Task1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Task1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNmUjYgZsjXiQhwKaT7+hSN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harshadbhale/TSF-Task-1/blob/main/Task1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MhvOc_X44NXW"
      },
      "source": [
        "# **THE SPARKS FOUNDATION**\n",
        "\n",
        "###**TASK 1 - Prediction using Supervised ML**\n",
        "\n",
        "To Predict the percentage of marks of the students based on the number of hours they studied\n",
        "\n",
        "\n",
        "\n",
        "###**Author - Harshad Sudam Bhale**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEyNUFDj-kDQ"
      },
      "source": [
        "**Import Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y74Z_WIC5Ani"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_absolute_error"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEXg2Ns34G0k"
      },
      "source": [
        "**Import Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbaUMtiU7jnr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6efd8443-211c-4a99-8699-e7b04105015a"
      },
      "source": [
        "dataset_url = \"http://bit.ly/w-data\"\n",
        "df = pd.read_csv(dataset_url)\n",
        "print(\"Dataset imported successfully!\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset imported successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "qNsGUobV-hr8",
        "outputId": "38fd237e-9ae9-4434-953c-bad2e8cfee3c"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 40,
      "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",
              "  </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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "1ZjOAoR-Fx27",
        "outputId": "28dcd265-8c06-49ca-fdaa-4d3f86af6c49"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 41,
      "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>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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iiftJdYn8ocr",
        "outputId": "eae2d735-f2f6-4773-e6ee-5e2a74066310"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "teNb3joW__W5",
        "outputId": "348d2abf-8b9b-49ac-b7ff-ee8c58086c4a"
      },
      "source": [
        "# Check if there any null value in the Dataset\n",
        "df.isnull == True"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRZz0XnVA11S"
      },
      "source": [
        "#**Data Visualization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "uRdssCQNA9ai",
        "outputId": "fa1835d3-e2f2-463c-a5a5-b96d80fb9445"
      },
      "source": [
        "sns.set_style('darkgrid')\n",
        "sns.scatterplot(y= df['Scores'], x= df['Hours'])\n",
        "plt.title('Marks Vs Study Hours',size=20)\n",
        "plt.ylabel('Marks Percentage', size=12)\n",
        "plt.xlabel('Hours Studied', size=12)\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "5DH26inCCWUq",
        "outputId": "6123d3f5-ce28-42c6-dfe2-326205a48366"
      },
      "source": [
        "sns.regplot(x= df['Hours'], y= df['Scores'])\n",
        "plt.title('Regression Plot',size=20)\n",
        "plt.ylabel('Marks Percentage', size=12)\n",
        "plt.xlabel('Hours Studied', size=12)\n",
        "plt.show()\n",
        "print(df.corr())"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "           Hours    Scores\n",
            "Hours   1.000000  0.976191\n",
            "Scores  0.976191  1.000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ot86eWI-Ct1J"
      },
      "source": [
        "#**Training the Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGBMxYwuC1oy"
      },
      "source": [
        "###**1) Splitting the Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "miDA5a_YCyCy"
      },
      "source": [
        "# Defining X and y from the Data\n",
        "X = df.iloc[:, :-1].values  \n",
        "y = df.iloc[:, 1].values\n",
        "\n",
        "# Spliting the Data in two\n",
        "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uX2TtPITDMhK"
      },
      "source": [
        "###**2) Fitting the Data into the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGDEpg7oDStg",
        "outputId": "22129240-3388-46a5-83b6-66f454600e1c"
      },
      "source": [
        "l = LinearRegression()\n",
        "l.fit(train_X, train_y)\n",
        "print(\"---------Model Trained---------\")"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---------Model Trained---------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1YeLK5lDZG-"
      },
      "source": [
        "##**Predicting the Percentage of Marks**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "5_U7PzT6Dfk9",
        "outputId": "25e73aa4-0e34-4e90-f188-2693aa3f54a9"
      },
      "source": [
        "pred_y = l.predict(val_X)\n",
        "prediction = pd.DataFrame({'Hours': [i[0] for i in val_X], 'Predicted Marks': [k for k in pred_y]})\n",
        "prediction"
      ],
      "execution_count": 48,
      "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>Predicted Marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.5</td>\n",
              "      <td>16.844722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.2</td>\n",
              "      <td>33.745575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.4</td>\n",
              "      <td>75.500624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.5</td>\n",
              "      <td>26.786400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.9</td>\n",
              "      <td>60.588106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.8</td>\n",
              "      <td>39.710582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.9</td>\n",
              "      <td>20.821393</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Predicted Marks\n",
              "0    1.5        16.844722\n",
              "1    3.2        33.745575\n",
              "2    7.4        75.500624\n",
              "3    2.5        26.786400\n",
              "4    5.9        60.588106\n",
              "5    3.8        39.710582\n",
              "6    1.9        20.821393"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9XpN7krEa0T"
      },
      "source": [
        "##**Visually Comparing the Predicted Marks with the Actual Marks**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "A3V3bbthEgm0",
        "outputId": "ff45c957-aa3e-45d4-f847-1a4704b057e0"
      },
      "source": [
        "plt.scatter(x=val_X, y=val_y, color='blue')\n",
        "plt.plot(val_X, pred_y, color='Black')\n",
        "plt.title('Actual vs Predicted', size=20)\n",
        "plt.ylabel('Marks Percentage', size=12)\n",
        "plt.xlabel('Hours Studied', size=12)\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jjBr3qfnGC5M"
      },
      "source": [
        "#**Evaluating the Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bx5zel5NGKG3",
        "outputId": "d3d45055-2fff-4a78-d854-38ed582a237c"
      },
      "source": [
        "# Calculating the accuracy of the model\n",
        "print('Mean absolute error: ',mean_absolute_error(val_y,pred_y))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean absolute error:  4.130879918502482\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9F-UuhiAGSFj"
      },
      "source": [
        "#****What will be the predicted score of a student if he/she studies for 9.25 hrs/ day?****"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dctJ2fCMEWNZ",
        "outputId": "f0d23a57-add3-4413-b367-5befc0553d82"
      },
      "source": [
        "hours =[[9.25]]\n",
        "pred_value = l.predict(hours)\n",
        "print('Number of total hours : {}'.format(hours))\n",
        "print('Predicted Score : {}'.format(pred_value[0]))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of total hours : [[9.25]]\n",
            "Predicted Score : 93.89272889341652\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3oEAtd-Q9CG"
      },
      "source": [
        "#**Predicted Score 93.89**"
      ]
    }
  ]
}