GRIP Task 1-  Prediction using Supervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cQlSZpWpCInd"
   },
   "source": [
    "# The Sparks Foundation\n",
    "\n",
    "# GRIP DATA SCIENCE AND BUSINESS ANALYTICS INTERNSHIP\n",
    "\n",
    "## Task 1 : Prediction using Supervised ML (Linear Regression)\n",
    "\n",
    "**Author M.Talib Farooq**\n",
    "\n",
    "**Problem: Predict the percentage of a student based on the no. of study hours, and What will be predicted score if a student studies for 9.25 hrs/ day?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_9XG6SGUEJM9"
   },
   "source": [
    "Numpy: It is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jd9uLjqFENlT"
   },
   "source": [
    "Pandas: It is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C5QhnXiWES9X"
   },
   "source": [
    "Matplotlib: It is a plotting library. Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lFm0NUG_CIni"
   },
   "source": [
    "# Importing all libraries as mentioned above for this problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "sKhLD0F1CInj"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MGNk7oM8CInk"
   },
   "source": [
    "# Exporting data from online source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "id": "VSr9ZuJBCInk",
    "outputId": "45b70011-ea2b-4ce3-9158-c113cb26b960",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entries of the data\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",
       "  </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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url=\"http://bit.ly/w-data\"\n",
    "data=pd.read_csv(url)\n",
    "print(\"Entries of the data\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "a-w2HAVBCInm",
    "outputId": "1dece76b-c72e-4687-fd17-04fbee0d4237"
   },
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KcksFE90CInn",
    "outputId": "0091c238-f4c8-4c6c-ab7d-ac6895417579"
   },
   "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: 464.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z8SLDtDICIno",
    "outputId": "6976d727-f387-4868-bb47-ba88c017f32a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kz1Y4clICInp"
   },
   "source": [
    "# Plotting data point in 2-d graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "sY2rr2FLCInp",
    "outputId": "90068a7c-873e-41c7-b492-66f0f927ff31"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the distribution of scores\n",
    "data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aZuF0rXiCInq"
   },
   "source": [
    "From above graph we get to know that there is a positive linear relation between the number of hours studied and percentage of score so we use linear regrssion model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fU2AcpwkCInr"
   },
   "source": [
    "# Preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "PRhVoYLxCInr"
   },
   "outputs": [],
   "source": [
    "X = data.iloc[:,0:1].values  #1st column of data is X\n",
    "Y = data.iloc[:,1].values  #2nd column of data is Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "C7W3M-GfCInr"
   },
   "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": {
    "id": "0RMAYoepCIns"
   },
   "source": [
    "# Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SIIFZGuNCIns",
    "outputId": "95f819f3-8ddf-40ef-f89c-25300b1ead70"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training is complete\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "regressor=LinearRegression()\n",
    "\n",
    "#calling fit method which finds the best value for intercept and slope for best fit line\n",
    "regressor.fit(X_train, Y_train) \n",
    "print(\"Training is complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "BuWmNBYiCInt",
    "outputId": "ef2a0110-8e4e-452b-df81-5bc66757b95a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope= 9.910656480642238\n",
      "intercept= 2.018160041434669\n"
     ]
    },
    {
     "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": [
    "print(\"slope=\",regressor.coef_[0])\n",
    "print(\"intercept=\",regressor.intercept_)\n",
    "\n",
    "# Plotting the linear regression line in the format y=mx+b \n",
    "line = regressor.coef_*X+regressor.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(X_train, Y_train,color=\"red\",label=\"Training data\")\n",
    "plt.scatter(X_test,Y_test,color=\"green\",label=\"Testing data\")\n",
    "plt.plot(X, line, label=\"Regression line\");\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F-2DDvXsCInu"
   },
   "source": [
    "# Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QDqaYuuPCInu",
    "outputId": "5a421d1d-8ce0-44e2-e1ee-f67a0e02355c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing data\n",
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Testing data\")\n",
    "print(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kicgBLU5CInu"
   },
   "source": [
    "# Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rjueLCGACInv",
    "outputId": "ac2fc829-c009-412e-826b-38286f2d4c1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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\n"
     ]
    }
   ],
   "source": [
    "Y_pred = regressor.predict(X_test)\n",
    "\n",
    "#comparing the predicitng scores with actual scores\n",
    "df = pd.DataFrame({'Actual': Y_test, 'Predicted': Y_pred})  \n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QYGG4NcQCInv",
    "outputId": "35d505b1-21a5-42ea-9a93-04676ce425da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on studying 9.25 hrs/day is  93.69173248737538 %\n"
     ]
    }
   ],
   "source": [
    "hours=[[9.25]]\n",
    "sol=regressor.predict(hours)\n",
    "\n",
    "print(\"Score on studying 9.25 hrs/day is \",sol[0],\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JEATZ07jCInw"
   },
   "source": [
    "# Score predicted on studying 9.25 hrs/day is  93.69173248737538 %"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "GRIP Task 1-  Prediction using Supervised ML",
   "provenance": []
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}