Sol_Task_1_Pranav_Yadav_Linear_Regression_GRIP21_SEP.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sol. Task#1 - Pranav Yadav - Linear Regression #GRIP21 SEP.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSopTaO0py8X"
      },
      "source": [
        "# **Author:** Pranav Yadav\n",
        "   - LinkedIn: [yadavpranav](https://www.linkedin.com/in/yadavpranav/)\n",
        "   - GitHub: [pranav-yadav](https://github.com/pranav-yadav)\n",
        "\n",
        "# **Data Science & Business Analytics Tasks**\n",
        "## **Task #1:** Prediction using Supervised ML\n",
        "Submitted in fulfillment of #GRIP21 SEP"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Odu6EV5bqfuT"
      },
      "source": [
        "* Task: Predicting the percentage score of a student based on number of study hours\n",
        "\n",
        "* Dataset: [Student Scores CSV](https://bit.ly/w-data)\n",
        "\n",
        "* ML Algo type: Linear Regression with Python Scikit Learn (Supervised ML)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pX6QX4ZZpR9B"
      },
      "source": [
        "# Importing required libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "4Qy_Bo7gs4vt",
        "outputId": "644019a4-dc42-43ed-8a2f-0801a72ca4a4"
      },
      "source": [
        "# Reading (Importing) data from remote link\n",
        "dataset_url = \"https://bit.ly/w-data\"\n",
        "data = pd.read_csv(dataset_url)\n",
        "\n",
        "print(\"Data imported successfully\")\n",
        "data.head(10)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data imported successfully\n"
          ]
        },
        {
          "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": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JTs1ChoCtn69"
      },
      "source": [
        "Graphical Analysis of data by plotting the dataset on 2D graph using Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "PHOagMsUti9e",
        "outputId": "5b938a1e-3cb9-46e8-e93e-f4ba6cd4d08c"
      },
      "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()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Zt8nNviu1ed"
      },
      "source": [
        "From the above graph, we can confer that there is direct **Positive Linear Relation** between the number of hours the student studied and the percentage score."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EiRy4inOvdXL"
      },
      "source": [
        "### Preprocessing the data\n",
        "Let's divide the data into Inputs (`Attributes`) and Outputs (`Labels`)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95-5lUDTvcfv"
      },
      "source": [
        "X = data.iloc[:, : -1].values\n",
        "y = data.iloc[:, 1].values"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aenRXraMwgJe"
      },
      "source": [
        "* Splitting of Data: Let's split the data inti \n",
        "training and testing sets; using Scikit-Learn's built-in `train_test_split()` method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVNhHPM1xEVl",
        "outputId": "667b0fdb-4991-4486-ffd1-358572227e77"
      },
      "source": [
        "# Importing from Scikit-Learn\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                    test_size = 0.2,\n",
        "                                                    train_size = 0.8,\n",
        "                                                    random_state = 0)\n",
        "\n",
        "print(\"Splitting Completed\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Splitting Completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxEW6C-LzaD2"
      },
      "source": [
        "### Training the model\n",
        "After splitting the data into training and testing sets, let's define and train our model.\n",
        "As, there is Linear Realation as seen after plotting the original data we can use `LinearRegression` algorithm from Scikit-Learn\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64oosGzqzGz7",
        "outputId": "92405a40-53c3-41bf-8b2c-66d52b2feaa4"
      },
      "source": [
        "# Importing from Scikit-Learn\n",
        "from sklearn.linear_model import LinearRegression\n",
        "# define the regressor\n",
        "reg_model = LinearRegression() \n",
        "# fitting the data to the model\n",
        "reg_model.fit(X_train, y_train)\n",
        "\n",
        "print(\"Training Completed\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "JnOKccin1VdN",
        "outputId": "c262e5b6-58aa-4614-a49e-c15ea4edf74a"
      },
      "source": [
        "# defining the Regression Line\n",
        "reg_line = reg_model.coef_ * X + reg_model.intercept_\n",
        "\n",
        "# Plotting the Regression Line on original data\n",
        "plt.scatter(X, y)\n",
        "plt.xlabel(\"Hours Studied\")\n",
        "plt.ylabel(\"Percentage Score\")\n",
        "plt.plot(X, reg_line)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFYyb_vp2OZk"
      },
      "source": [
        "### Predictions\n",
        "* Predictions on test data: After training (fitting) the model using `X_train, y_train` let's test our model using test data i.e., `X_test`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEuNsjJ-3CMc",
        "outputId": "cefbb8e4-25bf-49b0-93ac-33f5adf2299a"
      },
      "source": [
        "# testing data in hrs\n",
        "print(\"Testing Data:\\n\", X_test)\n",
        "\n",
        "# predicting the scores for test data\n",
        "y_preds = reg_model.predict(X_test)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Data:\n",
            " [[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "YdQcBPOT3_0K",
        "outputId": "83045439-8c31-47ef-8b58-2943875c64d6"
      },
      "source": [
        "# Compairing the Actual vs Predicted; test data\n",
        "df = pd.DataFrame({\"Actual\": y_test, \"Predicted\": y_preds})\n",
        "df"
      ],
      "execution_count": 10,
      "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>16.884145</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"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vALIKYnk40mC"
      },
      "source": [
        "* Predictions on unknown data: Let's predict the percentage score of a student who studied for `9.25hrs / day` using our model i.e.,\n",
        "\n",
        " - Given: Hours Studied = `9.25hrs / day`\n",
        " - To predict: Percentage Score / 100\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3VPYdVDy5QGI",
        "outputId": "4f85ba72-8806-49db-d615-6722cb066ed4"
      },
      "source": [
        "# predicting score for unknown data\n",
        "hrs = 9.25\n",
        "\n",
        "unk_pred = reg_model.predict([[hrs]])\n",
        "print(\"No. of Hours Studied = \", hrs, \"hrs/day\")\n",
        "print(\"Prdicted Percentage Score = \", unk_pred[0])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of Hours Studied =  9.25 hrs/day\n",
            "Prdicted Percentage Score =  93.69173248737539\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "taxPSTwVAVv2"
      },
      "source": [
        "Hence, student who studied for `9.25 hrs/day` should get the score `93.69173248737539 %` "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Pxs93mJ7RAY"
      },
      "source": [
        "### Model Evaluation\n",
        "Let's evaluate the performance of our model (algo) using MAE (Mean Absolute Error), with `y_test, y_preds`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPgIQAJB70pH",
        "outputId": "83325d7f-05ba-4689-8e69-6c952cf95a70"
      },
      "source": [
        "# Import from Scikit-Learn\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "print(\"Mean Absolute Error:\", mean_absolute_error(y_test, y_preds))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 4.183859899002982\n"
          ]
        }
      ]
    }
  ]
}