prob3.ipynb

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  "metadata": {
    "colab": {
      "name": "prob3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1KLuSB7nc0sRvMHYYQHE2Xv3SPHmcmqx7",
      "authorship_tag": "ABX9TyNSRvlrwA822Cr2xzdmt2jU",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/talalba/ML_exercises-/blob/master/prob3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NGLai8UYwrk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import the necessary packages\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import csv\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "from skimage.io import imread\n",
        "from sklearn.model_selection import KFold\n",
        "from pandas import read_excel\n",
        "import cv2\n",
        "import pathlib\n",
        "import multiprocessing\n",
        "import importlib\n",
        "import locale\n",
        "from random import randrange\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "import gzip\n",
        "from numpy.linalg import norm\n",
        "from numpy import dot\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHAoAKYsY1Cp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "outputId": "494c1c7e-d45c-4f48-e4f6-b87bcb442e72"
      },
      "source": [
        "data_path = '/content/drive/My Drive/exercise/'\n",
        "\n",
        "# load the data\n",
        "data = pd.read_csv(data_path+'adult-modified.csv',sep=',')\n",
        "display(data)\n",
        "\n",
        "print(data.dtypes)"
      ],
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\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>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>Public</td>\n",
              "      <td>13</td>\n",
              "      <td>Single</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp</td>\n",
              "      <td>13</td>\n",
              "      <td>Married</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>13</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>9</td>\n",
              "      <td>Single</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>7</td>\n",
              "      <td>Married</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>13</td>\n",
              "      <td>Married</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>10</td>\n",
              "      <td>Married</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>60</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>25</td>\n",
              "      <td>Private</td>\n",
              "      <td>9</td>\n",
              "      <td>Single</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>8</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>21</td>\n",
              "      <td>Private</td>\n",
              "      <td>10</td>\n",
              "      <td>Single</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>?</td>\n",
              "      <td>Private</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>53</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>39</td>\n",
              "      <td>Private</td>\n",
              "      <td>10</td>\n",
              "      <td>Single</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age workclass  education  ...     sex hours-per-week income\n",
              "0     39    Public         13  ...    Male             40  <=50K\n",
              "1     50  Self-emp         13  ...    Male             13  <=50K\n",
