20. XG_Boost.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "xg_boost.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8FLud1n-3pVm",
        "colab_type": "text"
      },
      "source": [
        "# XGBoost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sO8VPU6n3vES",
        "colab_type": "text"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clDSsF7P33NU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGpwK5XD386E",
        "colab_type": "text"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zcksk88u4Ae8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('Data.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNn2RnST6_Q-",
        "colab_type": "text"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajhBL-er7Gry",
        "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": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Y89ctGZ7Mcx",
        "colab_type": "text"
      },
      "source": [
        "## Training XGBoost on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ude1J0E47SKN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "classifier = XGBClassifier()\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivqmubzW7dFJ",
        "colab_type": "text"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUSZ3zm_7gRD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnbCjHgQ8XPn",
        "colab_type": "text"
      },
      "source": [
        "## Applying k-Fold Cross Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYbfiITD8ZAz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
        "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
        "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}