Task 1 Prediction using supervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SAKSHI GUPTA\n",
    "# Task-1\n",
    "# The percentage of an student based on the no. of study hours."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression involving two variables using Python.\n",
    "\n",
    "Simple Linear Regression\n",
    "In this regression, we will predict the percentage of marks of students based on the numbers of study hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all the required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as mt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "      <td>86</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\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the data\n",
    "link='http://bit.ly/w-data'\n",
    "data=pd.read_csv(link)\n",
    "data.head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will be ploting our data points in 2D graph to find any relationship between the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "data.plot(x=\"Hours\", y=\"Scores\", style=\"o\")\n",
    "mt.title(\"Study_Hours vs Percentage_Score\")\n",
    "mt.xlabel(\"Study_Hours\")\n",
    "mt.ylabel(\"Percentage_Score\")\n",
    "mt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph, we can conclude that there is positive relation between the number of hours studies and percentage of score.\n",
    "Now dividing the data into attributes and lables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.iloc[:,:-1].values\n",
    "y=data.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step, we will use Scikit-Learn's built-in train_set_split() method to split our data into training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "# Training the Algorithm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have splitted our data into training and testing sets, we will now train our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the model\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "R1=LinearRegression()\n",
    "R1.fit(X,y)\n",
    "print(\"Training the model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "#plotting regression line\n",
    "line=R1.coef_*X+R1.intercept_\n",
    "mt.scatter(X,y)\n",
    "mt.plot(X,line)\n",
    "mt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "#testing data\n",
    "print(X_test)\n",
    "y_pred=R1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>17.147378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.766244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>74.824618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.923182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.160913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  17.147378\n",
       "1      27  33.766244\n",
       "2      69  74.824618\n",
       "3      30  26.923182\n",
       "4      62  60.160913"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#comparing actual vs predicted\n",
    "df=pd.DataFrame({\"Actual\":y_test, \"Predicted\":y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.9512837351709387\n",
      "Testing Score:  0.9491748734859171\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Score:\",R1.score(X_train,y_train))\n",
    "print(\"Testing Score: \",R1.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the actual and predicted value into bar graph\n",
    "df.plot(kind=\"bar\", figsize=(7,5))\n",
    "mt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of hours= 9.25\n",
      "Predicted Score= 92.90985477015731\n"
     ]
    }
   ],
   "source": [
    "#predicting for 9.25 hrs per day\n",
    "hours=9.25\n",
    "test=np.array([hours])\n",
    "test=test.reshape(-1,1)\n",
    "pred_val=R1.predict(test)\n",
    "print(\"No. of hours=\", hours)\n",
    "print(\"Predicted Score=\", pred_val[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  4.071877793635608\n",
      "Mean Squared Error:  20.1389481299402\n",
      "Root Mean Sqaured Error:  4.487643939746134\n",
      "Explained Variance Score:  0.951522433518808\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "print(\"Mean Absolute Error: \",metrics.mean_absolute_error(y_test,y_pred))\n",
    "print(\"Mean Squared Error: \",metrics.mean_squared_error(y_test,y_pred))\n",
    "print(\"Root Mean Sqaured Error: \",np.sqrt(metrics.mean_squared_error(y_test,y_pred)))\n",
    "print(\"Explained Variance Score: \",metrics.explained_variance_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above step of calculating error is to evaluate the performance of algorithm. And its is important as it tells how it differently perform on different dataset."
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}