Prediction using Supervised ML #1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d3f04b56",
   "metadata": {},
   "source": [
    "<h1 style='text-align: center;'> ANSHUMAN PRATIK </h1>\n",
    "<h2 style='text-align: center;'> Data Science and Business Analytics </h2>\n",
    "<h3 style='text-align: center;'> GRIP SEPTEMBER22 - The Sparks Foundation </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b6c1d67",
   "metadata": {},
   "source": [
    "The model involves **Machine Learning** algorithm to implement **Linear regression** function using Scikit-Learn library of Python. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6a96993",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary Libraries\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bbd54dad",
   "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",
       "  </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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating Student Data frame and display first Records\n",
    "data=\"http://bit.ly/w-data\"\n",
    "student_df = pd.read_csv(data)\n",
    "student_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "894b2b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot to visualize the relationship between Percentage and Hours studied.\n",
    "student_df.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Percentage and Hours studied')  \n",
    "plt.xlabel('Number of Hours studied')  \n",
    "plt.ylabel('Percentage Obtained')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80bedea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting of dataset consisting of attributes and values into training and testing sets using Scikit-learn library.\n",
    "x = student_df.iloc[:, :-1].values  \n",
    "y = student_df.iloc[:, 1].values  \n",
    "\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=0.2, random_state=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "78f50a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training the model for Linear Regression function\n",
    "from sklearn.linear_model import LinearRegression  \n",
    "reg = LinearRegression()  \n",
    "reg.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eed0cb61",
   "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": [
    "#Plotting the Regression line of chracteristic\n",
    "reg_line = reg.coef_*x+reg.intercept_\n",
    "plt.plot(reg_line)\n",
    "plt.title('Characteristic of Percentage with Study Hours')\n",
    "plt.show()\n",
    "plt.scatter(x, y)\n",
    "plt.title('Regression Line')\n",
    "plt.plot(x, reg_line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "27277836",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([16.88414476, 33.73226078, 75.357018  , 26.79480124, 60.49103328])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting Scores with Testing data of Study Hours\n",
    "print(x_test)\n",
    "y_pred = reg.predict(x_test) \n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b6a92618",
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Comparing Actual and Predicted 'Percentage scores'\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f6d67bb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = [93.69173249]\n"
     ]
    }
   ],
   "source": [
    "#Estimating Percentage for 9.25 hours of study per day\n",
    "hours = 9.25\n",
    "pred_percentage=reg.coef_*hours+reg.intercept_\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(pred_percentage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "be023f25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "#Evaluation of model through 'Mean Square error' between Test and Predicted Values\n",
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4451ee54",
   "metadata": {},
   "source": [
    "   **CONCLUSION**\n",
    "   \n",
    "• The **positive Linear Correlation** between the Percentage and Study hours was observed and plotted.\n",
    "\n",
    "• We were able to predict the Percentage Score of student based on his Study hours through **Training and analysis** of model.\n",
    "\n",
    "• A Student studying for 9.25 hours per day is expected to obtain **93.69%** Score.\n"
   ]
  }
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