              "2     38   Private          9  ...    Male             40  <=50K\n",
              "3     53   Private          7  ...    Male             40  <=50K\n",
              "4     28   Private         13  ...  Female             40  <=50K\n",
              "...   ..       ...        ...  ...     ...            ...    ...\n",
              "9995  38   Private         10  ...    Male             60   >50K\n",
              "9996  25   Private          9  ...  Female              8  <=50K\n",
              "9997  21   Private         10  ...    Male             40  <=50K\n",
              "9998   ?   Private          2  ...    Male             53  <=50K\n",
              "9999  39   Private         10  ...  Female             40  <=50K\n",
              "\n",
              "[10000 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "age               object\n",
            "workclass         object\n",
            "education          int64\n",
            "marital-status    object\n",
            "race              object\n",
            "sex               object\n",
            "hours-per-week     int64\n",
            "income            object\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tA9grsxtZOY4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "outputId": "57cc8138-00f0-447e-b9cd-52969fabfd36"
      },
      "source": [
        "# data preprocessing\n",
        "\n",
        "# convert age to numeric\n",
        "\n",
        "# # convert ? to na\n",
        "data.age = data.age.replace('?',0)\n",
        "data.age = data.age.astype(np.int64)\n",
        "data['age']=data.age.mask(data.age == 0,data.age.mean())\n",
        "\n",
        "\n",
        "# data['age'].value_counts()\n",
        "\n",
        "# impute for numerical data\n",
        "data['education'].fillna((data['education'].mean()), inplace=True)\n",
        "data['hours-per-week'].fillna((data['hours-per-week'].mean()), inplace=True)\n",
        "data['age'].fillna((data['age'].mean()), inplace=True)\n",
        "\n",
        "# remove missing values for categorical data\n",
        "data = data.dropna()\n",
        "\n",
        "# check misisng values\n",
        "print(data.isna().sum())\n",
        "\n",
        "display(data)"
      ],
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "age               0\n",
            "workclass         0\n",
            "education         0\n",
            "marital-status    0\n",
            "race              0\n",
            "sex               0\n",
            "hours-per-week    0\n",
            "income            0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "\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>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39.0000</td>\n",
              "      <td>Public</td>\n",
              "      <td>13</td>\n",
              "      <td>Single</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50.0000</td>\n",
              "      <td>Self-emp</td>\n",
              "      <td>13</td>\n",
              "      <td>Married</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>13</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38.0000</td>\n",
              "      <td>Private</td>\n",
              "      <td>9</td>\n",
              "      <td>Single</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53.0000</td>\n",
              "      <td>Private</td>\n",
              "      <td>7</td>\n",
              "      <td>Married</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28.0000</td>\n",
