HOUSE_PRICE_PREDICTION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1053a337-4538-492f-86ed-52bd15896a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sb\n",
    "import tensorflow as tf \n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "27ee5837-b74f-4550-9111-5aa7c5beed8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"Housing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "6b749746-7a8f-4e6e-a1e6-951b5a65d4a2",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
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       "      <td>13300000</td>\n",
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       "      <td>12215000</td>\n",
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       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>yes</td>\n",
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       "      <td>yes</td>\n",
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       "<p>545 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0    13300000  7420         4          2        3      yes        no       no   \n",
       "1    12250000  8960         4          4        4      yes        no       no   \n",
       "2    12250000  9960         3          2        2      yes        no      yes   \n",
       "3    12215000  7500         4          2        2      yes        no      yes   \n",
       "4    11410000  7420         4          1        2      yes       yes      yes   \n",
       "..        ...   ...       ...        ...      ...      ...       ...      ...   \n",
       "540   1820000  3000         2          1        1      yes        no      yes   \n",
       "541   1767150  2400         3          1        1       no        no       no   \n",
       "542   1750000  3620         2          1        1      yes        no       no   \n",
       "543   1750000  2910         3          1        1       no        no       no   \n",
       "544   1750000  3850         3          1        2      yes        no       no   \n",
       "\n",
       "    hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0                no             yes        2      yes        furnished  \n",
       "1                no             yes        3       no        furnished  \n",
       "2                no              no        2      yes   semi-furnished  \n",
       "3                no             yes        3      yes        furnished  \n",
       "4                no             yes        2       no        furnished  \n",
       "..              ...             ...      ...      ...              ...  \n",
       "540              no              no        2       no      unfurnished  \n",
       "541              no              no        0       no   semi-furnished  \n",
       "542              no              no        0       no      unfurnished  \n",
       "543              no              no        0       no        furnished  \n",
       "544              no              no        0       no      unfurnished  \n",
       "\n",
       "[545 rows x 13 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
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    "dataset"
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   "id": "6d077913-85ce-4b0f-8ce9-b0d0cec094b1",
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       "      <th>count</th>\n",
       "      <td>5.450000e+02</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.766729e+06</td>\n",
       "      <td>5150.541284</td>\n",
       "      <td>2.965138</td>\n",
       "      <td>1.286239</td>\n",
       "      <td>1.805505</td>\n",
       "      <td>0.693578</td>\n",
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       "      <th>std</th>\n",
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       "      <td>2170.141023</td>\n",
       "      <td>0.738064</td>\n",
       "      <td>0.502470</td>\n",
       "      <td>0.867492</td>\n",
       "      <td>0.861586</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.750000e+06</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.430000e+06</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.340000e+06</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.740000e+06</td>\n",
       "      <td>6360.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>16200.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
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      "text/plain": [
       "              price          area    bedrooms   bathrooms     stories  \\\n",
       "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
       "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
       "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
       "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
       "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
       "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
       "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
       "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
       "\n",
       "          parking  \n",
       "count  545.000000  \n",
       "mean     0.693578  \n",
       "std      0.861586  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      3.000000  "
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     "metadata": {},
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   "source": [
    "dataset.describe()"
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   "id": "2d7e40f6-439f-46f7-9a2d-9949a9b4ee2b",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 545 entries, 0 to 544\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   price             545 non-null    int64 \n",
      " 1   area              545 non-null    int64 \n",
      " 2   bedrooms          545 non-null    int64 \n",
      " 3   bathrooms         545 non-null    int64 \n",
      " 4   stories           545 non-null    int64 \n",
      " 5   mainroad          545 non-null    object\n",
      " 6   guestroom         545 non-null    object\n",
      " 7   basement          545 non-null    object\n",
      " 8   hotwaterheating   545 non-null    object\n",
      " 9   airconditioning   545 non-null    object\n",
      " 10  parking           545 non-null    int64 \n",
      " 11  prefarea          545 non-null    object\n",
      " 12  furnishingstatus  545 non-null    object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 55.5+ KB\n"
     ]
    }
   ],
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    "dataset.info()"
   ]
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   "cell_type": "code",
   "execution_count": 109,
   "id": "ac1a24e0-e64f-446e-9f03-25163c9e89fe",
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       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0  13300000  7420         4          2        3      yes        no       no   \n",
       "1  12250000  8960         4          4        4      yes        no       no   \n",
       "2  12250000  9960         3          2        2      yes        no      yes   \n",
       "3  12215000  7500         4          2        2      yes        no      yes   \n",
       "4  11410000  7420         4          1        2      yes       yes      yes   \n",
       "\n",
       "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0              no             yes        2      yes        furnished  \n",
       "1              no             yes        3       no        furnished  \n",
       "2              no              no        2      yes   semi-furnished  \n",
       "3              no             yes        3      yes        furnished  \n",
       "4              no             yes        2       no        furnished  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cabd8418-a01a-4f46-9f32-c355d9194425",
   "metadata": {},
   "source": [
