Siddhant_Task1_GRIP_TSF_DataScience & Business Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# **Graduate Rotational Internship Program**\n",
    "\n",
    "# **THE SPARKS FOUNDATION**\n",
    "\n",
    "## **TASK 1 - Prediction using Supervised ML**\n",
    "● Predict the percentage of an student based on the no. of study hours.  \n",
    "● This is a simple linear regression task as it involves just 2 variables. \n",
    "● You can use R, Python, SAS Enterprise Miner or any other tool  \n",
    "● Data can be found at http://bit.ly/w-data \n",
    "● What will be predicted score if a student studies for 9.25 hrs/ day?\n",
    "\n",
    "\n",
    "### *Author - Siddhant Pradhan*\n",
    "### *College: GEC, Patan*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *To view the data we are using head()*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data is successfully Published\n"
     ]
    },
    {
     "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",
       "  </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"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the Data \n",
    "siddhantdata = pd.read_csv('http://bit.ly/w-data')\n",
    "print(\"The data is successfully Published\")\n",
    "siddhantdata.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there any null value in the Dataset\n",
    "siddhantdata.isnull == True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There is no null value in the Dataset so, we can now visualize our Data.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Distribution on the basis of Marks vs Study Hour ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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(y= data['Scores'], x= data['Hours'])\n",
    "plt.title('Marks Vs Study Hours',size=20)\n",
    "plt.ylabel('Marks Percentage', size=12)\n",
    "plt.xlabel('Hours Studied', size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From the above scatter plot there looks to be correlation between the 'Marks Percentage' and 'Hours Studied', Lets plot a regression line to confirm the correlation.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\sidth\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:331: MatplotlibDeprecationWarning: \n",
      "The is_first_col function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use ax.get_subplotspec().is_first_col() instead.\n",
      "  if ax.is_first_col():\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Hours'}>,\n",
       "        <AxesSubplot:title={'center':'Scores'}>]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "siddhantdata.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Hours    Scores\n",
      "Hours   1.000000  0.976191\n",
      "Scores  0.976191  1.000000\n"
     ]
    }
   ],
   "source": [
    "sns.regplot(x= data['Hours'], y= data['Scores'])\n",
    "plt.title('Regression Plot',size=20)\n",
    "plt.ylabel('Marks Percentage', size=12)\n",
    "plt.xlabel('Hours Studied', size=12)\n",
    "plt.show()\n",
    "print(data.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dividing Data into Attributes ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[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]] [21 47 27 75 30 20 88 60 81 25 85 62 41 42 17 95 30 24 67 69 30 54 35 76\n",
      " 86]\n"
     ]
    }
   ],
   "source": [
    "X = siddhantdata.iloc[:, :-1].values    \n",
    "y = siddhantdata.iloc[:, 1].values \n",
    "print(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It is confirmed that the variables are positively correlated.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "### 1) Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting the Data in two\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Fitting the Data into the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------Model Trained---------\n"
     ]
    }
   ],
   "source": [
    "regression = LinearRegression()\n",
    "regression.fit(train_X, train_y)\n",
    "print(\"---------Model Trained---------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Plotting a test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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 = regression.coef_*X+regression.intercept_  \n",
    "plt.scatter(X, y)  \n",
    "plt.plot(X, line);  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the Percentage of Marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Predicted Marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>16.844722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.2</td>\n",
       "      <td>33.745575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.4</td>\n",
       "      <td>75.500624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.5</td>\n",
       "      <td>26.786400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.9</td>\n",
       "      <td>60.588106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.8</td>\n",
       "      <td>39.710582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.9</td>\n",
       "      <td>20.821393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Predicted Marks\n",
       "0    1.5        16.844722\n",
       "1    3.2        33.745575\n",
       "2    7.4        75.500624\n",
       "3    2.5        26.786400\n",
       "4    5.9        60.588106\n",
       "5    3.8        39.710582\n",
       "6    1.9        20.821393"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y = regression.predict(val_X)\n",
    "prediction = pd.DataFrame({'Hours': [i[0] for i in val_X], 'Predicted Marks': [k for k in pred_y]})\n",
    "prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the Predicted Marks with the Actual Marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 Marks</th>\n",
       "      <th>Predicted Marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.844722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.745575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.500624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.786400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.588106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>39.710582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>20.821393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual Marks  Predicted Marks\n",
       "0            20        16.844722\n",
       "1            27        33.745575\n",
       "2            69        75.500624\n",
       "3            30        26.786400\n",
       "4            62        60.588106\n",
       "5            35        39.710582\n",
       "6            24        20.821393"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_scores = pd.DataFrame({'Actual Marks': val_y, 'Predicted Marks': pred_y})\n",
    "compare_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visually Comparing the Predicted Marks with the Actual Marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=val_X, y=val_y, color='blue')\n",
    "plt.plot(val_X, pred_y, color='Black')\n",
    "plt.title('Actual vs Predicted', size=20)\n",
    "plt.ylabel('Marks Percentage', size=12)\n",
    "plt.xlabel('Hours Studied', size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error:  4.130879918502486\n"
     ]
    }
   ],
   "source": [
    "# Calculating the accuracy of the model\n",
    "print('Mean absolute error: ',mean_absolute_error(val_y,pred_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Small value of Mean absolute error states that the chances of error or wrong forecasting through the model are very less.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What will be the predicted score of a student if he/she studies for 9.25 hrs/ day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score = 93.893\n"
     ]
    }
   ],
   "source": [
    "hours = [9.25]\n",
    "answer = regression.predict([hours])\n",
    "print(\"Score = {}\".format(round(answer[0],3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "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.**"
   ]
  }
 ],
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