              "      <td>Private</td>\n",
              "      <td>13</td>\n",
              "      <td>Married</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>38.0000</td>\n",
              "      <td>Private</td>\n",
              "      <td>10</td>\n",
              "      <td>Married</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>60</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>25.0000</td>\n",
              "      <td>Private</td>\n",
              "      <td>9</td>\n",
              "      <td>Single</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>8</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>21.0000</td>\n",
              "      <td>Private</td>\n",
              "      <td>10</td>\n",
              "      <td>Single</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>37.6885</td>\n",
              "      <td>Private</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>53</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>39.0000</td>\n",
              "      <td>Private</td>\n",
              "      <td>10</td>\n",
              "      <td>Single</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          age workclass  education  ...     sex hours-per-week income\n",
              "0     39.0000    Public         13  ...    Male             40  <=50K\n",
              "1     50.0000  Self-emp         13  ...    Male             13  <=50K\n",
              "2     38.0000   Private          9  ...    Male             40  <=50K\n",
              "3     53.0000   Private          7  ...    Male             40  <=50K\n",
              "4     28.0000   Private         13  ...  Female             40  <=50K\n",
              "...       ...       ...        ...  ...     ...            ...    ...\n",
              "9995  38.0000   Private         10  ...    Male             60   >50K\n",
              "9996  25.0000   Private          9  ...  Female              8  <=50K\n",
              "9997  21.0000   Private         10  ...    Male             40  <=50K\n",
              "9998  37.6885   Private          2  ...    Male             53  <=50K\n",
              "9999  39.0000   Private         10  ...  Female             40  <=50K\n",
              "\n",
              "[10000 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRBTqm-tdzbZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9016e11a-2a52-4659-a115-782a68d3aca1"
      },
      "source": [
        "# display stats\n",
        "plt.hist(data['education'])\n",
        "plt.xlabel('education')\n",
        "plt.show()\n",
        "\n",
        "plt.hist(data['hours-per-week'])\n",
        "plt.xlabel('hours-per-week')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.hist(data['age'],50)\n",
        "plt.xlabel('age')\n",
        "plt.show()\n",
        "\n",
        "plt.bar(data['race'].unique(),data['race'].value_counts())\n",
        "plt.xlabel('race')\n",
        "plt.show()\n",
        "\n",
        "plt.bar(data['income'].unique(),data['income'].value_counts())\n",
        "plt.xlabel('income')\n",
        "plt.show()\n",
        "\n",
        "plt.bar(data['workclass'].unique(),data['workclass'].value_counts())\n",
        "plt.xlabel('workclass')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.bar(data['sex'].unique(),data['sex'].value_counts())\n",
        "plt.xlabel('sex')\n",
        "plt.show()\n"
      ],
      "execution_count": 281,