    "### Encoding multiple columns at the same time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0a472c1f-fd4f-4793-a2a6-f59cc93deefa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "5044917d-7f89-498f-9916-e694f8ffd510",
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "25b015ac-267a-4335-a3e0-e3eb35d637fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
      "0    13300000  7420         4          2        3         1          0   \n",
      "1    12250000  8960         4          4        4         1          0   \n",
      "2    12250000  9960         3          2        2         1          0   \n",
      "3    12215000  7500         4          2        2         1          0   \n",
      "4    11410000  7420         4          1        2         1          1   \n",
      "..        ...   ...       ...        ...      ...       ...        ...   \n",
      "540   1820000  3000         2          1        1         1          0   \n",
      "541   1767150  2400         3          1        1         0          0   \n",
      "542   1750000  3620         2          1        1         1          0   \n",
      "543   1750000  2910         3          1        1         0          0   \n",
      "544   1750000  3850         3          1        2         1          0   \n",
      "\n",
      "     basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
      "0           0                0                1        2         1   \n",
      "1           0                0                1        3         0   \n",
      "2           1                0                0        2         1   \n",
      "3           1                0                1        3         1   \n",
      "4           1                0                1        2         0   \n",
      "..        ...              ...              ...      ...       ...   \n",
      "540         1                0                0        2         0   \n",
      "541         0                0                0        0         0   \n",
      "542         0                0                0        0         0   \n",
      "543         0                0                0        0         0   \n",
      "544         0                0                0        0         0   \n",
      "\n",
      "     furnishingstatus  \n",
      "0                   0  \n",
      "1                   0  \n",
      "2                   1  \n",
      "3                   0  \n",
      "4                   0  \n",
      "..                ...  \n",
      "540                 2  \n",
      "541                 1  \n",
      "542                 2  \n",
      "543                 0  \n",
      "544                 2  \n",
      "\n",
      "[545 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "cols = {\n",
    "    \"mainroad\" : [\"yes\" , \"no\"] , \n",
    "    \"guestroom\" : [\"yes\" , \"no\"] , \n",
    "    \"basement\" : [\"yes\" , \"no\"] , \n",
    "    \"hotwaterheating\" : [\"yes\" , \"no\"] ,\n",
    "    \"airconditioning\" : [\"yes\" , \"no\"] , \n",
    "    \"prefarea\" : [\"yes\" , \"no\"] , \n",
    "    \"furnishingstatus\" :[\"furnished\" , \"semi-furnished\" , \"unfurnished\"] \n",
    "    }\n",
    "\n",
    "cols_to_encode=[\"mainroad\",\"guestroom\",\"basement\",\"hotwaterheating\",\"airconditioning\",\"prefarea\",\"furnishingstatus\" ]\n",
    "\n",
    "for column in cols_to_encode:\n",
    "        dataset[column] = lb.fit_transform(dataset[column])\n",
    "\n",
    "print(dataset)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d21aa195-cfc1-4272-9b25-7c2445b21517",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import StandardScaler\n",
    "# sc = StandardScaler()\n",
    "\n",
    "# dataset= sc.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "a018d95d-5181-4257-9d4e-786f8b7f5d66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset = pd.DataFrame(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "db6a41f7-d011-4130-9e5e-0dba53512329",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
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       "      <th>bedrooms</th>\n",
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       "      <td>2400</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>1750000</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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       "<p>545 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "0    13300000  7420         4          2        3         1          0   \n",
       "1    12250000  8960         4          4        4         1          0   \n",
       "2    12250000  9960         3          2        2         1          0   \n",
       "3    12215000  7500         4          2        2         1          0   \n",
       "4    11410000  7420         4          1        2         1          1   \n",
       "..        ...   ...       ...        ...      ...       ...        ...   \n",
       "540   1820000  3000         2          1        1         1          0   \n",
       "541   1767150  2400         3          1        1         0          0   \n",
       "542   1750000  3620         2          1        1         1          0   \n",
       "543   1750000  2910         3          1        1         0          0   \n",
       "544   1750000  3850         3          1        2         1          0   \n",
       "\n",
       "     basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0           0                0                1        2         1   \n",
       "1           0                0                1        3         0   \n",
       "2           1                0                0        2         1   \n",
       "3           1                0                1        3         1   \n",
       "4           1                0                1        2         0   \n",
       "..        ...              ...              ...      ...       ...   \n",
       "540         1                0                0        2         0   \n",
       "541         0                0                0        0         0   \n",
       "542         0                0                0        0         0   \n",
       "543         0                0                0        0         0   \n",
       "544         0                0                0        0         0   \n",
       "\n",
       "     furnishingstatus  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   1  \n",
       "3                   0  \n",
       "4                   0  \n",
       "..                ...  \n",
       "540                 2  \n",
       "541                 1  \n",
       "542                 2  \n",
       "543                 0  \n",
       "544                 2  \n",
       "\n",
       "[545 rows x 13 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4024dc01-6d53-471e-b1c8-606220f0e022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price               0\n",
       "area                0\n",
       "bedrooms            0\n",
       "bathrooms           0\n",
       "stories             0\n",
       "mainroad            0\n",
       "guestroom           0\n",
       "basement            0\n",
       "hotwaterheating     0\n",
       "airconditioning     0\n",
       "parking             0\n",
       "prefarea            0\n",
       "furnishingstatus    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d56ac0e6-13dc-43c5-8119-495d26c8d5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset.columns = [\"price\",\"area\",\"bedrooms\",\"bathrooms\",\"stories\",\"mainroad\",\"guestroom\",\"basement\",\"hotwaterheating\",\"airconditioning\",\"parking\",\"prefarea\",\"furnishingstatus\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1afea797-604e-4f99-bec4-50663fb02b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>1820000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>1767150</td>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>1750000</td>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "0    13300000  7420         4          2        3         1          0   \n",
