Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "67a1a581",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas  as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "plt.rcParams['figure.figsize'] = [30,15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ee52bbcc",
   "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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</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\n",
       "5               2.9  56642.0\n",
       "6               3.0  60150.0\n",
       "7               3.2  54445.0\n",
       "8               3.2  64445.0\n",
       "9               3.7  57189.0\n",
       "10              3.9  63218.0\n",
       "11              4.0  55794.0\n",
       "12              4.0  56957.0\n",
       "13              4.1  57081.0\n",
       "14              4.5  61111.0\n",
       "15              4.9  67938.0\n",
       "16              5.1  66029.0\n",
       "17              5.3  83088.0\n",
       "18              5.9  81363.0\n",
       "19              6.0  93940.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Salary_Data.csv')\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "55f05b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:, :-1]\n",
    "y = df.iloc[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "933158a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c249b4b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "97acba0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40817.783270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123188.082589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65154.462615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63282.410357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>115699.873560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>108211.664531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>116635.899689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64218.436486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>76386.776158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               0\n",
       "0   40817.783270\n",
       "1  123188.082589\n",
       "2   65154.462615\n",
       "3   63282.410357\n",
       "4  115699.873560\n",
       "5  108211.664531\n",
       "6  116635.899689\n",
       "7   64218.436486\n",
       "8   76386.776158"
      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(x_test)\n",
    "pred = pd.DataFrame(y_pred)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "636a4ccd",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotted the actual & predicted \n",
    "plt.scatter(x_train, y_train, color = 'red', s = 200)\n",
    "plt.plot(x_train, lr.predict(x_train), color = 'blue')\n",
    "plt.xticks(fontsize =25,rotation = 0)\n",
    "plt.yticks(fontsize = 25)\n",
    "plt.title('Salary vs Experience (Actual vs. Predicted)', fontsize = 50)\n",
    "plt.xlabel('Years of Experience',fontsize = 30)\n",
    "plt.ylabel('Salary',fontsize = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4730b4d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The salary for 10 ==> 120380\n",
      "The salary for 11 ==> 129740\n",
      "The salary for 12 ==> 139100\n",
      "The salary for 13 ==> 148460\n",
      "The salary for 14 ==> 157821\n",
      "The salary for 15 ==> 167181\n",
      "The salary for 16 ==> 176541\n",
      "The salary for 17 ==> 185901\n",
      "The salary for 18 ==> 195262\n",
      "The salary for 19 ==> 204622\n",
      "The salary for 20 ==> 213982\n"
     ]
    }
   ],
   "source": [
    "# Output shows that the predicted salary when the years of experience exceeds 10\n",
    "i = 10\n",
    "while i <= 20:\n",
    "    print(\"The salary for %d ==>\" %(i) , int(lr.predict([[i]])))\n",
    "    i= i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cee1d8d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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