Task2_Linear_Regression_TSF.ipynb

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  "metadata": {
    "colab": {
      "name": "Task2-Linear Regression_TSF.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMryaaysv29NX9PQWfQ+hS7",
      "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/sachinkatageri/GRIP-TSF/blob/master/Task2_Linear_Regression_TSF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sFa6wk9cj3-R",
        "colab_type": "text"
      },
      "source": [
        "**TSFGRIP20-TCF06 - Sachin Katageri**\n",
        ">                                  \n",
        "**Task 2-Linear Regression with Python Scikit Learn** \n",
        "\n",
        "\n",
        "---\n",
        "In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MrMMP50fmC4W",
        "colab_type": "text"
      },
      "source": [
        "**Importing Requires libraries**\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zn44mjOojDO0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NEWQtcdvnFNF",
        "colab_type": "text"
      },
      "source": [
        "**Reading data from remote link**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lyc8dD0kjjKa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "953d704b-ed20-41bd-a599-935895223a72"
      },
      "source": [
        "url = \"http://bit.ly/w-data\"\n",
        "student_data=pd.read_csv(url)\n",
        "print(\"Student Data imported Succesfully\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Student Data imported Succesfully\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FQtBaqOn_ku",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "aa178747-9ae7-4c92-a81a-6780cb1c5809"
      },
      "source": [
        "student_data.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\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": "code",
      "metadata": {
        "id": "B9hBbATS11c6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "8b9f1b65-f1ad-4b0e-b6a3-e81e14ba2226"
      },
      "source": [
        "\n",
        "student_data.describe()"
      ],
      "execution_count": null,
      "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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sCj4RB4CpjKw",
        "colab_type": "text"
      },
      "source": [
        " **Plotting the distribution of scores** 2-D graph "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fyOzZMBhoi-f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "319b0fcb-ab38-4ac6-cfdb-cdf20e71abc8"
      },
      "source": [
        "student_data.plot(x='Hours', y='Scores', style='o')\n",
        "plt.title('Hours vs Percentage')\n",
        "plt.xlabel('Studied Hours')  \n",
        "plt.ylabel('Percentage Score')  \n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": "BkDzJiUlrldL",
        "colab_type": "text"
      },
      "source": [
        "**Preparing the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Jo_zku4rgPU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = student_data.iloc[:, :-1].values  \n",
        "y = student_data.iloc[:, 1].values "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgX5YJ4fsNup",
        "colab_type": "text"
      },
      "source": [
        "**Training and test sets**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqeFgefosUpQ",
        "colab_type": "code",
        "colab": {}
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EAJvuMspte2r",
        "colab_type": "text"
      },
      "source": [
        "**Training the Algorithm**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6BeVkGvtgrY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "019bc735-12bc-4eab-9227-361913966679"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression  \n",
        "regressor = LinearRegression()  \n",
        "regressor.fit(X_train, y_train) \n",
        "\n",
        "print(\"Training complete.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training complete.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jl083jdRtyJb",
        "colab_type": "text"
      },
      "source": [
        "**Plotting the regression line**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEdTSka0txoq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "44b31958-6608-45eb-e479-9df1961da404"
      },
      "source": [
        "line = regressor.coef_*X+regressor.intercept_\n",
        "\n",
        "# Plotting for the test data\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, line);\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": "NRRAm8qkvXTl",
        "colab_type": "text"
      },
      "source": [
        "**Making Predictions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sd-NSRjuvZJp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "4547320f-22e3-4d7a-e505-70dd2f1025a3"
      },
      "source": [
        "print(X_test) \n",
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": null,
      "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": {
        "id": "rmqPa3Ouvmk5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c706e3b2-7f8f-4077-dbcc-4530ec917afa"
      },
      "source": [
        "# Comparing Actual vs Predicted\n",
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
        "df "
      ],
      "execution_count": null,
      "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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDP5AlZF2bwR",
        "colab_type": "text"
      },
      "source": [
        "**Comparing the actual values and predicted values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2WvtY_n2SZT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "522d18c5-9c73-4710-fbe9-d275372ccb06"
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "df.plot(kind='line',figsize=(7,6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd4a1256d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPojRPNRwLdR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7e09836e-c2c7-4125-c267-9c440c208abd"
      },
      "source": [
        "hour=[[9.25]]\n",
        "predicted_score=regressor.predict(hour)\n",
        "print(f'Predicted Score: {predicted_score[0]} percent')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted Score: 93.69173248737539 percent\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwLdUmKDyhWF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "bc49ad2d-89fd-4fe6-8e51-a0df7e778577"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "mae=mean_absolute_error(y_pred,y_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "r_score=r2_score(y_pred,y_test)\n",
        "rmse=np.sqrt(mse)\n",
        "print('Mean Absoulte Error:',mae)\n",
        "print('Mean Squared Error:',mse)\n",
        "print('Root Mean Squared Error:',rmse)\n",
        "print('R-Squared:',r_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absoulte Error: 4.183859899002982\n",
            "Mean Squared Error: 21.598769307217456\n",
            "Root Mean Squared Error: 4.647447612100373\n",
            "R-Squared: 0.9546785947197246\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MWutDA2zCIv",
        "colab_type": "text"
      },
      "source": [
        "MADE BY - **SACHIN KATAGERI**"
      ]
    }
  ]
}