Task 1 Supervised ML Sparks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submitted By : Jainil Shah\n",
    "# Predict the percentage of an student based on the no. of study hours.\n",
    "## Simple linear regression task as it involves just 2 variables.\n",
    "## What will be predicted score if a student studies for 9.25 hrs/ day?\n",
    "### 1. Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# it offers data structures and operations for manipulating numerical tables and time series\n",
    "import pandas as pd\n",
    "# used for working with arrays\n",
    "import numpy as np  \n",
    "# data visualization and graphical plotting\n",
    "import matplotlib.pyplot as plt  \n",
    "# sets the backend of matplotlib to the 'inline' backend: With this backend, \n",
    "# the output of plotting commands is displayed inline within frontends\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Get csv dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('D://Machine Learning//Hours.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",
       "  </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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# used to return top n (5 by default) rows of a data frame or series.\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. understand data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "df.info()\n",
    "# method prints information about a DataFrame including the index dtype and column dtypes, non-null values and memory usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. understand statistical details of data"
   ]
  },
  {
   "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",
       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating some statistical data like percentile, mean and std of the numerical values of the Series or DataFrame\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Plotting the distribution of scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "# plot() function draws a line from point to point\n",
    "df.plot(x='Hours', y='Scores',style='o')  \n",
    "plt.title('Hours vs Scores')  \n",
    "plt.xlabel('Hours')  \n",
    "plt.ylabel('Scores')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. divide data into input(X) and output(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# it will select till the second last column of the data frame instead of the last column \n",
    "X = df.iloc[:, :-1].values  \n",
    "# it will select only last column of dataframe\n",
    "Y = df.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model_selection is a method for setting a blueprint to analyze data and then using it to measure new data\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Using Linear Regression , train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  used when we want to predict the value of a variable based on the value of another variable\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Testing our model using testing part of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': Y_test, 'Predicted': Y_pred})  \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Evaluating the model\n",
    "## we have chosen the mean square error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Printing the value of slope and intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept: 2.018160041434683\n",
      "slope: [9.91065648]\n"
     ]
    }
   ],
   "source": [
    "# intercept indicates the location where it intersects an axis\n",
    "print('intercept:', regressor.intercept_)\n",
    "# slope indicates the steepness of a line\n",
    "print('slope:', regressor.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Plot the regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "plt.scatter(X_train, Y_train, color = \"blue\")\n",
    "plt.plot(X_train, regressor.predict(X_train), color = \"black\")\n",
    "plt.title(\"Scores vs Hours of Study\")\n",
    "plt.xlabel(\"Hours of Study\")\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. Visualising the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "plt.scatter(X_test, Y_test, color = 'blue')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'black')\n",
    "plt.title('Salary vs Experience (Test set)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 13. Predicting the values of scores given the numbers of hours of study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar',figsize=(5,5))\n",
    "plt.grid(which='major', linewidth='0.5', color='red')\n",
    "plt.grid(which='minor', linewidth='0.5', color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14. What will be predicted score if a student studies for 9.25 hrs/ day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of hours studied:  9.25\n",
      "Percentage: [93.69173249]\n"
     ]
    }
   ],
   "source": [
    "# Manually taking input\n",
    "def score_pred(X,regressor):\n",
    "    print('No. of hours studied: ', X)\n",
    "    print('Percentage:', regressor.intercept_ + regressor.coef_ * X)\n",
    "score_pred(9.25, regressor)"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}