ML_for_IoT/Bot-IoT/notebooks/Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MACHINE LEARNING CLASSIFICATION AND COMPARISONS\n",
    "\n",
    "This notebook we have used 6 different ML classifiers and compared them to find the best one that can accurately classify our malicious dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing some libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 923
    },
    "colab_type": "code",
    "id": "qr2UdY33jeUQ",
    "outputId": "067623f2-6e7e-46d3-a548-47728b9f4d7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "pip install smote_variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "7czY2ZsRLL2q",
    "outputId": "04b9f2fb-8e2d-4e74-a7c3-00c7d6e764cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "pip install imbalanced_databases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "id": "b4FUTDEnT0qp",
    "outputId": "7f7cb1ac-bceb-4b6f-ed36-3bfe1102c954"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.6/dist-packages (0.4.3)\n",
      "Requirement already satisfied: scikit-learn>=0.20 in /usr/local/lib/python3.6/dist-packages (from imbalanced-learn) (0.22.2.post1)\n",
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     ]
    }
   ],
   "source": [
    "pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries for our needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "id": "JnKWN9B2LHP4",
    "outputId": "4aa45710-ba59-4375-e38f-ea76c707a3bf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import smote_variants as sv\n",
    "import pandas  as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import imbalanced_databases as imbd\n",
    "from sklearn import metrics\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the dataset to a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "K6gyDfH5PcxL",
    "outputId": "a3aa8059-3137-41cc-8971-cd751777b679"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5i6xdA4nJ_Me"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('/content/drive/My Drive/Major Project Works/dataset/ml_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rL13VhV0kb78"
   },
   "outputs": [],
   "source": [
    "df = df_train.copy()\n",
    "df.drop(['Unnamed: 0'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "32fIeDsCDP1T"
   },
   "outputs": [],
   "source": [
    "df_attack = df[df['attack'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7u0uBOUmDP4F"
   },
   "outputs": [],
   "source": [
    "df_normal = df[df['attack'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting genral idea about the weight of available classification packets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j8XjHHFUbd4t"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "colab_type": "code",
    "id": "ILzX8RoAJ6vd",
    "outputId": "2d25b5da-dcc2-4ff7-9d0a-d6a58cecdee7"
   },
   "outputs": [
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       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>700</td>\n",
       "      <td>1.26889</td>\n",
       "      <td>1.21662</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1.56107</td>\n",
       "      <td>1.21662</td>\n",
       "      <td>328</td>\n",
       "      <td>308</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>1708</td>\n",
       "      <td>6.401038</td>\n",
       "      <td>1.110847</td>\n",
       "      <td>1.110847</td>\n",
       "      <td>2.221694</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.221694</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1008</td>\n",
       "      <td>700</td>\n",
       "      <td>1.093573</td>\n",
       "      <td>0.624899</td>\n",
       "      <td>0.900214</td>\n",
       "      <td>56864</td>\n",
       "      <td>59969</td>\n",
       "      <td>308</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>700</td>\n",
       "      <td>1.26889</td>\n",
       "      <td>1.21662</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1.24980</td>\n",
       "      <td>1.21662</td>\n",
       "      <td>328</td>\n",
       "      <td>308</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>1462</td>\n",
       "      <td>6.400703</td>\n",
       "      <td>1.113328</td>\n",
       "      <td>1.113328</td>\n",
       "      <td>2.226655</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.226655</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>762</td>\n",
       "      <td>700</td>\n",
       "      <td>1.093630</td>\n",
       "      <td>0.624931</td>\n",
       "      <td>0.898208</td>\n",
       "      <td>56864</td>\n",
       "      <td>59969</td>\n",
       "      <td>308</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>700</td>\n",
       "      <td>1.26889</td>\n",
       "      <td>1.21662</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1.24986</td>\n",
       "      <td>1.21662</td>\n",
       "      <td>328</td>\n",
       "      <td>308</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>1296</td>\n",
       "      <td>6.400472</td>\n",
       "      <td>1.113098</td>\n",
       "      <td>1.113098</td>\n",
       "      <td>2.226195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.226195</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>596</td>\n",
       "      <td>700</td>\n",
       "      <td>1.093669</td>\n",
       "      <td>0.624954</td>\n",
       "      <td>0.898394</td>\n",
       "      <td>56864</td>\n",
       "      <td>59969</td>\n",
       "      <td>308</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>700</td>\n",
       "      <td>1.26889</td>\n",
       "      <td>1.21662</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1.24991</td>\n",
       "      <td>1.21662</td>\n",
       "      <td>328</td>\n",
       "      <td>308</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pkts  bytes  ...  Pkts_P_State_P_Protocol_P_SrcIP  attack\n",
       "0    10   1729  ...                              308       1\n",
       "1    10   1604  ...                              308       1\n",
       "2     8   1708  ...                              308       1\n",
