MachineLearningCLassificationWIthoutFlow.ipynb

{
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   "cell_type": "markdown",
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    "id": "jTTr50dJWlmm"
   },
   "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": {
    "colab_type": "text",
    "id": "SUPmMKPDWlmp"
   },
   "source": [
    "## Installing some libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "qr2UdY33jeUQ",
    "outputId": "396076f2-6b0b-405d-95ce-2543fa6c027f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting smote_variants\n",
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     ]
    }
   ],
   "source": [
    "pip install smote_variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "colab_type": "code",
    "id": "7czY2ZsRLL2q",
    "outputId": "dfa9f43e-6068-434a-a0f2-6cdce04ad560"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting imbalanced_databases\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/44/5bc6bf4d05a19e711fcdbceb0f4fcc48b46b85d3f140285bacafee2a40d8/imbalanced_databases-0.1.1-py3-none-any.whl (4.3MB)\n",
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      "Installing collected packages: imbalanced-databases\n",
      "Successfully installed imbalanced-databases-0.1.1\n"
     ]
    }
   ],
   "source": [
    "pip install imbalanced_databases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "b4FUTDEnT0qp",
    "outputId": "9b5d11f8-8f17-436e-bc74-a626b8380e04"
   },
   "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: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from imbalanced-learn) (1.18.5)\n",
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     ]
    }
   ],
   "source": [
    "pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "msNPOM1mWlnH"
   },
   "source": [
    "## Importing libraries for our needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "JnKWN9B2LHP4",
    "outputId": "05c2ccd8-dce0-4596-8949-e713042b5f52"
   },
   "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": {
    "colab_type": "text",
    "id": "yMcZTmIOWlnP"
   },
   "source": [
    "## Reading the dataset to a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "K6gyDfH5PcxL",
    "outputId": "dfce24ba-c5e5-4568-c97f-ec23fd89a6fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\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": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "lEIV2KpEXKWA",
    "outputId": "01d7abfe-fcd2-4077-9ac6-71c18856983f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pkts', 'bytes', 'dur', 'average_dur', 'stddev_dur', 'total_dur',\n",
       "       'min_dur', 'max_dur', 'spkts', 'dpkts', 'sbytes', 'dbytes', 'rate',\n",
       "       'srate', 'drate', 'TnBPSrcIP', 'TnBPDstIP', 'TnP_PSrcIP', 'TnP_PDstIP',\n",
       "       'TnP_PerProto', 'TnP_Per_Dport', 'AR_P_Proto_P_SrcIP',\n",
       "       'AR_P_Proto_P_DstIP', 'N_IN_Conn_P_DstIP', 'N_IN_Conn_P_SrcIP',\n",
       "       'AR_P_Proto_P_Sport', 'AR_P_Proto_P_Dport',\n",
       "       'Pkts_P_State_P_Protocol_P_DestIP', 'Pkts_P_State_P_Protocol_P_SrcIP',\n",
       "       'attack'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b5Xjb7hKWlnu"
   },
   "outputs": [],
   "source": [
    "df.drop([ 'TnBPSrcIP', 'TnBPDstIP', 'TnP_PSrcIP', 'TnP_PDstIP',\n",
    "       'TnP_PerProto', 'TnP_Per_Dport', 'AR_P_Proto_P_SrcIP',\n",
    "       'AR_P_Proto_P_DstIP', 'N_IN_Conn_P_DstIP', 'N_IN_Conn_P_SrcIP',\n",
    "       'AR_P_Proto_P_Sport', 'AR_P_Proto_P_Dport',\n",
    "       'Pkts_P_State_P_Protocol_P_DestIP', 'Pkts_P_State_P_Protocol_P_SrcIP'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ASmcr4G1Wlnz"
   },
   "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": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "ILzX8RoAJ6vd",
    "outputId": "6b9a8d9b-075a-4c80-8613-0194ab39181c"
   },
   "outputs": [
    {
     "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>pkts</th>\n",
