bangkityog3.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "bangkityog3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOGgyqVIR2Em5+dxx/MmQMf",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alkindiisda/AirAsia-DataGathering/blob/master/bangkityog3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6AFWKHTyBE1",
        "colab_type": "text"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "Assignment #05-W05-First Machine Learning Project - MLCC Project\n",
        "\n",
        "Project Members:\n",
        "\n",
        "*   Al Kindi Isda - alkindiisda@gmail.com\n",
        "*   Bayu Dwi P - mr.ilhamtohari@gmail.com\n",
        "*   Ilham Tohari - bayud40@gmail.com\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTCvWCp6yGUH",
        "colab_type": "text"
      },
      "source": [
        "# About the Dataset\n",
        "\n",
        "The csv file contains 5172 rows, each row for each email. There are 3002 columns. The first column indicates Email name. The name has been set with numbers and not recipients' name to protect privacy. The last column has the labels for prediction : 1 for spam, 0 for not spam. The remaining 3000 columns are the 3000 most common words in all the emails, after excluding the non-alphabetical characters/words. For each row, the count of each word(column) in that email(row) is stored in the respective cells. Thus, information regarding all 5172 emails are stored in a compact dataframe rather than as separate text files."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cuFCEyIqy50l",
        "colab_type": "text"
      },
      "source": [
        "# Pulling dataset from github"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMhWDDdhhBmw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install tensorflow==1.2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJjVlUR9y_vR",
        "colab_type": "code",
        "outputId": "0a6debea-38bc-4e23-d221-003b511cbcad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "import os\n",
        "import urllib.request\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import time\n",
        "import pickle\n",
        "import seaborn as sns\n",
        "import sys\n",
        "urllib.request.urlretrieve('https://github.com/alkindiisda/bangkityog3/releases/download/1.0/email-spam-data.zip', 'email-spam-data.zip')\n",
        "!unzip email-spam-data.zip"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  email-spam-data.zip\n",
            "replace emails.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: emails.csv              \n",
            "replace clean.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: clean.csv               \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pnd_jekW0X4s",
        "colab_type": "text"
      },
      "source": [
        "# Show raw data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgvud40p0biK",
        "colab_type": "code",
        "outputId": "cdb1dc01-f8f2-407c-b8af-2829903cd42c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "import pandas as pd\n",
        "import os.path\n",
        "\n",
        "if(os.path.isfile(\"clean.csv\")):\n",
        "  df = pd.read_csv(\"clean.csv\")\n",
        "else:\n",
        "  df = pd.read_csv(\"emails.csv\")\n",
        "  total_sum_ofR_row = df.sum(axis = 1, skipna = True)\n",
        "  label = []\n",
        "  token_count = []\n",
        "  tokenized_text = []\n",
        "\n",
        "  original = df.copy()\n",
        "  del original['Email No.']\n",
        "  del original['Prediction']\n",
        "  original_columns = original.columns\n",
        "  for index, row in df.iterrows():\n",
        "    txt = [ \" \".join([col_name] * row[col_name]) for col_name in original_columns if row[col_name] != 0]\n",
        "    tokenized_text.append(\" \".join(txt))\n",
        "    token_count.append(total_sum_ofR_row[index] - row['Prediction'])\n",
        "    if row['Prediction'] is 0:\n",
        "      label.append(\"not spam\")\n",
        "    else:\n",
        "      label.append(\"spam\")\n",
        "\n",
        "  df = pd.DataFrame({\"label\":label, \n",
        "                                    \"token_count\":token_count,\n",
        "                                    \"tokenized_text\":tokenized_text})\n",
        "  df.to_csv(\"clean.csv\",index=False)\n",
        "\n",
        "df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>token_count</th>\n",
              "      <th>tokenized_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>not spam</td>\n",
              "      <td>53</td>\n",
              "      <td>ect a a is i i s s s as re re e e e e t t t t ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>not spam</td>\n",
              "      <td>2203</td>\n",
              "      <td>the the the the the the the the to to to to to...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>not spam</td>\n",
