10.Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. importing the recuried  libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Importing the Data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"Salary_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 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]]\n"
     ]
    }
   ],
   "source": [
    "X = dataset.iloc[:,:-1].values\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 39343.  46205.  37731.  43525.  39891.  56642.  60150.  54445.  64445.\n",
      "  57189.  63218.  55794.  56957.  57081.  61111.  67938.  66029.  83088.\n",
      "  81363.  93940.  91738.  98273. 101302. 113812. 109431. 105582. 116969.\n",
      " 112635. 122391. 121872.]\n"
     ]
    }
   ],
   "source": [
    "Y = dataset.iloc[:,1].values\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. sPLITING THE DATASET INTO TARINING AND TEST SET "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 = 1/3,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.9],\n",
       "       [ 5.1],\n",
       "       [ 3.2],\n",
       "       [ 4.5],\n",
       "       [ 8.2],\n",
       "       [ 6.8],\n",
       "       [ 1.3],\n",
       "       [10.5],\n",
       "       [ 3. ],\n",
       "       [ 2.2],\n",
       "       [ 5.9],\n",
       "       [ 6. ],\n",
       "       [ 3.7],\n",
       "       [ 3.2],\n",
       "       [ 9. ],\n",
       "       [ 2. ],\n",
       "       [ 1.1],\n",
       "       [ 7.1],\n",
       "       [ 4.9],\n",
       "       [ 4. ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.5],\n",
       "       [10.3],\n",
       "       [ 4.1],\n",
       "       [ 3.9],\n",
       "       [ 9.5],\n",
       "       [ 8.7],\n",
       "       [ 9.6],\n",
       "       [ 4. ],\n",
       "       [ 5.3],\n",
       "       [ 7.9]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 56642.,  66029.,  64445.,  61111., 113812.,  91738.,  46205.,\n",
       "       121872.,  60150.,  39891.,  81363.,  93940.,  57189.,  54445.,\n",
       "       105582.,  43525.,  39343.,  98273.,  67938.,  56957.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431., 112635.,\n",
       "        55794.,  83088., 101302.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. fitting Simple linear Regression to the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. predicting the test set rrsults\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 40835.10590871 123079.39940819  65134.55626083  63265.36777221\n",
      " 115602.64545369 108125.8914992  116537.23969801  64199.96201652\n",
      "  76349.68719258 100649.1375447 ]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.Visualising the trining set result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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(X_train,Y_train,color = 'red')\n",
    "plt.plot(X_train,regressor.predict(X_train),color = 'blue')\n",
    "plt.title(\"Salary vs Experience ( TRAINING SET)\")\n",
    "plt.xlabel(\" Years of Experience\")\n",
    "plt.ylabel(\" Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.Visualising the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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(x_test,y_test,color = 'red')\n",
    "plt.plot(X_train,regressor.predict(X_train),color = 'blue')\n",
    "plt.title(\" Salary vs exprience (TEST SET)\")\n",
    "plt.xlabel(\" Years of exprience\")\n",
    "plt.ylabel( \" Salary \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9381900012894278"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9749154407708353"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(x_test,y_test)"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}