      "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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": "code",
      "metadata": {
        "id": "OwrxXs2ggvwb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7f01196c-7a03-4225-877c-69925dd384e2"
      },
      "source": [
        "# cross tabulations\n",
        "ct = pd.crosstab(data.education,data.race)\n",
        "ct.plot.bar(stacked=True)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "ct = pd.crosstab(data.workclass,data.income)\n",
        "ct.plot.bar(stacked=True)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "ct = pd.crosstab(data.workclass,data.race)\n",
        "ct.plot.bar(stacked=True)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "ct = pd.crosstab(data.income,data.race)\n",
        "ct.plot.bar(stacked=True)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "# cross tabe with percentages for low income group\n",
        "display(pd.crosstab(data.income, data.race).apply(lambda r: r/r.sum()*100, axis=1))\n",
        "\n",
        "# blacks formualte the majority of the low income group after white, whites have roughly the same percent in the low and high income group\n",
        "\n"
      ],
      "execution_count": 282,
      "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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+V0fE+ia7EjMzq1e9oR8RrwIDa2lfB5xcS3sAF9VxrsnA5IaXaWZmTcHfyDUzyxCHvplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEFh76kVpIWS/pNWu8j6RlJKyTdK6lNaj8gra9I23vnneM/UvvLkk5t6osxM7Pda8id/kRged769cCNEdEX2ACMT+3jgQ2p/ca0H5IGAOcCRwLDgf+W1GrPyjczs4YoKPQllQH/CNye1gWcBNyXdpkKjE7Lo9I6afvJaf9RwIyI2BwRK4EVwNCmuAgzMytMoXf6PwQuA7an9S7A2xGxNa1XAT3Tck/gNYC0fWPaf0d7LceYmVkzqDf0JZ0OvBkRzzZDPUiaIGmRpEXV1dXN8ZJmZplRyJ3+p4GRklYBM8h169wEdJLUOu1TBqxJy2uAXgBpe0dgXX57LcfsEBGTIqI8Isq7devW4AsyM7O61Rv6EfEfEVEWEb3JfRA7PyLGAI8AZ6bdxgGz0vLstE7aPj8iIrWfm0b39AH6AQua7ErMzKxerevfpU7/DsyQ9G1gMXBHar8DuEvSCmA9uT8URMQySTOBPwBbgYsiYtsevL6ZmTVQg0I/Ih4FHk3Lr1LL6JuI2AScVcfx1wLXNrRIMzNrGv5GrplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5llSL2hL6mtpAWSnpe0TNK3UnsfSc9IWiHpXkltUvsBaX1F2t4771z/kdpflnTq3rooMzOrXSF3+puBkyJiIDAIGC5pGHA9cGNE9AU2AOPT/uOBDan9xrQfkgYA5wJHAsOB/5bUqikvxszMdq/e0I+cd9Pq/ukngJOA+1L7VGB0Wh6V1knbT5ak1D4jIjZHxEpgBTC0Sa7CzMwKUlCfvqRWkpYAbwLzgD8Cb0fE1rRLFdAzLfcEXgNI2zcCXfLbazkm/7UmSFokaVF1dXXDr8jMzOpUUOhHxLaIGASUkbs777+3CoqISRFRHhHl3bp121svY2aWSQ0avRMRbwOPAH8PdJLUOm0qA9ak5TVAL4C0vSOwLr+9lmPMzKwZFDJ6p5ukTmm5HXAKsJxc+J+ZdhsHzErLs9M6afv8iIjUfm4a3dMH6AcsaKoLMTOz+rWufxd6AFPTSJv9gJkR8RtJfwBmSPo2sBi4I+1/B3CXpBXAenIjdoiIZZJmAn8AtgIXRcS2pr0cMzPbnXpDPyKWAsfU0v4qtYy+iYhNwFl1nOta4NqGl2lmZk3B38g1M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhjj0zcwyxKFvZpYhDn0zswypN/Ql9ZL0iKQ/SFomaWJqP0TSPEmV6Xfn1C5JN0taIWmppMF55xqX9q+UNG7vXZaZmdWmkDv9rcDXI2IAMAy4SNIA4HLg4YjoBzyc1gFGAP3SzwTgVsj9kQCuAo4FhgJX1fyhMDOz5lFv6EfE2oh4Li2/AywHegKjgKlpt6nA6LQ8CpgWOU8DnST1AE4F5kXE+ojYAMwDhjfp1ZiZ2W41qE9fUm/gGOAZoHtErE2bXge6p+WewGt5h1Wltrrad32NCZIWSVpUXV3dkPLMzKweBYe+pIOAXwBfi4i/5G+LiACiKQqKiEkRUR4R5d26dWuKU5qZWVJQ6Evan1zgT4+IX6bmN1K3Den3m6l9DdAr7/Cy1FZXu5mZNZNCRu8IuANYHhE/yNs0G6gZgTMOmJXXPjaN4hkGbEzdQHOBCkmd0we4FanNzMyaSesC9vk08M/AC5KWpLYrgOuAmZLGA6uBs9O2OcBpwArgfeACgIhYL+kaYGHa7+qIWN8kV2FmZgWpN/Qj4glAdWw+uZb9A7iojnNNBiY3pEAzM2s6/kaumVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZUshDVMysuXyzY7ErKMw3Nxa7Amsk3+mbmWWIQ9/MLEMc+mZmGeLQNzPLEH+Qa2b7Jn8oXqt67/QlTZb0pqQX89oOkTRPUmX63Tm1S9LNklZIWippcN4x49L+lZLG7Z3LMTOz3Smke2cKMHyXtsuBhyOiH/BwWgcYAfRLPxOAWyH3RwK4CjgWGApcVfOHwszMmk+9oR8RjwPrd2keBUxNy1OB0Xnt0yLnaaCTpB7AqcC8iFgfERuAeXz4D4mZme1ljf0gt3tErE3LrwPd03JP4LW8/apSW13tHyJpgqRFkhZVV1c3sjwzM6vNHo/eiYgAoglqqTnfpIgoj4jybt26NdVpzcyMxo/eeUNSj4hYm7pv3kzta4BeefuVpbY1wIm7tD/ayNc222f13nRPsUsoyKpiF2CN1tg7/dlAzQicccCsvPaxaRTPMGBj6gaaC1RI6pw+wK1IbWZm1ozqvdOX9DNyd+ldJVWRG4VzHTBT0nhgNXB22n0OcBqwAngfuAAgItZLugZYmPa7OiJ2/XDYzMz2snpDPyLOq2PTybXsG8BFdZxnMjC5QdWZmVmT8jQMZmYZ4tA3M8sQz71jZvskj4Sqne/0zcwyxKFvZpYhDn0zswzJXp++59g2swzznb6ZWYZk7k7fn+ibWZb5Tt/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhjR76EsaLullSSskXd7cr29mlmXNGvqSWgE/BkYAA4DzJA1ozhrMzLKsue/0hwIrIuLViPgAmAGMauYazMwySxHRfC8mnQkMj4gvpfV/Bo6NiIvz9pkATEirRwAvN1uBjdcVeKvYRexD/H42Lb+fTadU3svDIqJbbRta3ENUImISMKnYdTSEpEURUV7sOvYVfj+blt/PprMvvJfN3b2zBuiVt16W2szMrBk0d+gvBPpJ6iOpDXAuMLuZazAzy6xm7d6JiK2SLgbmAq2AyRGxrDlr2EtKqjuqBPj9bFp+P5tOyb+XzfpBrpmZFZe/kWtmliEOfTOzDHHom5llSIsbp9/SSToE+AqwCbg9Iv5S5JLMPkTSgcBfI2J7Wt8PaBsR7xe3Mis23+k33C+Ag4CewFOSDi9yPfsMSYdJ+lxabiepQ7FrKmEPA+3z1tsDvy1SLSVP0mBJ/yLpEkmDi13PnnDoN1yXiLgiIr4O/CvwmKQXJFVImlns4kqVpC8D9wE/SU1lwK+KV1HJaxsR79aspOX2u9nf6iDpSmAq0IXcNAx3SvrP4lbVeO7eabh3JPWOiFURMVfSocDHgA3AC0WurZRdRG5CvmcAIqJS0keKW1JJe0/S4Ih4DkDSp4C/FrmmUjUGGBgRmwAkXQcsAb5d1KoayaHfcF8E2tSsRO6LDjVTSbi/tPE2R8QHkgCQ1Brwl0ga72vAzyX9GRDwUeCc4pZUsv4MtCX3OR7AAZTw9DEO/QaKiFKY9bMUPSbpCqCdpFOAC4FfF7mmkhURCyX1JzdTLcDLEbGlmDWVsI3AMknzyN2InAIskHQzQET8SzGLayh/I9dahDS6ZDxQQe7OdG5E/LS4VZUeSSdFxHxJZ9S2PSJ+2dw1lTpJ43a3PSKmNlctTcGhby2CpIkRcVN9bbZ7kr4VEVdJurOWzRERX2z2oqxFcehbiyDpuYgYvEvb4og4plg1mQFIOh24BjiMXJe4yP0BPbiohTWSQ9+KStJ5wBeA44Hf5W3qAGyPiJOLUliJkvSvu9seET9orlr2FZJWAGcAL8Q+EJj+INeK7UlgLbnxz9/Pa38HWFqUikqbv9DW9F4DXtwXAh98p29mtluShpDr3nkM2FzTXqr/NfkbudYiSBomaaGkdyV9IGmbJM9r1EiSDpf0a0nVkt6UNMtThjTateS+g9OW3H9SNT8lyd071lLcQu7xmT8HyoGxwN8VtaLSdg/wY+B/pfVzgZ8BxxatotL1sYj4ZLGLaCq+07cWIyJWAK0iYltE3AkML3ZNJax9RNwVEVvTz93k7lSt4eZIqih2EU3Fd/rWUrwvqQ2wRNJ/kftw1zclDZSm/gZ4QNLlwAxy3yI9B5hTtMJK21eBf5P0AfABHrJptuckHQa8QW5eo0uBjsB/p7t/K5CkleRCXrVsjohwv37GOfStRUjTBvxPRGyud2ezZqTcLIBjgD4RcY2kXkCPiFhQ5NIaxaFvLUKaNuAk4HHgXuDBiNha3KpKl6SxtbVHxLTmrqXUSboV2A6cFBGfkNQZeCgihhS5tEZxn761CBFxgaT9gRHAecCPJc2LiC8VubRSlR9IbYGTgecAh37DHRsRgyUtBoiIDenzp5Lk0LcWIyK2SHqAXJ90O2A04NBvhIi4JH9dUidyH+paw22R1Ir0fAdJ3cjd+Zckj46wFkHSCElTgErgfwO3k3vwhzWN94A+xS6iRN0M3A98RNK1wBPAd4pbUuO5T99aBEk/I9eX/4A/zN1zkn7N3548th8wAJgZEZcXr6rSlR5IczK5UVEPR8TyIpfUaA59s32QpM/krW4FVkdEVbHq2VdImhARk4pdx55w6FtRSXoiIo6X9A47PxO3pL8AUyyS2gJfAfoCLwB3eBRU06ntuQ+lxh/kWlFFxPHpd8lOYNXCTAW2kHs2wQhy3ToTi1pRiZJ0QC1djbV96a2k+E7fii6NjFgWEf2LXUupk/RCRByVllsDC0r9zrRYau7qJd0VEf+c2spKvZvMd/pWdBGxTdLLkg6NiD8Vu54St6VmISK25r5Mao3URtIXgOPyHzQvaSiU7kPmHfrWUnQGlklaQG54IQARMbJ4JZWkgXnPIRDQLq37M5KG+wq56Rc6AZ/fZVsAJRn67t6xFmGX0SY7RMRjzV2LWT5J4yPijmLX0VQc+lZUHm1iLVV+l05t3L1j1jgebWIt1a5dOvncvWPWGB5tYta8PPeOFdtOo02KWYhZbSR1l3RHmgwQSQMkjS92XY3lO30rKknb+NtoHZGbXfN9PNrEWogU9ncC34iIgek/0sU1/6GWGt/pW1FFRKuIODj9dIiI1nnLDnxrCbpGxEzSdMrpP9JtxS2p8Rz6Zma7956kLvxtPv1hwMbiltR4Hr1jZrZ7/wrMBj4u6fdAN+DM4pbUeL7TNzOrhaQhkj4aEc8BnwGuADYDDwElO/+OQ9/MrHY/AT5Iy8cB3wB+DGwASnZOfXfvmJnVrlVErE/L5wCTIuIXwC8kLSliXXvEd/pmZrVrlYZnQu5RifPztpXsDXPJFm5mtpf9DHhM0lvAX8lNFYKkvpTw6B1/OcvMrA5peGYP4KGIeC+1/R1wUPqAt+Q49M3MMsR9+mZmGeLQNzPLEIe+WT0krZLUtcB9vynp3/Z2TWaN5dA32w1JrYpdg1lTcujbPkvS/5X0L2n5Rknz0/JJkqZLOk/SC5JelHR93nHvSvq+pOeBv89rbyfpAUlfTutjJS2V9Lyku2p5/S9LWpi2/0JS+9R+VnrN5yU9ntqOlLRA0pJ0zn579c2xzHLo277sd8AJabkcOEjS/qntFeB64CRgEDBE0ui074HAMxExMCKeSG0HAb8GfhYRP5V0JPCfwEkRMZDaH/H4y4gYkrYvB2oevHElcGpqH5navgLcFBGDUq0lO7eLtWwOfduXPQt8StLB5CbKeopcoJ4AvA08GhHVaX706cA/pOO2Ab/Y5VyzgDsjYlpaPwn4eUS8BZD3df18n5T0O0kvAGOAI1P774Ep6T+Gmu6jp4ArJP07cFhE/HVPLtysLg5922dFxBZgJXA+8CS5O//PAn2BVbs5dFNE7PqQjN8DwyWpASVMAS5OT1j6FtA21fUVcv8l9AKeldQlIuRiNy8AAADvSURBVO4hd9f/V2COpJMa8DpmBXPo277ud8C/AY+n5a8Ai4EFwGckdU0f1p4HPLab81xJbnbFH6f1+cBZ6eEaSDqklmM6AGtTl9KYmkZJH4+IZyLiSqAa6CXpcODViLiZ3H8VRzf2gs12x6Fv+7rfkfsa/VMR8QawCfhdRKwFLgceAZ4Hno2IWfWcayLQTtJ/RcQy4Fpyc7M8D/yglv3/H/AMuf8SXsprv6HmA2Ry/4E8D5wNvJhmb/wkMG3Xk5k1BU/DYGaWIb7TNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxD/j9ozqisvzUp/QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "output_type": "display_data",
          "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>race</th>\n",
              "      <th>Amer-Indian</th>\n",
              "      <th>Asian</th>\n",
              "      <th>Black</th>\n",
              "      <th>Hispanic</th>\n",
              "      <th>White</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>income</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>&lt;=50K</th>\n",
              "      <td>1.167826</td>\n",
              "      <td>3.162315</td>\n",
              "      <td>10.917202</td>\n",
              "      <td>1.010366</td>\n",
              "      <td>83.742291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>&gt;50K</th>\n",
              "      <td>0.420345</td>\n",
              "      <td>2.858344</td>\n",
              "      <td>5.086171</td>\n",
              "      <td>0.252207</td>\n",
              "      <td>91.382934</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "race    Amer-Indian     Asian      Black  Hispanic      White\n",
              "income                                                       \n",
              "<=50K      1.167826  3.162315  10.917202  1.010366  83.742291\n",
              ">50K       0.420345  2.858344   5.086171  0.252207  91.382934"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JvjKx7Gui7ZX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "ab4eb741-bba7-4c9a-d4e5-98fe020d542b"
      },
      "source": [
        "# create dummy variables\n",
        "lb_make = LabelEncoder()\n",
        "data[\"income\"] = lb_make.fit_transform(data[\"income\"])\n",
        "data[\"workclass\"] = lb_make.fit_transform(data[\"workclass\"])\n",
        "data[\"marital-status\"] = lb_make.fit_transform(data[\"marital-status\"])\n",
        "data[\"sex\"] = lb_make.fit_transform(data[\"sex\"])\n",
        "data[\"race\"] = lb_make.fit_transform(data[\"sex\"])\n",
        "\n",
        "display(data)\n",
        "display(data.dtypes)\n",
        "\n",
        "# define train data and labels\n",
        "train_data = data.drop('income',axis=1)\n",
        "labels = data['income']\n",
        "\n",
        "print(labels.value_counts())\n"
      ],
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "display_data",
          "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>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39.0000</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50.0000</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>38.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>25.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>21.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>37.6885</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>39.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          age  workclass  education  ...  sex  hours-per-week  income\n",