       "1    12250000  8960         4          4        4         1          0   \n",
       "2    12250000  9960         3          2        2         1          0   \n",
       "3    12215000  7500         4          2        2         1          0   \n",
       "4    11410000  7420         4          1        2         1          1   \n",
       "..        ...   ...       ...        ...      ...       ...        ...   \n",
       "540   1820000  3000         2          1        1         1          0   \n",
       "541   1767150  2400         3          1        1         0          0   \n",
       "542   1750000  3620         2          1        1         1          0   \n",
       "543   1750000  2910         3          1        1         0          0   \n",
       "544   1750000  3850         3          1        2         1          0   \n",
       "\n",
       "     basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0           0                0                1        2         1   \n",
       "1           0                0                1        3         0   \n",
       "2           1                0                0        2         1   \n",
       "3           1                0                1        3         1   \n",
       "4           1                0                1        2         0   \n",
       "..        ...              ...              ...      ...       ...   \n",
       "540         1                0                0        2         0   \n",
       "541         0                0                0        0         0   \n",
       "542         0                0                0        0         0   \n",
       "543         0                0                0        0         0   \n",
       "544         0                0                0        0         0   \n",
       "\n",
       "     furnishingstatus  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   1  \n",
       "3                   0  \n",
       "4                   0  \n",
       "..                ...  \n",
       "540                 2  \n",
       "541                 1  \n",
       "542                 2  \n",
       "543                 0  \n",
       "544                 2  \n",
       "\n",
       "[545 rows x 13 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42bc6091-9d7a-44c5-9d17-d9e9ef93fb13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95947e16-2da6-46eb-ad73-5108d48ef0bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "01218864-50a7-4d9e-b213-c9ea00149021",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6f42f884-6565-4951-b6a5-5b0232470de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c9676f46-a0e2-43fc-b220-75c33ce1bf6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.535997</td>\n",
       "      <td>0.366494</td>\n",
       "      <td>0.517545</td>\n",
       "      <td>0.420712</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>0.535997</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151858</td>\n",
       "      <td>0.193820</td>\n",
       "      <td>0.083996</td>\n",
       "      <td>0.288874</td>\n",
       "      <td>0.140297</td>\n",
       "      <td>0.047417</td>\n",
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       "      <td>0.234779</td>\n",
       "      <td>-0.171445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.366494</td>\n",
       "      <td>0.151858</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.373930</td>\n",
       "      <td>0.408564</td>\n",
       "      <td>-0.012033</td>\n",
       "      <td>0.080549</td>\n",
       "      <td>0.097312</td>\n",
       "      <td>0.046049</td>\n",
       "      <td>0.160603</td>\n",
       "      <td>0.139270</td>\n",
       "      <td>0.079023</td>\n",
       "      <td>-0.123244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.517545</td>\n",
       "      <td>0.193820</td>\n",
       "      <td>0.373930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.326165</td>\n",
       "      <td>0.042398</td>\n",
       "      <td>0.126469</td>\n",
       "      <td>0.102106</td>\n",
       "      <td>0.067159</td>\n",
       "      <td>0.186915</td>\n",
       "      <td>0.177496</td>\n",
       "      <td>0.063472</td>\n",
       "      <td>-0.143559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stories</th>\n",
       "      <td>0.420712</td>\n",
       "      <td>0.083996</td>\n",
       "      <td>0.408564</td>\n",
       "      <td>0.326165</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.121706</td>\n",
       "      <td>0.043538</td>\n",
       "      <td>-0.172394</td>\n",
       "      <td>0.018847</td>\n",
       "      <td>0.293602</td>\n",
       "      <td>0.045547</td>\n",
       "      <td>0.044425</td>\n",
       "      <td>-0.104672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mainroad</th>\n",
       "      <td>0.296898</td>\n",
       "      <td>0.288874</td>\n",
       "      <td>-0.012033</td>\n",
       "      <td>0.042398</td>\n",
       "      <td>0.121706</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.092337</td>\n",
       "      <td>0.044002</td>\n",
       "      <td>-0.011781</td>\n",
       "      <td>0.105423</td>\n",
       "      <td>0.204433</td>\n",
       "      <td>0.199876</td>\n",
       "      <td>-0.156726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guestroom</th>\n",
       "      <td>0.255517</td>\n",
       "      <td>0.140297</td>\n",
       "      <td>0.080549</td>\n",
       "      <td>0.126469</td>\n",
       "      <td>0.043538</td>\n",
       "      <td>0.092337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.372066</td>\n",
       "      <td>-0.010308</td>\n",
       "      <td>0.138179</td>\n",
       "      <td>0.037466</td>\n",
       "      <td>0.160897</td>\n",
       "      <td>-0.118328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basement</th>\n",
       "      <td>0.187057</td>\n",
       "      <td>0.047417</td>\n",
       "      <td>0.097312</td>\n",
       "      <td>0.102106</td>\n",
       "      <td>-0.172394</td>\n",
       "      <td>0.044002</td>\n",
       "      <td>0.372066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>0.047341</td>\n",
       "      <td>0.051497</td>\n",
       "      <td>0.228083</td>\n",
       "      <td>-0.112831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hotwaterheating</th>\n",
       "      <td>0.093073</td>\n",
       "      <td>-0.009229</td>\n",
       "      <td>0.046049</td>\n",
       "      <td>0.067159</td>\n",
       "      <td>0.018847</td>\n",
       "      <td>-0.011781</td>\n",
       "      <td>-0.010308</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.130023</td>\n",
       "      <td>0.067864</td>\n",
       "      <td>-0.059411</td>\n",
       "      <td>-0.031628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airconditioning</th>\n",
       "      <td>0.452954</td>\n",
       "      <td>0.222393</td>\n",
       "      <td>0.160603</td>\n",
       "      <td>0.186915</td>\n",
       "      <td>0.293602</td>\n",
       "      <td>0.105423</td>\n",
       "      <td>0.138179</td>\n",
       "      <td>0.047341</td>\n",
       "      <td>-0.130023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.159173</td>\n",
       "      <td>0.117382</td>\n",
       "      <td>-0.150477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parking</th>\n",
       "      <td>0.384394</td>\n",
       "      <td>0.352980</td>\n",
       "      <td>0.139270</td>\n",
       "      <td>0.177496</td>\n",
       "      <td>0.045547</td>\n",
       "      <td>0.204433</td>\n",
       "      <td>0.037466</td>\n",
       "      <td>0.051497</td>\n",