              "      <td>113</td>\n",
              "      <td>ect a a a a a a a a in in in in on on i i i i ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>not spam</td>\n",
              "      <td>1019</td>\n",
              "      <td>to to to to to ect ect ect ect ect ect ect ect...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>not spam</td>\n",
              "      <td>1075</td>\n",
              "      <td>the the the the the the the to to to to to to ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5167</th>\n",
              "      <td>not spam</td>\n",
              "      <td>505</td>\n",
              "      <td>the the to to ect ect and and and a a a a a a ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5168</th>\n",
              "      <td>not spam</td>\n",
              "      <td>2939</td>\n",
              "      <td>the the the the the the the the the the the th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5169</th>\n",
              "      <td>spam</td>\n",
              "      <td>178</td>\n",
              "      <td>ect and a a a a a a a a a a a in on is is is t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5170</th>\n",
              "      <td>spam</td>\n",
              "      <td>787</td>\n",
              "      <td>the the to to to to to to to ect for for of a ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5171</th>\n",
              "      <td>not spam</td>\n",
              "      <td>2717</td>\n",
              "      <td>the the the the the the the the the the the th...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5172 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         label  token_count                                     tokenized_text\n",
              "0     not spam           53  ect a a is i i s s s as re re e e e e t t t t ...\n",
              "1     not spam         2203  the the the the the the the the to to to to to...\n",
              "2     not spam          113  ect a a a a a a a a in in in in on on i i i i ...\n",
              "3     not spam         1019  to to to to to ect ect ect ect ect ect ect ect...\n",
              "4     not spam         1075  the the the the the the the to to to to to to ...\n",
              "...        ...          ...                                                ...\n",
              "5167  not spam          505  the the to to ect ect and and and a a a a a a ...\n",
              "5168  not spam         2939  the the the the the the the the the the the th...\n",
              "5169      spam          178  ect and a a a a a a a a a a a in on is is is t...\n",
              "5170      spam          787  the the to to to to to to to ect for for of a ...\n",
              "5171  not spam         2717  the the the the the the the the the the the th...\n",
              "\n",
              "[5172 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIOJw0Ah7Bsz",
        "colab_type": "code",
        "outputId": "6d565dd3-a216-43d3-acd5-b78ad5aedf8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "#plot email\n",
        "df1 = df.groupby(['label','label'])['label','label'].size().unstack()\n",
        "\n",
        "ax=df1.plot(kind='bar')\n",
        "ax.set_ylabel(\"Total Emails\")\n",
        "ax.set_xlabel(\"Category\")\n",
        "ax.set_title(\"Plot of Emails count with email type\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Plot of Emails count with email type')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LWLlyJW+//TaHHHIIAOPHjy86xUd9Q4cOZezYsfzmN79hyy0/7hkYM2YMXbp0oVevXowc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\nThZmZpbr/wNXdrkGh40r/QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iu8xP1Ks9fDU",
        "colab_type": "code",
        "outputId": "18040ed6-ec75-4e1f-e237-ee27909ac5eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "bins = [0,100,200,300,350,400,500,600,800,1000,1500,2000,3000,4000,5000,6000,10000,20000]\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(12, 6))\n",
        "fig.subplots_adjust(hspace=.5)\n",
        "\n",
        "df_sub=df[(df['label']=='not spam')]\n",
        "df1 = df_sub.groupby(pd.cut(df_sub['token_count'], bins=bins)).token_count.count()\n",
        "df1.index=[a.right for a in df1.index]\n",
        "res1=df1.plot(kind='bar',ax=axes[0])\n",
        "res1.set_xlabel('Email tokens length')\n",
        "res1.set_ylabel('Frequency')\n",
        "res1.set_title('Token length Vs Frequency for Enron Normal Emails')\n",
        "\n",
        "\n",
        "df_sub=df[ (df['label']=='spam')]\n",
        "df1 = df_sub.groupby(pd.cut(df_sub['token_count'], bins=bins)).token_count.count()\n",
        "df1.index=[a.right for a in df1.index]\n",
        "res2=df1.plot(kind='bar',ax=axes[1])\n",
        "res2.set_xlabel('Email tokens length')\n",
        "res2.set_ylabel('Frequency')\n",
