Capstone_Keras_Embeddings_Ghamy016.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Input Model Classification in Keras (LSTM Neural Network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\ghamy\\\\OneDrive - University of South Australia\\\\MS Data Science\\\\Semester 4\\\\Capstone Professional Project\\\\Me (Noman)\\\\keras-multi-input'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r\"C:\\Users\\ghamy\\OneDrive - University of South Australia\\MS Data Science\\Semester 4\\Capstone Professional Project\\Me (Noman)\\keras-multi-input\"\n",
    "os.chdir(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\ghamy\\\\OneDrive - University of South Australia\\\\MS Data Science\\\\Semester 4\\\\Capstone Professional Project\\\\Me (Noman)\\\\keras-multi-input'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Python libraries\n",
    "# numpy and pandas for data manipulation\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "\n",
    "# sklearn preprocessing for dealing with categorical variables\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# File system manangement\n",
    "import os\n",
    "\n",
    "# Suppress warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# matplotlib and seaborn for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# to read pickle files\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Data (4 files as dataframes)\n",
    "\n",
    "# function for laoding pickle files\n",
    "def loadObject(name):\n",
    "    try:\n",
    "        with open(name, 'rb') as f:\n",
    "            return pickle.load(f)\n",
    "    except FileNotFoundError:\n",
    "        print('Could not find file \"{}\"\". Please check it exists inc. the file extension'.format(str(name)))\n",
    "\n",
    "# using function to load pickle files\n",
    "myObject = loadObject('chatEmbeddings_2019-08-14-165706.pkl')\n",
    "myObject1 = loadObject('transactionEmbeddings_2019-08-14-165704.pkl')\n",
    "\n",
    "# Reading metadata and clickstream csv files \n",
    "\n",
    "data_df = pd.read_csv('Capstone_Final_Data.csv')\n",
    "\n",
    "\n",
    "# converting files to data frames for EDA and further analysis\n",
    "chat_df = pd.DataFrame(myObject)\n",
    "transaction_df = pd.DataFrame(myObject1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24104"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chat_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "607087"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(transaction_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "chat_df.loc[chat_df['status'] == 'withdrawn','status'] = 'declined'\n",
    "transaction_df.loc[transaction_df['status'] == 'withdrawn','status'] = 'declined'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Model with Chat Embeddings Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>applicationid</th>\n",
       "      <th>chatEmbedding</th>\n",
       "      <th>chatid</th>\n",
       "      <th>chatstarton</th>\n",
       "      <th>rating</th>\n",
       "      <th>sentiment_compound</th>\n",
       "      <th>status</th>\n",
       "      <th>user_type</th>\n",
       "      <th>utteranceid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0e730e97f5e23f177468cc459acb888c0ceb461eed15cb...</td>\n",
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       "      <td>e0375487b152725bfb21b96876b260a5de52bfd69018ba...</td>\n",
       "      <td>2017-08-08</td>\n",
       "      <td>not_rated</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>declined</td>\n",
       "      <td>visitor</td>\n",
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       "      <td>e0375487b152725bfb21b96876b260a5de52bfd69018ba...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>[[-0.015632771, 0.0395712, -0.021834029, -0.04...</td>\n",
       "      <td>4785521c6ff37491fd32d920faeb90871d85973cb81c01...</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>rated_good</td>\n",
       "      <td>0.2263</td>\n",
       "      <td>declined</td>\n",
       "      <td>visitor</td>\n",
       "      <td>0514e95a2ca7e0c14eb862d4a3ef7810eda207dedeb60c...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2017-08-16</td>\n",
       "      <td>rated_good</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>declined</td>\n",
       "      <td>visitor</td>\n",
       "      <td>f8e218879ec6a557ccc8905a5617ac191facfd0945aafe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d4cc10aa447414db05c8a6613065333f5de089475c3a5c...</td>\n",
       "      <td>[[0.013008025, 0.0027834661, 0.04260392, -0.01...</td>\n",
       "      <td>4785521c6ff37491fd32d920faeb90871d85973cb81c01...</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>rated_good</td>\n",
       "      <td>0.4019</td>\n",
       "      <td>declined</td>\n",
       "      <td>visitor</td>\n",
       "      <td>4b55bbd6c7cf7a68b2048e75dd304a361d5092743a5145...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                       applicationid  \\\n",
       "0  0e730e97f5e23f177468cc459acb888c0ceb461eed15cb...   \n",
       "1  0e730e97f5e23f177468cc459acb888c0ceb461eed15cb...   \n",
       "2  d4cc10aa447414db05c8a6613065333f5de089475c3a5c...   \n",
       "3  d4cc10aa447414db05c8a6613065333f5de089475c3a5c...   \n",
       "4  d4cc10aa447414db05c8a6613065333f5de089475c3a5c...   \n",
       "\n",
       "                                       chatEmbedding  \\\n",
       "0  [[0.012461126, 0.026670266, 0.0038820878, 0.04...   \n",
       "1  [[0.012461126, 0.026670266, 0.0038820878, 0.04...   \n",
       "2  [[-0.015632771, 0.0395712, -0.021834029, -0.04...   \n",
       "3  [[0.04322845, 0.023193743, -0.014702667, -0.04...   \n",
       "4  [[0.013008025, 0.0027834661, 0.04260392, -0.01...   \n",
       "\n",
       "                                              chatid chatstarton      rating  \\\n",
       "0  e0375487b152725bfb21b96876b260a5de52bfd69018ba...  2017-08-08   not_rated   \n",
       "1  e0375487b152725bfb21b96876b260a5de52bfd69018ba...  2017-08-08   not_rated   \n",
       "2  4785521c6ff37491fd32d920faeb90871d85973cb81c01...  2017-08-16  rated_good   \n",
       "3  4785521c6ff37491fd32d920faeb90871d85973cb81c01...  2017-08-16  rated_good   \n",
       "4  4785521c6ff37491fd32d920faeb90871d85973cb81c01...  2017-08-16  rated_good   \n",
       "\n",
       "   sentiment_compound    status user_type  \\\n",
       "0              0.0000  declined   visitor   \n",
       "1              0.0000  declined   visitor   \n",
       "2              0.2263  declined   visitor   \n",
       "3              0.0000  declined   visitor   \n",
       "4              0.4019  declined   visitor   \n",
       "\n",
       "                                         utteranceid  \n",
       "0  8bd6b80d2aa9d4bab974b34ba0319c8cd73ec2441fb25d...  \n",
       "1  851ec87283eaffc8375c0bf42c7b38d95707a27ebdaf05...  \n",
       "2  0514e95a2ca7e0c14eb862d4a3ef7810eda207dedeb60c...  \n",
       "3  f8e218879ec6a557ccc8905a5617ac191facfd0945aafe...  \n",
       "4  4b55bbd6c7cf7a68b2048e75dd304a361d5092743a5145...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from numpy import array\n",
    "from keras.preprocessing.text import one_hot\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Activation, Dropout, Dense\n",
    "from keras.layers import Flatten, LSTM\n",
    "from keras.layers import GlobalMaxPooling1D\n",
    "from keras.models import Model\n",
    "from keras.layers.embeddings import Embedding\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.layers import Input\n",
    "from keras.layers.merge import Concatenate\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = chat_df[['chatEmbedding']].copy()\n",
    "y = chat_df[['status']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24104, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_matrix = chat_df.chatEmbedding[0]\n",
    "from itertools import repeat\n",
    "for i in range(24103):\n",
    "    embedding_matrix = np.vstack((embedding_matrix, chat_df.chatEmbedding[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = embedding_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(385664, 9)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.loc[y.index.repeat(16)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>declined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>declined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>declined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>declined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>declined</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     status\n",
       "0  declined\n",
       "1  declined\n",
       "2  declined\n",
       "3  declined\n",
       "4  declined"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# label_encoder object knows how to understand word labels.\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "\n",
    "# Encode labels in column 'species'.\n",
