{
 "cells": [
 {
   "attachments": {
    "task11.jpeg": {
     "image/jpeg": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![task11.jpeg](attachment:task11.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task:  Predict the percentage of the marks of students based on the number of study hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_df=pd.read_csv(\"scores.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>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",
       "  </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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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"
    },
    {
     "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": [
    "for columns in score_df.columns:\n",
    "    plt.figure()\n",
    "    score_df.boxplot([columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- No missing values and outliers present in the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"darkgrid\")\n",
    "sns.scatterplot(data=score_df,x=\"Hours\",y=\"Scores\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- shows direct correlation between hours studied and scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training The Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Divide data into attributes and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=score_df.iloc[:,:-1].values\n",
    "y = score_df.iloc[:, 1].values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spliting data into train and test sets for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Complete\n"
     ]
    }
   ],
   "source": [
    "regressor.fit(X_train,y_train)\n",
    "print(\"Training Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 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",
   "metadata": {},
   "source": [
    "#### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)\n",
    "y_pred=regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':y_test,'Predicted':y_pred})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error=4.183859899002982\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "MAE=mean_absolute_error(y_pred,y_test)\n",
    "print(\"Mean Absolute Error={}\".format(MAE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluating performance of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of hours = 9.25\n",
      "Predicted Score=93.69173248737539\n"
     ]
    }
   ],
   "source": [
    "hours=9.25\n",
    "hours_arr=np.array(hours).reshape(-1,1)\n",
    "predict_score=regressor.predict(hours_arr)\n",
    "print(\"No of hours = {}\".format(hours))\n",
    "print(\"Predicted Score={}\".format(predict_score[0]))"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}