        "res2.set_title('Token length Vs Frequency for Enron Spam Emails')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Token length Vs Frequency for Enron Spam Emails')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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95epgzZa0NVusZm/VnekNGzZwxhlnrHQaktRLkvnfaLaqWbMlbc0Wq9kO85AkSZJ6sjMt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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Whwzhpjl32h9",
        "colab_type": "text"
      },
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZF1bxtR37EE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We randomize the rows to subset the dataframe\n",
        "df.reset_index(inplace=True)\n",
        "df=df.reindex(np.random.permutation(df.index))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "347-DCh5IHZb",
        "colab_type": "code",
        "outputId": "0e78cdb2-1289-4745-b2c6-40be4c034646",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "len_unseen=1000\n",
        "df_unseen_test= df.iloc[:len_unseen]\n",
        "df_model = df.iloc[len_unseen:]\n",
        "\n",
        "print('total emails for unseen test data : ', len(df_unseen_test))\n",
        "print('\\t total spam emails for enron  : ', len(df_unseen_test[ (df_unseen_test['label']=='spam')]))\n",
        "print('\\t total normal emails for enron  : ', len(df_unseen_test[(df_unseen_test['label']=='not spam')]))\n",
        "print()\n",
        "\n",
        "print('total emails for model training/validation : ', len(df_model))\n",
        "print('\\t total spam emails for enron  : ', len(df_model[ (df_model['label']=='spam')]))\n",
        "print('\\t total normal emails for enron  : ', len(df_model[ (df_model['label']=='not spam')]))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total emails for unseen test data :  1000\n",
            "\t total spam emails for enron  :  326\n",
            "\t total normal emails for enron  :  674\n",
            "\n",
            "total emails for model training/validation :  4172\n",
            "\t total spam emails for enron  :  1174\n",
            "\t total normal emails for enron  :  2998\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fYL4w7koIZjg",
        "colab_type": "text"
      },
      "source": [
        "# Training Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVIoPz1aId7Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Input, Dense\n",
        "from keras.models import Model,load_model\n",
        "from keras.layers import Input, Dense\n",
        "from keras.models import Model\n",
        "from keras import regularizers\n",
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation\n",
        "from keras.callbacks import ModelCheckpoint, TensorBoard"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DAKsRcVuIiXE",
        "colab_type": "code",
        "outputId": "a83b5333-8e6f-405c-ebb2-94d056943c8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "import sklearn\n",
        "from sklearn import metrics\n",
        "from sklearn import svm\n",
        "from sklearn.externals import joblib\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_7Q2WkdrInuW",
        "colab_type": "text"
      },
      "source": [
        "#  create tf-idf model with keras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "855Yon2GIpw4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# max number of features\n",
        "num_max = 4000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKK5kfVpIscF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_tf_idf_model(texts):\n",
        "    \"train tf idf model \"\n",
        "    tic = time.process_time()\n",
        "    \n",
        "\n",
        "    tok = Tokenizer(num_words=num_max)\n",
        "    tok.fit_on_texts(texts)\n",
        "    toc = time.process_time()\n",
        "\n",
        "    print (\" -----total Computation time = \" + str((toc - tic)) + \" seconds\")\n",
        "    return tok\n",
        "\n",
        "\n",
        "def prepare_model_input(tfidf_model,dataframe,mode='tfidf'):\n",
        "    \n",
        "    \"function to prepare data input features using tfidf model\"\n",
        "    tic = time.process_time()\n",
        "    \n",
        "    le = LabelEncoder()\n",
        "    sample_texts = list(dataframe['tokenized_text'])\n",
        "    sample_texts = [' '.join(x.split()) for x in sample_texts]\n",
        "    \n",
        "    targets=list(dataframe['label'])\n",
        "    targets = [1. if x=='spam' else 0. for x in targets]\n",
        "    sample_target = le.fit_transform(targets)\n",
        "    \n",
        "    if mode=='tfidf':\n",
        "        sample_texts=tfidf_model.texts_to_matrix(sample_texts,mode='tfidf')\n",
        "    else:\n",
        "        sample_texts=tfidf_model.texts_to_matrix(sample_texts)\n",
        "    \n",