    "y = label_encoder.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1_train, X1_test, y1_train, y1_test = train_test_split(X, y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(308531, 9)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01246113,  0.02667027,  0.00388209,  0.04182083, -0.03291637,\n",
       "       -0.01836227, -0.00864174,  0.02887807,  0.03490983], dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1_train[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77133, 9)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(308531,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77133,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983],\n",
       "       [-0.01285403, -0.03059145,  0.04359328, ...,  0.0282667 ,\n",
       "         0.03512987, -0.00883285],\n",
       "       ...,\n",
       "       [ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983]], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "y1_train = to_categorical(y1_train)\n",
    "y1_test = to_categorical(y1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxlen = 9\n",
    "vocab_size = 16*24104"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_inputs = Input(shape=(maxlen,))\n",
    "embedding_layer = Embedding(vocab_size, 9, weights=[embedding_matrix], trainable=False)(deep_inputs)\n",
    "LSTM_Layer_1 = LSTM(128)(embedding_layer)\n",
    "dense_layer_1 = Dense(2, activation='softmax')(LSTM_Layer_1)\n",
    "model = Model(inputs=deep_inputs, outputs=dense_layer_1)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 9)                 0         \n",
      "_________________________________________________________________\n",
      "embedding_1 (Embedding)      (None, 9, 9)              3470976   \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 128)               70656     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 3,541,890\n",
      "Trainable params: 70,914\n",
      "Non-trainable params: 3,470,976\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "plot_model(model, to_file='model_plot2.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 246824 samples, validate on 61707 samples\n",
      "Epoch 1/10\n",
      "246824/246824 [==============================] - 31s 124us/step - loss: 0.6902 - acc: 0.5394 - val_loss: 0.6903 - val_acc: 0.5381\n",
      "Epoch 2/10\n",
      "246824/246824 [==============================] - 32s 130us/step - loss: 0.6901 - acc: 0.5394 - val_loss: 0.6903 - val_acc: 0.5381\n",
      "Epoch 3/10\n",
      "246824/246824 [==============================] - 30s 121us/step - loss: 0.6901 - acc: 0.5394 - val_loss: 0.6903 - val_acc: 0.5381\n",
      "Epoch 4/10\n",
      "246824/246824 [==============================] - 32s 128us/step - loss: 0.6901 - acc: 0.5394 - val_loss: 0.6902 - val_acc: 0.5381\n",
      "Epoch 5/10\n",
      "246824/246824 [==============================] - 33s 133us/step - loss: 0.6901 - acc: 0.5394 - val_loss: 0.6903 - val_acc: 0.5381\n",
      "Epoch 6/10\n",
      "246824/246824 [==============================] - 33s 135us/step - loss: 0.6901 - acc: 0.5394 - val_loss: 0.6902 - val_acc: 0.5381\n",
      "Epoch 7/10\n",
      "246824/246824 [==============================] - 30s 123us/step - loss: 0.6901 - acc: 0.5394 - val_loss: 0.6903 - val_acc: 0.5381\n",
      "Epoch 8/10\n",
      "246824/246824 [==============================] - 32s 129us/step - loss: 0.6901 - acc: 0.5394 - val_loss: 0.6902 - val_acc: 0.5381\n",
      "Epoch 9/10\n",
      "246824/246824 [==============================] - 32s 131us/step - loss: 0.6901 - acc: 0.5392 - val_loss: 0.6902 - val_acc: 0.5381\n",
      "Epoch 10/10\n",
      "246824/246824 [==============================] - 33s 134us/step - loss: 0.6901 - acc: 0.5394 - val_loss: 0.6903 - val_acc: 0.5381\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X1_train, y1_train, batch_size=128, epochs=10, verbose=1, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77133/77133 [==============================] - 6s 82us/step\n",
      "Test Score: 0.6896745220773126\n",
      "Test Accuracy: 0.541674792766571\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X1_test, y1_test, verbose=1)\n",
    "\n",
    "print(\"Test Score:\", score[0])\n",
    "print(\"Test Accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_x_test = X1_test[0:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.01246113,  0.02667027,  0.00388209,  0.04182083, -0.03291637,\n",
       "        -0.01836227, -0.00864174,  0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209,  0.04182083, -0.03291637,\n",
       "        -0.01836227, -0.00864174,  0.02887807,  0.03490983],\n",
       "       [-0.01285403, -0.03059145,  0.04359328,  0.03636283, -0.04215313,\n",
       "         0.03540075,  0.0282667 ,  0.03512987, -0.00883285],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209,  0.04182083, -0.03291637,\n",
       "        -0.01836227, -0.00864174,  0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209,  0.04182083, -0.03291637,\n",
       "        -0.01836227, -0.00864174,  0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209,  0.04182083, -0.03291637,\n",
       "        -0.01836227, -0.00864174,  0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209,  0.04182083, -0.03291637,\n",
       "        -0.01836227, -0.00864174,  0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209,  0.04182083, -0.03291637,\n",
       "        -0.01836227, -0.00864174,  0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209,  0.04182083, -0.03291637,\n",
       "        -0.01836227, -0.00864174,  0.02887807,  0.03490983]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.45675665, 0.54324335],\n",
       "       [0.45675665, 0.54324335],\n",
       "       [0.45675665, 0.54324335],\n",
       "       [0.45675665, 0.54324335],\n",
       "       [0.45675665, 0.54324335],\n",
       "       [0.45675665, 0.54324335],\n",
       "       [0.45675665, 0.54324335],\n",
       "       [0.45675665, 0.54324335],\n",
       "       [0.45675665, 0.54324335]], dtype=float32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(single_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Model with Transaction Embeddings Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "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>accountid</th>\n",
       "      <th>applicationid</th>\n",
       "      <th>financialinstitution</th>\n",
       "      <th>status</th>\n",
       "      <th>transactionEmbedding</th>\n",
       "      <th>transactionamount</th>\n",
       "      <th>transactioncategory</th>\n",
       "      <th>transactionposteddate</th>\n",
       "      <th>txnid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>882a139cfcc370ed77493fa7940b9576c62cba020d2c39...</td>\n",
       "      <td>f38545158f037838b246a86459345fcebb094b3641fa5f...</td>\n",
       "      <td>ING</td>\n",
       "      <td>converted</td>\n",
       "      <td>[[0.039921653, 0.0076719522, -0.02605666, -0.0...</td>\n",
       "      <td>180.00</td>\n",
       "      <td>IncomeSalaryRegularIncome</td>\n",
       "      <td>2018-06-18</td>\n",
       "      <td>ea8b12252c4b3e6c85b5fb29cb4d02d1252cf54df38fcd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>882a139cfcc370ed77493fa7940b9576c62cba020d2c39...</td>\n",
       "      <td>f38545158f037838b246a86459345fcebb094b3641fa5f...</td>\n",
       "      <td>ING</td>\n",
       "      <td>converted</td>\n",
       "      <td>[[0.039921653, 0.0076719522, -0.02605666, -0.0...</td>\n",
       "      <td>174.00</td>\n",
       "      <td>IncomeSalaryRegularIncome</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>68aad105bc429721b1cd8c7dc70acbaaca06d99e66486f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>882a139cfcc370ed77493fa7940b9576c62cba020d2c39...</td>\n",
       "      <td>f38545158f037838b246a86459345fcebb094b3641fa5f...</td>\n",
       "      <td>ING</td>\n",
       "      <td>converted</td>\n",
       "      <td>[[0.0037002675, -0.032413684, 0.02330419, 0.00...</td>\n",
       "      <td>-1695.68</td>\n",
       "      <td>TransferTransfers</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>b4ff9d37964cd9f73a0ca4ff9c702cd261f3d6e2b2d052...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>882a139cfcc370ed77493fa7940b9576c62cba020d2c39...</td>\n",
       "      <td>f38545158f037838b246a86459345fcebb094b3641fa5f...</td>\n",
       "      <td>ING</td>\n",
       "      <td>converted</td>\n",
       "      <td>[[-0.044274665, -0.030233014, -0.030928208, -0...</td>\n",
       "      <td>10264.00</td>\n",
       "      <td>IncomeOtherIncome</td>\n",
       "      <td>2018-05-22</td>\n",
       "      <td>dccdd51da721e751df8ce0ad77971ba97fbe6a39f762d8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>882a139cfcc370ed77493fa7940b9576c62cba020d2c39...</td>\n",
       "      <td>f38545158f037838b246a86459345fcebb094b3641fa5f...</td>\n",
       "      <td>ING</td>\n",
       "      <td>converted</td>\n",
