Test_Score_prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Test_Score_prediction",
      "provenance": [],
      "authorship_tag": "ABX9TyO3Ik8X+nXoKzPky4jaQl7S",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HarshBhavsar023/TSP_GRIP-2021/blob/main/Test_Score_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvOpL6tnzcHw"
      },
      "source": [
        "#The Sparks Foundation \r\n",
        "###GRIP Task 1 : Prediction using Supervised ML\r\n",
        "\r\n",
        "Pridiction of percentage score based on the no. of hour he/she studies\r\n",
        "####Author : Harsh Bhavsar"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sqTMOknqoE37"
      },
      "source": [
        "###Importing Required Libraries\r\n",
        "---\r\n",
        "####matplotlib : \r\n",
        "It is a ploting library for the Python programming language and its numerical mathematics \r\n",
        "\r\n",
        "####pandas : \r\n",
        "Library for data Manipulation and Analysis\r\n",
        "\r\n",
        "####numpy : \r\n",
        "Library for supporting large, multi-dimensional arrays and matrices and high-level mathematical functions \r\n",
        "\r\n",
        "####sklearn:\r\n",
        "machine learning library provides useful machine Learning algorithms "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMmIxiZiy5sJ"
      },
      "source": [
        "import matplotlib.pyplot as plt \r\n",
        "import pandas as pd\r\n",
        "import numpy as np \r\n",
        "\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.metrics import mean_absolute_error"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TuEnR22Lmeex"
      },
      "source": [
        "Importing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "BLiPcumW0Lc9",
        "outputId": "59c5e014-4cae-4ffd-902d-cb5e770deee4"
      },
      "source": [
        "data = pd.read_csv('http://bit.ly/w-data')\r\n",
        "data.head(10)"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjSUXmM5rYTK"
      },
      "source": [
        "checking null values in Dataset, \r\n",
        "Here it is FALSE it means we can proceed .."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6Bf_0W61ERq",
        "outputId": "07714283-a567-426a-a24f-bfc904b81a6a"
      },
      "source": [
        "data.isnull == True "
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cLFw08ufrm-N"
      },
      "source": [
        "With this scatter plot, We can say that there is some co-relation with percentage scores and hours of studying..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "7JSlMpJd23bV",
        "outputId": "ef5a2a78-2189-44be-ca79-4d9096c18307"
      },
      "source": [
        "data.plot(x='Hours', y='Scores', style='x')\r\n",
        "plt.grid(b=None,alpha=0.25,axis='both', color='blue')\r\n",
        "plt.title('Percentage Vs Study Hours',size=20)\r\n",
        "plt.ylabel('Percentage', size=10)\r\n",
        "plt.xlabel('Hours Studied', size=10)\r\n",
        "plt.show()"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOYX02qksXsR"
      },
      "source": [
        "To prove that co-relation we will do regression, In this case there is co-releation... "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "SAN3nzmCYm6m",
        "outputId": "0851f16c-b0bd-439a-8542-4bcaa4fe9408"
      },
      "source": [
        "line = regressor.coef_*X+regressor.intercept_\r\n",
        "\r\n",
        "plt.scatter(X, y)\r\n",
        "plt.grid(b=None,alpha=0.25,axis='both', color='blue')\r\n",
        "plt.plot(X, line,color='red')\r\n",
        "plt.title('Regression : Hours v/s Percentage')  \r\n",
        "plt.xlabel('Hours Studied')  \r\n",
        "plt.ylabel('Percentage')  \r\n",
        "plt.show()"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "spPtBC-7s6Fk"
      },
      "source": [
        "#Training a model \r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCFD_7zDwHZ6"
      },
      "source": [
        "Data splitting into Training and Testing "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61k4mc3sJqC9"
      },
      "source": [
        "X = data.iloc[:, :-1].values  \r\n",
        "Y = data.iloc[:, 1].values\r\n",
        "\r\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=0)"
      ],
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0552vVLPwk2W"
      },
      "source": [
        "Fitting Data into model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkFpf8WXVbyn",
        "outputId": "5cc04346-4a43-40cb-fd0e-99e0271276a3"
      },
      "source": [
        "regressor = LinearRegression()\r\n",
        "regressor.fit(X_train, Y_train)\r\n",
        "print(\"Training Completed...\") "
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Completed...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WrLJuVcfwpfn"
      },
      "source": [
        "Pedicting Percentage "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pughNN2myBzG",
        "outputId": "9f1bf30f-9dd4-4e8a-f78b-fbb2d4c12d57"
      },
      "source": [
        "print(X_test)\r\n",
        "Y_pred = regressor.predict(X_test)\r\n",
        "print(Y_pred)"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]\n",
            " [3.8]\n",
            " [1.9]]\n",
            "[16.84472176 33.74557494 75.50062397 26.7864001  60.58810646 39.71058194\n",
            " 20.8213931 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnKDL-zHzWI-"
      },
      "source": [
        "Comparing the Actual marks with predicted"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "gSJwzYfCbzWk",
        "outputId": "f99330a7-5bc8-412b-f3b2-69e8adfc1a91"
      },
      "source": [
        "df = pd.DataFrame({'Actual': Y_test, 'Predicted': Y_pred}) \r\n",
        "df"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>16.844722</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  Predicted\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WB66l160zZtB"
      },
      "source": [
        "Visually comparing Acutal Values with Predicted Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "YAQvwKG9duUh",
        "outputId": "d562237a-8957-40f9-c50c-8361c24a7361"
      },
      "source": [
        "df.plot(kind='kde', figsize=(5,5))\r\n",
        "plt.grid(which='major', linewidth='0.5', color='cyan')\r\n",
        "plt.grid(which='minor', linewidth='0.5', color='red')\r\n",
        "plt.show()"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sbe-zFFmzk2E"
      },
      "source": [
        "###Finding What will be the score of a Student, If he/she studies for 9.25 hrs hours per day??"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIw7B-rTc1Tq",
        "outputId": "19364bc6-a76a-4b67-ccd1-5336b0bf4203"
      },
      "source": [
        "hours = [9.25]\r\n",
        "pred = regressor.predict(test)\r\n",
        "print(\"No of Hours = {}\".format(hours))\r\n",
        "print(\"Predicted Score = {}\".format(pred[0]))"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of Hours = [9.25]\n",
            "Predicted Score = 93.89272889341652\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTqo0g2p1Z85"
      },
      "source": [
        "According to this model Answer is 93.89"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I68u_tZE0Xkc"
      },
      "source": [
        "###Model Evaluation :\r\n",
        "Small no. of mean absolute error indicates the model is in a great shape "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eu06Rp0-iQe_",
        "outputId": "ece726e3-3e50-48c0-b5e1-631de37859fa"
      },
      "source": [
        "print('Mean Absolute error : ',  mean_absolute_error(Y_pred,Y_test))"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute error :  4.130879918502482\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "obMDtuOamnte"
      },
      "source": [
        "#CONCLUSION: \r\n",
        "---\r\n",
        "###when a student Studies for **9.25** hrs he/she tends to score **93.89%** marks "
      ]
    }
  ]
}