        "    toc = time.process_time()\n",
        "    \n",
        "    print('shape of labels: ', sample_target.shape)\n",
        "    print('shape of data: ', sample_texts.shape)\n",
        "    \n",
        "    print (\" -----total Computation time for preparing model data = \" + str((toc - tic)) + \" seconds\")\n",
        "    \n",
        "    return sample_texts,sample_target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6wcVcVHJ5Ij",
        "colab_type": "code",
        "outputId": "f3a6d674-67aa-4edf-be49-02f1bdaab325",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "texts=list(df_model['tokenized_text'])\n",
        "tfidf_model=train_tf_idf_model(texts)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " -----total Computation time = 1.30903056599999 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVrLqAfbLnPp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "d2135996-7188-4c1f-dde3-bcadb39e0eb5"
      },
      "source": [
        "# prepare model input data\n",
        "mat_texts,tags=prepare_model_input(tfidf_model,df_model,mode='tfidf')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape of labels:  (4172,)\n",
            "shape of data:  (4172, 4000)\n",
            " -----total Computation time for preparing model data = 4.363709831999998 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwyApYyXfUXq",
        "colab_type": "text"
      },
      "source": [
        "# Split Train/validation data\n",
        "\n",
        "We will use 85% for training, 15% for validation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptkwIERdfWBO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "7b4f5184-e3d0-4f23-f560-4e73dc116df9"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(mat_texts, tags, test_size=0.15)\n",
        "print ('train data shape: ', X_train.shape, y_train.shape)\n",
        "print ('validation data shape :' , X_val.shape, y_val.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train data shape:  (3546, 4000) (3546,)\n",
            "validation data shape : (626, 4000) (626,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "axL8GnnUfyQu",
        "colab_type": "text"
      },
      "source": [
        "# Build models\n",
        "**Deep learning model**\n",
        "We will build our 3 layer deep learning model using Keras and tensorflow.\n",
        "\n",
        "**Network**\n",
        "Input -> L1 : (Linear -> Relu) -> L2: (Linear -> Relu)-> (Linear -> Sigmoid)\n",
        "\n",
        "\n",
        "\n",
        "*   Layer L1 has 512 neurons with Relu activation\n",
        "*   Layer L2 has 256 neurons with Relu activation\n",
        "*   Regularization : We use dropout with probability 0.5 for L1, L2 to prevent overfitting\n",
        "*   Loss Function : binary cross entropy\n",
        "*   Optimizer : We use Adam optimizer for gradient descent estimation (faster optimization)\n",
        "*   Data Shuffling : Data shuffling is set to true\n",
        "*   Batch Size : 64\n",
        "*   Learning Rate = 0.001"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VhUjInogLgM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Define and initialize the network\n",
        "from tensorflow.contrib import keras\n",
        "model_save_path=\"spam_detector_enron_model.h5\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sAvsrfbgS12",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_simple_model():\n",
        "    model = Sequential()\n",
        "    model.add(Dense(512, activation='relu', input_shape=(num_max,)))\n",
        "    model.add(Dropout(0.5))\n",
        "    model.add(Dense(256, activation='relu'))\n",
        "    model.add(Dropout(0.5))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "    model.summary()\n",
        "    model.compile(loss='binary_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['acc',keras.metrics.binary_accuracy])\n",
        "    print('compile done')\n",
        "    return model\n",
        "\n",
        "def check_model(model,x,y,epochs=2):\n",
        "    history=model.fit(x,y,batch_size=32,epochs=epochs,verbose=1,shuffle=True,validation_split=0.2,\n",
        "              callbacks=[checkpointer, tensorboard]).history\n",
        "    return history\n",
        "\n",
        "\n",
        "def check_model2(model,x_train,y_train,x_val,y_val,epochs=10):\n",
        "    history=model.fit(x_train,y_train,batch_size=64,\n",
        "                      epochs=epochs,verbose=1,\n",
        "                      shuffle=True,\n",
        "                      validation_data=(x_val, y_val),\n",
        "                      callbacks=[checkpointer, tensorboard]).history\n",
        "    return history\n",
        "\n",
        "# define checkpointer\n",
        "checkpointer = ModelCheckpoint(filepath=model_save_path,\n",
        "                               verbose=1,\n",
