6.8-RandomForest.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RandomForest",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9OcqDFqEVo1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "ef63015d-31a6-400d-c4a7-35dd03b0a4de"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from sklearn import tree\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "import graphviz\n",
        "\n",
        "\n",
        "url='https://raw.githubusercontent.com/FazlyRabbiBD/Data-Science-Book/master/data-drugs.csv'\n",
        "df = pd.read_csv(url)\n",
        "df.head()"
      ],
      "execution_count": 17,
      "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>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>F</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>25.355</td>\n",
              "      <td>drugY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>18.043</td>\n",
              "      <td>drugY</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age Sex      BP Cholesterol  Na_to_K   Drug\n",
              "0   23   F    HIGH        HIGH   25.355  drugY\n",
              "1   47   M     LOW        HIGH   13.093  drugC\n",
              "2   47   M     LOW        HIGH   10.114  drugC\n",
              "3   28   F  NORMAL        HIGH    7.798  drugX\n",
              "4   61   F     LOW        HIGH   18.043  drugY"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1JgOWv7rCTh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d = {'HIGH': 2, 'LOW': 0, 'NORMAL': 1}\n",
        "df['BP'] = df['BP'].map(d)\n",
        "\n",
        "d = {'M': 1, 'F': 0}\n",
        "df['Sex'] = df['Sex'].map(d)\n",
        "\n",
        "d = {'HIGH': 1, 'NORMAL': 0}\n",
        "df['Cholesterol'] = df['Cholesterol'].map(d)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MDUX0kvqtdU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = ['Age', 'Sex', 'BP', 'Cholesterol','Na_to_K']\n",
        "x = df[features]\n",
        "y = df['Drug']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9f1yeAcq30H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4DHK5DA1EWwA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "outputId": "712ccf29-8377-40c6-c71d-5bd0c5f78daf"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier(n_estimators=10, max_depth=5, random_state=0)\n",
        "rf.fit(x_train, y_train)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=5, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bh6-2urUfjzV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "f8c6d4d4-9f76-4125-dba0-c4891331218a"
      },
      "source": [
        "y_pred = rf.predict(x_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "print('\\nAccuracy: {0:.4f}'.format(accuracy_score(y_test, y_pred)))\n",
        "print('Confusion Matrix:\\n',confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       drugA       1.00      1.00      1.00         7\n",
            "       drugB       1.00      1.00      1.00         5\n",
            "       drugC       1.00      1.00      1.00         5\n",
            "       drugX       1.00      0.95      0.98        21\n",
            "       drugY       0.96      1.00      0.98        22\n",
            "\n",
            "    accuracy                           0.98        60\n",
            "   macro avg       0.99      0.99      0.99        60\n",
            "weighted avg       0.98      0.98      0.98        60\n",
            "\n",
            "\n",
            "Accuracy: 0.9833\n",
            "Confusion Matrix:\n",
            " [[ 7  0  0  0  0]\n",
            " [ 0  5  0  0  0]\n",
            " [ 0  0  5  0  0]\n",
            " [ 0  0  0 20  1]\n",
            " [ 0  0  0  0 22]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2oTSVfPtrE4i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}