       "      <td>0.067864</td>\n",
       "      <td>0.159173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091627</td>\n",
       "      <td>-0.177539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prefarea</th>\n",
       "      <td>0.329777</td>\n",
       "      <td>0.234779</td>\n",
       "      <td>0.079023</td>\n",
       "      <td>0.063472</td>\n",
       "      <td>0.044425</td>\n",
       "      <td>0.199876</td>\n",
       "      <td>0.160897</td>\n",
       "      <td>0.228083</td>\n",
       "      <td>-0.059411</td>\n",
       "      <td>0.117382</td>\n",
       "      <td>0.091627</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.107686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>furnishingstatus</th>\n",
       "      <td>-0.304721</td>\n",
       "      <td>-0.171445</td>\n",
       "      <td>-0.123244</td>\n",
       "      <td>-0.143559</td>\n",
       "      <td>-0.104672</td>\n",
       "      <td>-0.156726</td>\n",
       "      <td>-0.118328</td>\n",
       "      <td>-0.112831</td>\n",
       "      <td>-0.031628</td>\n",
       "      <td>-0.150477</td>\n",
       "      <td>-0.177539</td>\n",
       "      <td>-0.107686</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price      area  bedrooms  bathrooms   stories  mainroad  \\\n",
       "price             1.000000  0.535997  0.366494   0.517545  0.420712  0.296898   \n",
       "area              0.535997  1.000000  0.151858   0.193820  0.083996  0.288874   \n",
       "bedrooms          0.366494  0.151858  1.000000   0.373930  0.408564 -0.012033   \n",
       "bathrooms         0.517545  0.193820  0.373930   1.000000  0.326165  0.042398   \n",
       "stories           0.420712  0.083996  0.408564   0.326165  1.000000  0.121706   \n",
       "mainroad          0.296898  0.288874 -0.012033   0.042398  0.121706  1.000000   \n",
       "guestroom         0.255517  0.140297  0.080549   0.126469  0.043538  0.092337   \n",
       "basement          0.187057  0.047417  0.097312   0.102106 -0.172394  0.044002   \n",
       "hotwaterheating   0.093073 -0.009229  0.046049   0.067159  0.018847 -0.011781   \n",
       "airconditioning   0.452954  0.222393  0.160603   0.186915  0.293602  0.105423   \n",
       "parking           0.384394  0.352980  0.139270   0.177496  0.045547  0.204433   \n",
       "prefarea          0.329777  0.234779  0.079023   0.063472  0.044425  0.199876   \n",
       "furnishingstatus -0.304721 -0.171445 -0.123244  -0.143559 -0.104672 -0.156726   \n",
       "\n",
       "                  guestroom  basement  hotwaterheating  airconditioning  \\\n",
       "price              0.255517  0.187057         0.093073         0.452954   \n",
       "area               0.140297  0.047417        -0.009229         0.222393   \n",
       "bedrooms           0.080549  0.097312         0.046049         0.160603   \n",
       "bathrooms          0.126469  0.102106         0.067159         0.186915   \n",
       "stories            0.043538 -0.172394         0.018847         0.293602   \n",
       "mainroad           0.092337  0.044002        -0.011781         0.105423   \n",
       "guestroom          1.000000  0.372066        -0.010308         0.138179   \n",
       "basement           0.372066  1.000000         0.004385         0.047341   \n",
       "hotwaterheating   -0.010308  0.004385         1.000000        -0.130023   \n",
       "airconditioning    0.138179  0.047341        -0.130023         1.000000   \n",
       "parking            0.037466  0.051497         0.067864         0.159173   \n",
       "prefarea           0.160897  0.228083        -0.059411         0.117382   \n",
       "furnishingstatus  -0.118328 -0.112831        -0.031628        -0.150477   \n",
       "\n",
       "                   parking  prefarea  furnishingstatus  \n",
       "price             0.384394  0.329777         -0.304721  \n",
       "area              0.352980  0.234779         -0.171445  \n",
       "bedrooms          0.139270  0.079023         -0.123244  \n",
       "bathrooms         0.177496  0.063472         -0.143559  \n",
       "stories           0.045547  0.044425         -0.104672  \n",
       "mainroad          0.204433  0.199876         -0.156726  \n",
       "guestroom         0.037466  0.160897         -0.118328  \n",
       "basement          0.051497  0.228083         -0.112831  \n",
       "hotwaterheating   0.067864 -0.059411         -0.031628  \n",
       "airconditioning   0.159173  0.117382         -0.150477  \n",
       "parking           1.000000  0.091627         -0.177539  \n",
       "prefarea          0.091627  1.000000         -0.107686  \n",
       "furnishingstatus -0.177539 -0.107686          1.000000  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "79383de2-870a-4a61-9cd5-a452543926c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sb.heatmap(correlation , cbar =True , cmap = \"Greens\" , annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "dcf4b9c5-0998-40f4-96bd-f6437863ba1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # plt.figure(figsize=(10,6))\n",
    "# for column in range(1,12):\n",
    "#     plt.hist(dataset, bins = 10 , alpha = 0.5 , label = column ,edgecolor = \"black\")\n",
    "# plt.xlabel('Value')\n",
    "# plt.ylabel('Frequency')\n",
    "# plt.title('Multiple Histograms Together')\n",
    "# plt.legend(loc='upper right')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "60f2319f-c097-4887-8349-a98c587d243d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(45.06947222222222, 0.5, 'price')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 621x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.relplot(x=dataset.area, y = dataset.price, hue =\"price\" , data = dataset)\n",
    "plt.xlabel('area')\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "6b971e12-f7c0-453f-858f-25feae8fc41d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(45.06947222222222, 0.5, 'price')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 621x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.relplot(x=dataset.bedrooms, y = dataset.price, hue =\"price\" , data = dataset)\n",
    "plt.xlabel('bedrooms')\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce69ba51-611c-4225-93f1-3871610094c9",
   "metadata": {},
   "source": [
    "# Model Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "1ee98784-ea9c-4845-8550-d750562d4d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "col = [\"area\" ,\"bathrooms\",\"bedrooms\" , \"stories\",\"parking\",\"price\"]\n",
    "cdf = dataset[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "8fb166d6-3566-4013-9443-9d7f786c52ea",
   "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>area</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7420</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>13300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>12250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9960</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7500</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7420</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>11410000</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>540</th>\n",
       "      <td>3000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>2400</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1767150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>3620</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>2910</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>3850</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     area  bathrooms  bedrooms  stories  parking     price\n",
       "0    7420          2         4        3        2  13300000\n",
       "1    8960          4         4        4        3  12250000\n",
       "2    9960          2         3        2        2  12250000\n",
