STUDENTDATA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "STUDENTDATA.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "CD8zoICH8W92"
      },
      "source": [
        "# Importing all libraries required in this notebook\r\n",
        "import pandas as pd\r\n",
        "import numpy as np  \r\n",
        "import matplotlib.pyplot as plt  \r\n",
        "%matplotlib inline"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUFM7dYu8ipR"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "-ghAvoVA8jOK",
        "outputId": "c831d994-516c-4317-a02a-c5149bc6e7e4"
      },
      "source": [
        "url = \"http://bit.ly/w-data\"\r\n",
        "s_data = pd.read_csv(url)\r\n",
        "print(\"Data imported successfully\")\r\n",
        "\r\n",
        "s_data.head(10)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data imported successfully\n"
          ],
          "name": "stdout"
        },
        {
          "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": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "lLJ0TqDK8mDs",
        "outputId": "42f58ff9-cc15-4fd2-9a27-16c6ddb3a7bf"
      },
      "source": [
        "s_data.plot(x='Hours', y='Scores', style='o')  \r\n",
        "plt.title('Hours vs Percentage')  \r\n",
        "plt.xlabel('Hours Studied')  \r\n",
        "plt.ylabel('Percentage Score')  \r\n",
        "plt.show()"
      ],
      "execution_count": 80,
      "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": {
        "id": "KTj4JfwE80cj"
      },
      "source": [
        "X = s_data.iloc[:, :-1].values  \r\n",
        "y = s_data.iloc[:, 1].values  "
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0dzGIZ_j9AGe"
      },
      "source": [
        "from sklearn.model_selection import train_test_split  \r\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \r\n",
        "                            test_size=0.2, random_state=0) "
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Lb8kRzL9CB8",
        "outputId": "ba68c498-8a26-4d9f-894e-f6da2ca4172b"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression  \r\n",
        "regressor = LinearRegression()  \r\n",
        "regressor.fit(X_train, y_train) \r\n",
        "\r\n",
        "print(\"Training completed.\")"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training completed.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "OGnnsguB9Mu8",
        "outputId": "1457c4d1-c988-4f1d-b1bb-c85168096105"
      },
      "source": [
        "line = regressor.coef_*X+regressor.intercept_\r\n",
        "\r\n",
        "plt.scatter(X, y)\r\n",
        "plt.plot(X, line);\r\n",
        "plt.show()"
      ],
      "execution_count": 84,
      "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": 265
        },
        "id": "d5vqe-fU9Zdm",
        "outputId": "f7c8410e-030a-4230-d75b-ad55f243bf36"
      },
      "source": [
        "line = regressor.coef_*X+regressor.intercept_\r\n",
        "\r\n",
        "plt.scatter(X, y)\r\n",
        "plt.plot(y, line);\r\n",
        "plt.show()"
      ],
      "execution_count": 85,
      "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/"
        },
        "id": "XxIHIVxR9g5N",
        "outputId": "ca9e3287-67db-4ca6-de1e-5b32f0466ec9"
      },
      "source": [
        "print(X_test) \r\n",
        "y_pred = regressor.predict(X_test) "
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "0EUCbv5JCd94",
        "outputId": "5ec796fd-ffc3-40fc-8bb5-0eb97696ab13"
      },
      "source": [
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \r\n",
        "df "
      ],
      "execution_count": 87,
      "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": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAFO8zbx-AH1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4c5305e-f691-4b28-e0ee-f2209c6a3ea3"
      },
      "source": [
        "print(X_test)\n",
        "y_pred = regressor.predict([[9.25]])\n",
        "print(y_pred)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]]\n",
            "[93.69173249]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5UOrRH-5VCQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "277fb016-b45d-4498-8b09-8eeae9aba02d"
      },
      "source": [
        "from sklearn import metrics  \n",
        "print('Mean Absolute Error:', \n",
        "      metrics.mean_absolute_error(y_test, y_pred)) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 4.183859899002982\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sXDWfVlKs64"
      },
      "source": [
        "AUTHOR :SAKTHIRAAMBALASUNDARAM"
      ]
    }
  ]
}