       "      <th>bytes</th>\n",
       "      <th>dur</th>\n",
       "      <th>average_dur</th>\n",
       "      <th>stddev_dur</th>\n",
       "      <th>total_dur</th>\n",
       "      <th>min_dur</th>\n",
       "      <th>max_dur</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>sbytes</th>\n",
       "      <th>dbytes</th>\n",
       "      <th>rate</th>\n",
       "      <th>srate</th>\n",
       "      <th>drate</th>\n",
       "      <th>attack</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>1729</td>\n",
       "      <td>6.406424</td>\n",
       "      <td>0.679473</td>\n",
       "      <td>0.544126</td>\n",
       "      <td>1.358946</td>\n",
       "      <td>0.135347</td>\n",
       "      <td>1.223599</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>963</td>\n",
       "      <td>766</td>\n",
       "      <td>1.404840</td>\n",
       "      <td>0.780467</td>\n",
       "      <td>0.468280</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>1604</td>\n",
       "      <td>6.405851</td>\n",
       "      <td>0.679572</td>\n",
       "      <td>0.544197</td>\n",
       "      <td>1.359144</td>\n",
       "      <td>0.135375</td>\n",
       "      <td>1.223769</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>838</td>\n",
       "      <td>766</td>\n",
       "      <td>1.404966</td>\n",
       "      <td>0.780536</td>\n",
       "      <td>0.468322</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>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>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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pkts  bytes       dur  average_dur  ...      rate     srate     drate  attack\n",
       "0    10   1729  6.406424     0.679473  ...  1.404840  0.780467  0.468280       1\n",
       "1    10   1604  6.405851     0.679572  ...  1.404966  0.780536  0.468322       1\n",
       "2     8   1708  6.401038     1.110847  ...  1.093573  0.624899  0.900214       1\n",
       "3     8   1462  6.400703     1.113328  ...  1.093630  0.624931  0.898208       1\n",
       "4     8   1296  6.400472     1.113098  ...  1.093669  0.624954  0.898394       1\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "colab_type": "code",
    "id": "gq1OUFUMmbxT",
    "outputId": "acba8751-a26d-4579-bf0a-ede9019eea43"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1926624\n",
       "0        477\n",
       "Name: attack, dtype: int64"
      ]
     },
     "execution_count": 12,
     "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": {
    "colab_type": "text",
    "id": "h6Pf_-VcWloC"
   },
   "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": {
    "colab_type": "text",
    "id": "RCg4KirBWloC"
   },
   "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",
    "\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": {
    "colab_type": "text",
    "id": "8_F_K1RnWloI"
   },
   "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": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "4I_gzBXwBJTp",
    "outputId": "ed309fd7-b3ec-4aa9-ae9c-fb8787893606"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FCCKBLFBeK98"
   },
   "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": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "ZCxyQRcQNnbB",
    "outputId": "9c117d1e-1efb-4792-8c9c-6a68c58c2b84"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "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": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "zq3cY5zdNAij",
    "outputId": "0c9fd1e6-920a-484a-cc3b-0f92b3e6aff1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[766800   4774]\n",
      " [146920 622806]]\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 0.9015804840070071\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.99      0.91    771574\n",
      "           1       0.99      0.81      0.89    769726\n",
      "\n",