        "                               save_best_only=True)    \n",
        "\n",
        "\n",
        "# define tensorboard\n",
        "tensorboard = TensorBoard(log_dir='./logs',\n",
        "                          histogram_freq=0,\n",
        "                          write_graph=True,\n",
        "                          write_images=True)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# define the predict function for the deep learning model for later use\n",
        "def predict(data):\n",
        "    result=spam_model_dl.predict(data)\n",
        "    prediction = [round(x[0]) for x in result]\n",
        "    return prediction"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8IrOlYPjGS5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "15accfe6-3eee-4499-ae21-a94428ceb8a0"
      },
      "source": [
        "# get the compiled model\n",
        "model = get_simple_model()\n",
        "\n",
        "# load history\n",
        "# history=check_model(m,mat_texts,tags,epochs=10)\n",
        "history=check_model2(model,X_train,y_train,X_val,y_val,epochs=10)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_13 (Dense)             (None, 512)               2048512   \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 1)                 257       \n",
            "=================================================================\n",
            "Total params: 2,180,097\n",
            "Trainable params: 2,180,097\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "compile done\n",
            "Train on 3546 samples, validate on 626 samples\n",
            "Epoch 1/10\n",
            "3546/3546 [==============================] - 4s 1ms/step - loss: 0.2452 - acc: 0.9033 - binary_accuracy: 0.9033 - val_loss: 0.0887 - val_acc: 0.9792 - val_binary_accuracy: 0.9792\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.08873, saving model to spam_detector_enron_model.h5\n",
            "Epoch 2/10\n",
            "3546/3546 [==============================] - 4s 1ms/step - loss: 0.0740 - acc: 0.9822 - binary_accuracy: 0.9822 - val_loss: 0.1117 - val_acc: 0.9792 - val_binary_accuracy: 0.9792\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 0.08873\n",
            "Epoch 3/10\n",
            "3546/3546 [==============================] - 4s 1ms/step - loss: 0.0311 - acc: 0.9921 - binary_accuracy: 0.9921 - val_loss: 0.1035 - val_acc: 0.9808 - val_binary_accuracy: 0.9808\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.08873\n",
            "Epoch 4/10\n",
            "3546/3546 [==============================] - 4s 1ms/step - loss: 0.0386 - acc: 0.9915 - binary_accuracy: 0.9915 - val_loss: 0.0883 - val_acc: 0.9824 - val_binary_accuracy: 0.9824\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.08873 to 0.08834, saving model to spam_detector_enron_model.h5\n",
            "Epoch 5/10\n",
            "3546/3546 [==============================] - 4s 1ms/step - loss: 0.0174 - acc: 0.9952 - binary_accuracy: 0.9952 - val_loss: 0.0860 - val_acc: 0.9904 - val_binary_accuracy: 0.9904\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.08834 to 0.08600, saving model to spam_detector_enron_model.h5\n",
            "Epoch 6/10\n",
            "3546/3546 [==============================] - 4s 1ms/step - loss: 0.0134 - acc: 0.9952 - binary_accuracy: 0.9952 - val_loss: 0.0821 - val_acc: 0.9888 - val_binary_accuracy: 0.9888\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.08600 to 0.08205, saving model to spam_detector_enron_model.h5\n",
            "Epoch 7/10\n",
            "3546/3546 [==============================] - 4s 1ms/step - loss: 0.0090 - acc: 0.9969 - binary_accuracy: 0.9969 - val_loss: 0.0940 - val_acc: 0.9856 - val_binary_accuracy: 0.9856\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.08205\n",
            "Epoch 8/10\n",
            "3546/3546 [==============================] - 4s 1ms/step - loss: 0.0061 - acc: 0.9977 - binary_accuracy: 0.9977 - val_loss: 0.0910 - val_acc: 0.9824 - val_binary_accuracy: 0.9824\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 0.08205\n",
            "Epoch 9/10\n",
            "3546/3546 [==============================] - 4s 1ms/step - loss: 0.0079 - acc: 0.9983 - binary_accuracy: 0.9983 - val_loss: 0.1119 - val_acc: 0.9872 - val_binary_accuracy: 0.9872\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.08205\n",
            "Epoch 10/10\n",
            "3546/3546 [==============================] - 4s 1ms/step - loss: 0.0067 - acc: 0.9992 - binary_accuracy: 0.9992 - val_loss: 0.1299 - val_acc: 0.9872 - val_binary_accuracy: 0.9872\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.08205\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NxUYGpnxjPWZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "c8d54888-d18f-42db-923e-5571603cbb9b"
      },
      "source": [
        "plt.plot(history['loss'])\n",
        "plt.plot(history['val_loss'])\n",
