prob_2.ipynb

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    "colab": {
      "name": "prob_2.ipynb",
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      "mount_file_id": "1TVE7WpbQ9Zx4jQUL2EqDF4dCxE8pAl8K",
      "authorship_tag": "ABX9TyMs2nHl3jUtjAbyjMX9OzRw",
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    "kernelspec": {
      "name": "python3",
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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/prob_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hG0Oya9BXQ9W",
        "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",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "km_QFUKRXepX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "outputId": "f539d7c6-0569-40d4-e739-ce27a627d624"
      },
      "source": [
        "data_path = '/content/drive/My Drive/exercise/'\n",
        "\n",
        "# load the data\n",
        "data = pd.read_csv(data_path+'bank_data.csv',sep=',')\n",
        "display(data)\n",
        "print(data.dtypes)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>income</th>\n",
              "      <th>children</th>\n",
              "      <th>gender</th>\n",
              "      <th>region</th>\n",
              "      <th>married</th>\n",
              "      <th>car</th>\n",
              "      <th>savings_acct</th>\n",
              "      <th>current_acct</th>\n",
              "      <th>mortgage</th>\n",
              "      <th>pep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID12101</td>\n",
              "      <td>48</td>\n",
              "      <td>17546.00</td>\n",
              "      <td>1</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>INNER_CITY</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID12102</td>\n",
              "      <td>40</td>\n",
              "      <td>30085.10</td>\n",
              "      <td>3</td>\n",
              "      <td>MALE</td>\n",
              "      <td>TOWN</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID12103</td>\n",
              "      <td>51</td>\n",
              "      <td>16575.40</td>\n",
              "      <td>0</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>INNER_CITY</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID12104</td>\n",
              "      <td>23</td>\n",
              "      <td>20375.40</td>\n",
              "      <td>3</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>TOWN</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID12105</td>\n",
              "      <td>57</td>\n",
              "      <td>50576.30</td>\n",
              "      <td>0</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>RURAL</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>ID12696</td>\n",
              "      <td>61</td>\n",
              "      <td>47025.00</td>\n",
              "      <td>2</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>INNER_CITY</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>ID12697</td>\n",
              "      <td>30</td>\n",
              "      <td>9672.25</td>\n",
              "      <td>0</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>INNER_CITY</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>ID12698</td>\n",
              "      <td>31</td>\n",
              "      <td>15976.30</td>\n",
              "      <td>0</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>TOWN</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>ID12699</td>\n",
              "      <td>29</td>\n",
              "      <td>14711.80</td>\n",
              "      <td>0</td>\n",
              "      <td>MALE</td>\n",
              "      <td>INNER_CITY</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>ID12700</td>\n",
              "      <td>38</td>\n",
              "      <td>26671.60</td>\n",
              "      <td>0</td>\n",
              "      <td>MALE</td>\n",
              "      <td>TOWN</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  age    income  children  ... savings_acct current_acct mortgage  pep\n",
              "0    ID12101   48  17546.00         1  ...           NO           NO       NO  YES\n",
              "1    ID12102   40  30085.10         3  ...           NO          YES      YES   NO\n",
              "2    ID12103   51  16575.40         0  ...          YES          YES       NO   NO\n",
              "3    ID12104   23  20375.40         3  ...           NO          YES       NO   NO\n",
              "4    ID12105   57  50576.30         0  ...          YES           NO       NO   NO\n",
              "..       ...  ...       ...       ...  ...          ...          ...      ...  ...\n",
              "595  ID12696   61  47025.00         2  ...          YES          YES      YES   NO\n",
              "596  ID12697   30   9672.25         0  ...          YES          YES       NO   NO\n",
              "597  ID12698   31  15976.30         0  ...          YES           NO       NO  YES\n",
              "598  ID12699   29  14711.80         0  ...          YES           NO      YES   NO\n",
              "599  ID12700   38  26671.60         0  ...           NO          YES      YES  YES\n",
              "\n",
              "[600 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "id               object\n",
