Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e890180",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "42312531",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f7fec88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d497591",
   "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>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</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</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</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</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  distance  YearsExperience  Salary\n",
       "0  31.1     77.75              1.1   39343\n",
       "1  31.3     78.25              1.3   46205\n",
       "2  31.5     78.75              1.5   37731\n",
       "3  32.0     80.00              2.0   43525\n",
       "4  32.2     80.50              2.2   39891"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dataset\n",
    "df = pd.read_csv(\"ml_data_salary.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c01ec834",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['age'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7687b1eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience  Salary\n",
       "0              1.1   39343\n",
       "1              1.3   46205\n",
       "2              1.5   37731\n",
       "3              2.0   43525\n",
       "4              2.2   39891"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['distance'], axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5c6b8109",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['YearsExperience']];\n",
    "y = df['Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "595ce83e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience\n",
       "0              1.1\n",
       "1              1.3\n",
       "2              1.5\n",
       "3              2.0\n",
       "4              2.2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "80fc04f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    39343\n",
       "1    46205\n",
       "2    37731\n",
       "3    43525\n",
       "4    39891\n",
       "Name: Salary, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d8d9eb64",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/5, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0bd39c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression().fit(X_train,y_train)\n",
    "reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1a8f2414",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dc45b5c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40748.96184072, 122699.62295594,  64961.65717022,  63099.14214487,\n",
       "       115249.56285456, 107799.50275317])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1903fff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13811d81a90>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(X_train, y_train)\n",
    "plt.plot(X_train, reg.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ff2c4095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13811dd7bb0>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(X_test, y_test)\n",
    "plt.plot(X_test, reg.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f036c516",
   "metadata": {},
   "source": [
    "We checl both tesmt and train both are linear so these are hwai baty now we will chek this by a score\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f53219",
   "metadata": {},
   "source": [
    "# Testing and evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b429237",
   "metadata": {},
   "source": [
    "Model Fitness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bb885ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.988169515729126"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f932693e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9411949620562126"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "382eb6c8",
   "metadata": {},
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f12db637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([73342.97478427])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "667d62e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 73342.97478427, 119905.85041792, 166468.72605157])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[5],[10],[15]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04fe24c8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}