Task 1-TSF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "254ceb55",
   "metadata": {},
   "source": [
    "# Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32b14a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os,glob,random\n",
    "from sklearn.metrics import accuracy_score\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25b123b5",
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours  Scores\n",
       "0     2.5      21\n",
       "1     5.1      47\n",
       "2     3.2      27\n",
       "3     8.5      75\n",
       "4     3.5      30\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "link=\"http://bit.ly/w-data\"\n",
    "data=pd.read_csv(link)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ef9f726",
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b59a56d",
   "metadata": {},
   "source": [
    "# Plot a graph of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "15110290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Hours',y='Scores',style='o')\n",
    "plt.title('Hours VS Score in Percentage')\n",
    "plt.xlabel('Hours Studied')\n",
    "plt.ylabel('Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c294d333",
   "metadata": {},
   "source": [
    "# Splitting the training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c2d803b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import train_test_split\n",
    "x=data.iloc[:,:-1].values\n",
    "\n",
    "y=data.iloc[:,1].values\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.80,test_size=0.20,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "0c13a1e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 27, 69, 30, 62], dtype=int64)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117990be",
   "metadata": {},
   "source": [
    "# Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "f8ff3e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linearregressor=LinearRegression()\n",
    "linearregressor.fit(x_train,y_train)\n",
    "y_predict=linearregressor.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fee2ea2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39.67865467, 20.84840735, 79.32128059, 70.40168976, 12.91988217,\n",
       "       52.56250809, 78.33021494, 34.72332643, 84.27660883, 93.19619966,\n",
       "       62.47316457, 36.70545772, 28.77693254, 56.52677068, 28.77693254,\n",
       "       86.25874013, 26.79480124, 49.58931115, 90.22300272, 46.6161142 ])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "bc43f195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Complete\n"
     ]
    }
   ],
   "source": [
    "regressor=LinearRegression()\n",
    "regressor.fit(x_train,y_train)\n",
    "print(\"Training Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f404c854",
   "metadata": {},
   "source": [
    "# Plot the line according to the equation y=mx+c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "00248134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "line=regressor.coef_*x+regressor.intercept_\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,line)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "282d3111",
   "metadata": {},
   "source": [
    "# Evaluating the Training and Testing metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "8f102a2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score\n",
      "0.9515510725211552\n",
      "Testing Score\n",
      "0.9454906892105355\n"
     ]
    }
   ],
   "source": [
    "print('Training Score')\n",
    "print(regressor.score(x_train,y_train))\n",
    "print('Testing Score')\n",
    "print(regressor.score(x_test,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "502866b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39.67865467, 20.84840735, 79.32128059, 70.40168976, 12.91988217])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict1=y_predict[:5]\n",
    "y_predict1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "96b7c0fb",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>39.678655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>20.848407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>79.321281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>70.401690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>12.919882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  39.678655\n",
       "1      27  20.848407\n",
       "2      69  79.321281\n",
       "3      30  70.401690\n",
       "4      62  12.919882"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.DataFrame({\"Actual\":y_test,\"Predicted\":y_predict1})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "76469186",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores of student who studied for 9.25 hours a day [93.69173249]\n"
     ]
    }
   ],
   "source": [
    "#Let's predict the score for 9.25 hours\n",
    "print('Scores of student who studied for 9.25 hours a day', regressor.predict([[9.25]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "46dd44a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error:  914.5549752244239\n",
      "Mean Absolute Error:  25.126667098277863\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score\n",
    "mean_squ_error=mean_squared_error(y_test,y_predict1)\n",
    "mean_abs_error=mean_absolute_error(y_test,y_predict1)\n",
    "print(\"Mean Square Error: \",mean_squ_error)\n",
    "print(\"Mean Absolute Error: \",mean_abs_error)"
   ]
  }
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