       "      <td>[[-0.023104716, -0.030675078, -0.017270565, 0....</td>\n",
       "      <td>-41.43</td>\n",
       "      <td>TransferSecuritiesTrades</td>\n",
       "      <td>2018-05-21</td>\n",
       "      <td>f29a19b4120787ce8cc90be800f0f6d62cc9180c31c293...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           accountid  \\\n",
       "0  882a139cfcc370ed77493fa7940b9576c62cba020d2c39...   \n",
       "1  882a139cfcc370ed77493fa7940b9576c62cba020d2c39...   \n",
       "2  882a139cfcc370ed77493fa7940b9576c62cba020d2c39...   \n",
       "3  882a139cfcc370ed77493fa7940b9576c62cba020d2c39...   \n",
       "4  882a139cfcc370ed77493fa7940b9576c62cba020d2c39...   \n",
       "\n",
       "                                       applicationid financialinstitution  \\\n",
       "0  f38545158f037838b246a86459345fcebb094b3641fa5f...                  ING   \n",
       "1  f38545158f037838b246a86459345fcebb094b3641fa5f...                  ING   \n",
       "2  f38545158f037838b246a86459345fcebb094b3641fa5f...                  ING   \n",
       "3  f38545158f037838b246a86459345fcebb094b3641fa5f...                  ING   \n",
       "4  f38545158f037838b246a86459345fcebb094b3641fa5f...                  ING   \n",
       "\n",
       "      status                               transactionEmbedding  \\\n",
       "0  converted  [[0.039921653, 0.0076719522, -0.02605666, -0.0...   \n",
       "1  converted  [[0.039921653, 0.0076719522, -0.02605666, -0.0...   \n",
       "2  converted  [[0.0037002675, -0.032413684, 0.02330419, 0.00...   \n",
       "3  converted  [[-0.044274665, -0.030233014, -0.030928208, -0...   \n",
       "4  converted  [[-0.023104716, -0.030675078, -0.017270565, 0....   \n",
       "\n",
       "   transactionamount        transactioncategory transactionposteddate  \\\n",
       "0             180.00  IncomeSalaryRegularIncome            2018-06-18   \n",
       "1             174.00  IncomeSalaryRegularIncome            2018-06-04   \n",
       "2           -1695.68          TransferTransfers            2018-06-01   \n",
       "3           10264.00          IncomeOtherIncome            2018-05-22   \n",
       "4             -41.43   TransferSecuritiesTrades            2018-05-21   \n",
       "\n",
       "                                               txnid  \n",
       "0  ea8b12252c4b3e6c85b5fb29cb4d02d1252cf54df38fcd...  \n",
       "1  68aad105bc429721b1cd8c7dc70acbaaca06d99e66486f...  \n",
       "2  b4ff9d37964cd9f73a0ca4ff9c702cd261f3d6e2b2d052...  \n",
       "3  dccdd51da721e751df8ce0ad77971ba97fbe6a39f762d8...  \n",
       "4  f29a19b4120787ce8cc90be800f0f6d62cc9180c31c293...  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction_df1 = transaction_df.sample(frac =.5) \n",
    "transaction_df1 = transaction_df1.sample(frac =.5) \n",
    "transaction_df1 = transaction_df1.sample(frac =.5) \n",
    "transaction_df1 = transaction_df1.sample(frac =.5).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37943"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(transaction_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 16)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_df1.transactionEmbedding[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = transaction_df1[['transactionEmbedding']].copy()\n",
    "y1 = transaction_df1[['status']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37943, 1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37943"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_matrix1 = transaction_df.transactionEmbedding[0]\n",
    "from itertools import repeat\n",
    "for i in range(37942):\n",
    "    embedding_matrix1 = np.vstack((embedding_matrix1, transaction_df.transactionEmbedding[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = embedding_matrix1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(607088, 16)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37943"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 = y1.loc[y1.index.repeat(16)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "607088"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# label_encoder object knows how to understand word labels.\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "\n",
    "# Encode labels in column 'species'.\n",
    "y1 = label_encoder.fit_transform(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(X1, y1, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(485670, 16)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121418, 16)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(485670,)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121418,)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "y_train1 = to_categorical(y_train1)\n",
    "y_test1 = to_categorical(y_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxlen1 = 16\n",
    "vocab_size1 = 16*37943"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_inputs1 = Input(shape=(maxlen1,))\n",
    "embedding_layer1 = Embedding(vocab_size1, 16, weights=[embedding_matrix1], trainable=False)(deep_inputs1)\n",
    "LSTM1_Layer_1 = LSTM(128)(embedding_layer1)\n",
    "dense1_layer_1 = Dense(2, activation='softmax')(LSTM1_Layer_1)\n",
    "model1 = Model(inputs=deep_inputs1, outputs=dense1_layer_1)\n",
    "\n",
    "model1.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "embedding_2 (Embedding)      (None, 16, 16)            9713408   \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 128)               74240     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 9,787,906\n",
      "Trainable params: 74,498\n",
      "Non-trainable params: 9,713,408\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from keras.utils import plot_model\n",
    "plot_model(model1, to_file='model_plot1.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 388536 samples, validate on 97134 samples\n",
      "Epoch 1/10\n",
      "388536/388536 [==============================] - 97s 249us/step - loss: 0.5687 - acc: 0.7448 - val_loss: 0.5690 - val_acc: 0.7454\n",
      "Epoch 2/10\n",
      "388536/388536 [==============================] - 97s 249us/step - loss: 0.5681 - acc: 0.7449 - val_loss: 0.5676 - val_acc: 0.7454\n",
      "Epoch 3/10\n",
      "388536/388536 [==============================] - 91s 235us/step - loss: 0.5680 - acc: 0.7449 - val_loss: 0.5673 - val_acc: 0.7454\n",
      "Epoch 4/10\n",
      "388536/388536 [==============================] - 91s 235us/step - loss: 0.5680 - acc: 0.7449 - val_loss: 0.5673 - val_acc: 0.7454\n",
      "Epoch 5/10\n",
      "388536/388536 [==============================] - 89s 229us/step - loss: 0.5680 - acc: 0.7449 - val_loss: 0.5674 - val_acc: 0.7454\n",
      "Epoch 6/10\n",
      "388536/388536 [==============================] - 91s 235us/step - loss: 0.5680 - acc: 0.7449 - val_loss: 0.5676 - val_acc: 0.7454\n",
      "Epoch 7/10\n",
      "388536/388536 [==============================] - 90s 232us/step - loss: 0.5680 - acc: 0.7449 - val_loss: 0.5676 - val_acc: 0.7454\n",
      "Epoch 8/10\n",
      "388536/388536 [==============================] - 92s 237us/step - loss: 0.5680 - acc: 0.7449 - val_loss: 0.5673 - val_acc: 0.7454\n",
      "Epoch 9/10\n",
      "388536/388536 [==============================] - 92s 237us/step - loss: 0.5679 - acc: 0.7449 - val_loss: 0.5673 - val_acc: 0.7454\n",
      "Epoch 10/10\n",
      "388536/388536 [==============================] - 93s 239us/step - loss: 0.5679 - acc: 0.7449 - val_loss: 0.5674 - val_acc: 0.7454\n"
     ]
    }
   ],
   "source": [
    "history1 = model1.fit(X_train1, y_train1, batch_size=128, epochs=10, verbose=1, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121418/121418 [==============================] - 16s 131us/step\n",
      "Test Score: 0.5689661954254294\n",
      "Test Accuracy: 0.74391770362854\n"
     ]
    }
   ],
   "source": [
    "score1 = model1.evaluate(X_test1, y_test1, verbose=1)\n",
    "\n",
    "print(\"Test Score:\", score1[0])\n",
    "print(\"Test Accuracy:\", score1[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history1.history['acc'])\n",
    "plt.plot(history1.history['val_acc'])\n",
    "\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history1.history['loss'])\n",
    "plt.plot(history1.history['val_loss'])\n",
    "\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Model with Numerical and Categorical Features Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "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>applicationid</th>\n",
       "      <th>status</th>\n",
       "      <th>propertystate</th>\n",
       "      <th>loanpurpose</th>\n",
       "      <th>repaymenttype</th>\n",
       "      <th>loanusage</th>\n",
       "      <th>ratetype</th>\n",
       "      <th>loanamount</th>\n",
       "      <th>loantovalueratio</th>\n",
       "      <th>applicationtype</th>\n",
       "      <th>...</th>\n",
       "      <th>finalpage</th>\n",
       "      <th>pagesvisited</th>\n",
