Prediction using Supervised ML - .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a7e2cc8",
   "metadata": {},
   "source": [
    "# TASK 1 - Prediction using Supervised ML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b804d6ee",
   "metadata": {},
   "source": [
    "To Predict the _percentage of marks_ of the students based on the _number of hours they studied_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aeb57d8",
   "metadata": {},
   "source": [
    "### Author - Sujeet Kumar Pandit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "37abc3c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "387fee86",
   "metadata": {},
   "source": [
    "## Data Acquisition & Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "237df59e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataset: (25, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
       "  </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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing the dataset\n",
    "data = pd.read_csv(r\"data/student_scores.csv\")\n",
    "print('Shape of the dataset:', data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924e86ee",
   "metadata": {},
   "source": [
    "- The dataset contains **25** rows and **2** columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2415b65f",
   "metadata": {},
   "source": [
    "### Data Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bad169ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f023647",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "\n",
    "- The **average** percentage of **Scores** is **51.48**\n",
    "\n",
    "- The **maximum** percentage of **Scores** is **95**\n",
    "\n",
    "- The **maximum** study hours is **9.2**\n",
    "\n",
    "- The **least** study hours is **1.10**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f1e83b",
   "metadata": {},
   "source": [
    "### Data Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4636c2f1",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f575153",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "\n",
    "- There are **no null values present** in this data set.\n",
    "\n",
    "- **Each feature** seems to have **correct data type**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6017d5ff",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f50e198",
   "metadata": {},
   "source": [
    "**Is there a relationship between Hours and Scores?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6afd963c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\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",
   "id": "fcc636dd",
   "metadata": {},
   "source": [
    "__Observation__<br/>\n",
    "\n",
    "- From the graph above, Hours and Study are positively correlated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ccf2897",
   "metadata": {},
   "source": [
    "### Visualising Pairwise correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "51f0e68d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2d0ef311640>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data, size = 2, aspect = 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89c80dd",
   "metadata": {},
   "source": [
    "### Calculating and plotting heatmap correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f1982666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(), annot=True );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da7f8d88",
   "metadata": {},
   "source": [
    "__Observation__\n",
    "\n",
    "- **Strong** relationship between **Hours** and **Scores**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee7e1fa",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61f9538a",
   "metadata": {},
   "source": [
    "- Now we will **split** our **data** into **dependent** and **independent** variables for further development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cfa98429",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:, :-1]\n",
    "y = data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c3c344cb",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours\n",
       "0    2.5\n",
       "1    5.1\n",
       "2    3.2\n",
       "3    8.5\n",
       "4    3.5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "61bc09cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    21\n",
       "1    47\n",
       "2    27\n",
       "3    75\n",
       "4    30\n",
       "Name: Scores, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9348ea58",
   "metadata": {},
   "source": [
    "- Now we will **split** our **data** into **training** and **testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7614aa34",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split     #import train test library\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": 54,
   "id": "ef2436b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train cases as below\n",
      "x_train shape:  (20, 1)\n",
      "y_train shape:  (20,)\n",
      "\n",
      "Test cases as below\n",
      "x_test shape:  (5, 1)\n",
      "y_test shape:  (5,)\n"
     ]
    }
   ],
   "source": [
    "print('Train cases as below')\n",
    "print('x_train shape: ',x_train.shape)\n",
    "print('y_train shape: ',y_train.shape)\n",
    "print('\\nTest cases as below')\n",
    "print('x_test shape: ',x_test.shape)\n",
    "print('y_test shape: ',y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623db245",
   "metadata": {},
   "source": [
    "## Model Development & Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2910d60d",
   "metadata": {},
   "source": [
    "### Linear regression in scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8c803129",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "linreg = LinearRegression()  \n",
    "linreg.fit(x_train, y_train) \n",
    "\n",
    "print(\"Training complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66349b9a",
   "metadata": {},
   "source": [
    "**Interpreting Model Coefficients**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5f2a478a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: 2.018160041434683\n",
      "Coefficients: [9.91065648]\n"
     ]
    }
   ],
   "source": [
    "print('Intercept:',linreg.intercept_)          # print the intercept \n",
    "print('Coefficients:',linreg.coef_)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526239a9",
   "metadata": {},
   "source": [
    "### Visually Comparing the Predicted Marks with the Actual Marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "af79811f",
   "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 = linreg.coef_*x+linreg.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ef5360",
   "metadata": {},
   "source": [
    "### Using the Model for Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30dfa16a",
   "metadata": {},
   "source": [
    "Now that we have trained our algorithm, it's time to make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b17df040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Hours\n",
      "5     1.5\n",
      "2     3.2\n",
      "19    7.4\n",
      "16    2.5\n",
      "11    5.9\n"
     ]
    }
   ],
   "source": [
    "print(x_test)                       # Testing data - In Hours\n",
    "y_pred = linreg.predict(x_test)     # Predicting the scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "915c161c",
   "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>5</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "5       20  16.884145\n",
       "2       27  33.732261\n",
       "19      69  75.357018\n",
       "16      30  26.794801\n",
       "11      62  60.491033"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "dataframe = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b4c437",
   "metadata": {},
   "source": [
    "### What will be predicted score if a student studies for 9.25 hrs/ day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "74c9c806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = [9.25]\n",
      "Predicted Score = 93.69173248737538\n"
     ]
    }
   ],
   "source": [
    "hours = [9.25]\n",
    "own_pred = linreg.predict([hours])\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5631c74d",
   "metadata": {},
   "source": [
    "### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f3d798ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba7c6bbe",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a217d02f",
   "metadata": {},
   "source": [
    "- **According to the regression model if a student studies for 9.25 hours a day he/she is likely to score 93.89 marks.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55309b27",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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