02_linearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MACHINE LEARNING\n",
    "1. SIMPLE LINEAR REGRESSION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step-1 import libararies and load datastep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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</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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset path\n",
    "file_path = 'data/salary_data.csv'\n",
    "\n",
    "# reading our dataset as a dataframe\n",
    "df= pd.read_csv(file_path)\n",
    "\n",
    "# taking a first look on our data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## step-2 spliting data set into training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#assigning x and y from the dataset\n",
    "X = df.iloc[:, :-1].values #get a copy of dataset exclude last column\n",
    "y = df.iloc[:, 1].values #get array of dataset in column 1st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 39343,  46205,  37731,  43525,  39891,  56642,  60150,  54445,\n",
       "        64445,  57189,  63218,  55794,  56957,  57081,  61111,  67938,\n",
       "        66029,  83088,  81363,  93940,  91738,  98273, 101302, 113812,\n",
       "       109431, 105582, 116969, 112635, 122391, 121872], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libarary\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=1/5, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## step_3 fit linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model= LinearRegression().fit(X_train, y_train)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## step_4 plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(X_train, y_train)\n",
    "plt.plot((X_train), model.predict(X_train), color=\"Purple\")\n",
    "plt.xlabel(\"yearsExperience\")\n",
    "plt.ylabel(\"salary\")\n",
    "plt.title(\"jado of train set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(X_test, y_test)\n",
    "plt.plot(X_test,model.predict(X_test),color=\"Purple\")\n",
    "plt.xlabel(\"yearsExperience\")\n",
    "plt.ylabel(\"salary\")\n",
    "plt.title(\"jado of train set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## step-5 testing/ evaluating your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.988169515729126"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model fitness of test set\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9411949620562126"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model fitness of train set\n",
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or you can do like ,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score of training data =  0.988169515729126\n",
      "score of testing data =  0.9411949620562126\n"
     ]
    }
   ],
   "source": [
    "print(\"score of training data = \",model.score(X_test, y_test) )\n",
    "print(\"score of testing data = \",model.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## step_6 prediction of unknown values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([73342.97478427])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([213031.60168521])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[20]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 73342.97478427, 147843.57579811, 678660.35802167])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[5],[13],[70]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40748.96184072, 122699.62295594,  64961.65717022,  63099.14214487,\n",
       "       115249.56285456, 107799.50275317])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  }
 ],
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