       "      <th>howentered</th>\n",
       "      <th>conversionpathlen</th>\n",
       "      <th>financialinstitutionmode</th>\n",
       "      <th>transactioncategorymode</th>\n",
       "      <th>transactionamountmedian</th>\n",
       "      <th>chatratedpercentage</th>\n",
       "      <th>chatsentimentmean</th>\n",
       "      <th>chatlength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000429e2882bd4555885bacf9678c0a60e10610ab9b7f8...</td>\n",
       "      <td>declined</td>\n",
       "      <td>QLD</td>\n",
       "      <td>PUR</td>\n",
       "      <td>Principal &amp; Interest</td>\n",
       "      <td>livein</td>\n",
       "      <td>variable</td>\n",
       "      <td>12.917403</td>\n",
       "      <td>0.657669</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>...</td>\n",
       "      <td>home |  worlds first instant home loan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PaidSearch</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ING</td>\n",
       "      <td>TransferCreditCardPayments</td>\n",
       "      <td>-13.27</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>003dec44f66a6d3f85bdc0080ec7a49bb7f4e02bf3c81e...</td>\n",
       "      <td>declined</td>\n",
       "      <td>WA</td>\n",
       "      <td>REF</td>\n",
       "      <td>Principal &amp; Interest</td>\n",
       "      <td>livein</td>\n",
       "      <td>variable</td>\n",
       "      <td>13.575731</td>\n",
       "      <td>0.619600</td>\n",
       "      <td>JOINT</td>\n",
       "      <td>...</td>\n",
       "      <td>own your home loan | tic:toc</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Native</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00696bdea8b77f36209e48a29ea490b32acd26c33c0dbd...</td>\n",
       "      <td>declined</td>\n",
       "      <td>QLD</td>\n",
       "      <td>REF</td>\n",
       "      <td>Principal &amp; Interest</td>\n",
       "      <td>investment</td>\n",
       "      <td>variable</td>\n",
       "      <td>12.681971</td>\n",
       "      <td>0.794400</td>\n",
       "      <td>JOINT</td>\n",
       "      <td>...</td>\n",
       "      <td>am i eligible for a tic:toc loan?</td>\n",
       "      <td>2.0</td>\n",
       "      <td>PaidSearch</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Westpac</td>\n",
       "      <td>ExpensesAtmCashWithdraws</td>\n",
       "      <td>-51.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00776b64bf03fe8de18742eb413ccd1ad5a57264febcaa...</td>\n",
       "      <td>declined</td>\n",
       "      <td>NSW</td>\n",
       "      <td>REF</td>\n",
       "      <td>Principal &amp; Interest</td>\n",
       "      <td>livein</td>\n",
       "      <td>fixed</td>\n",
       "      <td>12.980556</td>\n",
       "      <td>0.494100</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>...</td>\n",
       "      <td>our home loans | tic:toc</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Affiliates</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Commonwealth Bank CBA</td>\n",
       "      <td>TransferTransfers</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0077be1ca38bcfa8b4d2cc8fc9ff2efdce1c106b4a8ef6...</td>\n",
       "      <td>converted</td>\n",
       "      <td>VIC</td>\n",
       "      <td>REF</td>\n",
       "      <td>Principal &amp; Interest</td>\n",
       "      <td>livein</td>\n",
       "      <td>variable</td>\n",
       "      <td>12.282987</td>\n",
       "      <td>0.160800</td>\n",
       "      <td>JOINT</td>\n",
       "      <td>...</td>\n",
       "      <td>rates  fees</td>\n",
       "      <td>6.0</td>\n",
       "      <td>PaidSearch</td>\n",
       "      <td>8.0</td>\n",
       "      <td>ANZ</td>\n",
       "      <td>ExpensesGroceries</td>\n",
       "      <td>-35.96</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.349618</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       applicationid     status propertystate  \\\n",
       "0  000429e2882bd4555885bacf9678c0a60e10610ab9b7f8...   declined           QLD   \n",
       "1  003dec44f66a6d3f85bdc0080ec7a49bb7f4e02bf3c81e...   declined            WA   \n",
       "2  00696bdea8b77f36209e48a29ea490b32acd26c33c0dbd...   declined           QLD   \n",
       "3  00776b64bf03fe8de18742eb413ccd1ad5a57264febcaa...   declined           NSW   \n",
       "4  0077be1ca38bcfa8b4d2cc8fc9ff2efdce1c106b4a8ef6...  converted           VIC   \n",
       "\n",
       "  loanpurpose         repaymenttype   loanusage  ratetype  loanamount  \\\n",
       "0         PUR  Principal & Interest      livein  variable   12.917403   \n",
       "1         REF  Principal & Interest      livein  variable   13.575731   \n",
       "2         REF  Principal & Interest  investment  variable   12.681971   \n",
       "3         REF  Principal & Interest      livein     fixed   12.980556   \n",
       "4         REF  Principal & Interest      livein  variable   12.282987   \n",
       "\n",
       "   loantovalueratio applicationtype  ...  \\\n",
       "0          0.657669          SINGLE  ...   \n",
       "1          0.619600           JOINT  ...   \n",
       "2          0.794400           JOINT  ...   \n",
       "3          0.494100          SINGLE  ...   \n",
       "4          0.160800           JOINT  ...   \n",
       "\n",
       "                                finalpage  pagesvisited  howentered  \\\n",
       "0  home |  worlds first instant home loan           1.0  PaidSearch   \n",
       "1            own your home loan | tic:toc           1.0      Native   \n",
       "2       am i eligible for a tic:toc loan?           2.0  PaidSearch   \n",
       "3                our home loans | tic:toc           1.0  Affiliates   \n",
       "4                             rates  fees           6.0  PaidSearch   \n",
       "\n",
       "   conversionpathlen  financialinstitutionmode     transactioncategorymode  \\\n",
       "0                1.0                       ING  TransferCreditCardPayments   \n",
       "1                1.0                      None                        None   \n",
       "2                4.0                   Westpac    ExpensesAtmCashWithdraws   \n",
       "3                1.0     Commonwealth Bank CBA           TransferTransfers   \n",
       "4                8.0                       ANZ           ExpensesGroceries   \n",
       "\n",
       "   transactionamountmedian chatratedpercentage chatsentimentmean  chatlength  \n",
       "0                   -13.27            0.000000          0.000000         0.0  \n",
       "1                     0.00            0.000000          0.000000         0.0  \n",
       "2                   -51.00            0.000000          0.000000         0.0  \n",
       "3                    -5.00            0.000000          0.000000         0.0  \n",
       "4                   -35.96            0.727273          0.349618        11.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data_df['applicationid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "        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>status</th>\n",
       "      <th>propertystate</th>\n",
       "      <th>loanpurpose</th>\n",
       "      <th>repaymenttype</th>\n",
       "      <th>loanusage</th>\n",
       "      <th>ratetype</th>\n",
       "      <th>loanamount</th>\n",
       "      <th>loantovalueratio</th>\n",
       "      <th>applicationtype</th>\n",
       "      <th>agecreatedon</th>\n",
       "      <th>...</th>\n",
       "      <th>finalpage</th>\n",
       "      <th>pagesvisited</th>\n",
       "      <th>howentered</th>\n",
       "      <th>conversionpathlen</th>\n",
       "      <th>financialinstitutionmode</th>\n",
       "      <th>transactioncategorymode</th>\n",
       "      <th>transactionamountmedian</th>\n",
       "      <th>chatratedpercentage</th>\n",
       "      <th>chatsentimentmean</th>\n",
       "      <th>chatlength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>declined</td>\n",
       "      <td>QLD</td>\n",
       "      <td>PUR</td>\n",
       "      <td>Principal &amp; Interest</td>\n",
       "      <td>livein</td>\n",
       "      <td>variable</td>\n",
       "      <td>12.917403</td>\n",
       "      <td>0.657669</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
       "      <td>home |  worlds first instant home loan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PaidSearch</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ING</td>\n",
       "      <td>TransferCreditCardPayments</td>\n",
       "      <td>-13.27</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>declined</td>\n",
       "      <td>WA</td>\n",
       "      <td>REF</td>\n",
       "      <td>Principal &amp; Interest</td>\n",
       "      <td>livein</td>\n",
       "      <td>variable</td>\n",
       "      <td>13.575731</td>\n",
       "      <td>0.619600</td>\n",
       "      <td>JOINT</td>\n",
       "      <td>72.0</td>\n",