        "plt.title('Email Spam Filter Model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper right');"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S2qrExfWxP8hAgrvubd4pbtbJj/7urna18DZY+EsYd6ILi8lzIdnOkzT9SGsr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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Us6W8iwYlAJr",
        "colab_type": "text"
      },
      "source": [
        "# Other Machine Learning Models\n",
        "We will build 3 more models and compare the performance in the same way. For this purpose we will use the same tf-idf as input feature . We will train following models :\n",
        "\n",
        "\n",
        "\n",
        "*   SVM\n",
        "*   Random Forest\n",
        "*   XGboost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNIpQhpMlLUo",
        "colab_type": "text"
      },
      "source": [
        "# Lets train the svm model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6x8y6OD5lOtx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "a212e536-f0c8-4a28-99d4-2631534cf0d4"
      },
      "source": [
        "spam_model_svm = svm.SVC(verbose=1)\n",
        "spam_model_svm.fit(X_train,y_train)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[LibSVM]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJY9dMphlTTN",
        "colab_type": "text"
      },
      "source": [
        "# Lets build random forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQZBdX1OlUkq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "d53f8c7c-83df-4093-eb42-4a0eb4edc084"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "spam_model_rf = RandomForestClassifier(n_jobs=2, random_state=0,n_estimators=50)\n",
        "\n",
        "# Train the Classifier to take the training features and learn how they relate\n",
        "# to the training y (the species)\n",
        "spam_model_rf.fit(X_train,y_train)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=2,\n",
              "                       oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "byyWgsDalcMS",
        "colab_type": "text"
      },
      "source": [
        "# Lets train xgboost model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLPQR3lOldgj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "232f1be4-afb7-44b0-f9a9-1084b0a10f43"
      },
      "source": [
        "import xgboost as xgb\n",
        "spam_model_xgboost = xgb.XGBClassifier()\n",
        "spam_model_xgboost.fit(X_train,y_train)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2QwTkWwlkor",
        "colab_type": "text"
      },
      "source": [
        "# Evaluate Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzwxN3dull6K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "212e6ee9-df32-49f9-a4e7-f4887a4aa981"
      },
      "source": [
        "sample_texts,sample_target=prepare_model_input(tfidf_model,df_unseen_test,mode='')\n",
        "\n",
        "model_dict={}\n",
        "model_dict['random_forest']=spam_model_rf\n",
        "model_dict['svm']=spam_model_svm\n",
        "model_dict['xgboost']=spam_model_xgboost\n",
        "\n",
        "\n",
        "def getResults(model_dict,sample_texts,sample_target):\n",
        "    '''\n",
        "    Get results from different models\n",
        "    '''\n",
        "    results=[]\n",
        "    \n",
        "    results_cm={}\n",
        "    \n",
        "    for name,model in model_dict.items():\n",
        "#         print(name)\n",
        "        tic1 = time.process_time()\n",
        "        if name in 'deep_learning':\n",
        "            predicted_sample = predict(sample_texts)\n",
        "        else:    \n",
        "            predicted_sample = model.predict(sample_texts)\n",
        "        toc1 = time.process_time()\n",
        "#         print(predicted_sample)\n",
        "\n",
        "        cm=sklearn.metrics.confusion_matrix(sample_target, predicted_sample)\n",
        "        results_cm[name]=cm\n",
        "        \n",
        "        total=len(predicted_sample)\n",
        "        TP = cm[0][0]\n",
        "        FP = cm[0][1]\n",
        "        FN = cm[1][0]\n",
        "        TN = cm[1][1]\n",
        "        \n",
        "        time_taken=round(toc1 - tic1,4)\n",
        "        res=sklearn.metrics.precision_recall_fscore_support(sample_target, predicted_sample)\n",
        "        results.append([name,np.mean(res[0]),np.mean(res[1]),np.mean(res[2]),total,TP,FP,FN,TN,str(time_taken)] )\n",
        "        \n",
        "        \n",
        "    \n",
        "    df_cols=['model','precision','recall','f1_score','Total_samples','TP','FP','FN','TN','execution_time']\n",
        "    result_df=pd.DataFrame(results,columns=df_cols)\n",
        "    \n",
        "    return result_df,results_cm\n",
        "    "
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape of labels:  (1000,)\n",