       "3     8   1462  ...                              308       1\n",
       "4     8   1296  ...                              308       1\n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 344
    },
    "colab_type": "code",
    "id": "gq1OUFUMmbxT",
    "outputId": "b3c39210-7e47-487b-b487-caa9ca4e23c5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1926624\n",
       "0        477\n",
       "Name: attack, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.value_counts(df['attack']).plot.bar()\n",
    "plt.title('Attack histogram')\n",
    "plt.xlabel('attack')\n",
    "plt.ylabel('Value')\n",
    "df['attack'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### Here we can find that there is a lot of imbalance in the dataset, so we can tell the data is highly-imballanced. Thus we need to synthtically oversample the minority class to get a balanced dataset for training and testing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining some methods which are later used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qD0Yxvv2qQGR"
   },
   "outputs": [],
   "source": [
    "# Used to plot the roc curve.\n",
    "def plot_roc_curve(fpr, tpr):\n",
    "    plt.plot(fpr, tpr, color='orange', label='ROC')\n",
    "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "# Used for classification of dataset.\n",
    "def classif_results(): \n",
    "  conf_mat = confusion_matrix(y_true=y_test, y_pred=y_pred)\n",
    "  print('Confusion matrix:\\n', conf_mat)\n",
    "\n",
    "  labels = ['Class 0', 'Class 1']\n",
    "  fig = plt.figure()\n",
    "  ax = fig.add_subplot(111)\n",
    "  cax = ax.matshow(conf_mat, cmap=plt.cm.Blues)\n",
    "  fig.colorbar(cax)\n",
    "  ax.set_xticklabels([''] + labels)\n",
    "  ax.set_yticklabels([''] + labels)\n",
    "  plt.xlabel('Predicted')\n",
    "  plt.ylabel('Expected')\n",
    "  plt.show()\n",
    "\n",
    "  print(\"Accuracy\", metrics.accuracy_score(y_test, y_pred))\n",
    "\n",
    "  from sklearn.metrics import classification_report\n",
    "  print(classification_report(y_test, y_pred))\n",
    "\n",
    "  auc = roc_auc_score(y_test, y_pred)\n",
    "  print(\"AUC Score: \")\n",
    "  print(auc)\n",
    "\n",
    "  fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "  plot_roc_curve(fpr, tpr)\n",
    "\n",
    "# Used for splitting and normalizing dataset.\n",
    "def test_scale():\n",
    "  from sklearn.model_selection import train_test_split\n",
    "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 0)\n",
    "\n",
    "  from sklearn.preprocessing import StandardScaler\n",
    "  sc_X = StandardScaler()\n",
    "  X_train = sc_X.fit_transform(X_train)\n",
    "  X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here, we are applying SMOTE method, and applying it to dataset. We use the daataset by applying attack packets to X and normal to Y and oversample Y sythetically to length of X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "id": "4I_gzBXwBJTp",
    "outputId": "86841c67-4987-42c2-e971-e90bc66ce8f2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "X = df.iloc[:, df.columns != 'attack']\n",
    "y = df.iloc[:, df.columns == 'attack']\n",
    "X, y = SMOTE().fit_sample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 0)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WaBPuri0Sk4O"
   },
   "source": [
    "# Logistic Regression:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZCxyQRcQNnbB"
   },
   "outputs": [],
   "source": [
    "test_scale()\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train,y_train)\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "zq3cY5zdNAij",
    "outputId": "eb6bdabf-194b-46b0-8e92-15b39e8fc844"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[771574      0]\n",
      " [    91 769635]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9999409589307727\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    771574\n",
      "           1       1.00      1.00      1.00    769726\n",
      "\n",
      "    accuracy                           1.00   1541300\n",
      "   macro avg       1.00      1.00      1.00   1541300\n",
      "weighted avg       1.00      1.00      1.00   1541300\n",
      "\n",
      "AUC Score: \n",
      "0.9999408880562694\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classif_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1_oX5TSiZlYN"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XgAdLKxwYz4a"
   },
   "source": [
    "# Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u8Eq9ptJY17w"
   },
   "outputs": [],
   "source": [
    "test_scale()\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier()\n",
    "classifier.fit(X_train,y_train)\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "mc1cZCHAY1-0",
    "outputId": "4243bc06-ca50-4700-9557-a6fe81727e4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[771574      0]\n",
      " [     1 769725]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9999993511970414\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    771574\n",
      "           1       1.00      1.00      1.00    769726\n",
      "\n",
      "    accuracy                           1.00   1541300\n",
      "   macro avg       1.00      1.00      1.00   1541300\n",
      "weighted avg       1.00      1.00      1.00   1541300\n",
      "\n",
      "AUC Score: \n",
      "0.9999993504182008\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classif_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4U1rSQSJY2Bq"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F7eoKyO8gvU0"
   },
   "source": [
    "# Random Forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "v0xYdJZGgugZ"
   },
   "outputs": [],
   "source": [
    "test_scale()\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "SxnUXQK6gulj",
    "outputId": "caf4b42c-7cdd-4a62-f954-32f4162408b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[771574      0]\n",
      " [     0 769726]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    771574\n",