       "      <td>...</td>\n",
       "      <td>own your home loan | tic:toc</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Native</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>declined</td>\n",
       "      <td>QLD</td>\n",
       "      <td>REF</td>\n",
       "      <td>Principal &amp; Interest</td>\n",
       "      <td>investment</td>\n",
       "      <td>variable</td>\n",
       "      <td>12.681971</td>\n",
       "      <td>0.794400</td>\n",
       "      <td>JOINT</td>\n",
       "      <td>42.0</td>\n",
       "      <td>...</td>\n",
       "      <td>am i eligible for a tic:toc loan?</td>\n",
       "      <td>2.0</td>\n",
       "      <td>PaidSearch</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Westpac</td>\n",
       "      <td>ExpensesAtmCashWithdraws</td>\n",
       "      <td>-51.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>declined</td>\n",
       "      <td>NSW</td>\n",
       "      <td>REF</td>\n",
       "      <td>Principal &amp; Interest</td>\n",
       "      <td>livein</td>\n",
       "      <td>fixed</td>\n",
       "      <td>12.980556</td>\n",
       "      <td>0.494100</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>our home loans | tic:toc</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Affiliates</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Commonwealth Bank CBA</td>\n",
       "      <td>TransferTransfers</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>converted</td>\n",
       "      <td>VIC</td>\n",
       "      <td>REF</td>\n",
       "      <td>Principal &amp; Interest</td>\n",
       "      <td>livein</td>\n",
       "      <td>variable</td>\n",
       "      <td>12.282987</td>\n",
       "      <td>0.160800</td>\n",
       "      <td>JOINT</td>\n",
       "      <td>65.0</td>\n",
       "      <td>...</td>\n",
       "      <td>rates  fees</td>\n",
       "      <td>6.0</td>\n",
       "      <td>PaidSearch</td>\n",
       "      <td>8.0</td>\n",
       "      <td>ANZ</td>\n",
       "      <td>ExpensesGroceries</td>\n",
       "      <td>-35.96</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.349618</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      status propertystate loanpurpose         repaymenttype   loanusage  \\\n",
       "0   declined           QLD         PUR  Principal & Interest      livein   \n",
       "1   declined            WA         REF  Principal & Interest      livein   \n",
       "2   declined           QLD         REF  Principal & Interest  investment   \n",
       "3   declined           NSW         REF  Principal & Interest      livein   \n",
       "4  converted           VIC         REF  Principal & Interest      livein   \n",
       "\n",
       "   ratetype  loanamount  loantovalueratio applicationtype  agecreatedon  ...  \\\n",
       "0  variable   12.917403          0.657669          SINGLE          32.0  ...   \n",
       "1  variable   13.575731          0.619600           JOINT          72.0  ...   \n",
       "2  variable   12.681971          0.794400           JOINT          42.0  ...   \n",
       "3     fixed   12.980556          0.494100          SINGLE          30.0  ...   \n",
       "4  variable   12.282987          0.160800           JOINT          65.0  ...   \n",
       "\n",
       "                                finalpage  pagesvisited  howentered  \\\n",
       "0  home |  worlds first instant home loan           1.0  PaidSearch   \n",
       "1            own your home loan | tic:toc           1.0      Native   \n",
       "2       am i eligible for a tic:toc loan?           2.0  PaidSearch   \n",
       "3                our home loans | tic:toc           1.0  Affiliates   \n",
       "4                             rates  fees           6.0  PaidSearch   \n",
       "\n",
       "   conversionpathlen  financialinstitutionmode     transactioncategorymode  \\\n",
       "0                1.0                       ING  TransferCreditCardPayments   \n",
       "1                1.0                      None                        None   \n",
       "2                4.0                   Westpac    ExpensesAtmCashWithdraws   \n",
       "3                1.0     Commonwealth Bank CBA           TransferTransfers   \n",
       "4                8.0                       ANZ           ExpensesGroceries   \n",
       "\n",
       "  transactionamountmedian chatratedpercentage  chatsentimentmean chatlength  \n",
       "0                  -13.27            0.000000           0.000000        0.0  \n",
       "1                    0.00            0.000000           0.000000        0.0  \n",
       "2                  -51.00            0.000000           0.000000        0.0  \n",
       "3                   -5.00            0.000000           0.000000        0.0  \n",
       "4                  -35.96            0.727273           0.349618       11.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['status',\n",
       " 'propertystate',\n",
       " 'loanpurpose',\n",
       " 'repaymenttype',\n",
       " 'loanusage',\n",
       " 'ratetype',\n",
       " 'loanamount',\n",
       " 'loantovalueratio',\n",
       " 'applicationtype',\n",
       " 'agecreatedon',\n",
       " 'hasduplicate',\n",
       " 'loanpercentdifference',\n",
       " 'datedifference',\n",
       " 'sessioncount',\n",
       " 'visitdays',\n",
       " 'daysjourney',\n",
       " 'landingpage',\n",
       " 'finalpage',\n",
       " 'pagesvisited',\n",
       " 'howentered',\n",
       " 'conversionpathlen',\n",
       " 'financialinstitutionmode',\n",
       " 'transactioncategorymode',\n",
       " 'transactionamountmedian',\n",
       " 'chatratedpercentage',\n",
       " 'chatsentimentmean',\n",
       " 'chatlength']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_meta = data_df[['propertystate',\n",
    " 'loanpurpose',\n",
    " 'repaymenttype',\n",
    " 'loanusage',\n",
    " 'ratetype',\n",
    " 'loanamount',\n",
    " 'loantovalueratio',\n",
    " 'applicationtype',\n",
    " 'agecreatedon',\n",
    " 'hasduplicate',\n",
    " 'loanpercentdifference',\n",
    " 'datedifference',\n",
    " 'sessioncount',\n",
    " 'visitdays',\n",
    " 'daysjourney',\n",
    " 'landingpage',\n",
    " 'finalpage',\n",
    " 'pagesvisited',\n",
    " 'howentered',\n",
    " 'conversionpathlen',\n",
    " 'financialinstitutionmode',\n",
    " 'transactioncategorymode',\n",
    " 'transactionamountmedian',\n",
    " 'chatratedpercentage',\n",
    " 'chatsentimentmean',\n",
    " 'chatlength']]\n",
    "Y_meta = data_df['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = X_meta.select_dtypes(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_columns = X_meta.select_dtypes(exclude = ['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loanamount',\n",
       " 'loantovalueratio',\n",
       " 'agecreatedon',\n",
       " 'hasduplicate',\n",
       " 'loanpercentdifference',\n",
       " 'datedifference',\n",
       " 'sessioncount',\n",
       " 'visitdays',\n",
       " 'daysjourney',\n",
       " 'pagesvisited',\n",
       " 'conversionpathlen',\n",
       " 'transactionamountmedian',\n",
       " 'chatratedpercentage',\n",
       " 'chatsentimentmean',\n",
       " 'chatlength']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(numerical_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loanamount</th>\n",
       "      <th>loantovalueratio</th>\n",
       "      <th>agecreatedon</th>\n",
       "      <th>hasduplicate</th>\n",
       "      <th>loanpercentdifference</th>\n",
       "      <th>datedifference</th>\n",
       "      <th>sessioncount</th>\n",
       "      <th>visitdays</th>\n",
       "      <th>daysjourney</th>\n",
       "      <th>pagesvisited</th>\n",
       "      <th>...</th>\n",
       "      <th>transactioncategorymode_IncomeInterestIncome</th>\n",
       "      <th>transactioncategorymode_IncomeInvestmentRetirementIncome</th>\n",
       "      <th>transactioncategorymode_IncomeOtherIncome</th>\n",
       "      <th>transactioncategorymode_IncomeRefundsAdjustments</th>\n",
       "      <th>transactioncategorymode_IncomeSalaryRegularIncome</th>\n",
       "      <th>transactioncategorymode_IncomeSalesServiceIncome</th>\n",
       "      <th>transactioncategorymode_None</th>\n",
       "      <th>transactioncategorymode_TransferCreditCardPayments</th>\n",
       "      <th>transactioncategorymode_TransferSavings</th>\n",
       "      <th>transactioncategorymode_TransferTransfers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.917403</td>\n",
       "      <td>0.657669</td>\n",
       "      <td>32.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.575731</td>\n",
       "      <td>0.619600</td>\n",
       "      <td>72.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.681971</td>\n",
       "      <td>0.794400</td>\n",
       "      <td>42.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.980556</td>\n",
       "      <td>0.494100</td>\n",
