kirthik-d/prediction-using-supervised-machine-learning.ipynb

## prediction-using-supervised-machine-learning.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Prediction using Supervised Machine Learning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/gist/kirthikmicrosoft/b3527423b83cd09cbc079bc45ce47f41/prediction-using-supervised-machine-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": "Qc6iV5SZnvNB"
      },
      "source": [
        "Name: Kirthik.D\n",
        "<br>\n",
        "Task: Prediction using Supervised Machine Learning.Supervised Learning is to prediction with a labelled data <br>\n",
        "Algorithm: Linear Regression\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81oKB31Ungtn"
      },
      "source": [
        "#Import Libraries \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "ATb_PffGoRyQ",
        "outputId": "33a1be03-d8d1-447b-8b86-b7d2de7992ba"
      },
      "source": [
        "#Read data from a link\n",
        "url = \"http://bit.ly/w-data\"\n",
        "s_data = pd.read_csv(url)\n",
        "print(\"Data has been imported\")\n",
        "\n",
        "s_data.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data has been imported\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",
              "  </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": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b40NkBFj0_b1"
      },
      "source": [
        "##Plotting the data\n",
        "Plotting the dataset to find if any relationship exists between the data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "H-JAhfMUqadF",
        "outputId": "02404a51-8709-45bd-85e6-198e90112e4f"
      },
      "source": [
        "s_data.plot(x='Hours', y='Scores', style='o', grid=True)  \n",
        "plt.title('Hours vs Percentage')\n",
        "plt.xlabel('Hours Studied')\n",
        "plt.ylabel('Percentage Score')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "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": "rgTz8t5HvrWH"
      },
      "source": [
        "**From the graph above, we can clearly see that there is a positive linear relation between the number of hours studied and percentage of score.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCSSt9Cj9Ebf"
      },
      "source": [
        "#Prepearing the Data\n",
        "The next step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMuRsvoU87d6"
      },
      "source": [
        "X = s_data.iloc[:, :-1].values\n",
        "y = s_data.iloc[:, 1].values\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U6xdceinv3SI"
      },
      "source": [
        "Split data to training and testing set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWMM3cHL_2lw"
      },
      "source": [
        "from sklearn.model_selection import train_test_split  \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                            test_size=0.2, random_state=0) "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZiPiwTwrwE_U"
      },
      "source": [
        "### **Training the Algorithm**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qoRPJJRFBCTr",
        "outputId": "c79e83dc-e79e-410c-ff5f-58eca797db79"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)\n",
        "\n",
        "print(\"Training Complete\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Complete\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ph32Ya8iwW3W"
      },
      "source": [
        "### **Plotting Regression Line**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "1rNFEWjlBUYh",
        "outputId": "78e94b84-98a4-49e7-cc42-1eeb138c5658"
      },
      "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": 12,
      "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": "VU2B1xy9wljT"
      },
      "source": [
        "### **Making Predictions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvCLn4QZwzkJ",
        "outputId": "7bce7727-6703-4540-eaf4-d597adba0c29"
      },
      "source": [
        "#Testing data in hours\n",
        "print(X_test)\n",
        "\n",
        "#Making predictions for the scores \n",
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aq49u0nRxFpY"
      },
      "source": [
        "Comparing actual vs predicted data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qxzkaj6-CAKB",
        "outputId": "8038694f-436d-45da-e7c4-7cf78d4a6392"
      },
      "source": [
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
        "df "
      ],
      "execution_count": 14,
      "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": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AkmMrBDrxMQz"
      },
      "source": [
        "Testing again with a new data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkWYu2-8seMo",
        "outputId": "b69cb85f-dd91-488e-ddf5-e806aee3009e"
      },
      "source": [
        "#Testing with new data\n",
        "hours = [[9.25]]\n",
        "my_pred = regressor.predict(hours)\n",
        "print(\"No of Hours = {}\".format(hours[0][0]))\n",
        "print(\"Predicted Score = {}\".format(my_pred[0]))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of Hours = 9.25\n",
            "Predicted Score = 93.69173248737539\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NSiCGAz4xXxd"
      },
      "source": [
        "###**Evaluating the model**\n",
        "Evaluating the performance of algorithm. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Gz6ovPZt_B8",
        "outputId": "269682b9-54f7-4e01-858d-2dc0bf4a5363"
      },
      "source": [
        "from sklearn import metrics\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 4.183859899002982\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}