      "           1       1.00      1.00      1.00    769726\n",
      "\n",
      "    accuracy                           1.00   1541300\n",
      "   macro avg       1.00      1.00      1.00   1541300\n",
      "weighted avg       1.00      1.00      1.00   1541300\n",
      "\n",
      "AUC Score: \n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classif_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q7-wWafQguo8"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k3RQYB8WSSSY"
   },
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uJNUH0aVSd4P"
   },
   "outputs": [],
   "source": [
    "test_scale()\n",
    "\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier()\n",
    "classifier.fit(X_train,y_train)\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "UPjbK4lIV__f",
    "outputId": "697c1484-0d78-4e41-a28b-261ae26bc88e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[771574      0]\n",
      " [     6 769720]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9999961071822487\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    771574\n",
      "           1       1.00      1.00      1.00    769726\n",
      "\n",
      "    accuracy                           1.00   1541300\n",
      "   macro avg       1.00      1.00      1.00   1541300\n",
      "weighted avg       1.00      1.00      1.00   1541300\n",
      "\n",
      "AUC Score: \n",
      "0.9999961025092046\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classif_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RtcmLQ1rVewF"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q802V5OiXG8F"
   },
   "source": [
    "# Support Vector Machines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dZB9pl0bVwwU"
   },
   "outputs": [],
   "source": [
    "test_scale()\n",
    "\n",
    "# Fitting SVM to the Training set\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC()\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "rBko4G0GXaw7",
    "outputId": "00adbfea-3981-4194-8145-1f4bedf21f99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[771574      0]\n",
      " [    13 769713]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.999991565561539\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    771574\n",
      "           1       1.00      1.00      1.00    769726\n",
      "\n",
      "    accuracy                           1.00   1541300\n",
      "   macro avg       1.00      1.00      1.00   1541300\n",
      "weighted avg       1.00      1.00      1.00   1541300\n",
      "\n",
      "AUC Score: \n",
      "0.9999915554366099\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classif_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zNttbb-TXtxS"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uC67IAVcYtOz"
   },
   "source": [
    "# Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zBwG-YYncej7"
   },
   "outputs": [],
   "source": [
    "test_scale()\n",
    "\n",
    "# Fitting SVM to the Training set\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "#Create a Gaussian Classifier\n",
    "classifier = GaussianNB()\n",
    "\n",
    "# Train the model using the training sets\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "hLkdNWmZksZC",
    "outputId": "e9d12527-da3f-410e-b671-bc088e04b049"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[771574      0]\n",
      " [   837 768889]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9994569519237008\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    771574\n",
      "           1       1.00      1.00      1.00    769726\n",
      "\n",
      "    accuracy                           1.00   1541300\n",
      "   macro avg       1.00      1.00      1.00   1541300\n",
      "weighted avg       1.00      1.00      1.00   1541300\n",
      "\n",
      "AUC Score: \n",
      "0.9994563000340381\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classif_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M6HUBYufk7zw"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RFhSu26Epqrp"
   },
   "source": [
    "# Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "LhH1Fh49ptq6",
    "outputId": "62aa0660-e341-410b-bc48-7ed53e812a75"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "2311948/2311948 [==============================] - 263s 114us/step - loss: 0.0029 - accuracy: 0.9998\n",
      "Epoch 2/3\n",
      "2311948/2311948 [==============================] - 260s 113us/step - loss: 0.0043 - accuracy: 1.0000\n",
      "Epoch 3/3\n",
      "2311948/2311948 [==============================] - 260s 112us/step - loss: 0.0031 - accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "\n",
    "classifier = Sequential()\n",
    "classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 29))\n",
    "classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "\n",
    "\n",
    "# Fitting the ANN to the Training set\n",
    "classifier.fit(X_train, y_train, batch_size = 10, epochs = 3)\n",
    "\n",
    "# Part 3 - Making predictions and evaluating the model\n",
    "\n",
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)\n",
    "\n",
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "JoAWOUZNpvr2",
    "outputId": "b7bd97e5-c37b-4279-91af-5475341dac74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[771574      0]\n",
      " [    12 769714]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9999922143644975\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    771574\n",
      "           1       1.00      1.00      1.00    769726\n",
      "\n",
      "    accuracy                           1.00   1541300\n",
      "   macro avg       1.00      1.00      1.00   1541300\n",
      "weighted avg       1.00      1.00      1.00   1541300\n",
      "\n",
      "AUC Score: \n",
      "0.9999922050184091\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classif_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MszcdtWl0KcK"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "MachineLearningCLassification.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}