            "age               int64\n",
            "income          float64\n",
            "children          int64\n",
            "gender           object\n",
            "region           object\n",
            "married          object\n",
            "car              object\n",
            "savings_acct     object\n",
            "current_acct     object\n",
            "mortgage         object\n",
            "pep              object\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qBYG6GY8_13",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "c39f7086-7afb-40c6-dcf2-83196810b8da"
      },
      "source": [
        "data2 = data.drop('id',axis=1)\n",
        "# create dummy variables\n",
        "le = LabelEncoder()\n",
        "\n",
        "\n",
        "# Categorical boolean mask\n",
        "categorical_feature_mask = data2.dtypes==object\n",
        "# filter categorical columns using mask and turn it into a list\n",
        "categorical_cols = data2.columns[categorical_feature_mask].tolist()\n",
        "\n",
        "# apply le on categorical feature columns\n",
        "data2[categorical_cols] = data2[categorical_cols].apply(lambda col: le.fit_transform(col))\n",
        "\n",
        "display(data2)\n",
        "\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>income</th>\n",
              "      <th>children</th>\n",
              "      <th>gender</th>\n",
              "      <th>region</th>\n",
              "      <th>married</th>\n",
              "      <th>car</th>\n",
              "      <th>savings_acct</th>\n",
              "      <th>current_acct</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23</td>\n",
              "      <td>20375.40</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>50576.30</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>61</td>\n",
              "      <td>47025.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>30</td>\n",
              "      <td>9672.25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>31</td>\n",
              "      <td>15976.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>29</td>\n",
              "      <td>14711.80</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>38</td>\n",
              "      <td>26671.60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age    income  children  gender  ...  savings_acct  current_acct  mortgage  pep\n",
              "0     48  17546.00         1       0  ...             0             0         0    1\n",
              "1     40  30085.10         3       1  ...             0             1         1    0\n",
              "2     51  16575.40         0       0  ...             1             1         0    0\n",
              "3     23  20375.40         3       0  ...             0             1         0    0\n",
              "4     57  50576.30         0       0  ...             1             0         0    0\n",
              "..   ...       ...       ...     ...  ...           ...           ...       ...  ...\n",
              "595   61  47025.00         2       0  ...             1             1         1    0\n",
              "596   30   9672.25         0       0  ...             1             1         0    0\n",
              "597   31  15976.30         0       0  ...             1             0         0    1\n",
              "598   29  14711.80         0       1  ...             1             0         1    0\n",
              "599   38  26671.60         0       1  ...             0             1         1    1\n",
              "\n",
              "[600 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vo83h-Jg-qA1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c5401f2-bf12-4dfa-958e-653580b84e79"
      },
      "source": [
        "\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(600, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPsUmrVkYArX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "eb8bcb45-2c34-41b7-fda4-8f0c8969449f"
      },
      "source": [
        "data3 = data2.drop('pep',axis=1)\n",
        "labels = np.array(data2.pep)\n",
        "# split data into train and test set\n",
        "\n",
        "# normalize data\n",
        "\n",
        "cs = MinMaxScaler()\n",
        "data3 = cs.fit_transform(data3)\n",
        "\n",
        "data3.shape\n",
        "no_runs = 50\n",
        "\n",
        "actual_labels = []\n",
        "predicted_labels = []\n",
        "\n",
        "predicted_labels_dt = []\n",
        "predicted_labels_gnb = []\n",
        "\n",
        "for ii in range(0,no_runs):\n",
        "\n",
        "  split = train_test_split(data2,labels, test_size=0.2)\n",
        "  (X_train, X_test, Y_train, Y_test) = split\n",
        "\n",
        "  # neigh = KNeighborsClassifier(n_neighbors=12, weights='distance')\n",
        "  neigh = KNeighborsClassifier(n_neighbors=10)\n",
        "  neigh.fit(X_train, Y_train)\n",
        "\n",
        "  # decison tree\n",
        "  dt = DecisionTreeClassifier(criterion='entropy')\n",
        "  dt.fit(X_train, Y_train)\n",
        "  predicted_labels_dt = np.append(predicted_labels_dt,dt.predict(X_test))\n",
        "\n",
        "  # naive bayes\n",
        "  gnb = GaussianNB()\n",