      "    accuracy                           0.90   1541300\n",
      "   macro avg       0.92      0.90      0.90   1541300\n",
      "weighted avg       0.92      0.90      0.90   1541300\n",
      "\n",
      "AUC Score: \n",
      "0.9014697660119697\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": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "mc1cZCHAY1-0",
    "outputId": "9b941012-1cb9-4b1d-bbd9-d853d3645d96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[771574      0]\n",
      " [     7 769719]]\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 0.9999954583792902\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",
      "0.9999954529274053\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": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "SxnUXQK6gulj",
    "outputId": "9d1bd09f-1401-4044-82f5-1723ae26489e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[771574      0]\n",
      " [     5 769721]]\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 0.9999967559852073\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",
      "0.9999967520910038\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": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "UPjbK4lIV__f",
    "outputId": "0c660e3a-4de6-4d5e-c3b4-d52a2c62e10d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[771554     20]\n",
      " [    23 769703]]\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 0.9999721014727827\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",
      "0.9999720990989526\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": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "dZB9pl0bVwwU",
    "outputId": "d0841b52-a1e2-4d9f-e0e8-4e196316c59d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    " test_scale()\n",
    "\n",
    "# Fitting SVM to the Training set\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.svm import LinearSVC\n",
    "classifier = LinearSVC()\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": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 812
    },
    "colab_type": "code",
    "id": "rBko4G0GXaw7",
    "outputId": "1ffef72f-a940-4e66-e51c-3bc4adc03793"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[746148  25426]\n",
      " [146942 622784]]\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 0.8881671316421202\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.97      0.90    771574\n",
      "           1       0.96      0.81      0.88    769726\n",
      "\n",
      "    accuracy                           0.89   1541300\n",
      "   macro avg       0.90      0.89      0.89   1541300\n",
      "weighted avg       0.90      0.89      0.89   1541300\n",
      "\n",
      "AUC Score: \n",
      "0.8880724426063111\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": "zLo3JLgWOPQF"
   },
   "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": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "hLkdNWmZksZC",
    "outputId": "56c3cc77-ed78-47da-dd7d-7e0bae3ad85e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[222418 549156]\n",
      " [   265 769461]]\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 0.6435340297151755\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.29      0.45    771574\n",
      "           1       0.58      1.00      0.74    769726\n",
      "\n",
      "    accuracy                           0.64   1541300\n",
      "   macro avg       0.79      0.64      0.59   1541300\n",
      "weighted avg       0.79      0.64      0.59   1541300\n",
      "\n",
      "AUC Score: \n",
      "0.6439605039656615\n"
     ]
    },
    {
     "data": {