       "3    7500          2         4        2        3  12215000\n",
       "4    7420          1         4        2        2  11410000\n",
       "..    ...        ...       ...      ...      ...       ...\n",
       "540  3000          1         2        1        2   1820000\n",
       "541  2400          1         3        1        0   1767150\n",
       "542  3620          1         2        1        0   1750000\n",
       "543  2910          1         3        1        0   1750000\n",
       "544  3850          1         3        2        0   1750000\n",
       "\n",
       "[545 rows x 6 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f3575f-bbe2-42a6-88d2-1196db3981ea",
   "metadata": {},
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "540993ed-406e-4223-bdf2-083c264562c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "cdf = pd.DataFrame(scaler.fit_transform(cdf),columns = cdf.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b8f028-9e37-41ce-ba00-ef39ba603e03",
   "metadata": {},
   "source": [
    "### determine x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "feefcf6c-15cd-4a35-9f52-3c09d72f225a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = cdf.drop(columns=\"price\" , axis=1)\n",
    "y = cdf[\"price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d7a612-7997-4b61-a5ca-2099bbf3aae1",
   "metadata": {},
   "source": [
    "## Train-Test-Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "2e2682d2-0761-4922-86c5-798bbd825827",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "4a812bb2-f734-467b-bfbc-33fd323f9bd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train , x_test , y_train , y_test = train_test_split(x,y,test_size = 0.3, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "32d8c7d8-7224-40ab-8004-fe318df8545c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         area  bathrooms  bedrooms   stories   parking\n",
      "166  0.422680        0.0       0.4  0.000000  0.666667\n",
      "378  0.042955        0.0       0.4  0.666667  0.000000\n",
      "349  0.217869        0.0       0.4  0.333333  0.000000\n",
      "368  0.274227        0.0       0.2  0.000000  0.000000\n",
      "306  0.219244        0.0       0.4  0.333333  0.333333\n",
      "..        ...        ...       ...       ...       ...\n",
      "299  0.367698        0.0       0.4  0.000000  1.000000\n",
      "534  0.092784        0.0       0.6  0.333333  0.000000\n",
      "493  0.158763        0.0       0.4  0.000000  0.000000\n",
      "527  0.012784        0.0       0.2  0.000000  0.000000\n",
      "168  0.179381        0.0       0.6  0.333333  0.000000\n",
      "\n",
      "[381 rows x 5 columns] 166    0.309091\n",
      "378    0.163636\n",
      "349    0.175758\n",
      "368    0.166667\n",
      "306    0.209091\n",
      "         ...   \n",
      "299    0.212121\n",
      "534    0.030303\n",
      "493    0.090909\n",
      "527    0.045455\n",
      "168    0.303030\n",
      "Name: price, Length: 381, dtype: float64          area  bathrooms  bedrooms   stories   parking\n",
      "333  0.092784   0.000000       0.4  0.333333  0.000000\n",
      "84   0.145017   0.000000       0.4  0.333333  0.666667\n",
      "439  0.156701   0.000000       0.2  0.000000  0.000000\n",
      "396  0.136770   0.000000       0.2  0.000000  0.333333\n",
      "161  0.305842   0.000000       0.4  0.666667  0.000000\n",
      "..        ...        ...       ...       ...       ...\n",
      "117  0.140893   0.000000       0.6  0.333333  0.000000\n",
      "314  0.164261   0.000000       0.2  0.333333  0.333333\n",
      "340  0.250859   0.333333       0.8  0.333333  0.000000\n",
      "444  0.101031   0.000000       0.4  0.333333  0.000000\n",
      "307  0.167010   0.000000       0.4  0.333333  0.666667\n",
      "\n",
      "[164 rows x 5 columns] 333    0.187879\n",
      "84     0.412121\n",
      "439    0.130303\n",
      "396    0.151515\n",
      "161    0.321212\n",
      "         ...   \n",
      "117    0.363636\n",
      "314    0.203030\n",
      "340    0.181818\n",
      "444    0.127273\n",
      "307    0.209091\n",
      "Name: price, Length: 164, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(x_train , y_train ,x_test , y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c859f8f2-4d6e-4ff7-b371-40dbf2dbe277",
   "metadata": {},
   "source": [
    "## 1. Linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ebad7f35-e2f7-445c-bfc4-f9f8c7654580",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "cf3eb537-2f38-462f-92cf-312d1ade959d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf760dd-f9b2-4730-b07f-c59147158983",
   "metadata": {},
   "source": [
    "#### Using cross validation to evaluate our model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "97b094be-ea26-4f99-a3cf-197ce12ea275",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import cross_val_score , cross_val_predict\n",
    "# cv_scores = cross_val_score(lr, x_train , y_train , cv=5 ,scoring=\"r2\")\n",
    "# cv_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "86ec06d3-6f43-423f-a2cd-ac7f579debca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(f'The cross-validated accuracy is:{np.mean(cv_scores)*100}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d07690-36eb-44d1-bd60-bc94cab455a2",
   "metadata": {},
   "source": [
    "the closer they are to 0 the better the model is "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a25177dd-c7b8-4923-92ac-0f5adb38fdfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cv_scores = cross_val_score(lr, x_train , y_train , cv=5 ,scoring=\"neg_mean_squared_error\")\n",
    "# cv_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "52863178-8f01-4a6e-b532-fa8f710cd381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d7b908d1-f9fd-408f-87b6-db111c00011e",
   "metadata": {},
   "source": [
    "# Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "c3718f4f-f888-450d-8737-cac0faef9fef",
   "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>area</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>0.092784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.145017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.156701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0.136770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.305842</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</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>117</th>\n",
       "      <td>0.140893</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>0.164261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>0.250859</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>0.101031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>0.167010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         area  bathrooms  bedrooms   stories   parking\n",
       "333  0.092784   0.000000       0.4  0.333333  0.000000\n",
       "84   0.145017   0.000000       0.4  0.333333  0.666667\n",
       "439  0.156701   0.000000       0.2  0.000000  0.000000\n",
       "396  0.136770   0.000000       0.2  0.000000  0.333333\n",
       "161  0.305842   0.000000       0.4  0.666667  0.000000\n",
       "..        ...        ...       ...       ...       ...\n",
       "117  0.140893   0.000000       0.6  0.333333  0.000000\n",
       "314  0.164261   0.000000       0.2  0.333333  0.333333\n",
       "340  0.250859   0.333333       0.8  0.333333  0.000000\n",
       "444  0.101031   0.000000       0.4  0.333333  0.000000\n",
       "307  0.167010   0.000000       0.4  0.333333  0.666667\n",
       "\n",
       "[164 rows x 5 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "386a143f-8afa-4649-b489-c230b40073fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "333    0.187879\n",
       "84     0.412121\n",
       "439    0.130303\n",
       "396    0.151515\n",
       "161    0.321212\n",
