pipe.predict(X_test)
+r2_score(y_test,y_pred)
+
+import pickle
+
+pickle.dump(pipe,open('LinearRegressionModel.pkl','wb'))
+
+pipe.predict(pd.DataFrame(columns=['name','company','year','kms_driven','fuel_type'],data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))
+
+pipe.steps[0][1].transformers[0][1].categories[0]
+