Assignment 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c41d81f8-d8f4-42da-992f-796bf9f1ebfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b50bef66-28f5-4667-b753-7aece5f96535",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.1</td>\n",
       "      <td>77884.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.1</td>\n",
       "      <td>127213.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.9</td>\n",
       "      <td>103343.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.4</td>\n",
       "      <td>88988.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>35214.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience     Salary\n",
       "0              8.1   77884.67\n",
       "1             19.1  127213.68\n",
       "2             14.9  103343.52\n",
       "3             12.4   88988.96\n",
       "4              4.0   35214.78"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('extended_salary_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60b0f255-c529-46fd-81f6-7ca624b03721",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(data['YearsExperience'],data['Salary'])\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aaedc74b-ccaa-45d9-b13e-a57a939f41d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean\n",
      "{'YearsExperience': 9.48, 'Salary': 77370.5474}\n",
      "Median\n",
      "{'YearsExperience': 9.3, 'Salary': 78598.65}\n",
      "Variance\n",
      "{'YearsExperience': 30.09061224489796, 'Salary': 800829123.3381461}\n",
      "Maximum\n",
      "{'YearsExperience': 19.4, 'Salary': 140646.44}\n",
      "Minimum\n",
      "{'YearsExperience': 1.4, 'Salary': 33048.8}\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean\")\n",
    "print(data.mean().to_dict())\n",
    "print(\"Median\")\n",
    "print(data.median().to_dict())\n",
    "print(\"Variance\")\n",
    "print(data.var().to_dict())\n",
    "print(\"Maximum\")\n",
    "print(data.max().to_dict())\n",
    "print(\"Minimum\")\n",
    "print(data.min().to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b3ebb707-1811-4f03-aa9b-44238a80d373",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = data['YearsExperience'].values.reshape(-1, 1)\n",
    "y = data['Salary'].values.reshape(-1, 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "print(len(X_train))#verifying size\n",
    "print(len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2b4c1a4a-5420-4452-8204-8befc3219a1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4854.18497207]]\n",
      "[31387.44340406]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "slope = model.coef_  \n",
    "intercept = model.intercept_ \n",
    "\n",
    "print(slope)\n",
    "print(intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f10f5598-e7f0-4eec-8ee4-a69073685d11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 6836.210895894568\n",
      "Mean Squared Error (MSE): 79580620.27310547\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(\"Mean Absolute Error (MAE):\", mae)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error (MSE):\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "92000294-74ba-4e6d-bbc2-9df81f2039b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test = X_test.flatten()\n",
    "sorted_indices = np.argsort(X_test)\n",
    "X_test_sorted = X_test[sorted_indices]\n",
    "y_pred_sorted = y_pred[sorted_indices]\n",
    "plt.plot(X_test_sorted, y_pred_sorted, color=\"red\", label=\"Regression line\")\n",
    "plt.scatter(data['YearsExperience'],data['Salary'])\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4d2835-f88a-49d2-97a2-df579abae4ea",
   "metadata": {},
   "source": [
    "- Bonus Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "35fd4ff9-4daa-4706-9f29-7cb76c7c704f",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NumProjectsCompleted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.10</td>\n",
       "      <td>77,884.67</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.10</td>\n",
       "      <td>127,213.68</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.90</td>\n",
       "      <td>103,343.52</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.40</td>\n",
       "      <td>88,988.96</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.00</td>\n",
       "      <td>35,214.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience     Salary  NumProjectsCompleted\n",
       "0             8.10  77,884.67                     2\n",
       "1            19.10 127,213.68                     6\n",
       "2            14.90 103,343.52                     4\n",
       "3            12.40  88,988.96                     4\n",
       "4             4.00  35,214.78                     0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_projects = (data['YearsExperience'] / 3).round().astype(int)\n",
    "\n",
    "# Generate random integer noise in the range of -1 to 1\n",
    "noise = np.random.randint(-1, 2, size=data.shape[0])\n",
    "\n",
    "# Add noise to the base projects and ensure positivity\n",
    "data['NumProjectsCompleted'] = (base_projects + noise).clip(lower=0)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "82c547ff-589d-4d5a-9515-3eb964a3e164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Metrics  Simple Linear Regression  Multiple Linear Regression\n",
      "0     MAE                  6,836.21                    7,303.22\n",
      "1     MSE             79,580,620.27               81,595,811.52\n"
     ]
    }
   ],
   "source": [
    "X_multiple = data[['YearsExperience', 'NumProjectsCompleted']] \n",
    "model_multiple = LinearRegression()\n",
    "model_multiple.fit(X_multiple, y)\n",
    "y_pred_multiple = model_multiple.predict(X_multiple)\n",
    "mae_multiple = mean_absolute_error(y, y_pred_multiple)\n",
    "mse_multiple = mean_squared_error(y, y_pred_multiple)\n",
    "\n",
    "comparison_data = {\n",
    "    'Metrics': ['MAE', 'MSE'],\n",
    "    'Simple Linear Regression': [mae, mse],  # Placeholder values\n",
    "    'Multiple Linear Regression': [mae_multiple, mse_multiple]  # Placeholder values\n",
    "}\n",
    "\n",
    "comparison_table = pd.DataFrame(comparison_data)\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "print(comparison_table)"
   ]
  }
 ],
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