       "         ...   \n",
       "117    0.363636\n",
       "314    0.203030\n",
       "340    0.181818\n",
       "444    0.127273\n",
       "307    0.209091\n",
       "Name: price, Length: 164, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "7dc011be-300b-4034-854e-7c8535e2fc2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "8d34b8d5-c80a-4b7e-bd22-d7c00af561ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_predictions = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8825dbc-0ee2-49cf-84cf-2b9c6f25aa53",
   "metadata": {},
   "source": [
    "# Evaluating our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "d3dbff03-8d22-468d-87aa-bdd687c71444",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error,mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "85acac8b-a740-43ae-95a3-275e60188fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean sqauare error = 0.012153094405361995\n"
     ]
    }
   ],
   "source": [
    "MSE = mean_squared_error(y_test_predictions,y_test)\n",
    "print(\"Mean sqauare error =\",MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "d68acfe5-b70a-4af0-8070-2fe6bdcba2cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error =  0.079023252084935\n"
     ]
    }
   ],
   "source": [
    "MAE = mean_absolute_error(y_test_predictions , y_test)\n",
    "print(\"Mean absolute error = \", MAE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ed4707-a470-4fb1-95d0-425ea3a3986a",
   "metadata": {},
   "source": [
    "# 2. XG Boost Regressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "24a1abea-eb41-481e-af51-fecd88dd6989",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "49304405-fb37-4729-b487-f75032dc5623",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBRegressor()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bedf0cc-34c9-43f7-94a9-cbaf85aeb4f0",
   "metadata": {},
   "source": [
    "# Final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "aa5fc821-91bc-4717-a609-14c5ca950b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "a781e75a-ad82-41fa-b717-e5007499e28a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_predictions = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea35acd-eb8e-4e4a-ab58-2399ca17acf1",
   "metadata": {},
   "source": [
    "### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "086fa2f7-5ec1-4bbd-b9b3-698dd2534d0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean sqauare error = 0.023453321878824245\n"
     ]
    }
   ],
   "source": [
    "MSE = mean_squared_error(y_test_predictions,y_test)\n",
    "print(\"Mean sqauare error =\",MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "632c3e74-97dd-4d3b-aff6-e4e09dabc440",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error =  0.10803826212949076\n"
     ]
    }
   ],
   "source": [
    "MAE = mean_absolute_error(y_test_predictions , y_test)\n",
    "print(\"Mean absolute error = \", MAE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04701afa-c791-4680-8169-c61eaa159c32",
   "metadata": {},
   "source": [
    "# 3. Using Tensorflow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "427283fa-e635-4d77-8816-86b85118a48b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_arr = x.values\n",
    "Y_arr = y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "c2a6ed1c-415b-4352-94a7-2a18e57df33f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (539, 5)\n",
      "Y_train shape: (539,)\n",
      "X_test shape: (6, 5)\n",
      "Y_test shape: (6,)\n"
     ]
    }
   ],
   "source": [
    "X_train , X_test , Y_train , Y_test = train_test_split(X_arr , Y_arr , test_size = 0.01 , shuffle=True ,random_state=1)\n",
    "\n",
    "\n",
    "print(\"X_train shape:\",X_train.shape)\n",
    "print(\"Y_train shape:\",Y_train.shape)\n",
    "print(\"X_test shape:\",X_test.shape)\n",
    "print(\"Y_test shape:\",Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a271a6f0-c356-4a80-a966-246e7af54278",
   "metadata": {},
   "source": [
    "## Create a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "c556aec8-8577-43de-861d-1486d7c5b08f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,Dropout\n",
    "\n",
    "def get_model():\n",
    "\n",
    "    model = Sequential([\n",
    "        Dense(10,input_shape=(5,),activation=\"relu\"),\n",
    "        Dense(20 , activation=\"relu\"),\n",
    "        Dense(5 , activation=\"sigmoid\"),\n",
    "        Dense(1)\n",
    "    ])\n",
    "\n",
    "    model.compile(\n",
    "        loss=\"mse\" ,\n",
    "        optimizer=\"adadelta\"\n",
    "    )\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "d044d729-b363-4a39-8a0d-5f215b0f2c08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_8\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_32 (Dense)            (None, 10)                60        \n",
      "                                                                 \n",
      " dense_33 (Dense)            (None, 20)                220       \n",
      "                                                                 \n",
      " dense_34 (Dense)            (None, 5)                 105       \n",
      "                                                                 \n",
      " dense_35 (Dense)            (None, 1)                 6         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 391\n",
      "Trainable params: 391\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "MODEL = get_model()\n",
    "MODEL.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d27e34-10ce-45d5-aca7-0e25421593de",
   "metadata": {},
   "source": [
    "## Model training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "0ea3c3c3-a21a-4aa8-8562-3aa82f7d2668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 10 calls to <function Model.make_predict_function.<locals>.predict_function at 0x00000289B23CF430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 162ms/step\n"
     ]
    }
   ],
   "source": [
    "MODEL = get_model()\n",
    "\n",
    "# this prediction is before training the model\n",
    "preds_on_untrained = MODEL.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "43e6adb8-e46e-4eac-a6b9-24e9e00e0f5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "17/17 [==============================] - 1s 28ms/step - loss: 0.7941 - val_loss: 0.8196\n",
      "Epoch 2/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7938 - val_loss: 0.8193\n",
      "Epoch 3/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7936 - val_loss: 0.8191\n",
      "Epoch 4/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7933 - val_loss: 0.8188\n",
      "Epoch 5/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7931 - val_loss: 0.8186\n",
      "Epoch 6/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7928 - val_loss: 0.8183\n",
      "Epoch 7/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7926 - val_loss: 0.8180\n",
      "Epoch 8/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7923 - val_loss: 0.8178\n",
      "Epoch 9/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7921 - val_loss: 0.8175\n",
      "Epoch 10/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7918 - val_loss: 0.8172\n",
      "Epoch 11/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7915 - val_loss: 0.8169\n",
      "Epoch 12/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7913 - val_loss: 0.8167\n",
      "Epoch 13/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7910 - val_loss: 0.8164\n",
      "Epoch 14/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7907 - val_loss: 0.8161\n",
      "Epoch 15/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7904 - val_loss: 0.8158\n",
      "Epoch 16/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7901 - val_loss: 0.8155\n",
      "Epoch 17/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7898 - val_loss: 0.8152\n",
      "Epoch 18/1000\n",
