Task-1 Prediction Using Supervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE SPARKS FOUNDATION\n",
    "\n",
    "# Data Science and Business analytics\n",
    "\n",
    "# Name:- SANGITA MAHALA\n",
    "\n",
    "# Task-1: Prediction using Supervised ML\n",
    "    \n",
    "# Predict the percentage of an student based on the no. of study hours.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"http://bit.ly/w-data\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      "Hours     25 non-null float64\n",
      "Scores    25 non-null int64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "      <td>count</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boxplot of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19a968620c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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smDxzVhOKSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df[[\"Hours\",\"Scores\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatterplot of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "df.plot.scatter(x=\"Hours\",y=\"Scores\")\n",
    "plt.title(\"Hours vs. Scores\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly we can see that there is positive linear relationship between hours and scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting our Dataset into Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                    test_size = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are using 80% of our dataset for training and 20% of the data for testing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now we use the training data to train our Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "\n",
    "print(\"Training complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "line = regressor.coef_*X+regressor.intercept_\n",
    "\n",
    "df.plot.scatter(x=\"Hours\",y=\"Scores\")\n",
    "plt.plot(X, line);\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then make some Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16.88414476 33.73226078 75.357018   26.79480124 60.49103328]\n"
     ]
    }
   ],
   "source": [
    "# Here we predict the scores \n",
    "y_pred = regressor.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Actual Score vs. Predicted Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 Score</th>\n",
       "      <th>Predicted Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual Score  Predicted Score\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_compare = pd.DataFrame({\"Actual Score\":y_test,\"Predicted Score\":y_pred})\n",
    "df_compare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now Check The Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 93.69173248737538\n"
     ]
    }
   ],
   "source": [
    "my_hours = np.array([[9.25]])\n",
    "my_pred = regressor.predict(my_hours)\n",
    "print(\"No of Hours = {}\".format(my_hours[0][0]))\n",
    "print(\"Predicted Score = {}\".format(my_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance:  0.9483\n",
      "mean_squared_log_error:  0.0185\n",
      "r2:  0.9455\n",
      "MAE:  4.1839\n",
      "MSE:  21.5988\n",
      "RMSE:  4.6474\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "\n",
    "explained_variance=metrics.explained_variance_score(y_test, y_pred)\n",
    "mean_absolute_error=metrics.mean_absolute_error(y_test, y_pred) \n",
    "mse=metrics.mean_squared_error(y_test, y_pred) \n",
    "mean_squared_log_error=metrics.mean_squared_log_error(y_test, y_pred)\n",
    "median_absolute_error=metrics.median_absolute_error(y_test, y_pred)\n",
    "r2=metrics.r2_score(y_test, y_pred)\n",
    "\n",
    "print('Explained Variance: ', round(explained_variance,4))    \n",
    "print('mean_squared_log_error: ', round(mean_squared_log_error,4))\n",
    "print('r2: ', round(r2,4))\n",
    "print('MAE: ', round(mean_absolute_error,4))\n",
    "print('MSE: ', round(mse,4))\n",
    "print('RMSE: ', round(np.sqrt(mse),4))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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