TSF_INTERNSHIP_TASK_1_SUPERVISED_LEARNING.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TSF INTERNSHIP TASK 1 - SUPERVISED LEARNING.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNw6No+hXtNtM3V611es8WC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Amey-Thakur/TSF-SUPERVISED-LEARNING-ML/blob/main/TSF_INTERNSHIP_TASK_1_SUPERVISED_LEARNING.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywassMkdwGcd"
      },
      "source": [
        "# The Sparks Foundation - Supervised Learning ML\n",
        "- Created By Amey Mahendra Thakur"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIAFM4p_wNvM"
      },
      "source": [
        "## Task: To Predict the percentage of a student based on the no. of study hours."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NfA7KZIPwTTA"
      },
      "source": [
        "## Step 1- Importing Libraries and Loading Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTbcIPrWwbOr"
      },
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRA0xBh1whgf"
      },
      "source": [
        "data = pd.read_csv(r\"/content/scores.csv\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "v25b_41VwmjE",
        "outputId": "359d21f5-2bc6-4d0a-ed1d-2337498e3921"
      },
      "source": [
        "data.head(10)"
      ],
      "execution_count": 3,
      "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": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-hWTfBNwoWU"
      },
      "source": [
        "## Step 2: Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KxHgtFpwrZU",
        "outputId": "41e75db1-285f-4261-d71b-9ad271bb935e"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NbhgZ91HwtrY",
        "outputId": "149469e5-fbeb-4f94-e176-19752f913699"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 5,
      "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/",
          "height": 295
        },
        "id": "QF0fg5DVw_Ko",
        "outputId": "1b9aefbc-491b-4b5f-9f47-a74ec6409661"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 6,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "wPGbTZY1xDGw",
        "outputId": "ff4f0d98-2bce-4b55-bd86-de4d5a63ecaf"
      },
      "source": [
        "#Plotting the distribution of scores\n",
        "data.plot(x='Hours', y='Scores', color='green', style='*')  \n",
        "plt.title('Hours vs Percentage')  \n",
        "plt.xlabel('Hours Studied')  \n",
        "plt.ylabel('Percentage Scored')  \n",
        "plt.show()"
      ],
      "execution_count": 7,
      "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": "az7_XBsPxFyu"
      },
      "source": [
        "### Preparing the Data\n",
        "The next step is to divide the data into attributes(inputs) and labels(outputs)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ViQojoBlxGt8"
      },
      "source": [
        "X = data.iloc[:, :-1].values\n",
        "y = data.iloc[:, 1].values"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGbIDfnHxOMa"
      },
      "source": [
        "### Splitting the data into Training and Testing sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGrd1JXyxO_8"
      },
      "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)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "MpHsQJrMxRB5",
        "outputId": "4c949b89-8789-4448-9749-d36138293048"
      },
      "source": [
        "sns.distplot(y_train, kde=True, color='green',)\n",
        "plt.title('Distribution of Scores')\n",
        "plt.xlabel('Hours Studied')  \n",
        "plt.ylabel('Percentage Scored')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Percentage Scored')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "PtfW6as-xTHe",
        "outputId": "dd7e1401-a020-4273-fd8e-183c5cdb56ce"
      },
      "source": [
        "sns.regplot(X_train, y_train, color='green', )\n",
        "plt.title('Hours vs Scores')\n",
        "plt.xlabel('Hours Studied')  \n",
        "plt.ylabel('Percentage Scored')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Percentage Scored')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "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": "QmAHul7jxYCA"
      },
      "source": [
        "## Step 3: Training the Algorithm\n",
        "After splitting the data into training and testing sets, We have to train our Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rgtapWo-xY1P",
        "outputId": "48fab480-b49a-4ae9-e9db-dc07b863bcd8"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression  \n",
        "training = LinearRegression()  \n",
        "training.fit(X_train, y_train) \n",
        "print(\"Training completed using \" + str(training))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training completed using LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "IegzrqXqxalP",
        "outputId": "d118a89c-97c3-4d4b-8417-50f71fe62fb7"
      },
      "source": [
        "#predicting the test set results\n",
        "y_pred = training.predict(X_test)\n",
        "\n",
        "# Comparing Actual vs Predicted\n",
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
        "df.head(5)"
      ],
      "execution_count": 13,
      "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": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ud2MVz18xhGy"
      },
      "source": [
        "## Step 4: Visualizing the Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "BHF795T9xhxj",
        "outputId": "a0f26f74-0dbc-4fb0-edb8-47e1e193c1a1"
      },
      "source": [
        "#visualising the training set results\n",
        "plt.scatter(X_train, y_train, color = 'green')\n",
        "plt.plot(X_train,  training.predict(X_train), color= 'blue')\n",
        "plt.title('Hours vs Scores (Training set)')\n",
        "plt.xlabel('Hours')\n",
        "plt.ylabel('Scores')\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "xN6UXMDXxj4N",
        "outputId": "6e9681e5-de72-42d0-f966-9b71bfe0c5f3"
      },
      "source": [
        "#visualising the test set results\n",
        "plt.scatter(X_test, y_test, color = 'green')\n",
        "plt.plot(X_train,  training.predict(X_train), color= 'blue')\n",
        "plt.title('Hours vs Scores (Test set)')\n",
        "plt.xlabel('Hours')\n",
        "plt.ylabel('Scores')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "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": "oy7UwfGsxsNr"
      },
      "source": [
        "## Step 5: Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3OdeeIJpN7Ha",
        "outputId": "aa3de23d-0674-4e98-9da2-f55a4cbf05ee"
      },
      "source": [
        "k = X_test.shape[1]\n",
        "n = len(X_test)\n",
        "n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lR6YUnLhOB6j",
        "outputId": "1d63f30a-60ad-47d4-dfaf-82cc0f5e268d"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "from math import sqrt\n",
        "from sklearn import metrics  \n",
        "r2 = r2_score(y_test, y_pred)\n",
        "adj_r2 = 1-(1-r2)*(n-1)/(n-k-1)\n",
        "\n",
        "print('R2 =', r2, '\\nAdjusted R2 =', adj_r2)\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2 = 0.9454906892105354 \n",
            "Adjusted R2 = 0.9273209189473804\n",
            "Mean Absolute Error: 4.183859899002982\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6_gxtHEPx4gd"
      },
      "source": [
        "## Step 6: Predicting Future Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zwHZrysx5Fa",
        "outputId": "ab01201e-b27a-41f1-c347-8bec5e22bb69"
      },
      "source": [
        "Hours = 9.25\n",
        "prediction = training.predict([[Hours]])\n",
        "print(\"Number of Hours = {}\".format(Hours))\n",
        "print(\"Predicted Score = {}\".format(prediction[0]))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Hours = 9.25\n",
            "Predicted Score = 93.69173248737539\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}