      "17/17 [==============================] - 0s 11ms/step - loss: 0.7896 - val_loss: 0.8149\n",
      "Epoch 19/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7893 - val_loss: 0.8146\n",
      "Epoch 20/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7890 - val_loss: 0.8142\n",
      "Epoch 21/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7887 - val_loss: 0.8139\n",
      "Epoch 22/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7884 - val_loss: 0.8136\n",
      "Epoch 23/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7880 - val_loss: 0.8133\n",
      "Epoch 24/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7877 - val_loss: 0.8130\n",
      "Epoch 25/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7874 - val_loss: 0.8126\n",
      "Epoch 26/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7871 - val_loss: 0.8123\n",
      "Epoch 27/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7868 - val_loss: 0.8120\n",
      "Epoch 28/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7865 - val_loss: 0.8116\n",
      "Epoch 29/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7861 - val_loss: 0.8113\n",
      "Epoch 30/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7858 - val_loss: 0.8109\n",
      "Epoch 31/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7855 - val_loss: 0.8106\n",
      "Epoch 32/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.7852 - val_loss: 0.8102\n",
      "Epoch 33/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.7848 - val_loss: 0.8099\n",
      "Epoch 34/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.7845 - val_loss: 0.8095\n",
      "Epoch 35/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7841 - val_loss: 0.8092\n",
      "Epoch 36/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7838 - val_loss: 0.8088\n",
      "Epoch 37/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.7835 - val_loss: 0.8085\n",
      "Epoch 38/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7831 - val_loss: 0.8081\n",
      "Epoch 39/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.7828 - val_loss: 0.8077\n",
      "Epoch 40/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.7824 - val_loss: 0.8074\n",
      "Epoch 41/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7820 - val_loss: 0.8070\n",
      "Epoch 42/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.7817 - val_loss: 0.8066\n",
      "Epoch 43/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.7813 - val_loss: 0.8062\n",
      "Epoch 44/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.7810 - val_loss: 0.8059\n",
      "Epoch 45/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.7806 - val_loss: 0.8055\n",
      "Epoch 46/1000\n",
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      "Epoch 885/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2901 - val_loss: 0.2940\n",
      "Epoch 886/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2896 - val_loss: 0.2935\n",
      "Epoch 887/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2891 - val_loss: 0.2930\n",
      "Epoch 888/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2886 - val_loss: 0.2924\n",
      "Epoch 889/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2881 - val_loss: 0.2919\n",
      "Epoch 890/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2876 - val_loss: 0.2914\n",
      "Epoch 891/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2870 - val_loss: 0.2908\n",
      "Epoch 892/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2865 - val_loss: 0.2903\n",
      "Epoch 893/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2860 - val_loss: 0.2898\n",
      "Epoch 894/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2855 - val_loss: 0.2893\n",
      "Epoch 895/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2850 - val_loss: 0.2887\n",
      "Epoch 896/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2845 - val_loss: 0.2882\n",
      "Epoch 897/1000\n",
      "17/17 [==============================] - 0s 10ms/step - loss: 0.2840 - val_loss: 0.2877\n",
      "Epoch 898/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2835 - val_loss: 0.2871\n",
      "Epoch 899/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2830 - val_loss: 0.2866\n",
      "Epoch 900/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2825 - val_loss: 0.2861\n",
      "Epoch 901/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2820 - val_loss: 0.2856\n",
      "Epoch 902/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2815 - val_loss: 0.2850\n",
      "Epoch 903/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2810 - val_loss: 0.2845\n",
      "Epoch 904/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2805 - val_loss: 0.2840\n",
      "Epoch 905/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2800 - val_loss: 0.2835\n",
      "Epoch 906/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2795 - val_loss: 0.2830\n",
      "Epoch 907/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2790 - val_loss: 0.2824\n",
      "Epoch 908/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2785 - val_loss: 0.2819\n",
      "Epoch 909/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2780 - val_loss: 0.2814\n",
      "Epoch 910/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2775 - val_loss: 0.2809\n",
      "Epoch 911/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2770 - val_loss: 0.2804\n",
      "Epoch 912/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2765 - val_loss: 0.2798\n",
      "Epoch 913/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2760 - val_loss: 0.2793\n",
      "Epoch 914/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2755 - val_loss: 0.2788\n",
      "Epoch 915/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2750 - val_loss: 0.2783\n",
      "Epoch 916/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2745 - val_loss: 0.2778\n",
      "Epoch 917/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2740 - val_loss: 0.2773\n",
      "Epoch 918/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2735 - val_loss: 0.2767\n",
      "Epoch 919/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2730 - val_loss: 0.2762\n",
      "Epoch 920/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2725 - val_loss: 0.2757\n",
      "Epoch 921/1000\n",
      "17/17 [==============================] - 0s 10ms/step - loss: 0.2720 - val_loss: 0.2752\n",
      "Epoch 922/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2715 - val_loss: 0.2747\n",
      "Epoch 923/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2710 - val_loss: 0.2742\n",
      "Epoch 924/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2705 - val_loss: 0.2737\n",
      "Epoch 925/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2701 - val_loss: 0.2732\n",
      "Epoch 926/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2696 - val_loss: 0.2726\n",
      "Epoch 927/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2691 - val_loss: 0.2721\n",
      "Epoch 928/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2686 - val_loss: 0.2716\n",
      "Epoch 929/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2681 - val_loss: 0.2711\n",
      "Epoch 930/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2676 - val_loss: 0.2706\n",
      "Epoch 931/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2671 - val_loss: 0.2701\n",
      "Epoch 932/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2666 - val_loss: 0.2696\n",
      "Epoch 933/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2661 - val_loss: 0.2691\n",
      "Epoch 934/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2657 - val_loss: 0.2686\n",
      "Epoch 935/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2652 - val_loss: 0.2681\n",