            "shape of data:  (1000, 4000)\n",
            " -----total Computation time for preparing model data = 0.5178589359999819 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09HCdx0-l9Pn",
        "colab_type": "text"
      },
      "source": [
        "# Result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvQkwzoEl-iS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "69626a53-5e0d-459a-eea9-798d09d2e575"
      },
      "source": [
        "result_df,results_cm= getResults(model_dict,sample_texts,sample_target)\n",
        "result_df"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1_score</th>\n",
              "      <th>Total_samples</th>\n",
              "      <th>TP</th>\n",
              "      <th>FP</th>\n",
              "      <th>FN</th>\n",
              "      <th>TN</th>\n",
              "      <th>execution_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>random_forest</td>\n",
              "      <td>0.892526</td>\n",
              "      <td>0.820211</td>\n",
              "      <td>0.843750</td>\n",
              "      <td>1000</td>\n",
              "      <td>657</td>\n",
              "      <td>17</td>\n",
              "      <td>109</td>\n",
              "      <td>217</td>\n",
              "      <td>0.0396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>svm</td>\n",
              "      <td>0.931751</td>\n",
              "      <td>0.959049</td>\n",
              "      <td>0.942781</td>\n",
              "      <td>1000</td>\n",
              "      <td>625</td>\n",
              "      <td>49</td>\n",
              "      <td>3</td>\n",
              "      <td>323</td>\n",
              "      <td>7.9163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>xgboost</td>\n",
              "      <td>0.905070</td>\n",
              "      <td>0.882653</td>\n",
              "      <td>0.892473</td>\n",
              "      <td>1000</td>\n",
              "      <td>644</td>\n",
              "      <td>30</td>\n",
              "      <td>62</td>\n",
              "      <td>264</td>\n",
              "      <td>0.0367</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           model  precision    recall  f1_score  ...  FP   FN   TN  execution_time\n",
              "0  random_forest   0.892526  0.820211  0.843750  ...  17  109  217          0.0396\n",
              "1            svm   0.931751  0.959049  0.942781  ...  49    3  323          7.9163\n",
              "2        xgboost   0.905070  0.882653  0.892473  ...  30   62  264          0.0367\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0KgKFh99mF4K",
        "colab_type": "text"
      },
      "source": [
        "# Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fq9giar0mLgA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_heatmap(cm,title):\n",
        "    df_cm2 = pd.DataFrame(cm, index = ['normal', 'spam'])\n",
        "    df_cm2.columns=['normal','spam']\n",
        "\n",
        "    ax = plt.axes()\n",
        "    sns.heatmap(df_cm2, annot=True, fmt=\"d\", linewidths=.5,ax=ax)\n",
        "    ax.set_title(title)\n",
        "    plt.show()\n",
        "\n",
        "    \n",
        "    return"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4r0yDQjGmXhD",
        "colab_type": "text"
      },
      "source": [
        "# SVM model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wA2AAvnMmbPO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "113ca18f-5c0b-4b3e-8a39-0505324665b9"
      },
      "source": [
        "plot_heatmap(results_cm['svm'],'SVM')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eAZuZZcTL0MzMMtLqVRBmZtnwCNjMLCOeAzYzy4hXQZiZZcQjYDOzjLTm0r5pLXtOwGZW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\ngM3MMuIEbGaWkf8FrQW8Nz7QvVAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFAgviwVmdpr",
        "colab_type": "text"
      },
      "source": [
        "# Random Forest Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWxaB8lzmhMh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "6801b9c2-4ad7-4538-cade-9a58006a5592"
      },
      "source": [
        "plot_heatmap(results_cm['random_forest'],'Random Forest')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SJkty/dfMOpxKJ2DgR5TugzW6BLg8IramNCHhhNR+ArAgtV+e+jUrawnigMyhmpnlKOpV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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hma0bpmYmlCa",
        "colab_type": "text"
      },
      "source": [
        "# Xgboost Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_ojDxOEmmCo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "25d22553-c179-4e44-b247-82956ad4492f"
      },
      "source": [
        "plot_heatmap(results_cm['xgboost'],'xgboost')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEICAYAAABhxi57AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wfqXtXYV7LxDAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}