        "  gnb.fit(X_train, Y_train)\n",
        "  predicted_labels_gnb = np.append(predicted_labels_gnb,gnb.predict(X_test))\n",
        "\n",
        "  predicted_labels = np.append(predicted_labels,neigh.predict(X_test))\n",
        "  actual_labels = np.append(actual_labels,Y_test)\n",
        "\n",
        "print(\"KNN results\")\n",
        "\n",
        "# print classification performance \n",
        "cr = classification_report(actual_labels,predicted_labels)\n",
        "print(cr)\n",
        "\n",
        "# print confusion matrix\n",
        "cm = confusion_matrix(actual_labels, predicted_labels)\n",
        "print(cm)\n",
        "\n",
        "# without weights workd better and best value of K is 12\n",
        "\n",
        "print(\"GNB results\")\n",
        "# print classification performance \n",
        "cr = classification_report(actual_labels,predicted_labels_gnb)\n",
        "print(cr)\n",
        "\n",
        "# print confusion matrix\n",
        "cm = confusion_matrix(actual_labels, predicted_labels_gnb)\n",
        "print(cm)\n",
        "\n",
        "\n",
        "print(\"DT results\")\n",
        "# print classification performance \n",
        "cr = classification_report(actual_labels,predicted_labels_dt)\n",
        "print(cr)\n",
        "\n",
        "# print confusion matrix\n",
        "cm = confusion_matrix(actual_labels, predicted_labels_dt)\n",
        "print(cm)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN results\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.59      0.73      0.65      3280\n",
            "         1.0       0.54      0.38      0.44      2720\n",
            "\n",
            "    accuracy                           0.57      6000\n",
            "   macro avg       0.56      0.55      0.55      6000\n",
            "weighted avg       0.56      0.57      0.56      6000\n",
            "\n",
            "[[2393  887]\n",
            " [1690 1030]]\n",
            "GNB results\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00      3280\n",
            "         1.0       1.00      1.00      1.00      2720\n",
            "\n",
            "    accuracy                           1.00      6000\n",
            "   macro avg       1.00      1.00      1.00      6000\n",
            "weighted avg       1.00      1.00      1.00      6000\n",
            "\n",
            "[[3276    4]\n",
            " [   0 2720]]\n",
            "DT results\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00      3280\n",
            "         1.0       1.00      1.00      1.00      2720\n",
            "\n",
            "    accuracy                           1.00      6000\n",
            "   macro avg       1.00      1.00      1.00      6000\n",
            "weighted avg       1.00      1.00      1.00      6000\n",
            "\n",
            "[[3280    0]\n",
            " [   0 2720]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhwAf_YDB0KD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "deeb5c76-fec5-4e79-8000-76f1923fc417"
      },
      "source": [
        "# sklearn cross valiadtion module is deprecated so using traintest split\n",
        "# naive bayes\n",
        "gnb = GaussianNB()\n",
        "gnb.fit(X_train, Y_train)\n",
        "predicted_labels_gnb = gnb.predict(X_test)\n",
        "\n",
        "# decison tree\n",
        "dt = DecisionTreeClassifier(criterion='entropy')\n",
        "dt.fit(X_train, Y_train)\n",
        "predicted_labels_dt = dt.predict(X_test)\n",
        "\n",
        "\n",
        "# print classification performance \n",
        "cr = classification_report(Y_test,predicted_labels_dt)\n",
        "print(cr)\n",
        "\n",
        "# print confusion matrix\n",
        "cm = confusion_matrix(Y_test, predicted_labels_dt)\n",
        "print(cm)\n",
        "\n",
        "\n",
        "# print classification performance \n",
        "cr = classification_report(Y_test,predicted_labels_gnb)\n",
        "print(cr)\n",
        "\n",
        "# print confusion matrix\n",
        "cm = confusion_matrix(Y_test, predicted_labels_gnb)\n",
        "print(cm)\n",
        "\n",
        "\n"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        69\n",
            "           1       1.00      1.00      1.00        51\n",
            "\n",
            "    accuracy                           1.00       120\n",
            "   macro avg       1.00      1.00      1.00       120\n",
            "weighted avg       1.00      1.00      1.00       120\n",
            "\n",
            "[[69  0]\n",
            " [ 0 51]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        69\n",
            "           1       1.00      1.00      1.00        51\n",
            "\n",
            "    accuracy                           1.00       120\n",
            "   macro avg       1.00      1.00      1.00       120\n",
            "weighted avg       1.00      1.00      1.00       120\n",
            "\n",
            "[[69  0]\n",
            " [ 0 51]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCSM5ymVVxnE",
        "colab_type": "text"
      },
      "source": [
        "KNN has high variance, whereas deceion tree and naive bayes gaussian classifier have higher bias "
      ]
    }
  ]
}