       "      <td>30.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.282987</td>\n",
       "      <td>0.160800</td>\n",
       "      <td>65.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loanamount  loantovalueratio  agecreatedon  hasduplicate  \\\n",
       "0   12.917403          0.657669          32.0         False   \n",
       "1   13.575731          0.619600          72.0         False   \n",
       "2   12.681971          0.794400          42.0         False   \n",
       "3   12.980556          0.494100          30.0         False   \n",
       "4   12.282987          0.160800          65.0         False   \n",
       "\n",
       "   loanpercentdifference  datedifference  sessioncount  visitdays  \\\n",
       "0                    0.0             0.0           1.0        1.0   \n",
       "1                    0.0             0.0           1.0        1.0   \n",
       "2                    0.0             0.0           4.0        3.0   \n",
       "3                    0.0             0.0           1.0        1.0   \n",
       "4                    0.0             0.0           8.0        4.0   \n",
       "\n",
       "   daysjourney  pagesvisited  ...  \\\n",
       "0          0.0           1.0  ...   \n",
       "1          0.0           1.0  ...   \n",
       "2          7.0           2.0  ...   \n",
       "3          0.0           1.0  ...   \n",
       "4         15.0           6.0  ...   \n",
       "\n",
       "   transactioncategorymode_IncomeInterestIncome  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "\n",
       "   transactioncategorymode_IncomeInvestmentRetirementIncome  \\\n",
       "0                                                  0          \n",
       "1                                                  0          \n",
       "2                                                  0          \n",
       "3                                                  0          \n",
       "4                                                  0          \n",
       "\n",
       "   transactioncategorymode_IncomeOtherIncome  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "\n",
       "   transactioncategorymode_IncomeRefundsAdjustments  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "\n",
       "   transactioncategorymode_IncomeSalaryRegularIncome  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "\n",
       "   transactioncategorymode_IncomeSalesServiceIncome  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "\n",
       "   transactioncategorymode_None  \\\n",
       "0                             0   \n",
       "1                             1   \n",
       "2                             0   \n",
       "3                             0   \n",
       "4                             0   \n",
       "\n",
       "   transactioncategorymode_TransferCreditCardPayments  \\\n",
       "0                                                  1    \n",
       "1                                                  0    \n",
       "2                                                  0    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "\n",
       "   transactioncategorymode_TransferSavings  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "\n",
       "   transactioncategorymode_TransferTransfers  \n",
       "0                                          0  \n",
       "1                                          0  \n",
       "2                                          0  \n",
       "3                                          1  \n",
       "4                                          0  \n",
       "\n",
       "[5 rows x 165 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot = pd.get_dummies(categorical_columns)\n",
    "# Join the encoded df\n",
    "X_meta = numerical_columns.join(one_hot)\n",
    "X_meta.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loanamount',\n",
       " 'loantovalueratio',\n",
       " 'agecreatedon',\n",
       " 'hasduplicate',\n",
       " 'loanpercentdifference',\n",
       " 'datedifference',\n",
       " 'sessioncount',\n",
       " 'visitdays',\n",
       " 'daysjourney',\n",
       " 'pagesvisited',\n",
       " 'conversionpathlen',\n",
       " 'transactionamountmedian',\n",
       " 'chatratedpercentage',\n",
       " 'chatsentimentmean',\n",
       " 'chatlength',\n",
       " 'propertystate_ACT',\n",
       " 'propertystate_NSW',\n",
       " 'propertystate_NT',\n",
       " 'propertystate_QLD',\n",
       " 'propertystate_SA',\n",
       " 'propertystate_TAS',\n",
       " 'propertystate_VIC',\n",
       " 'propertystate_WA',\n",
       " 'loanpurpose_PUR',\n",
       " 'loanpurpose_REF',\n",
       " 'repaymenttype_Interest Only',\n",
       " 'repaymenttype_Principal & Interest',\n",
       " 'loanusage_investment',\n",
       " 'loanusage_livein',\n",
       " 'ratetype_fixed',\n",
       " 'ratetype_variable',\n",
       " 'applicationtype_JOINT',\n",
       " 'applicationtype_SINGLE',\n",
       " 'landingpage_(not set)',\n",
       " 'landingpage_None',\n",
       " 'landingpage_a smarter online home loan',\n",
       " 'landingpage_am i eligible for a tic:toc loan?',\n",
       " 'landingpage_calculators',\n",
       " 'landingpage_get your instant fix | tic:toc',\n",
       " 'landingpage_home',\n",
       " 'landingpage_home loans made simple',\n",
       " 'landingpage_home |  worlds first instant home loan',\n",
       " 'landingpage_low freq',\n",
       " 'landingpage_our home loans',\n",
       " 'landingpage_our home loans | tic:toc',\n",
       " 'landingpage_own your home loan | tic:toc',\n",
       " 'landingpage_rates  fees',\n",
       " 'landingpage_rates  fees | tic:toc',\n",
       " 'landingpage_worlds first instant home loan',\n",
       " 'finalpage_(Odd)',\n",
       " 'finalpage_None',\n",
       " 'finalpage_a smarter online home loan',\n",
       " 'finalpage_about us: smart technology, smart people',\n",
       " 'finalpage_am i eligible for a tic:toc loan?',\n",
       " 'finalpage_calculators',\n",
       " 'finalpage_frequently asked questions',\n",
       " 'finalpage_home',\n",
       " 'finalpage_home loans made simple',\n",
       " 'finalpage_home |  worlds first instant home loan',\n",
       " 'finalpage_instant decision',\n",
       " 'finalpage_low freq',\n",
       " 'finalpage_more on your financials (we#x2019;re nearly re)',\n",
       " 'finalpage_need help or want to chat? livechat, phone, or email us',\n",
       " 'finalpage_our home loans',\n",
       " 'finalpage_our home loans | tic:toc',\n",
       " 'finalpage_own your home loan | tic:toc',\n",
       " 'finalpage_rates  fees',\n",
       " 'finalpage_rates  fees | tic:toc',\n",
       " 'finalpage_settlement with tic:toc',\n",
       " 'finalpage_tic:toc online banking login',\n",
       " 'finalpage_worlds first instant home loan',\n",
       " 'howentered_Affiliates',\n",
       " 'howentered_Direct',\n",
       " 'howentered_Display',\n",
       " 'howentered_LiveChat',\n",
       " 'howentered_Native',\n",
       " 'howentered_None',\n",
       " 'howentered_OrganicSearch',\n",
       " 'howentered_PaidSearch',\n",
       " 'howentered_Referral',\n",
       " 'howentered_SaveResume',\n",
       " 'howentered_Social',\n",
       " 'financialinstitutionmode_28 Degrees MasterCard',\n",
       " 'financialinstitutionmode_AFG Home Loans Retro/Direct',\n",
       " 'financialinstitutionmode_AMP',\n",
       " 'financialinstitutionmode_ANZ',\n",
       " 'financialinstitutionmode_Adelaide Bank',\n",
       " 'financialinstitutionmode_American Express Cards',\n",
       " 'financialinstitutionmode_BOQ Specialist',\n",
       " 'financialinstitutionmode_Bank Australia',\n",
       " 'financialinstitutionmode_Bank First',\n",
       " 'financialinstitutionmode_Bank of Melbourne',\n",
       " 'financialinstitutionmode_Bank of Queensland',\n",
       " 'financialinstitutionmode_Bank of sydney Personal Bank',\n",
       " 'financialinstitutionmode_BankSA',\n",
       " 'financialinstitutionmode_BankVic',\n",
       " 'financialinstitutionmode_BankWest',\n",
       " 'financialinstitutionmode_Bendigo Bank',\n",
       " 'financialinstitutionmode_Beyond Bank',\n",
       " 'financialinstitutionmode_Big Sky',\n",
       " 'financialinstitutionmode_CUA',\n",
       " 'financialinstitutionmode_CardServices Online',\n",
       " 'financialinstitutionmode_Citibank',\n",
       " 'financialinstitutionmode_Coles MasterCard',\n",
       " 'financialinstitutionmode_Commonwealth Bank CBA',\n",
       " 'financialinstitutionmode_David Jones (American Express Card)',\n",
       " 'financialinstitutionmode_Defence Bank',\n",
       " 'financialinstitutionmode_Firstmac',\n",
       " 'financialinstitutionmode_Greater Bank',\n",
       " 'financialinstitutionmode_HSBC Bank',\n",
       " 'financialinstitutionmode_Heritage Bank',\n",
       " 'financialinstitutionmode_IMB Building Society',\n",
       " 'financialinstitutionmode_ING',\n",
