TSF_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TSF_2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHkGzoxgMJrZ",
        "colab_type": "text"
      },
      "source": [
        "# **TASK 2: To Explore Supervised Machine Learning**\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A99-rBZJMhox",
        "colab_type": "text"
      },
      "source": [
        "This is a simple linear regression task as it involves just two variables. We will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TaF7QYka657P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Importing needed packages\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.style as style\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cU7jgNDOkmRg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "style.use('seaborn-poster')\n",
        "sns.set_style('darkgrid')"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1e5Q8GVM5rF",
        "colab_type": "text"
      },
      "source": [
        "# **Understanding the data**\n",
        "\n",
        "\n",
        "*   **Hours** : Number of hours studied\n",
        "*   **Scores** : Percentage Score\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g28atW7g7VAZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5e4e0840-524e-4ec4-f2c9-c2a62c3deffb"
      },
      "source": [
        "#Getting the data\n",
        "data = pd.read_csv('http://www.bitly.com/w-data')\n",
        "data.head()"
      ],
      "execution_count": 100,
      "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": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugAzOdyXN0mh",
        "colab_type": "text"
      },
      "source": [
        "Plotting Hours vs Scores, to see how linear is their relation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRZ6ccs07VDb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "e97f02c2-a4be-45f3-ace9-b59fe71f22e5"
      },
      "source": [
        "plt.scatter(data.Hours, data.Scores)\n",
        "plt.show()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 921.6x633.6 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GaYfUiIOVIz",
        "colab_type": "text"
      },
      "source": [
        "# **Train and Test dataset**\n",
        "\n",
        "\n",
        "*   Train/Test Split involves splitting the dataset into training and testing sets respectively to provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the data. It is more realistic for real world problems.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBL9u74h7VIo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#80% training set, 20% testing set\n",
        "msk = np.random.rand(len(data)) < 0.8\n",
        "train = data[msk]\n",
        "test = data[~msk]"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfrHTqWM7VPx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Using sklearn package to model data\n",
        "from sklearn import linear_model\n",
        "regr = linear_model.LinearRegression()"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHbPXpQBPvag",
        "colab_type": "text"
      },
      "source": [
        "Coefficient and Intercept in the simple linear regression, are the parameters of the fit line.\n",
        "Given that it is a simple linear regression, sklearn can estimate them directly from our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8vWtlDb7VW0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1342020b-33b6-4437-cf8d-d843d4b20c14"
      },
      "source": [
        "train_x = np.asanyarray(train[['Hours']])\n",
        "train_y = np.asanyarray(train[['Scores']])\n",
        "regr.fit (train_x, train_y)\n",
        "# The coefficients\n",
        "print ('Coefficients: ', regr.coef_)\n",
        "print ('Intercept: ',regr.intercept_)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients:  [[9.98754768]]\n",
            "Intercept:  [2.01369363]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWJS45IE7VcH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "edfa109b-6342-4cfa-c017-5efa5a5be93d"
      },
      "source": [
        "plt.scatter(data.Hours, data.Scores)\n",
        "#Plotting best fit line over the data\n",
        "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
        "plt.show()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 921.6x633.6 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0TVQ4mfLQN5a",
        "colab_type": "text"
      },
      "source": [
        "# **Evaluation**\n",
        "\n",
        "> - Mean absolute error: It is the mean of the absolute value of the errors.\n",
        "- Mean Squared Error (MSE): It) is the mean of the squared error. It is geared more towards large errors due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n",
        "- R-squared represents how close the data are to the fitted regression line. The higher the R-squared, the better the model fits your data. Best possible score is 1.0.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0fznWpv7VZ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0a8ce5e4-7bf5-47e9-8cc4-b73fe89fdd75"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "test_x = np.asanyarray(train[['Hours']])\n",
        "test_y = np.asanyarray(train[['Scores']])\n",
        "test_y_1 = regr.predict(test_x)\n",
        "\n",
        "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_1 - test_y)))\n",
        "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_1 - test_y) ** 2))\n",
        "print(\"R2-score: %.2f\" % r2_score(test_y_1 , test_y) )"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean absolute error: 4.86\n",
            "Residual sum of squares (MSE): 26.85\n",
            "R2-score: 0.96\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLbgch_HRQjs",
        "colab_type": "text"
      },
      "source": [
        "# **Making Predictions**\n",
        "\n",
        "\n",
        "*   What will be predicted score if a student studies for 9.25 hours in a day?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfBoYPU7-iod",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "b8fca761-b541-4c73-db71-74dcf49544d9"
      },
      "source": [
        "Test_hours = np.array([9.25,9.5,9.75,9.90])\n",
        "Test_hours = Test_hours.reshape(-1,1)\n",
        "Pred_Scores = regr.predict(Test_hours)\n",
        "\n",
        "print('Hours\\tPredicted Score')\n",
        "for h,s in zip(Test_hours,Pred_Scores):\n",
        "  print('{}\\t{}'.format(h,s))"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hours\tPredicted Score\n",
            "[9.25]\t[94.39850965]\n",
            "[9.5]\t[96.89539657]\n",
            "[9.75]\t[99.39228349]\n",
            "[9.9]\t[100.89041564]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}