4-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.313333</td>\n",
       "      <td>76003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>37731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>56720.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>65237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>100544.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.500000</td>\n",
       "      <td>122391.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       YearsExperience         Salary\n",
       "count        30.000000      30.000000\n",
       "mean          5.313333   76003.000000\n",
       "std           2.837888   27414.429785\n",
       "min           1.100000   37731.000000\n",
       "25%           3.200000   56720.750000\n",
       "50%           4.700000   65237.000000\n",
       "75%           7.700000  100544.750000\n",
       "max          10.500000  122391.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv(\"C:/Users/H_jam/OneDrive/Uni-MA/Term2/DataMining/DataSets/Regression/Simple Linear Regression/Salary_Data.csv\")\n",
    "dataset.describe() "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define X , Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=dataset.iloc[:,:-1].values\n",
    "y=dataset.iloc[:,1].values\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which Regression?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1be37b07100>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,color=\"red\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split test and train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (1.21.5)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit Simple Linear Regression to training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the test set result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([120275.61667525])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=regressor.predict(x_test)\n",
    "regressor.predict([[10]])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation by using Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23a2304fb20>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(0)\n",
    "plt.scatter(x_train , y_train, color='red')\n",
    "plt.plot(x_train,regressor.predict(x_train),color='blue')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation by R-Squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9381900012894278\n"
     ]
    }
   ],
   "source": [
    "r_squared_train=regressor.score(x_train,y_train)\n",
    "print(r_squared_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9749154407708353\n"
     ]
    }
   ],
   "source": [
    "r_squared_test=regressor.score(x_test,y_test)\n",
    "print(r_squared_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9749154407708353\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "rsquared=r2_score(y_test,y_pred)\n",
    "print(rsquared)"
   ]
  }
 ],
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