      "image/png": 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w3qedyFI5tdFXL56XiWAX548pW9KdFvKaNGnCypUrAfjyyy9p1qwZHTs6IzLmzp2bjz76iNatW/Pwww/z9ttv069fP6pUqQI4JYsHH3wwxXUbk2EkxMOSR2DTMCjXAxp+AllC4twyw4uPT6Bp0y/ZsCGGZ55pwMsvNyVXrsBdOu7luzYZ6CMiE3AGej6aLu0DSx6DmOWXvJrzFKwD9Qf7tWh8fDy//fYbPXs6Q5yuWbOG+vXrn7dM+fLlOXHiBMeOHWP16tU8+eST6RuvMYEWfwYW3Ak7J0LVZ6DOm36d5ZrUHTp0ikKFchIRkYXXXmtBqVJ5iYoqFvDtBvLy0fE4I0VVFpFoEekpIr1FpLe7yFRgC86YvZ8ADwUqlmA4deoUderUoVixYuzbt48OHTp4HZIxgXHuGMzu4iSBuoOg7luWBC6RqvLFF2upVGkkn366CoDrr68YlCQAgb1q6LY05ivwcLpv2M8z9/SW2EYQGxtLp06dGDJkCH379qVatWrMnTv3vGW3bNlCnjx5yJcvH9WrV2fJkiXUrl3bk7iNuSCn98OsznBkBTT5HMqmNTa9ScvOncfo3XsGU6dupXHjK2nWrHjQY7C+htJZ7ty5+eCDDxg0aBBxcXHccccd/P777/z666+AU3Lo27cvzzzjjKv99NNP8/rrr7Nx40YAEhIS+Pjjjz2L35gUndgK05vBsXXQcrIlgXQwfvw6qlcfzezZOxk8uA2//34b1aoVCXoclggCoG7dutSqVYvx48eTK1cufvjhB1599VUqV65MzZo1adCgAX369AGgVq1aDB48mNtuu42qVatSo0YNtmzZ4vEeGJNEzEqY3hTOHoK2v0GJLl5HlCkULJiTRo2uZPXqHjz6aH0iIrz5SQ65MYujoqI06cA069ato2rVqh5FFDzhsp8mg9k/D+ZcA1nzQJtpUKC61xGFrLi4BN57bzFnzybQr19jwGkfCEbPASKyRFWjkptnJQJjTMqiJ8OsjpCrGHRcYEngEqxYsZ/GjcfxzDNzWbnyAIkn4Rmh+xhLBMaY5P09CuZdD/lrQvvf4bKMeVd/RnfmTBwvvvg7UVFfsHPncb755homTOiaIRJAokxz90ewildeCbUqPBPCVGHd27D8WSjWEVp8C9nyeB1VyNq0KYa33lrI7bdX4d1321C4cC6vQ/qXTFEiyJkzJ4cOHcq0P5aJ4xHkzJnT61BMZqcJsPRJJwmUuQ1a/WhJ4CKcOHGWcePWAlCjRlHWr7+PMWO6ZMgkAJmkRFCyZEmio6M5cOCA16EETOIIZcYETMI5+PM+2PYFVHrEuSdHMsW5YlDNmLGNXr2ms337MerVu4KqVQtTrlwBr8NKVaZIBNmyZbORu4y5FHEnYd7NsOdnqPUqVH/e7ha+QDExp3nqqdmMGrWaSpUKMmdOd6pWLex1WH7JFInAGHMJzhyC2V3h8EJoOAIqPOB1RCEnPj6BZs2+ZOPGGJ57rhH9+zchZ87Q+XkNnUiNMenv5E6Y1QlObIHmE6HU9V5HFFIOHoylUKFcRERk4fXXW1C6dD7q1bvC67AumFUAGhOujq6DGc3g1C5o84slgQugqnz++RoqVRrFp586Xc5fd13FkEwCYCUCY8LTwb+cHkSzZIP2c5yu1o1ftm8/yn/+M4Np07bRtGlxWrYM/Ys4LBEYE252T4N5Nzh3C7eZDnnLex1RyPjii7U8+OAMVOHDD9vy0EN1yZIl9BvVLREYE062fQl/3AMFakDrn51kYPxWtGgumjUrwfDhHShTJr/X4aQbSwTGhIv178PSx+DyVtDyB8ieeX7IAuXcuXgGDVrMuXMJvPhiEzp1KkvHjpGZrhcDayw2JrNThRX9nCRQ8nqnYdiSQJqWLdtHo0bjeO65eaxdeyhDdRKX3iwRGJOZJcTBwl6w5nUo/wA0/wYirKuS1Jw+Hcfzz8+jQYMv2L37BN9+ey3jx2esTuLSm1UNGZNZxZ2CBbdD9PdQ/QWoNcDuFvbD5s0xDBy4iLvvrs6gQa0pWDDzJ05LBMZkRmePwtxrYf9cqP8BVH7E64gytBMnzjJp0ibuuqs6NWoUZcOG+yhbNmP3D5SeLBEYk9mc2uMMMH9sLTT9EiJv8zqiDG3atK306jWdnTuPExVVjKpVC4dVEgBrIzAmczn+N8xoDic2Q6ufLAmk4tChU9xzz1SuuupbcufOxrx5t4VMJ3HpzUoExmQWh5fB7KtA46HtTCjS0OuIMiynk7jxbN4cQ79+jXnhhcYh1UlcegvfPTcmM9k3C+Z0g+wFnQHm81fxOqIM6cCBWAoXdjqJe+utlpQpk486dS73OizPWdWQMaFu53cw6yq4rBR0nG9JIBmqymefraJSpZF88onTSVy3bhUsCbgsERgTyjaPgN9vhkL1of08yB36HaClt23bjtKp00Tuu28aNWsWpU2bUl6HlOFY1ZAxoUgVVr8Kq/pD8S7OjWJZc3sdVYYzduwaHnzwV0Rg6ND2/Oc/tTNFJ3HpzRKBMaFGE2DJo7DxI4i8CxqPdLqTNv9yxRWX0bJlST7+uAOlS+fzOpwMyxKBMaEk/iz8eQ9snwBVnoS6b9sA8z7OnYvn7bcXER+fQP/+TenYMZKOHSO9DivDs0RgTKg4dxzm3Qh7Z0Cdt6Ha015HlKEsXbqP++77hRUrDnD77VVR1UzdP1B6skRgTCg4fQBmXw0xS6HRKCh/r9cRZRinTp3jlVf+YODARRQtmptJk7px3XUVvQ4rpAS0TCkiV4nIBhHZLCLPJjO/tIjMEpFlIrJSRLoEMh5jQtLJ7c7dwkdXQYtJlgSS2LLlKO++u5gePWqwdu29lgQuQsBKBCISAQwBOgDRwCIRmayqa30WewH4WlWHiUg1YCoQGaiYjAk5R1bDrE4QdxLazIDLm3sdUYZw7NgZvvtuEz161KB69SJs2tQzU40YFmyBLBE0BDar6hZVPQtMALolWUaBxKb8/MDuAMZjTGg5MB9mtAAUOsyzJOCaOnULNWqMpmfPaaxbdwjAksAlCmQiKAHs9Hke7U7z9TJwp4hE45QGku0rV0R6ichiEVl84MCBQMRqTMayawrM7AA5i0KHBVCgptcRee7gwVjuumsqV1/9HXnzZmf+/PDtJC69eX3d2W3AaFUtCXQBxor8+1o4VR2hqlGqGlW0aNGgB2lMUG35HOZ2g/zVoMPvkCfS64g8l9hJ3IQJ6+nfvwlLl95F48bFvQ4r0wjkVUO7AN97uUu603z1BK4CUNU/RCQnUATYH8C4jMm41g2EZU/DFe2g5STIltfriDy1b99JihbNTUREFgYObE2ZMvmoVctOBtNbIEsEi4CKIlJWRLID3YHJSZbZAbQDEJGqQE7A6n5M+FGFZc84SaD0LdB6SlgnAVVl5MhVVK48ihEjVgBwzTXlLQkESMBKBKoaJyJ9gGlABDBKVdeIyABgsapOBp4EPhGRx3EajnuoqgYqJmMypIQ4+Ot+2DoGKj7kDC2ZJcLrqDyzZcsRHnhgOjNn7qBVq5K0b1/G65AyvYDeUKaqU3EagX2n9fd5vBZoFsgYjMnQ4mLh91th909Q82Wo0T+sB5gfM2Y1Dz30KxERWfj44w488EAt6yQuCOzOYmO8cjYG5lwDBxZAg6FQ8UGvI/Jc8eJ5aNu2NMOGdaBkyfCtGgs2SwTGeCF2lzOYzPGN0PwrKH2z1xF54uzZeN588y8SEpSXX25Ghw6RdOgQ6XVYYccSgTHBdmyDc7fwmUPQ+mco1tbriDyxaNEe7rtvGqtXH+Suu6pZJ3Ee8vo+AmPCy6FFTr9BcbHQfnZYJoHY2HM89dRsGjf+kpiY00yefD2ff97FkoCHrERgTLDsmQHzroccRaHNdMgXnp2jbd16lA8/XMYDD9Tirbdakj9/Dq9DCnuWCIwJhu1fwR93Qb4q0PoXyB1ed8UePXqG777byL331qR69SJs3tyTUqVsxLCMwqqGjAm0DR/B/NugcGNoPzfsksCUKX9Tvfpn3H//dNavdzqJsySQsVgiMCZQVGHlS7DkEShxDbSZBtkLeB1V0Bw4EMsdd0yha9dJFCyYkz/+uJ0qVayTuIzIqoaMCYSEeFj8MGweDuXug4bDIUv4fN3i4xNo3nw8W7ce5ZVXmvLss43Inj1875bO6MLnk2lMsMSfgQV3wM5vodqzUPv1sLlbeO/ek1x+udNJ3KBBrYmMzEeNGtY