              "0     39.0000          2         13  ...    1              40       0\n",
              "1     50.0000          3         13  ...    1              13       0\n",
              "2     38.0000          1          9  ...    1              40       0\n",
              "3     53.0000          1          7  ...    1              40       0\n",
              "4     28.0000          1         13  ...    0              40       0\n",
              "...       ...        ...        ...  ...  ...             ...     ...\n",
              "9995  38.0000          1         10  ...    1              60       1\n",
              "9996  25.0000          1          9  ...    0               8       0\n",
              "9997  21.0000          1         10  ...    1              40       0\n",
              "9998  37.6885          1          2  ...    1              53       0\n",
              "9999  39.0000          1         10  ...    0              40       0\n",
              "\n",
              "[10000 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "age               float64\n",
              "workclass           int64\n",
              "education           int64\n",
              "marital-status      int64\n",
              "race                int64\n",
              "sex                 int64\n",
              "hours-per-week      int64\n",
              "income              int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "0    7621\n",
            "1    2379\n",
            "Name: income, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJDIGqNtrsdF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "a91d0909-4171-44d7-e7c7-f68dcf4ec4db"
      },
      "source": [
        "# perform modeling \n",
        "kf = KFold(n_splits=10)\n",
        "\n",
        "X = np.array(train_data)\n",
        "Y = np.array(labels)\n",
        "\n",
        "\n",
        "# sklearn cross valiadtion module is deprecated so using traintest split\n",
        "# naive bayes\n",
        "gnb = GaussianNB()\n",
        "acc_gnb = cross_val_score(gnb, X, Y, cv=10)\n",
        "\n",
        "print(\"mean_accuracy_gnb = \"+ round(np.mean(acc_gnb),2).astype(str))\n",
        "\n",
        "# decison tree\n",
        "dt = DecisionTreeClassifier(criterion='entropy')\n",
        "acc_dt = cross_val_score(dt, X, Y, cv=10)\n",
        "\n",
        "print(\"mean_accuracy_decison_tree = \"+ round(np.mean(acc_dt),2).astype(str))\n",
        "\n",
        "# lda\n",
        "lda_clf = LDA()\n",
        "acc_lda = cross_val_score(lda_clf, X, Y, cv=10)\n",
        "\n",
        "print(\"mean_accuracy_lda = \"+ round(np.mean(acc_lda),2).astype(str))\n",
        "\n"
      ],
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mean_accuracy_gnb = 0.77\n",
            "mean_accuracy_decison_tree = 0.78\n",
            "mean_accuracy_lda = 0.81\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjOZwff_3ahJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5419d2c5-fa03-40ca-a8a0-7b31252fbad6"
      },
      "source": [
        "# generate tree \n",
        "from sklearn import tree\n",
        "tree.plot_tree(dt.fit(X, Y)) \n",
        "\n",
        "import graphviz \n",
        "dot_data = tree.export_graphviz(dt.fit(X, Y), out_file=data_path+'tree1.pdf') \n",
        "graph = graphviz.Source(dot_data) \n",
        "graph.render()\n"
      ],
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "execute_result",
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}