Decision_Trees_Practice.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Decision Trees Practice",
      "provenance": [],
      "authorship_tag": "ABX9TyNDvXkiquJkbiSQ/zPW1QvI"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TmZlKdwjPRe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Decision Tree Classifier Building in Scikit-learn\n",
        "# Towards Data Science Tutorial: https://towardsdatascience.com/decision-tree-algorithm-explained-83beb6e78ef4\n",
        "# KD Nuggets Tutorial: https://www.kdnuggets.com/2020/01/decision-tree-algorithm-explained.html\n",
        "\n",
        "# Setup\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split         # For splitting into training & testing\n",
        "from sklearn.preprocessing import StandardScaler         # For feature scaling\n",
        "from sklearn.tree import DecisionTreeClassifier         # For fitting to Decision Tree\n",
        "from sklearn import metrics         # To Check accuracy\n",
        "from sklearn.metrics import confusion_matrix          # For confusion matrix\n",
        "from matplotlib.colors import ListedColormap          # For visualization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dJYBjdvWjjAz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "b24a9d8e-d2dc-4588-e049-cbd103832da4"
      },
      "source": [
        "# Load data\n",
        "data = pd.read_csv('/content/Social_Network_Ads.csv')\n",
        "data.head()"
      ],
      "execution_count": 2,
      "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>User ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15624510</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15810944</td>\n",
              "      <td>Male</td>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15668575</td>\n",
              "      <td>Female</td>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15603246</td>\n",
              "      <td>Female</td>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15804002</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
              "0  15624510    Male   19            19000          0\n",
              "1  15810944    Male   35            20000          0\n",
              "2  15668575  Female   26            43000          0\n",
              "3  15603246  Female   27            57000          0\n",
              "4  15804002    Male   19            76000          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BH7yVgmzjpSa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Age and EstimatedSalary will be independet variables X because Gender and User ID have no impact on purchasing ability\n",
        "feature_cols = ['Age','EstimatedSalary']\n",
        "X = data.iloc[:,[2,3]].values\n",
        "y = data.iloc[:,4].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XymTUsgQkQcR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split dataset into training and test\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25, random_state= 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lq_Xen7xopBI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Scale features\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4j-O7tWypPqF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fit model to Decision Tree classifier\n",
        "classifier = DecisionTreeClassifier()\n",
        "classifier = classifier.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnb4KRUUplPs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a9c1b572-f27d-482b-ba89-d6384b18c4e9"
      },
      "source": [
        "# Make Predictions & Check Accuracy\n",
        "# Predict against testing data\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# Check Accuracy\n",
        "print(f'Accuracy Score: {metrics.accuracy_score(y_test, y_pred)}')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score: 0.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9n7eTRoDqVlf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "dbcbc2ea-c6ae-4b25-9656-06f5f5b839e8"
      },
      "source": [
        "# Create a confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm\n",
        "# 6 observations have been classified as false"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[62,  6],\n",
              "       [ 4, 28]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hC6-F5GrrHuS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "cd6defcd-2108-49b2-930b-f9aa943faa81"
      },
      "source": [
        "# Visualize Model Predictions\n",
        "X_set, y_set = X_test, y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:,0].min()-1, stop= X_set[:,0].max()+1, step = 0.01),np.arange(start = X_set[:,1].min()-1, stop= X_set[:,1].max()+1, step = 0.01))\n",
        "plt.contourf(X1,X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape), alpha=0.75, cmap=ListedColormap(('red','green')))\n",
        "\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "\n",
        "for i,j in enumerate(np.unique(y_set)):\n",
        "  plt.scatter(X_set[y_set==j,0], X_set[y_set==j,1], c=ListedColormap(('white','black'))(i),label=j)\n",
        "\n",
        "plt.title('Decision Tree (Test Set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vuD/2e0dUj8j76T6uOG3o5RKr9E9qCcDMDLgFeMrdi7vPmUgFa2vbz8i98d67rW1rssH0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\n4xvUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}