Sparks Foundation .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Sparks Foundation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Science and Business Analytics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1 : Prediction using Supervised ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Author: Subramanya K S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict the percentage of an student based on the no. of study hours.\n",
    "Dataset:  http://bit.ly/w-data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### import required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### import and Read the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
       "  </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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('student_scores.csv')\n",
    "df = data.copy()\n",
    "# view the first five rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. We are using simple linear regression as it involves just two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Visualization and Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "#getting the information of data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describing the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rows and column in dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     float64\n",
       "Scores      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#datatypes of each columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>Hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scores</th>\n",
       "      <td>0.976191</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours    Scores\n",
       "Hours   1.000000  0.976191\n",
       "Scores  0.976191  1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for correlation\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24c877f1148>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap to analyse correlation\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x24c87f33c48>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(y_vars = 'Scores', x_vars = 'Hours' ,data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24c8800e248>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.distplot(df['Scores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000024C87FE43C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000024C8810A948>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for the null value\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "# Plotting the distribution of scores\n",
    "data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create dependent and Independent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values\n",
    "Y = df.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25, 1)\n",
      "(25,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.5],\n",
       "       [5.1],\n",
       "       [3.2],\n",
       "       [8.5],\n",
       "       [3.5],\n",
       "       [1.5],\n",
       "       [9.2],\n",
       "       [5.5],\n",
       "       [8.3],\n",
       "       [2.7],\n",
       "       [7.7],\n",
       "       [5.9],\n",
       "       [4.5],\n",
       "       [3.3],\n",
       "       [1.1],\n",
       "       [8.9],\n",
       "       [2.5],\n",
       "       [1.9],\n",
       "       [6.1],\n",
       "       [7.4],\n",
       "       [2.7],\n",
       "       [4.8],\n",
       "       [3.8],\n",
       "       [6.9],\n",
       "       [7.8]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 47, 27, 75, 30, 20, 88, 60, 81, 25, 85, 62, 41, 42, 17, 95, 30,\n",
       "       24, 67, 69, 30, 54, 35, 76, 86], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split the dataset into training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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=0.2,random_state =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 1)\n",
      "(5, 1)\n",
      "(20,)\n",
      "(5,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply Linear Regression to Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lreg = LinearRegression()\n",
    "lreg.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "# Plotting the regression line\n",
    "line = lreg.coef_*X+lreg.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict the Test set value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.88414476, 33.73226078, 75.357018  , 26.79480124, 60.49103328])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = (lreg.predict(X_test))\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare actual and Predicted Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "      <th>varience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "      <td>3.115855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "      <td>-6.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "      <td>-6.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "      <td>3.205199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "      <td>1.508967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted  varience\n",
       "0      20  16.884145  3.115855\n",
       "1      27  33.732261 -6.732261\n",
       "2      69  75.357018 -6.357018\n",
       "3      30  26.794801  3.205199\n",
       "4      62  60.491033  1.508967"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({\"Actual\":Y_test, \"Predicted\": y_pred, 'varience':Y_test-y_pred})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.5],\n",
       "       [3.2],\n",
       "       [7.4],\n",
       "       [2.5],\n",
       "       [5.9]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding intercept and co-efficient for linear equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.018160041434683\n",
      "[9.91065648]\n"
     ]
    }
   ],
   "source": [
    "print(lreg.intercept_)\n",
    "print(lreg.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Linear Regression Equation is:<br>\n",
    "y = 2.01816+9.911 * x \n",
    "<br>or<br>\n",
    "Scores = 2.01816+9.911 * Hours of Study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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, lreg.predict(X_train), color = 'blue')\n",
    "plt.title('Scores vs Hours of Study (Training set)')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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, lreg.predict(X_train), color = 'blue')\n",
    "plt.title('Scores vs Hours of Study (Training set)')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation of model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "finding Root Mean Square Errore, Mean Square Error, Mean Absolute Eroor, R-Square."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared Error =  21.5987693072174\n",
      "Mean absolute Error =  4.183859899002975\n",
      "Root Mean Square error = 4.6474476121003665\n",
      "R square =  0.9454906892105356\n"
     ]
    }
   ],
   "source": [
    "print('Mean squared Error = ',metrics.mean_squared_error( Y_test, y_pred))\n",
    "print('Mean absolute Error = ',metrics.mean_absolute_error( Y_test, y_pred))\n",
    "print(\"Root Mean Square error =\",np.sqrt(metrics.mean_squared_error(Y_test, y_pred)))\n",
    "print('R square = ',metrics.r2_score(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training and testing Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy 95.16%\n",
      "Testing accuracy 94.55%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training accuracy {round(lreg.score(X_train,Y_train)*100,2)}%\")\n",
    "print(f\"Testing accuracy {round(lreg.score(X_test,Y_test)*100,2)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predicting scores when hour of stdy is 9.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 93.69173248737538\n"
     ]
    }
   ],
   "source": [
    "# You can also test with your own data\n",
    "hours = 9.25\n",
    "own_pred = lreg.predict([[hours]])\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ________________________END____________________________________"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}