/UEbnd9WQiOQOZCDGZArnjsHszk4SqPcu1HkjLJJAQoIyfPgKKlUayfDhTidxXbuWtyQQItJMBCLSVETWAuvd57VFZGjAIzMm1JzaB7+2hv3zoMlYqPK41xEFxebNMbRr9zW9e8+gQYNidOoU6XVI5gL5UzX0HtAJtwtpVV0hIi0DGpUxoebEFpjZEU7thlaToXhnryMKis8+W8VDD/1G9uxZ+OSTjvTsWdNuDAtBfrURqOrOJG9ufGDCMSYExaxw+g1KOANtf4OiTbyOKGhKl85Hp06RDBnSjhIlrJO4UOVPItgpIk0BFZFswKPAusCGZUyI2D/X6UE0Wz5o95szvGQmduZMHG+84XQSN2BAc9q1K0O7djZeQKjzp7G4N/AwzsDzu4A6wEOBDMqYkBD9g1MdlKs4dJif6ZPAX3/toX79sbzyyh/s2HEcG0Mq8/CnRFBZVe/wnSAizYD5gQnJmBDw90hY2AsKNYBWP0HOIl5HFDAnT57lxRfnM3jwEkqUyMtPP13P1VeX9zosk478KRF86Oc0YzI/VVjzhjO0ZLEO0PbXTJ0EALZvP8bQocvp3bs2a9b0sCSQCaVYIhCRJkBToKiIPOEzKx/OGMTGhBdNgKVPwobBUOY2aDwaIrJ7HVVAHDlymokTN3L//bWoVq0ImzffbyOGZWKpVQ1lB/K4y/h+Ao4BNwUyKGMynPiz8Nd9sG0cVH7UuVlMMmdXXT/8sJkHH5zB/v2xNG9egipVClsSyORSTASqOgeYIyKjVXV7EGMyJmOJOwnzboQ905zuIqo9mynvFt6//yR9+87kq682UKtWUSZPvt46iQsT/jQWx4rIO0B1IGfiRFUNv6GVTPg5cwhmXw2HF0HDT6DC/V5HFBDx8Qk0azaeHTuO8+qrzXnmmQZky2Y1wOHCn0QwDvgK6IpzKek9wIFABmVMhnBypzO28Ikt0PxbKHWd1xGlu927T1Cs2GVERGTh/ffbEhmZj2rVMnfjt/k3fyo5C6vqSOCcqs5R1fsAKw2YzO3oOpjRFE7tcsYRyGRJICFBGTZsOVWqjOLjj5cD0KVLOUsCYcqfEsE59/8eEbka2A0UClxIxnjs4J9OdVCWbNB+DhSs43VE6WrjxsM88MB05s6Npn37MnTuXNbrkIzH/EkEr4pIfuBJnPsH8gGPBTQqY7yy+2eYdxPkuhLaToc85byOKF2NHLmKPn1+I2fOCEaN6kSPHjWskziTdiJQ1Z/ch0eBNvDPncXGZC5bx8GfPaBADWeA+VxXeB1RuouMzEfnzmUZMqQdV16Zx+twTAaR2g1lEcAtOH0M/aKqq0WkK/A8kAuoG5wQjQmC9YNh6eNweWto+T1kz+91ROnizJk4/u///gTg1VetkziTvNRKBCOBUsBC4AMR2Q1EAc+q6vfBCM6YgFOFFc/D2jeh1A3QdBxE5Ez7dSFgwYJd9Ow5jfXrD3PffTVQVasGMslKLRFEAbVUNUFEcgJ7gfKqeig4oRkTYAlxsPA/sGUUVOgFUUMhS+hfO3/ixFn69fudDz9cSqlSefnllxvp1MkahE3KUrt89KyqJgCo6mlgy4UmARG5SkQ2iMhmEXk2hWVuEZG1IrJGRL68kPUbc9HiTsHvNzlJoMaL0ODjTJEEAHbsOMbw4St4+OG6rF59ryUBk6bUSgRVRGSl+1iA8u5zAVRVa6W2YreNYQjQAYgGFonIZFVd67NMReA5oJmqxojI5ZewL8b45+wRmHMtHPgd6n8Ilft4HdEli4k5zTffbKBXr9pUq1aELVseoHhxaww2/kktEVS9xHU3BDar6hYAEZkAdAPW+izzADBEVWMAVHX/JW7TmNSd2uMMK3lsHTT9EiK7ex3RJZs0aRMPPfQrBw7E0qpVKSpXLmRJwFyQ1Dqdu9SO5rU3DD0AACAASURBVEoAO32eRwONkixTCUBE5uN0bf2yqv6SdEUi0gvoBVC6dOlLDMuErWObnC4jzuyHVlPgyg5eR3RJ9u49ySOP/MbEiRupU+dypky5gcqV7V5Pc+H8Grw+wNuvCLQGSgJzRaSmqh7xXUhVRwAjAKKiomx8PHPhDi91SgIkQLtZULiB1xFdkvj4BFq0GM/Oncd5/fUWPPVUlHUSZy5aIBPBLpzLTxOVdKf5igb+UtVzwFYR2YiTGBYFMC4TbvbOhLnXQfaCzt3C+Sp7HdFFi44+TvHieYiIyMIHH7SlbNn81lW0uWR+jawhIrlE5EK/PYuAiiJSVkSyA92ByUmW+R6nNICIFMGpKtpygdsxJmU7JsLsznBZaei4IGSTQEKC8uGHS6lSZRTDhjmdxHXuXM6SgEkXaSYCEbkGWA784j6vIyJJf9D/RVXjgD7ANGAd8LWqrhGRASJyrbvYNOCQiKwFZgFP230KJt1s+hh+vwUKRUH7uZC7hNcRXZT16w/RsuUE+vadSfPmJejaNXP1f2S8J6qpV7mLyBKcbqdnq2pdd9oqVa0ZhPj+JSoqShcvXuzFpk2oUIXV/werXoLiV0PzryFrbq+juiiffrqSPn1+I3fubAwe3Ia77qpmdwebiyIiS1Q1Krl5fnVDrapHk3z4rMHWZEwJ8bDkUdg0BMreA40+cbqTDlHlyxfgmmvK89FH7bjiisu8DsdkUv4kgjUicjsQ4d4A1hdYENiwjLkI8Wfgj7thx9dQ9Smo83bIjS18+nQcAwb8AcDrr7egTZvStGljl0ybwPKnsfgRnPGKzwBf4nRHbeMRmIzl3HGY09VJAnXehrrvhFwSmD9/F3XqfM4bb/zFgQOxpFVta0x68adEUEVV+wH9Ah2MMRfl9AGY3QVilkHjz6BcD68juiDHj5/l+efnMWTIMsqUyce0aTfRsWOk12GZMOJPIhgkIsWAicBXqro6wDEZ478T25y7hWN3QItJUPIaryO6YNHRx/n001U88kg9XnutOXnyZPc6JBNm0qwaUtU2OCOTHQCGi8gqEXkh4JEZk5Yjq2BGMzi9H9r+GlJJ4NChU//cD1C1amG2bLmf999va0nAeMKvG8pUda+qfgD0xrmnoH9AozImLft/hxktnccd5kHR0Bg9VVWZOHED1ap9Rt++M9mw4TCADRtpPOXPDWVVReRlEVmFM3j9ApzuIozxRvSPMKsD5LwcOsx3xhgOAXv2nODGGydz880/UqpUXhYvvtM6iTMZgj9tBKOAr4BOqro7wPEYk7oto+Gv+6FgXWg9FXIW9ToivzidxE1g164TvP12Sx5/PIqsWf0qkBsTcGkmAlVtEoxAjEnT2ndg+TNQrD20+A6y5fU6ojTt3HmMEiXyEhGRhSFD2lG2bH4qVbJSgMlYUjwlEZGv3f+rRGSlz98qn5HLjAk8TYBlTztJoPSt0OqnDJ8E4uMT+OCD8zuJ69SprCUBkyGlViJ41P3fNRiBGJOshHNOVdDWz6FSH6j/PkjGrlJZt+4QPXtO448/dtO5c1muuaa81yEZk6oUv1Gqusd9+JCqbvf9Ax4KTngmrMXFwtzrnSRQcwDU/yDDJ4ERI1ZQp87nbNwYw9ixXZgy5QZKl87ndVjGpMqfb1Vy4/l1Tu9AjDnPmcMwswPsngoNhkHNF0Oiy4iKFQty/fUVWLu2B3feaT2FmtCQYtWQiDyIc+ZfLkmbQF5gfqADM2EsNtoZVvL4Jmj+DZS+0euIUnTq1DlefnkBIsKbb7a0TuJMSEqtjeBL4GfgDeBZn+nHVfVwQKMy4evYBpjZEc7GQJtf4Io2XkeUorlzd3L//dPZtCmG3r1ro6pWAjAhKbVEoKq6TUQeTjpDRApZMjDp7tAip/M4yQLtZ0Ohel5HlKxjx87w7LNzGTZsBeXK5ee3326hbVsrBZjQlVaJoCuwBGcgGt9THQVsvDyTfvZMh3k3QI7LnQHm81bwOqIU7d59gtGj1/DEE/UZMKAZl11m/QOZ0JZiIlDVru7/ssELx4SlbRPgz7shX1WnOijXlV5H9C8HD8by9dcbeOihulSpUpitWx+wEcNMpuFPX0PNROQy9/GdIvKuiFg52KSPDR/CgtuhSBNoPyfDJQFV5auv1lOt2mc89tgsNm50akQtCZjMxJ/LR4cBsSJSG3gS+BsYG9CoTOanCitehCV9oeS10PoXyF7A66jOs3v3Ca677nu6d/+JMmXysWTJXXZnsMmU/Ol0Lk5VVUS6AR+p6kgR6RnowEwmlhAPix+CzSOg3H3QcDhk8eejGDzx8Qm0bOl0EjdwYCsefbS+dRJnMi1/vn3HReQ54C6ghYhkAbIFNiyTacWfhgV3wM7voNpzUPu1DHWj2PbtRylZ0ukkbujQ9pQrl58KFQp6HZYxAeXPKc6tOAPX36eqe3HGIngnoFGZzOnsUZjV2UkC9d6DOq9nmCQQH5/Au+8upmrVz/7pJK5jx0hLAiYs+NMN9V4RGQc0EJGuwEJV/TzwoZlM5dQ+mH0VHFkNTb6Asnd4HdE/Vq8+QM+e01i4cC9du5bjuusqeh2SMUHlz1VDtwALgZuBW4C/ROSmQAdmMpETW5yxhY9thFY/Zqgk8PHHy6lXbyxbthzlyy+vZvLk6ylZMmN3cW1MevOnjaAf0EBV9wOISFHgV2BiIAMzmUTMcqffoIRz0G4mFGnkdUQA/3QHUbVqYW6+uTKDB7ehaNHcXodljCf8SQRZEpOA6xB+Dnpvwty+OTD3WsiWD9rNgvxVvY6I2Nhz9O8/n4gI4a23WtGqVSlatSrldVjGeMqfH/RfRGSaiPQQkR7AFGBqYMMyIW/nJJjVCXKVgA4LMkQSmD17B7VqjWHQoMWcOHEOVfU6JGMyBH8ai58WkRuA5u6kEao6KbBhmZC2+VNY9B8o1ABaT4EchT0N5+jRMzzzzBxGjFhJ+fIFmDnzFusq2hgfqY1HUBEYCJQHVgFPqequYAVmQpAqrH0DVvSDK6+CFhMhq/ddMezZc4IvvljLU09F8corzcid226DMcZXalVDo4CfgBtxeiD98EJXLiJXicgGEdksIs+mstyNIqIiEnWh2zAZhCbAksecJBB5B7Sa7GkSOHAglg8/XApAlSqF2batF++809qSgDHJSK1qKK+qfuI+3iAiSy9kxSISAQzBGeoyGlgkIpNVdW2S5fICjwJ/Xcj6TQYSfxb+7AHbx0Plx6DeIM/GFlZVxo9fT9++Mzl27AydOkVSqVIhuyLImFSk9m3NKSJ1RaSeiNQDciV5npaGwGZV3aKqZ4EJQLdklvs/4C3g9AVHb7x37oRzZdD28VD7Daj3rmdJYOfOY1xzzSTuuGMKFSoUYNmyu62TOGP8kFqJYA/wrs/zvT7PFWibxrpLADt9nkcD511E7iaUUqo6RUSeTmlFItIL6AVQurQ18mUYpw/CnKvh8GJo9CmU964vwri4BFq3/oq9e0/y3ntteOSRukRE2FXOxvgjtYFpAjpYrNt53btAj7SWVdURwAiAqKgou+YvIzi5A2Z1hBPboPm3UOo6T8LYtu0opUrlJWvWLAwf3pFy5fJTrlzG6s7amIwukKdMuwDfO3VKutMS5QVqALNFZBvQGJhsDcYh4Ohap8uIU3udYSU9SAJxcQkMHLiIqlU/Y+hQp5O49u3LWBIw5iIEshP4RUBFESmLkwC6A7cnzlTVo0CRxOciMhvnEtXFAYzJXKoDfzjVQVlyOCOKFawd9BBWrjxAz56/sHjxPrp1q8CNN1YKegzGZCYBKxGoahzQB5gGrAO+VtU1IjJARK4N1HZNAO3+GWa2g+yFoeN8T5LA0KHLqF9/LNu3H+Orr7oyaVI3ihfPE/Q4jMlM0iwRiIgAdwDlVHWAO15xMVVdmNZrVXUqSbqjUNX+KSzb2q+IjTe2fgF/3gsFakLrnyHXFUHdfGIncTVqFKF79yq8915rihSxS0KNSQ/+VA0NBRJwrhIaABwHvgUaBDAuk5Gsfw+WPgFXtIGW3zudyAXJyZNneeGF+WTNKrzzTmtatixFy5bWSZwx6cmfqqFGqvow7nX+qhoDZA9oVCZjUIXlzzpJoNSN0HpqUJPAb79tp2bNMQwevIQzZ+KtkzhjAsSfEsE59y5hhX/GI0gIaFTGewlxsPA/sGUUVPgPRA2BLBFB2fSRI6d56qk5jBy5iooVCzJ3bndatCgZlG0bE478KRF8AEwCLheR14DfgdcDGpXxVtwpmHejkwRq9IcGw4KWBAD27YtlwoT1/Pe/DVmx4m5LAsYEmD/dUI8TkSVAO0CA61R1XcAjM944ewTmXAsHfoeoj6DSw0HZ7L59J5kwYT2PPlqfypULsW3bA9YYbEyQ+HPVUGkgFvjRd5qq7ghkYMYDsbudAeaPrYdm46HMrQHfpKoybtw6Hn10JidOnKNLl3JUrFjQkoAxQeRPG8EUnPYBAXICZYENQPUAxmWC7dgmp8uIMweg1RS4skPAN7ljxzF6957Bzz9vpUmT4owc2YmKFQsGfLvGmPP5UzVU0/e521HcQwGLyATf4SUwqzOg0G42FA58Lx+JncTt3x/LBx+05aGH6lgnccZ45IK7mFDVpSLSKO0lTUjY+xvMvc4ZTrLNdMgX2O4atmw5Qpky+ciaNQuffNKR8uULEBmZP6DbNMakzp82gid8nmYB6gG7AxaRCZ4d38CCOyFvJWjzC+QuEbBNxcUlMGjQIl56aQFvv92Kvn3r0a5dmYBtzxjjP39KBHl9HsfhtBl8G5hwTNBsGgaLHoaiTaHVj5A9cHXzy5fvp2fPaSxduo/rr6/IzTdbJ3HGZCSpJgL3RrK8qvpUkOIxgaYKq16B1a9A8a7Q/CvIGrgrdD76aCmPPz6bwoVzMnHitdZTqDEZUIqJQESyqmqciDQLZkAmgBLiYUlf2DQUyt4DjT6BLIEZzD2xk7hatYpyxx1Veffd1hQqlCsg2zLGXJrUSgQLcdoDlovIZOAb4GTiTFX9LsCxmfQUfwb+uMtpF6j6DNR5E0TSfTMnTpylX7/fyZYtCwMHWidxxoQCf9oIcgKHcHofTbyfQAFLBKHi3HGYez3s+w3qvgNVA1PTN336Nnr1ms6OHcd45JF6/5QKjDEZW2qJ4HL3iqHV/C8BJLJuIEPF6f0wuwvELIfGY6Dc3em+iZiY0zzxxCxGj15D5cqFmDu3O82bW/9AxoSK1BJBBJCH8xNAIksEoeDEVpjVCWKjoeUPUOLqgGxm//5YJk7cyHPPNaJ//ybkzBnIEVCNMekttW/sHlUdELRITPo6sspJAnGnoO2vzmWi6Wjv3pOMH7+Oxx+PcjuJ60XhwtYYbEwoSu2efqvcDVX7f4cZLQGBDvPSNQmoKmPGrKZatc947rl5bNoUA2BJwJgQlloiaBe0KEz6iZ4MszpAzsuh4wIoUCPdVr1t21GuuupbevT4hWrVCrN8+d3WSZwxmUCKVUOqejiYgZh08PdnsPABKFgPWk+BnEXTbdVxcQm0afMVBw+eYsiQdvTuXYcsWazQaExmYK16mYEqrHsHlv8XinWAFt9BtjzpsurNm2MoWzY/WbNmYdSoqyhXLj9lylgnccZkJtbvb6jTBFj2lJMESt8KrX5KlyRw7lw8r7/+J9Wrj2bIkOUAtGlT2pKAMZmQlQhCWcI5+LMnbBsLlfpA/fdBLj23L126j549p7F8+X5uvrkSt95aOR2CNcZkVJYIQlXcSfj9Ftg9FWr9H1Tvly5dRnzwwVKeeGIWRYvm5rvvunH99RXTIVhjTEZmiSAUnTkMs6+Gwwuh4XCo0OuSV5nYHUTdupdz993VGTSoNQUL5kyHYI0xGZ0lglATG+3cKHZ8MzT/BkrdcEmrO378LM89N5ccOSIYNKgNLVqUpEUL6x7CmHBijcWh5Oh6mN4UTu50RhS7xCTwyy9bqVHjM4YOXY6qUyowxoQfKxGEioMLYU4XkAhoPwcK1b3oVR06dIonnpjF55