      "Epoch 936/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2647 - val_loss: 0.2676\n",
      "Epoch 937/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2642 - val_loss: 0.2671\n",
      "Epoch 938/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2637 - val_loss: 0.2666\n",
      "Epoch 939/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2632 - val_loss: 0.2661\n",
      "Epoch 940/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2627 - val_loss: 0.2656\n",
      "Epoch 941/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2623 - val_loss: 0.2650\n",
      "Epoch 942/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2618 - val_loss: 0.2645\n",
      "Epoch 943/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2613 - val_loss: 0.2640\n",
      "Epoch 944/1000\n",
      "17/17 [==============================] - 0s 14ms/step - loss: 0.2608 - val_loss: 0.2635\n",
      "Epoch 945/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2603 - val_loss: 0.2630\n",
      "Epoch 946/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2599 - val_loss: 0.2625\n",
      "Epoch 947/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2594 - val_loss: 0.2620\n",
      "Epoch 948/1000\n",
      "17/17 [==============================] - 0s 11ms/step - loss: 0.2589 - val_loss: 0.2615\n",
      "Epoch 949/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2584 - val_loss: 0.2610\n",
      "Epoch 950/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2579 - val_loss: 0.2606\n",
      "Epoch 951/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2575 - val_loss: 0.2601\n",
      "Epoch 952/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2570 - val_loss: 0.2596\n",
      "Epoch 953/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2565 - val_loss: 0.2591\n",
      "Epoch 954/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2560 - val_loss: 0.2586\n",
      "Epoch 955/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2556 - val_loss: 0.2581\n",
      "Epoch 956/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2551 - val_loss: 0.2576\n",
      "Epoch 957/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2546 - val_loss: 0.2571\n",
      "Epoch 958/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2541 - val_loss: 0.2566\n",
      "Epoch 959/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2537 - val_loss: 0.2561\n",
      "Epoch 960/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2532 - val_loss: 0.2556\n",
      "Epoch 961/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2527 - val_loss: 0.2551\n",
      "Epoch 962/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2522 - val_loss: 0.2546\n",
      "Epoch 963/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2518 - val_loss: 0.2541\n",
      "Epoch 964/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2513 - val_loss: 0.2536\n",
      "Epoch 965/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2508 - val_loss: 0.2532\n",
      "Epoch 966/1000\n",
      "17/17 [==============================] - 0s 11ms/step - loss: 0.2504 - val_loss: 0.2527\n",
      "Epoch 967/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2499 - val_loss: 0.2522\n",
      "Epoch 968/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2494 - val_loss: 0.2517\n",
      "Epoch 969/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2490 - val_loss: 0.2512\n",
      "Epoch 970/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2485 - val_loss: 0.2507\n",
      "Epoch 971/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2480 - val_loss: 0.2502\n",
      "Epoch 972/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2476 - val_loss: 0.2498\n",
      "Epoch 973/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2471 - val_loss: 0.2493\n",
      "Epoch 974/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2466 - val_loss: 0.2488\n",
      "Epoch 975/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2462 - val_loss: 0.2483\n",
      "Epoch 976/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2457 - val_loss: 0.2478\n",
      "Epoch 977/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2452 - val_loss: 0.2473\n",
      "Epoch 978/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2448 - val_loss: 0.2469\n",
      "Epoch 979/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2443 - val_loss: 0.2464\n",
      "Epoch 980/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2438 - val_loss: 0.2459\n",
      "Epoch 981/1000\n",
      "17/17 [==============================] - 0s 10ms/step - loss: 0.2434 - val_loss: 0.2454\n",
      "Epoch 982/1000\n",
      "17/17 [==============================] - 0s 9ms/step - loss: 0.2429 - val_loss: 0.2449\n",
      "Epoch 983/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2425 - val_loss: 0.2445\n",
      "Epoch 984/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2420 - val_loss: 0.2440\n",
      "Epoch 985/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2415 - val_loss: 0.2435\n",
      "Epoch 986/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2411 - val_loss: 0.2430\n",
      "Epoch 987/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2406 - val_loss: 0.2425\n",
      "Epoch 988/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2402 - val_loss: 0.2421\n",
      "Epoch 989/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2397 - val_loss: 0.2416\n",
      "Epoch 990/1000\n",
      "17/17 [==============================] - 0s 5ms/step - loss: 0.2392 - val_loss: 0.2411\n",
      "Epoch 991/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2388 - val_loss: 0.2406\n",
      "Epoch 992/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2383 - val_loss: 0.2402\n",
      "Epoch 993/1000\n",
      "17/17 [==============================] - 0s 8ms/step - loss: 0.2379 - val_loss: 0.2397\n",
      "Epoch 994/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2374 - val_loss: 0.2392\n",
      "Epoch 995/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2370 - val_loss: 0.2388\n",
      "Epoch 996/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2365 - val_loss: 0.2383\n",
      "Epoch 997/1000\n",
      "17/17 [==============================] - 0s 7ms/step - loss: 0.2361 - val_loss: 0.2378\n",
      "Epoch 998/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2356 - val_loss: 0.2373\n",
      "Epoch 999/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2352 - val_loss: 0.2369\n",
      "Epoch 1000/1000\n",
      "17/17 [==============================] - 0s 6ms/step - loss: 0.2347 - val_loss: 0.2364\n"
     ]
    }
   ],
   "source": [
    "# train model and store in the object history\n",
    "history = MODEL.fit(X_train , Y_train , validation_data=(X_test , Y_test),epochs=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a8c95e-ee2b-4f97-b6e3-556f2611756d",
   "metadata": {},
   "source": [
    "# Predictions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "b5d81776-7d6a-41f4-b6ca-6202b8319700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 48ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = MODEL.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "debb589d-525d-4d16-81ca-4b97056d7701",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "0e914ce9-7756-4099-8e9c-e715746fe0a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 39ms/step - loss: 0.2364\n"
     ]
    }
   ],
   "source": [
    "evaluation = MODEL.evaluate(X_test , Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ad5caf-60cd-44ba-ad69-32d245ac86d3",
   "metadata": {},
   "source": [
    "## PLotting the loss graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "91973a07-e24f-4575-8315-650ef53b6cb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history[\"loss\"],label=\"Training loss\")\n",
    "plt.plot(history.history[\"val_loss\"] , label=\"Validation loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}