Day10_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bit.ly/apssdc-git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/AP-State-Skill-Development-Corporation/Datasets/master/Regression/Salary_Data.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    float64\n",
       "Salary             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "plt.scatter(df['YearsExperience'], df['Salary'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['YearsExperience'].values.reshape(-1, 1)\n",
    "y = df['Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.1],\n",
       "       [ 1.3],\n",
       "       [ 1.5],\n",
       "       [ 2. ],\n",
       "       [ 2.2],\n",
       "       [ 2.9],\n",
       "       [ 3. ],\n",
       "       [ 3.2],\n",
       "       [ 3.2],\n",
       "       [ 3.7],\n",
       "       [ 3.9],\n",
       "       [ 4. ],\n",
       "       [ 4. ],\n",
       "       [ 4.1],\n",
       "       [ 4.5],\n",
       "       [ 4.9],\n",
       "       [ 5.1],\n",
       "       [ 5.3],\n",
       "       [ 5.9],\n",
       "       [ 6. ],\n",
       "       [ 6.8],\n",
       "       [ 7.1],\n",
       "       [ 7.9],\n",
       "       [ 8.2],\n",
       "       [ 8.7],\n",
       "       [ 9. ],\n",
       "       [ 9.5],\n",
       "       [ 9.6],\n",
       "       [10.3],\n",
       "       [10.5]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['YearsExperience'].values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25792.20019866871"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9449.96232146])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "experience = np.arange(1, 15, 0.1).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1. ],\n",
       "       [ 1.1],\n",
       "       [ 1.2],\n",
       "       [ 1.3],\n",
       "       [ 1.4],\n",
       "       [ 1.5],\n",
       "       [ 1.6],\n",
       "       [ 1.7],\n",
       "       [ 1.8],\n",
       "       [ 1.9],\n",
       "       [ 2. ],\n",
       "       [ 2.1],\n",
       "       [ 2.2],\n",
       "       [ 2.3],\n",
       "       [ 2.4],\n",
       "       [ 2.5],\n",
       "       [ 2.6],\n",
       "       [ 2.7],\n",
       "       [ 2.8],\n",
       "       [ 2.9],\n",
       "       [ 3. ],\n",
       "       [ 3.1],\n",
       "       [ 3.2],\n",
       "       [ 3.3],\n",
       "       [ 3.4],\n",
       "       [ 3.5],\n",
       "       [ 3.6],\n",
       "       [ 3.7],\n",
       "       [ 3.8],\n",
       "       [ 3.9],\n",
       "       [ 4. ],\n",
       "       [ 4.1],\n",
       "       [ 4.2],\n",
       "       [ 4.3],\n",
       "       [ 4.4],\n",
       "       [ 4.5],\n",
       "       [ 4.6],\n",
       "       [ 4.7],\n",
       "       [ 4.8],\n",
       "       [ 4.9],\n",
       "       [ 5. ],\n",
       "       [ 5.1],\n",
       "       [ 5.2],\n",
       "       [ 5.3],\n",
       "       [ 5.4],\n",
       "       [ 5.5],\n",
       "       [ 5.6],\n",
       "       [ 5.7],\n",
       "       [ 5.8],\n",
       "       [ 5.9],\n",
       "       [ 6. ],\n",
       "       [ 6.1],\n",
       "       [ 6.2],\n",
       "       [ 6.3],\n",
       "       [ 6.4],\n",
       "       [ 6.5],\n",
       "       [ 6.6],\n",
       "       [ 6.7],\n",
       "       [ 6.8],\n",
       "       [ 6.9],\n",
       "       [ 7. ],\n",
       "       [ 7.1],\n",
       "       [ 7.2],\n",
       "       [ 7.3],\n",
       "       [ 7.4],\n",
       "       [ 7.5],\n",
       "       [ 7.6],\n",
       "       [ 7.7],\n",
       "       [ 7.8],\n",
       "       [ 7.9],\n",
       "       [ 8. ],\n",
       "       [ 8.1],\n",
       "       [ 8.2],\n",
       "       [ 8.3],\n",
       "       [ 8.4],\n",
       "       [ 8.5],\n",
       "       [ 8.6],\n",
       "       [ 8.7],\n",
       "       [ 8.8],\n",
       "       [ 8.9],\n",
       "       [ 9. ],\n",
       "       [ 9.1],\n",
       "       [ 9.2],\n",
       "       [ 9.3],\n",
       "       [ 9.4],\n",
       "       [ 9.5],\n",
       "       [ 9.6],\n",
       "       [ 9.7],\n",
       "       [ 9.8],\n",
       "       [ 9.9],\n",
       "       [10. ],\n",
       "       [10.1],\n",
       "       [10.2],\n",
       "       [10.3],\n",
       "       [10.4],\n",
       "       [10.5],\n",
       "       [10.6],\n",
       "       [10.7],\n",
       "       [10.8],\n",
       "       [10.9],\n",
       "       [11. ],\n",
       "       [11.1],\n",
       "       [11.2],\n",
       "       [11.3],\n",
       "       [11.4],\n",
       "       [11.5],\n",
       "       [11.6],\n",
       "       [11.7],\n",
       "       [11.8],\n",
       "       [11.9],\n",
       "       [12. ],\n",
       "       [12.1],\n",
       "       [12.2],\n",
       "       [12.3],\n",
       "       [12.4],\n",
       "       [12.5],\n",
       "       [12.6],\n",
       "       [12.7],\n",
       "       [12.8],\n",
       "       [12.9],\n",
       "       [13. ],\n",
       "       [13.1],\n",
       "       [13.2],\n",
       "       [13.3],\n",
       "       [13.4],\n",
       "       [13.5],\n",
       "       [13.6],\n",
       "       [13.7],\n",
       "       [13.8],\n",
       "       [13.9],\n",
       "       [14. ],\n",
       "       [14.1],\n",
       "       [14.2],\n",
       "       [14.3],\n",
       "       [14.4],\n",
       "       [14.5],\n",
       "       [14.6],\n",
       "       [14.7],\n",
       "       [14.8],\n",
       "       [14.9]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(experience).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": 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": [
    "plt.scatter(df['YearsExperience'], df['Salary'], label = 'Data')\n",
    "plt.plot(experience, y_pred, c = 'r', label = 'Regression Line')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5592.043608760662"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y, predicted) ** 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9569566641435086"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}