+vpWrVQsyffztNmhRPx2CNMaHESgShYM90mNkWsuWHDvMvKQmAkwgmTdrMiy82Ztmyuy0JGBPmApoIROQqEdkgIptF5Nlk5j8hImtFZKWI/CYiNpp5UtvGOw3DeSpAh98hb4WLWs2ePScYOHARqkqlSoXYvr0XAwY0J0cOKxQaE+4Clgjc8Y6HAJ2BasBtIlItyWLLgChVrQVMBN4OVDwhacMHsOB2p9O49nMg15UXvApVZdSoVVSt+hkvvjifzZuPANgVQcaYfwSyRNAQ2KyqW1T1LDAB6Oa7gKrOUtVY9+mfgF2uAk6XEStegCWPQsnroM00yH7hd/Ru3XqEjh0n0rPnNGrXLsqKFdZJnDHm3wJZL1AC2OnzPBpolMryPYGfk5shIr2AXgClS5dOr/gypoQ4WPQQ/P0JlL8fGgyDLBf+NsXFJdC27dccOnSaYcPa06tXbeskzhiTrAxRQSwidwJRQKvk5qvqCGAEQFRUVOa9xjH+NMy/HaInQfXnodarF3y38KZNMZQr53QS99lnV1G+fAFKlcoXoICNMZlBIKuGdgGlfJ6XdKedR0TaA/2Aa1X1TADjydjOHoVZVzlJoP77UPu1C0oC587F8+qrf1Cjxmg++mgZAK1bl7YkYIxJUyBLBIuAiiJSFicBdAdu911AROoCw4GrVHV/AGPJ2E7tdZLA0TXQdBxE3p72a3wsXryXnj2nsXLlAbp3r8Jtt1UJUKDGmMwoYIlAVeNEpA8wDYgARqnqGhEZACxW1cnAO0Ae4Btxzn53qOq1gYopQzr+N8zq6CSDVj9C8asu6OXvv7+EJ56YTbFil/HDD9dx7bUXd3mpMSZ8BbSNQFWnAlOTTOvv87h9ILef4R1eBrM7g8ZBu5lQJLW29PMldhIXFVWMnj1r8vbbLSlQwC4JNcZcuAzRWByW9s2GOddC9gLQZhbkr+rXy44dO8N//zuXnDmz8t57bWjWrATNmpUIbKzGmEzNupjwws7vnB5Ec5d0uozwMwlMnbqF6tVHM2LESrJmFeskzhiTLqxEEGybP4FFvaFQQ2j9E+QonOZLDh6M5bHHZjFu3DqqVy/MxIm306jRhd9lbIwxybESQbCowurXYGEvKNYJ2v3qVxIAiIk5w48//s1LLzVh6dK7LQkYY9KVlQiCQRNgyWOw8UOIvBMaj4Is2VJ9ya5dxxk3bh1PP92AihULsn17L2sMNsYEhCWCQIs/C3/eA9snQOXHod7AVAeYV1U+/XQVTz01m3PnErjhhopUqFDQkoAxJmCsaiiQzp2AOdc4SaDOm1BvUKpJ4O+/j9Cu3df06jWdevWuYOXKe6hQwTqJM8YElpUIAuX0QZhzNRxeDI1GQvn7Ul08Li6Bdu2+5vDh0wwf3oH7769lncQZY4LCEkEgnNzuXB56cju0mAQlU75ZesOGw5QvX4CsWbMwZkxnypcvQMmSeYMYrDEm3FnVUHo7sgamN3O6jGgzPcUkcPZsPK+8soCaNUczZIjTSVyrVqUsCRhjgs5KBOnpwAKY0xUickL7uVCwVrKLLVy4h549p7F69UFuv70qd9zh3w1lxhgTCFYiSC+7psDM9pC9sHO3cApJYPDgJTRp8iUxMaf58cfrGTfuaooUyR3kYI0x5n+sRJAetnwOf90HBWpDm58h5+X/WiSxk7iGDYvxwAO1eOutluTPn8ODYI0x5nyWCC7VukGw7Cm4oi20nATZzh8I5ujRMzzzzBxy5crK4MFtadq0BE2bWidxxpiMw6qGLpYqLHvGSQKlboLWU/+VBH788W+qVfuMTz9dRY4cEdZJnDEmQ7ISwcVIiIOFD8CW0VDxQaj/IWSJ+Gf2gQOxPProTMaPX0/NmkX4/vtuNGhg/QMZYzImSwQXKi4W5neHXT9CzZehRv9/jS189OgZpk7dyiuvNOXZZxuRPXtE8usyxpgMwBLBhTgb4wwmc2A+RA2BSg/9M2vnzmN88cU6nn22IRUqOJ3EWWOwMSYUWCLwV+xu527h4xug2QQocwsACQnKiBEreOaZucTHJ3DzzZWoUKGgJQFjTMiwxmJ/HNsIM5rCyW1Oo7CbBDZtiqFt26948MFfadiwGKtW9bBO4owxIcdKBGk5tNgZYB6BdrOgcBTgdBLXocM3HDlyhpEjO3HvvTUQsU7ijDGhxxJBavb+CnOvd0YSazMd8lVi3bpDVKxYkKxZszB2bBfKly9A8eJ5vI7UGGMumlUNpWT71zC7C1wWCR0WcCZHOV56aT61ao3ho4+cTuJatChpScAYE/KsRJCcjUNhcR8o2gxaTebPpafo2XMsa9ce4q67qnHXXdW8jtAYY9KNlQh8qcLKl2Hxw1CiK7SZzqAPN9O06ZccP36WqVNv4PPPu1C4cC6vIzXGmHRjJYJECfFOKWDzx1CuBwlRI8iSNRtNmhSnd+/avPlmS/Lls0tCjTGZj4Ra/zdRUVG6ePHi9F1p/BlYcCfsnMiRkv/lydGtyX1ZNj78sF36bscYYzwiIktUNSq5eVY1dO6Y0yi8cyLfHxpIte5lGPP5GvLmzW6dxBljwkJ4Vw2d2gezu7B/+2b6/PgB30w9Q506l/HTTzdQr94VXkdnjDFBEb4lghNbYUZzOLaOYzXGMGMBvPZacxYuvMOSgDEmrIRniSBmJTu+vomxsyvz/PtjqHB5U3bsOEvevNm9jswYY4IuoCUCEblKRDaIyGYReTaZ+TlE5Ct3/l8iEhnIeAAS9s5l6NOPU/2xHrw+uR1/H3MGjrckYIwJVwFLBCISAQwBOgPVgNtEJOmdWD2BGFWtALwHvBWoeAA2zJlI6zZf8/DIzjRpWoo1a+6zTuKMMWEvkFVDDYHNqroFQEQmAN2AtT7LdANedh9PBD4SEdEAXK4Tt+lzOt20nqOni/PZiGbcc39j6yTOGGMIbCIoAez0eR4NNEppGVWNE5GjQGHgoO9CItIL6AVQunTpiwoma4HyfPHSr5S/9h2uLG2NwcYYkygkGotVdQQwApwbyi5qJUWb0bxPs/QMyxhjMoVANhbvAkr5PC/pTkt2GRHJCuQHDgUwJmOMMUkEMhEsAiqKSFkRyQ50ByYnWWYycI/7+CZgZiDaB4wxxqQsYFVDbp1/H2AaEAGMUtU1IjIAWKyqk4GRwFgR2QwcxkkWxhhjgiigbQSqOhWYmmRaf5/Hp4GbAxmDMcaY1IVvFxPGGGMASwTGGBP2LBEYY0yYs0RgjDFhLuRGKBORA8D2i3x5EZLctRwGbJ/Dg+1zeLiUfS6jqkWTmxFyieBSiMjilIZqy6xsn8OD7XN4CNQ+W9WQMcaEOUsExhgT5sItEYzwOgAP2D6HB9vn8BCQfQ6rNgJjjDH/Fm4lAmOMMUlYIjDGmDCXKROBiFwlIhtEZLOIPJvM/Bwi8pU7/y8RiQx+lOnLj31+QkTWishKEflNRMp4EWd6SmuffZa7UURUREL+UkN/9llEbnHf6zUi8mWwY0xvfny2S4vILBFZ5n6+u3gRZ3oRkVEisl9EVqcwX0TkA/d4rBSRepe8UVXNVH84XV7/DZQDsgMrgGpJlnkI+Nh93B34yuu4g7DPbYDc7uMHw2Gf3eXyAnOBP4Eor+MOwvtcEVgGFHSfX+513EHY5xHAg+7jasA2r+O+xH1uCdQDVqcwvwvwMyBAY+CvS91mZiwRNAQ2q+oWVT0LTAC6JVmmGzDGfTwRaCehPZJ9mvusqrNUNdZ9+ifOiHGhzJ/3GeD/gLeA08EMLkD82ecHgCGqGgOgqvuDHGN682efFcjnPs4P7A5ifOlOVefijM+Skm7A5+r4EyggIldeyjYzYyIoAez0eR7tTkt2GVWNA44ChYMSXWD4s8++euKcUYSyNPfZLTKXUtUpwQwsgPx5nysBlURkvoj8KSJXBS26wPBnn18G7hSRaJzxTx4JTmieudDve5pCYvB6k35E5E4gCmjldSyBJCJZgHeBHh6HEmxZcaqHWuOU+uaKSE1VPeJpVIF1GzBaVQeJSBOcUQ9rqGqC14GFisxYItgFlPJ5XtKdluwyIpIVpzh5KCjRBYY/+4yItAf6Adeq6pkgxRYoae1zXqAGMFtEtuHUpU4O8QZjf97naGCyqp5T1a3ARpzEEKr82eeewNcAqvoHkBOnc7bMyq/v+4XIjIlgEVBRRMqKSHacxuDJSZaZDNzjPr4JmKluK0yISnOfRaQuMBwnCYR6vTGksc+qelRVi6hqpKpG4rSLXKuqi70JN13489n+Hqc0gIgUwakq2hLMINOZP/u8A2gHICJVcRLBgaBGGVyTgbvdq4caA0dVdc+lrDDTVQ2papyI9AGm4VxxMEpV14jIAGCxqk4GRuIUHzfjNMp09y7iS+fnPr8D5AG+cdvFd6jqtZ4FfYn83OdMxc99ngZ0FJG1QDzwtKqGbGnXz31+EvhERB7HaTjuEcondiIyHieZF3HbPV4CsgGo6sc47SBdgM1ALHDvJW8zhI+XMcaYdJAZq4aMMcZcAEsExhgT5iwRGGNMmLNEYIwxYc4SgTHGhDlLBCZDEpF4EVnu8xeZyrIn0mF7o0Vkq7utpe4dqhe6jk9FpJr7+Pkk8xZcaozuehKPy2oR+VFECqSxfJ1Q743TBJ5dPmoyJBE5oap50nvZVNYxGvhJVSeKSEdgoKrWuoT1XXJMaa1XRMYAG1X1tVSW74HT62qf9I7FZB5WIjAhQUTyuOMoLBWRVSLyr55GReRKEZnrc8bcwp3eUUT+cF/7jYik9QM9F6jgvvYJd12rReQxd9plIjJFRFa40291p88WkSgReRPI5cYxzp13wv0/QUSu9ol5tIjcJCIRIvKOiCxy+5j/jx+H5Q/czsZEpKG7j8tEZIGIVHbvxB0A3OrGcqsb+ygRWegum1yPrSbceN33tv3ZX3J/OHfFLnf/JuHcBZ/PnVcE567KxBLtCff/k0A/93EETn9DSBLMIQAAAvxJREFURXB+2C9zp/8X6J/M9kYDN7mPbwb+AuoDq4DLcO7KXgPUBW4EPvF5bX73/2zcMQ8SY/JZJjHG64Ex7uPsOL1I5gJ6/X975xJSVRSF4e+HTFPIJhE0CBokERRCzaIXRUlBFAURRQiNImxSzYQgxKgoMJpUEgVFRM9ZmYWiBGGhZvScNO0xaFBpILQarH3oYke8jUTP+uDA2Ze1z96bC3edvfbl/4Hm9Hkl8AJYmDPPHyXruwU0pPZsYEa63wDcSfeNwPmS/q3A3nQ/B9ciqpns7zuuyb2mncREMG0YMbP6rCGpAmiVtBr4jb8JzwM+lfR5DlxOsffNbFDSGtys5GmS1piJv0nncVpSM65Tsx/Xr7lnZj/THO4Cq4CHwBlJJ/FyUu9/rOsB0CapEmgAesxsJJWjlknameJqcbG4j2P6z5I0mNb/Fugsib8qaREus1Axzvgbga2SjqR2FbAgPSsoKJEIgqnCHmAusNzMRuWKolWlAWbWkxLFFuCKpLPAN6DTzHaXMcZRM7udNSStzwsysw9yr4PNQIukJ2Z2vJxFmNkvSd3AJmAXbrQC7jbVZGYdEzxixMzqJVXj+jsHgXO4AU+XmW1PB+vd4/QXsMPM3pcz36AYxBlBMFWoBb6kJLAO+MdzWe7D/NnMLgHtuN3fM2ClpKzmXyOprswxe4Ftkqol1eBlnV5J84FhM7uGi/nlecaOpp1JHjdxobBsdwH+o34g6yOpLo2Zi7nb3CHgsP5KqWdSxI0lod/xEllGB9CktD2Sq9IGBScSQTBVuA6skPQK2Ae8y4lZC7yUNIC/bbeZ2Vf8h/GGpCG8LLS4nAHNrB8/O+jDzwzazWwAWAr0pRLNMaAlp/tFYCg7LB7DI9wY6LG5/SJ44noD9MtNyy8wwY49zWUIN2Y5BZxIay/t1wUsyQ6L8Z1DRZrb69QOCk78fTQIgqDgxI4gCIKg4EQiCIIgKDiRCIIgCApOJIIgCIKCE4kgCIKg4EQiCIIgKDiRCIIgCArOHyNheFfrVhXHAAAAAElFTkSuQmCC\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": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "LhH1Fh49ptq6",
    "outputId": "4d7cbabd-0b9d-4f21-c095-9a69d7ef5ac5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "2311948/2311948 [==============================] - 247s 107us/step - loss: 0.1406 - accuracy: 0.9542\n",
      "Epoch 2/3\n",
      "2311948/2311948 [==============================] - 245s 106us/step - loss: 0.1170 - accuracy: 0.9655\n",
      "Epoch 3/3\n",
      "2311948/2311948 [==============================] - 245s 106us/step - loss: 0.1095 - accuracy: 0.9664\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 = 15))\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": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "colab_type": "code",
    "id": "JoAWOUZNpvr2",
    "outputId": "c0aa3f13-3c7b-4a53-d7d4-22935682857f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[769993   1581]\n",
      " [ 50382 719344]]\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 0.9662862518653085\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97    771574\n",
      "           1       1.00      0.93      0.97    769726\n",
      "\n",
      "    accuracy                           0.97   1541300\n",
      "   macro avg       0.97      0.97      0.97   1541300\n",
      "weighted avg       0.97      0.97      0.97   1541300\n",
      "\n",
      "AUC Score: \n",
      "0.9662482407112907\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": "MszcdtWl0KcK"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7N_XtMXDvH0H"
   },
   "outputs": [],
   "source": [
    "# RESULTS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m8XYR-eRhE5q"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "MachineLearningCLassificationWIthoutFlow.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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
}