Arjav Tongia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "renewable-smith",
   "metadata": {},
   "source": [
    "# Linear Regression with Python Scikit Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "strong-necklace",
   "metadata": {},
   "source": [
    "## SALARY PREDICTION MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "organizational-combine",
   "metadata": {},
   "source": [
    "## Author: Arjav Tongia (EN19CS303013)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ongoing-colleague",
   "metadata": {},
   "source": [
    "#### Importing required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "mobile-conservation",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression  \n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "corporate-sampling",
   "metadata": {},
   "source": [
    "#### Reading data from link."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "characteristic-blogger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience   Salary\n",
       "0              1.1  39343.0\n",
       "1              1.3  46205.0\n",
       "2              1.5  37731.0\n",
       "3              2.0  43525.0\n",
       "4              2.2  39891.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Salary_Data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extra-flesh",
   "metadata": {},
   "source": [
    "#### Summary of the Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "typical-longer",
   "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>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.313333</td>\n",
       "      <td>76003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>37731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>56720.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>65237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>100544.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.500000</td>\n",
       "      <td>122391.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       YearsExperience         Salary\n",
       "count        30.000000      30.000000\n",
       "mean          5.313333   76003.000000\n",
       "std           2.837888   27414.429785\n",
       "min           1.100000   37731.000000\n",
       "25%           3.200000   56720.750000\n",
       "50%           4.700000   65237.000000\n",
       "75%           7.700000  100544.750000\n",
       "max          10.500000  122391.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "least-copper",
   "metadata": {},
   "source": [
    "#### Shape of the Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "certified-copper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flush-denial",
   "metadata": {},
   "source": [
    "#### Checking for any null value in our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "regulation-canvas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull == True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effective-colleague",
   "metadata": {},
   "source": [
    "#### Plotting the Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "jewish-pharmaceutical",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='YearsExperience', y='Salary', style='o')\n",
    "plt.title('Years of Experience vs Salary')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recognized-distribution",
   "metadata": {},
   "source": [
    "### **From the graph above, we can clearly see that there is a positive linear relation between the years of experience and salary.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "permanent-translation",
   "metadata": {},
   "source": [
    "#### Now, we will divide the data into attributes (inputs) and labels (output):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "narrow-split",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, :-1].values  \n",
    "y = data.iloc[:, 1].values  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "official-louisiana",
   "metadata": {},
   "source": [
    "#### Now, we will split the data into training and test sets by using SCIKIT-LEARN'S built-in train_test_split() Method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "naughty-agency",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fallen-remedy",
   "metadata": {},
   "source": [
    "#### We will split our data into training and testing sets, and then finally train our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "biological-excitement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training is completed.\n"
     ]
    }
   ],
   "source": [
    "regressor  = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "print(\"Training is completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dimensional-skating",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line= regressor.coef_*X + regressor.intercept_\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excellent-brooks",
   "metadata": {},
   "source": [
    "####  Making Predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "corrected-insurance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.5]\n",
      " [10.3]\n",
      " [ 4.1]\n",
      " [ 3.9]\n",
      " [ 9.5]\n",
      " [ 8.7]\n",
      " [ 9.6]\n",
      " [ 4. ]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "understood-newsletter",
   "metadata": {},
   "source": [
    "#### Comparing Actual vs Predicted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "moving-interest",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37731.0</td>\n",
       "      <td>41056.257055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122391.0</td>\n",
       "      <td>123597.709384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57081.0</td>\n",
       "      <td>65443.504334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63218.0</td>\n",
       "      <td>63567.562235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116969.0</td>\n",
       "      <td>116093.940990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>109431.0</td>\n",
       "      <td>108590.172597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>112635.0</td>\n",
       "      <td>117031.912039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55794.0</td>\n",
       "      <td>64505.533285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual      Predicted\n",
       "0   37731.0   41056.257055\n",
       "1  122391.0  123597.709384\n",
       "2   57081.0   65443.504334\n",
       "3   63218.0   63567.562235\n",
       "4  116969.0  116093.940990\n",
       "5  109431.0  108590.172597\n",
       "6  112635.0  117031.912039\n",
       "7   55794.0   64505.533285"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "olive-regulation",
   "metadata": {},
   "source": [
    "#### Testing with Custom Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "heated-industry",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Years of Experience = 9\n",
      "Predicted Salary = 111404.08574425138\n"
     ]
    }
   ],
   "source": [
    "years = 9\n",
    "prediction = regressor.predict([[years]])\n",
    "print(\"Years of Experience = {}\".format(years))\n",
    "print(\"Predicted Salary = {}\".format(prediction[0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thirty-audit",
   "metadata": {},
   "source": [
    "#### Evaluating The Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "brief-impact",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  3508.5455930660555\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error: \",metrics.mean_absolute_error(y_test, y_pred))"
   ]
  }
 ],
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