Simple_linear_regression_for_machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f9ddfaa0",
   "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>age</th>\n",
       "      <th>distance</th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.1</td>\n",
       "      <td>77.75</td>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.3</td>\n",
       "      <td>78.25</td>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.5</td>\n",
       "      <td>78.75</td>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.2</td>\n",
       "      <td>80.50</td>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32.9</td>\n",
       "      <td>82.25</td>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33.0</td>\n",
       "      <td>82.50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33.2</td>\n",
       "      <td>83.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33.2</td>\n",
       "      <td>83.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33.7</td>\n",
       "      <td>84.25</td>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>33.9</td>\n",
       "      <td>84.75</td>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>34.0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>34.0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>35.0</td>\n",
       "      <td>85.50</td>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>35.3</td>\n",
       "      <td>85.90</td>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>35.8</td>\n",
       "      <td>86.30</td>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>36.0</td>\n",
       "      <td>87.00</td>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>36.5</td>\n",
       "      <td>87.30</td>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40.0</td>\n",
       "      <td>87.90</td>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>41.2</td>\n",
       "      <td>88.20</td>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>42.0</td>\n",
       "      <td>88.60</td>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>43.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>44.0</td>\n",
       "      <td>90.30</td>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>45.0</td>\n",
       "      <td>90.80</td>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>46.0</td>\n",
       "      <td>91.20</td>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>47.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>48.0</td>\n",
       "      <td>96.00</td>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>49.0</td>\n",
       "      <td>97.00</td>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>50.0</td>\n",
       "      <td>98.00</td>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>50.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  distance  YearsExperience    Salary\n",
       "0   31.1     77.75              1.1   39343.0\n",
       "1   31.3     78.25              1.3   46205.0\n",
       "2   31.5     78.75              1.5   37731.0\n",
       "3   32.0     80.00              2.0   43525.0\n",
       "4   32.2     80.50              2.2   39891.0\n",
       "5   32.9     82.25              2.9   56642.0\n",
       "6   33.0     82.50              3.0   60150.0\n",
       "7   33.2     83.00              3.2   54445.0\n",
       "8   33.2     83.00              3.2   64445.0\n",
       "9   33.7     84.25              3.7   57189.0\n",
       "10  33.9     84.75              3.9   63218.0\n",
       "11  34.0     85.00              4.0   55794.0\n",
       "12  34.0     85.00              4.0   56957.0\n",
       "13  35.0     85.50              4.1   57081.0\n",
       "14  35.3     85.90              4.5   61111.0\n",
       "15  35.8     86.30              4.9   67938.0\n",
       "16  36.0     87.00              5.1   66029.0\n",
       "17  36.5     87.30              5.3   83088.0\n",
       "18  40.0     87.90              5.9   81363.0\n",
       "19  41.2     88.20              6.0   93940.0\n",
       "20  42.0     88.60              6.8   91738.0\n",
       "21  43.0     90.00              7.1   98273.0\n",
       "22  44.0     90.30              7.9  101302.0\n",
       "23  45.0     90.80              8.2  113812.0\n",
       "24  46.0     91.20              8.7  109431.0\n",
       "25  47.0     95.00              9.0  105582.0\n",
       "26  48.0     96.00              9.5  116969.0\n",
       "27  49.0     97.00              9.6  112635.0\n",
       "28  50.0     98.00             10.3  122391.0\n",
       "29  50.0     99.00             10.5  121872.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df = pd.read_csv(\"Salary_Data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "642677dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the dataset into the training and testing\n",
    "X = df[[\"YearsExperience\"]]\n",
    "y = df[[\"Salary\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47f79c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the linear-regression library\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,  test_size=1/3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a344c15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step-3 fit linear regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model = model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ac2781c",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.scatter(X_train, y_train)\n",
    "plt.plot(X_train, model.predict(X_train), color=\"red\")\n",
    "plt.xlabel = (\"tajarba\")\n",
    "plt.ylabel = (\"tankhwa\")\n",
    "plt.title = (\"apna plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "454ce5ed",
   "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.scatter(X_test, y_test)\n",
    "plt.plot(X_train, model.predict(X_train), color=\"gray\")\n",
    "plt.xlabel = (\"tajarba\")\n",
    "plt.ylabel = (\"tankhwa\")\n",
    "plt.title = (\"apna plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0bbac8e",
   "metadata": {},
   "source": [
    "\n",
    "# testing or evaluating your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01e9dbee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9749154407708353"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test) # score for the testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "298b3024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9381900012894278"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train,y_train) # score for the traing data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd9abc19",
   "metadata": {},
   "source": [
    "# prediction of the unknown value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "925e527c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[73545.90445964]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70b997cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 40835.10590871],\n",
       "       [123079.39940819],\n",
       "       [ 65134.55626083],\n",
       "       [ 63265.36777221],\n",
       "       [115602.64545369],\n",
       "       [108125.8914992 ],\n",
       "       [116537.23969801],\n",
       "       [ 64199.96201652],\n",
       "       [ 76349.68719258],\n",
       "       [100649.1375447 ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0268767",
   "metadata": {},
   "outputs": [],
   "source": [
    "p=model.predict([[1],[2],[3],[4],[5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1bdf7ee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[36162.13468715],\n",
       "       [45508.07713028],\n",
       "       [54854.0195734 ],\n",
       "       [64199.96201652],\n",
       "       [73545.90445964]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f6758b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# in this way the moel prediction works in the machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f7d4ba3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4ead4f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f038ec8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eb6c702",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75d2026c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}