       " 'financialinstitutionmode_Loans.Com.AU',\n",
       " 'financialinstitutionmode_Macquarie Bank',\n",
       " 'financialinstitutionmode_Members Equity Bank (ME Bank)',\n",
       " 'financialinstitutionmode_MyState Bank',\n",
       " 'financialinstitutionmode_Mystate(Personal)',\n",
       " 'financialinstitutionmode_NAB Internet',\n",
       " 'financialinstitutionmode_Newcastle Permanent',\n",
       " 'financialinstitutionmode_None',\n",
       " 'financialinstitutionmode_PN Bank',\n",
       " 'financialinstitutionmode_Peoples Choice CU',\n",
       " 'financialinstitutionmode_Police CU',\n",
       " 'financialinstitutionmode_Qantas Money',\n",
       " 'financialinstitutionmode_Qudos Bank',\n",
       " 'financialinstitutionmode_RACQ Bank',\n",
       " 'financialinstitutionmode_RAMS',\n",
       " 'financialinstitutionmode_St. George Bank',\n",
       " 'financialinstitutionmode_State Custodians',\n",
       " 'financialinstitutionmode_Suncorp',\n",
       " 'financialinstitutionmode_Teachers Mutual Bank',\n",
       " 'financialinstitutionmode_UBank',\n",
       " 'financialinstitutionmode_Unity Bank',\n",
       " 'financialinstitutionmode_Westpac',\n",
       " 'financialinstitutionmode_Woolworths Everyday Money Credit Card',\n",
       " 'transactioncategorymode_ExpensesAtmCashWithdraws',\n",
       " 'transactioncategorymode_ExpensesAutomotiveFuel',\n",
       " 'transactioncategorymode_ExpensesCableSatelliteTelecom',\n",
       " 'transactioncategorymode_ExpensesElectronicsGeneralMerchandise',\n",
       " 'transactioncategorymode_ExpensesEntertainmentRecreation',\n",
       " 'transactioncategorymode_ExpensesGroceries',\n",
       " 'transactioncategorymode_ExpensesHomeImprovement',\n",
       " 'transactioncategorymode_ExpensesInsurance',\n",
       " 'transactioncategorymode_ExpensesLoans',\n",
       " 'transactioncategorymode_ExpensesOfficeExpenses',\n",
       " 'transactioncategorymode_ExpensesOtherExpenses',\n",
       " 'transactioncategorymode_ExpensesPersonalFamily',\n",
       " 'transactioncategorymode_ExpensesRestaurants',\n",
       " 'transactioncategorymode_ExpensesServiceChargesFees',\n",
       " 'transactioncategorymode_ExpensesServicesSupplies',\n",
       " 'transactioncategorymode_ExpensesTravel',\n",
       " 'transactioncategorymode_ExpensesUtilities',\n",
       " 'transactioncategorymode_GeneralUncategorized',\n",
       " 'transactioncategorymode_IncomeDeposits',\n",
       " 'transactioncategorymode_IncomeInterestIncome',\n",
       " 'transactioncategorymode_IncomeInvestmentRetirementIncome',\n",
       " 'transactioncategorymode_IncomeOtherIncome',\n",
       " 'transactioncategorymode_IncomeRefundsAdjustments',\n",
       " 'transactioncategorymode_IncomeSalaryRegularIncome',\n",
       " 'transactioncategorymode_IncomeSalesServiceIncome',\n",
       " 'transactioncategorymode_None',\n",
       " 'transactioncategorymode_TransferCreditCardPayments',\n",
       " 'transactioncategorymode_TransferSavings',\n",
       " 'transactioncategorymode_TransferTransfers']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(X_meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_meta = X_meta.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# label_encoder object knows how to understand word labels.\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "\n",
    "# Encode labels in column 'species'.\n",
    "Y_meta = label_encoder.fit_transform(Y_meta)\n",
    "\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X_meta, Y_meta, test_size=0.20, random_state=42)\n",
    "\n",
    "from keras.utils import to_categorical\n",
    "y2_train = to_categorical(y2_train)\n",
    "y2_test = to_categorical(y2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2067, 165)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       ...,\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2584, 165)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_meta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "input2 = Input(shape=(165,))\n",
    "dense_layer_1 = Dense(10, activation='relu')(input2)\n",
    "dense_layer_2 = Dense(10, activation='relu')(dense_layer_1)\n",
    "output = Dense(2, activation='softmax')(dense_layer_2)\n",
    "\n",
    "model3 = Model(inputs=input2, outputs=output)\n",
    "model3.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 165)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                1660      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                110       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 2)                 22        \n",
      "=================================================================\n",
      "Total params: 1,792\n",
      "Trainable params: 1,792\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lCu7msD55MVE/IkIen/5hxeqiW6t2tyq30b4apewV4vHNo1ul7LU6Fqvj0dfZ/F7qh/QlEommQwvlqJh20j7LevZUelWiTvTi32evpsftNL2qPLOfnp5u63WqqjacaNIt4XAYuVzO0zroJZNJDAwMtN2WTK9KRLZEo1HcuXOn6Qw+K14H60KhgHg87mkd9EqlEkqlEqLRqGNlMmAT7YKf0uPaFQwGsbi4iLm5OVt9wr1gfX0dBw8erLu/qFc2NzexsLCAxcVFR7/IGLCJdsFP6XHbMTg4iGw2i1u3bnldFVtGR0e1C6a9IJ/P48qVK44nw9rvaGlEe0yv9Vs7KRgMtt33SjvcajeeYRMR+QQDNhGRTzBgExH5BAM2EZFPNLzouLq62s16ENly7949APz7tENOi2Zb9Y+6mY7379/HCy+84FV9iIgIOycn5hwkdQGbyO86nZJN1OvYh01E5BMM2EREPsGATUTkEwzYREQ+wYBNROQTDNhERD7BgE1E5BMM2EREPsGATUTkEwzYREQ+wYBNROQTDNhERD7BgE1E5BMM2EREPsGATUTkEwzYREQ+wYBNROQTDNhERD7BgE1E5BMM2EREPsGATUTkEwzYREQ+wYBNROQTDNhERD7BgE1E5BMM2EREPsGATUTkEwzYREQ+wYBNROQTDNhERD7BgE1E5BMM2EREPsGATUTkE/u9rgDRbvz2t7/FtWvX8OjRI23Zz372MwDA3//932vL9u3bh9deew1/8id/0vU6EjklIIQQXleCqFP//M//jL/7u78DgIbB+A9/+AMA4N69ezhx4kTX6kbkNAZs8rVHjx5haGgIv/nNb5pu99nPfhaVSgX79u3rUs2InMc+bPK1ffv24ZVXXsGTTz7ZcJsnn3wSr7zyCoM1+R4DNvnexMQEPvnkk4brP/nkE0xMTHSxRkTuYJcI9YXh4WH88pe/tFz3hS98Advb212uEZHzeIZNfeH8+fM4cOBA3fIDBw7g/PnzHtSIyHk8w6a+8LOf/Qxf/vKXLdf99Kc/xZe+9KUu14jIeTzDpr7wpS99CV/+8pcRCAS0ZYFAAF/+8pcZrKlvMGBT3zh//jz27388F2z//v3sDqG+wi4R6hvlchl/9Vd/BfknHQgE8Itf/ALPPfecxzUjcgbPsKlvPPfcczhx4gSeeOIJPPHEEzhx4gSDNfUVBmzqKxcuXMCnn36KTz/9FBcuXPC6OkSOYpcI9ZVf//rX+Iu/+AsAwH/913/hc5/7nMc1InKQcNBbb70lAPDBBx988AGIt956y8kQKxxNr/qLX/wCBw4cwNLSkpPFUh96+eWX8frrr+Mb3/iG42X/7//+LwKBAJ566inHy+62H//4x3jnnXdw8+ZNr6tCbTp37hx+8YtfOFqm4/mwx8bGMDY25nSx1IdeeOEF/q208PDhQwBgO/nQu+++63iZvOhIROQTDNhERD7BgE1E5BMM2EREPsGATUTkEwzY5GvJZBLJZNLravSsarWK+fl5r6vhS/Pz81BV1etqGDBgE+2CqqqGlK69pFqt4vLlyzh+/DgCgQACgUDDLze5Xv/oVaqqolAoIJPJIBwOt9y+VCpp25qPq1QqGY55ampKW3f69GlMTk6iWq06fgydcnwcNlE3Xb161dP9371719P9N6KqKqLRKOLxOEZGRlCr1bC2toZIJAKgvt2EEKhWqxgaGkKlUsHg4KAX1bYllUoBAGZnZ1tuOz8/jzt37uDixYv44Q9/iFwuZ1h///59w/OXXnpJ+38oFEI8Hkc0GkU2m0UwGHSg9rvDM2yiDqmqikwm43U1LC0uLiIUCmFkZAQAEAwGMT4+DmAn0K2srNS9RgbpXg7WwM6XjZ0v6qmpKdRqNWSzWSiKguHh4bptDh06BCGE9lAUxbB+ZGQEhw8fxuLiomP13w0GbPKtarWKlZUV7Wex+Xk+n0cgEEA4HNZuwlutVpHP57VtMpmM9lN4c3NTK9uqa8C8LJVKIZ/PG9YB3verV6tVzMzM4OTJk5brU6kUIpGIZdC2oqoqVlZWtGPMZDKGbgI77a7fdn5+Xlu/vr7e4VE2J9v/6tWrDc+Mt7e3EQ6HkUwmUSgUGpY1NjaGmZmZ3ugacTIxycTEhJiYmHCySOpTAMTS0tKuylAURUuyY36+sbEhhBCiXC4LACIWi2n7NW9Tq9VELBYTAMT7778vhBCiUqkYytaXpV9mfi6EEIlEQiQSiV0dm7S0tFRXfiu5XE4AEOVyuW6dLCuRSAgAolgsWq7XUxRFpNNpIcROuyiKIhRFEbVaTVvfqt31r11eXhZCCHH79m3LOthl1fZCCFEsFgUAkcvlRDqdFgCEoiji9u3bhu1kO8mHoiiiUqnUlSePJZfLtVU/N+IhAzZ5womALctpFUDtbCM/5KlUatdlOamTgC2DsRW5vFaraYFWfknp10syqOoD2cbGhgCgBV75ulZttby8bLlNp19ujdo+lUoZvgj0X8jyC0Wq1WqiWCxqbSa/mMzbmP827GDApr7RawHb6bKc0knAblYn/XL5K0J/Zml+nQx0ejKAKYrSdJ/mZfozcfOjE3bfRyEefyHrz/jN0um04Zjs7KsZN+Ih+7CJ9qjBwUEUi0Xk83lEo1HLMccLCwt1y2SfsOy/t0tuL3QX+eTDbaFQCID18Ugvv/xy28fUbQzYRDqxWMzrKnRVKBRCLpdDPp/XhsvpyVETVhfcOm0r/cVdN8h6WX0BmUeB6AWDwZ5//xmwifA4iOjH4fqVDLx2Z+kpioLl5WXLcc0TExMAgK2tLW2ZLLfdHN3pdBoAkM1mtTLcmIkp6/XBBx9oy+T+5PFYUVW16TElEglnKrgLDNjkW+ahZfrn8gOqD1rms0Q5rE1VVW2srv4MTJ5tyWCuH/olZ8Tpz0Bl4PF6WN+RI0cA1AdsefxWZ8vj4+OWAenMmTNQFAVzc3Pa69bW1hCLxTA6OlpXXrN2P3v2LICdceADAwMIBAIYGhrSgqQc7lcqlVoeo75883GOjo4ikUggmUxq+7558yYURdHGoq+srBiGFG5vb+Pu3bvaMenJoYknTpxoWS+3MWCTbw0NDRn+r38+MDBg+Ne8PQAcO3YM4XAYAwMDGB4eRjabNaz/7ne/C0VRcPToUeTzeYyMjGhno1euXAHweMbgD37wA0xOTjp7gB164YUXAAAPHjzQlsngCOy0g9XU86tXr9Z1GQSDQSwuLkJRFMPrvv/972vb2G33wcFBlMtl7YshFouhXC5rE1pqtRpisVjLL7tAIGAoXwZ/q2PR11n//j799NM4deqUNl3/o48+athdIttRtquXHL1r+rlz5wCA93SklgKBAJaWlpr+RHVz3wC6crFrt27cuIFz5861XVd5tj89Pd3W61RV9XwKdjgcrptC7qVkMomBgYG229KNeMgzbKI+FI1GcefOnaYz+Kx4HawLhQLi8binddArlUoolUqIRqNeVwVAjwZs81RXIqeY+737lezKmJubs9Un3AvW19dx8OBBLf+J1zY3N7GwsIDFxUXPv8ikngzYly9fRiQS6fkxkY1Uq1Ukk0kt94LdnA16Vuku5WN+fh75fL7ncvX6gbnfu58NDg4im83i1q1bXlfFltHRUe2CaS/I5/O4cuVKTyXD6smAfe3aNa+r0LFqtYqtrS1cvXoVQggsLy8jEom0PXRJCIFKpaI9r9Vq2iSD06dPI5PJ9FyuXj/o9oQNrwWDwbb7XmnH9PR0TwVroEcDtp9tbW0ZftLJYUQzMzNtl6X/Y9H/JAuFQlq6x0Yz1Iio//REwNanbwyHww1nQjVKzdhOekf5epki0jwcaLfpH839bzKYmse47nas7uDgIN544w3k8/m6JPp+aCci6oCTiUk6TXaiKIqIxWJaukaZ1QumRDWNUjPaTe+YSqW0lJO1Wq0uq5nT6R/L5bK2D31GNCHsp+A0t4OeTMJjN4VlL7UTHEr+1O86Sf5EvaEvs/XJnLT6gCYDkf4PtVVqRqvAZl4GU5pIma3M7j7aoc+djA5SM+r33+wD69d2YsC2hwHbv9wI2J7f0/Gf/umfAMBwddhqCM2NGzcAoO6n+ezsrO37+sViMQwNDWF5eRlnzpzB4OCg4cKTE/uQhoeHIYRAqVTCj370I8zMzODP//zPcfHixbbKaZef2unevXs4cOCA7e33onv37gEAVldXPa4JtWt7e9vytmS74mT07+QbBbvITdyqHPOy999/39AtYD7rbbWPTr3//vsdl93sdfKXiP7M1i/tJMvgg49+fuz5fNi7Sc145MgR5HI5FItFxGIxzMzMWA63czr9o1tjS3/yk58AgOW9+/zQTktLS5a5kfl4/JDTmr2uBx/tP9xIu+B5wJYpF1vNxnIiNWMgEICqqgiFQrh27RqKxaJhuJ1b6R9lWcvLy7sqR69areLtt9+GoiiGDGN+biciakE4qJMuEXlxTlEUbWSCHHUAPB69oL8pqv5RLpcN6+RIE/2FS/3tjxKJhLafcrls+LnfbB92KYpiOcrCfEHOzigR/THI4xJCaCM+rG4a6pd2AnjR0Q5edPSvvhwlIsROQJD3jovFYoZhY/qApB8mF4vFtABhDhzNllUqFe0mnVYjNxrtwy7znZhTqVTdjT+FaB2wrQJiqzL91E4M2PYwYPuXGwGb6VXJE16mV/WTTtOrkveYXpWIaA9jwCYi8gkGbJuapTvVP4h6GUfzWJufn/dFEjUGbJuEzbGX1PtUVXX1y9Xt8jtVrVZx+fJlHD9+XDvBaJSAzE8nI6qqolAoIJPJNL3pST6fRzgcRjgcrsu1f/r0aV+kK/Z8ajpRt5mzG/qt/E6oqopoNIp4PI6RkRHUajWsra0hEokAQF1KASEEqtUqhoaGUKlUei4vtF4qlQKwkxqhkZWVFdy4cUO7Ee+bb76JDz/8UEsVEQqFEI/HEY1Gkc1me+YOM2Y8w6Y9RVVVZDIZ35bfqcXFRYRCIS39bzAY1HK1z87OWt4VSQbpXg7WwM6XTbMcNtvb24hEIojH4wgGgwgGg4jFYrh06ZJhwt7IyAgOHz6s5ZrvRQzY5Bv6vOn6XN2S1c9387JUKqX9HJbLq9Wq9nMZADKZDAKBAKampgzT7zstH9h9/vPdqFarmJmZsUxhAOzUORKJ2L6VXav3oZ28693Iq/7ee+8BAJ599llt2TPPPAMAuH//vmHbsbExzMzM9GzXCAM2+cbk5CQ+/vhjCLFz+7R8Pm+4447+lmpSuVw2PNeficnrDkNDQ1q/ZqFQwMWLF1Gr1QAAR48e1YJ2p+V7TWb8++IXv2i5fnp6GolEApFIxNYNe1u9D9FoVLsna6FQgKIoKJfLyOfz+N73vqeVU61WEY1GcfjwYQgh8MYbb+DUqVOO3zT4zp07AGDInCd/NZj7smUbyTbrOU7OwnFjZg/1J7Q501GmK9DPfN3Y2BAAtBspyHLNf9bmZXa2EWInBQBMMz07Lb9TTsx0NN+AQk8ur9VqWoZGfW568+ucfB+czD/faJ/tLpepGjrNX6/nRjzkGTb5gswHre9PPXbsGIDH+bmdFgqFAHR2P85e0uxinBQMBrW+22ZdAk6+D/q86vruIzv1dYu82Nir7zkDNvnCwsJC3TL54TL/rKXODA4Oolgs1nVx6Dn5PsjthcvDYxVFabguFos5ui+3MWCTL8gPndWZn9sfOr99qHcjFAohl8shn89rw+X03HgfnM4/b2ZVZ3nx8/nnn3d1305jwCZfkEmitra2tGXyDHBsbMyVfcpA8tJLL7lSfrfIwGt3Jp+iKFheXrbsmnDyfehWXvUXX3wRgLHODx48MKwzSyQSjtbBKQzY5AtnzpyBoiiYm5vTzpTW1tYQi8UMN3CQZ3ky2BYKBW3d1NQUAOMZlzk4yKFtqqoim81CURTDT+pOy/dyWJ+845E5YMt2tDpbHh8ftwxadt4HfXlyn/p9y/Vnz54FsNNnPTAwgEAggKGhIS3wy+F+dkaN6Ms3H+fw8DDS6daWcx8AACAASURBVDSuX78OVVWhqiquX7+OdDpdd89FeeZ94sSJlvv0hJNXMDlKhOxCB/mwK5WKSKfT2tX95eVlw40dhNjJ0y1HO+RyOSGEqMutLkd/JBIJw00bAGg3hwAg0um0Y+XbuWGFFSdGicgbTuhzqMvj1T+sKIpiWV6z98Gq3Eb7apZXPZFIiFgsZlkHPatjsToemateURRx+/Zty7LkiBfzjUE6wXzY1Dd6LR+2HKHg4MfBEU7lw5Zn+tPT0229TlVVz6dph8Nh5HK5ruwrmUxiYGCg7XaywnzYRNSRaDSKO3fuGLpw7PA6WBcKBcTj8a7sq1QqoVQqIRqNdmV/nWDApj3PPK26H8lx1nNzc47PJHTL+vo6Dh48qOU/cdPm5iYWFhawuLjo+ZdUMwzYtOcNDQ1Z/r/fDA4OIpvN4tatW15XxZbR0VHtgqnb8vk8rly50vOJrphelfa8Xuu3dlMwGHSkf7bf+KVNeIZNROQTDNhERD7BgE1E5BMM2EREPuH4RccbN27g4cOHThdLfeidd97Bu+++63U1epqcKv3yyy97XBNq1+rqquMTwxyd6ZjP57WbXBJ55d///d8BAF/5ylc8rgntdZOTk03Tu7bL0YBN1AuYIoH6FfuwiYh8ggGbiMgnGLCJiHyCAZuIyCcYsImIfIIBm4jIJxiwiYh8ggGbiMgnGLCJiHyCAZuIyCcYsImIfIIBm4jIJxiwiYh8ggGbiMgnGLCJiHyCAZuIyCcYsImIfIIBm4jIJxiwiYh8ggGbiMgnGLCJiHyCAZuIyCcYsImIfIIBm4jIJxiwiYh8ggGbiMgnGLCJiHyCAZuIyCcYsImIfIIBm4jIJxiwiYh8ggGbiMgnGLCJiHwiIIQQXleCqFM///nPEQqF8Jd/+Zd44omd84/f/OY3AIDPfvazAIBPP/0UH3zwAf7jP/4Dhw4d8qyuRLu13+sKEO3Go0eP8Lvf/Q4/+9nP6tb953/+p+G5qqoM2ORr7BIhXzt69Ci++tWvIhAINNwmEAjgq1/9Ko4ePdrFmhE5jwGbfO/ChQvYt29fw/X79u3DhQsXulgjInewD5t878GDB/j85z+PRn/KgUAAv/rVr/Dss892uWZEzuIZNvnes88+i69//evaRUe9J554Al//+tcZrKkvMGBTXzh//rxlP3YgEMD58+c9qBGR89glQn3hv//7vzE0NIQ//vGPhuX79+9HpVLBwYMHPaoZkXN4hk194eDBg3jxxRexf//jkar79+/Hiy++yGBNfYMBm/rGxMQEPv30U+35p59+iomJCQ9rROQsdolQ3/jtb3+Lz33uc/j9738PAHjqqafw61//Gk8//bTHNSNyBs+wqW88/fTT+Na3voUDBw7gwIED+Na3vsVgTX2FAZv6yiuvvIKHDx/i4cOHeOWVV7yuDpGjXM0l8stf/hKFQsHNXRAZPHr0SPv/xx9/jNXVVQ9rQ3vNyMgIvvCFL7i3A+GiV199VQDggw8++NgTj1dffdXNkCpcPcP+wx/+gImJCSwtLbm5G+oDgUAAS0tLHNXRwo0bN3Du3LmG0/DJO+fOncMf/vAHV/fBPmwiIp9gwCYi8gkGbCIin2DAJiLyCQZsIiKfYMAmIvIJBmzqK8lkEslk0utq9KxqtYr5+Xmvq9Fz5ufnoaqq19VoiQGbyEGqqja9IbCXqtUqLl++jOPHjyMQCCAQCDT8cpPr9Y9epaoqCoUCMpkMwuFww+3y+TzC4TDC4TDy+bxh3enTpzE5OYlqtep2dXfF1YkzRN129epVT/d/9+5dT/ffiKqqiEajiMfjGBkZQa1Ww9raGiKRCID6dhNCoFqtYmhoCJVKBYODg15U25ZUKgUAmJ2dbbjNysoKbty4gWw2CwB488038eGHH+LixYsAgFAohHg8jmg0imw2i2Aw6H7FO8AzbCKHqKqKTCbjdTUsLS4uIhQKYWRkBAAQDAYxPj4OYCfQrays1L1GBuleDtbAzpdNsy/q7e1tRCIRxONxBINBBINBxGIxXLp0CaVSSdtuZGQEhw8fxuLiYjeq3REGbOob1WoVKysr2s9i8/N8Po9AIIBwOIzt7W1tG/lTGQAymQwCgQCmpqawubmplW3VNWBelkqltJ/a+uVe96tXq1XMzMzg5MmTlutTqRQikYhl0LaiqipWVla0Y8xkMoauBDvtrt92fn5eW7++vt7hUTb23nvvAYDhRszPPPMMAOD+/fuGbcfGxjAzM9O7XSNuJiqZmJgQExMTbu6C+gQAsbS0tKsyFEXRkvCYn29sbAghhCiXywKAiMVi2n7N29RqNRGLxQQA8f777wshhKhUKoay9WXpl5mfCyFEIpEQiURiV8cmLS0t1ZXfSi6XEwBEuVyuWyfLSiQSAoAoFouW6/UURRHpdFoIsdMuiqIIRVFErVbT1rdqd/1rl5eXhRBC3L5927IOdlm1vRBCey+ttlcUxbBM1jOXy7W9/27EOwZs6glOBGxZTqsAamebYrEoAIhUKrXrspzUScCWwdiKXF6r1bRAK7+k9OslGVQrlYq2bGNjQwDQAq98Xau2Wl5ettym0y+3Rm3fzvJarVb3vtvVjXjHLhEiC6FQCAAwMzPjcU12r9nFOCkYDGp9t826BGR+cX2/9rFjxwDsZBJsh9ze3LVkp75ukRcbe/V9Z8AmIgA7QbhYLCKfzyMajVqOS15YWKhbJoOceahcK3J7sfNL3/BwkqIoDdfFYjFH9+U2BmyiJvz2gd6tUCiEXC6HfD6vDZfTk8HP6gy807bSX9x1g1Wd5cXP559/3tV9O40Bm8iCDCIvvfSSxzXZPRl47c7kUxQFy8vLll0T8gYTW1tb2jJZ7tjYWFv1SqfTAIBsNquV4cZMzBdffBGAsc4PHjwwrDNLJBKO1sEpDNjUN8xDy/TPZUDQBy3zWaIc1qaqKrLZLBRFMfyclmeQMpjr71c6NTUFwHg2JwOP18P6jhw5AqA+YMvjtzpbHh8ftwxaZ86cgaIomJub0163traGWCyG0dHRuvKatfvZs2cB7PRZDwwMIBAIYGhoSAv8criffqx0I/ryzcc5PDyMdDqN69evQ1VVqKqK69evI51OY3h42LCtPPM+ceJEy316ws0rmhwlQnbBgVEiaHG/Patt9MuKxaI2UiKdTmvD1KRyuaytl8O+5LA0OWpCji5JJBLaMq+H9ckhiXKInRDWbWXFPOxNlpdOp7XXLS8vG9rKbrsLsdOmchRLLBYzDD1MJBIiFotZ1kGv2futJ4c3Kooibt++bVmWHPGiHwVjVzfiXUAI924Od+7cOQDgPR2pJS/v6ShHJ7j4UXBMp/d0lGf709PTbb1OVVXPp2mHw2Hkcrmu7CuZTGJgYKDtdgK6E+/YJUK0B0SjUdy5c8fQjWOH18G6UCggHo93ZV+lUgmlUgnRaLQr++uELwK2eaorkVPM/d79So6znpubs9Un3AvW19dx8OBBLf+JmzY3N7GwsIDFxUXPv6Sa8UXAvnz5MiKRSNvjPHuVzFfRDqt0l/IxPz+PfD7vi3y+vWZoaMjy//1ocHAQ2WwWt27d8roqtoyOjmoXTN2Wz+dx5cqVnk905YuAfe3aNa+r4JhSqYRLly61/TohBCqViva8VqtpkwxOnz6NTCbji3y+vUa4OGGjFwWDwY76Z/vd9PR0zwdrwCcBu1+oqoof/ehHHb9e/wel/9kWCoW0acWNZqgRkf/1ZMDWp28Mh8MNZ0I1Ss3YTnpH+XqZItLcVeFk+sfFxUW89tprlut2O1Z3cHAQb7zxBvL5fF0Sfb+1ExE14OaYwU7HJSqKImKxmDa2U2b10le3WWpGu+kdU6mUNu6zVqvVZTVzMv3j7du3tbqYj0UI+2N1rV4ryUxjdlNY9lI7waFsff2uk3HY1B17Mr2qHNyuT/EoA5H+D7VVakarwGZeBtMAeTnBwO4+7JITDRrVox2tXuvXdmLAtocBu3ftyYkzU1NTWFhYqLsAZJ7cYHUjTUkIYTkZwrxM7mt5eRlnzpypG87Tah92ZTIZ7d5xVvVoR6vX+rWdAoEAXnjhhbqpwmS0vb2Ne/futZ23g9x37949fOMb39hbE2es0jdacSI143e+8x0oioJIJIKBgYG6pDNO7COfzzdMMOM0ebFRnwPCL+1ERDa4efreyU8E2Lw7hHyu7zppVU6jsovFonYbIas7jDTahx2yjEaPTsuzIvuO9XkS/NRO7BJpjV0ivWtP3nFGplxsNRvLidSMgUAAqqoiFArh2rVrKBaLhjtNOLEP0eSsUzh4BlqtVvH2229DURQtaxrgn3YiIhvc/Dbo5BtHjlJQFEUbmSDPHKEbvaC/Kar+US6XDevkSBP9hUt5AQ3/f2FM7qdcLhvOHJvtYzdgcQZrZ5SI/hj02dHkiA9FUeqyjPmlncAzbFt4ht279uQZ9vDwMMrlMg4fPoznnnsOU1NT+MpXvqIlVb9y5QqAnXHH5XJZ66+NxWIol8sYHh42TDEeGBgw/AsYpyC/9tprWF1dRSAQwOrqqmEWWLN9dFsgEDAcg8wfHAgEcOvWLcTjceRyubrZWnutnYj6Wc+NEqG9ycv0qn7SaXpVch/TqxIRkYYBm2gP4cVga/Pz877IwcOA3aFm6U71D+p9qqq6+l65Xb5d1WoVly9fxvHjx7W/z0b5a/z0t6yqKgqFAjKZTNOc+fl8HuFw2HKi1+nTp32R7XK/1xXwK/Yh9g9zsiy/lW+HqqqIRqOIx+MYGRlBrVbD2toaIpEIAODq1auG7YUQqFarGBoaQqVS6enUo/Ku8FZ3eZdWVlZw48YNZLNZAMCbb76JDz/8UJuBHAqFEI/HEY1Gkc1me/YmBjzDpj1NVVVkMhnflm/X4uIiQqGQdveWYDCI8fFxADuBTt4xXk8G6V4O1sDOl435C0dve3sbkUgE8XgcwWAQwWAQsVgMly5dMsz3GBkZweHDh7VUxb2IAZt8S5+GV5/6VbL6OW9elkqltJ/Hcnm1WtV+PgOP7xA0NTVlSPXbafnA7tPptqNarWJmZgYnT560XJ9KpRCJRCyDtpVW7d5O2t5upOV97733AADPPvustuyZZ54BANy/f9+w7djYGGZmZnq2a4QBm3xrcnISH3/8sXY3nnw+b7iBg/4OPVK5XDY815+Zif+fiTo0NKT1cxYKBVy8eBG1Wg0AcPToUS1od1p+t927dw8A8MUvftFy/fT0NBKJBCKRiK37PbZq92g0qt3Sr1AoQFEUlMtl5PN5fO9739PKqVariEajOHz4MIQQeOONN3Dq1CnH7zl5584dADDMC5C/Gsx92bKNZJv1HDdn5XRj5g/1B7Q501HOftXP7NzY2BAAtLzcslzzn7l5mZ1thNiZUYoGeVTaLb9Tncx0NOcv15PLa7Walh9dnxPG/Don292p9MXN9tnucjnTV/8e27UnZzoS2bG6ugrA2L967NgxADuTS9wQCoUAwJBHxQ+aXYyT5F3VATTtEnCy3eX25m4kO/V1i7zY2KvvMQM2+ZJVGl75YWuUm5uaGxwcRLFYrOvi0HOy3buVlldRlIbrYrGYo/tyGwM2+ZL8EFqdCbr9IfTbh7wdoVAIuVwO+XxeGy6n50a7N7pnq1Os6iwvfj7//POu7ttpDNjkSzLnyNbWlrZMnhG6dTcWGVheeuklV8p3iwy8dmfyyURrVl0TTrZ7t9LyyhuI6Ov84MEDwzoz/U1AegkDNvnSmTNnoCgK5ubmtDOntbU1xGIxQz5wedYng22hUNDWTU1NATCegZmDhRzqpqoqstksFEUx/MTutPxuDus7cuQIgPqALdvN6mx5fHzcMmjZaXd9eXKf+n3L9WfPngWw02cts08ODQ1pgV8O97MzakRfvvk4h4eHkU6ncf36daiqClVVcf36daTT6bqMkvLM+8SJEy336Qk3r2hylAjZhQ7yYcubG+P/r/YvLy8b8oQLsZO7W45+yOVyQgih3eFdjnSQoz8SiYQhBzhgvLt8Op12rHw7+c+tdDJKROYr39jY0JbJ49M/rCiKYlles3a3KrfRvsrlsjaKJRaLGXKoJxIJEYvFLOugZ3UsVscjb/CtKIrhrkx6csSLOa+8HXvyJry0N/VaetXd3CjZTZ2mV5Vn9vo85naoqur5NO1wOIxcLteVfSWTSQwMDLTdTgDTqxKRQ6LRKO7cuWPosrHD62BdKBQQj8e7sq9SqYRSqYRoNNqV/XWCAZvIxDzNuh/IcdZzc3OOzyR0y/r6Og4ePKjlP3HT5uYmFhYWsLi46PmXVDMM2EQm+luj6f/vd4ODg8hms7h165bXVbFldHRUu2Dqtnw+jytXrvR8oiumVyUy6bV+aycFg8GO+mf7nV/ahGfYREQ+wYBNROQTDNhERD7BgE1E5BMM2EREfuHmNMpXX3214bRRPvjgg49+e7z66qtuhlR3p6b/8pe/bHtmFdFuvfPOOwCA119/3eOa0F4zMjKCL3zhC66V72rAJvICc9hQv2IfNhGRTzBgExH5BAM2EZFPMGATEfkEAzYRkU8wYBMR+QQDNhGRTzBgExH5BAM2EZFPMGATEfkEAzYRkU8wYBMR+QQDNhGRTzBgExH5BAM2EZFPMGATEfkEAzYRkU8wYBMR+QQDNhGRTzBgExH5BAM2EZFPMGATEfkEAzYRkU8wYBMR+QQDNhGRTzBgExH5BAM2EZFPMGATEfkEAzYRkU8wYBMR+QQDNhGRTzBgExH5xH6vK0C0W+VyGY8ePdKe/8///A8AYGtrS1u2b98+PPfcc12vG5GTAkII4XUliDr14x//GH/7t39ra9t/+Zd/wde+9jWXa0TkHgZs8rVarYbPfOYztrb96KOPMDAw4HKNiNzDPmzytYGBAYTDYezf37h3b//+/QiHwwzW5HsM2OR7k5OThj5ss0ePHmFycrKLNSJyB7tEyPd+//vf47Of/Sx+97vfWa7/sz/7M/zmN7/BU0891eWaETmLZ9jke0899RS+/e1v48CBA3XrDhw4gG9/+9sM1tQXGLCpL5w7dw4PHz6sW/7w4UOcO3fOgxoROY9dItQX/vjHP2JwcBAfffSRYflnPvMZVKvVphclifyCZ9jUF/bv34+JiQk8+eST2rInn3wSExMTDNbUNxiwqW+Mj4/jk08+0Z5/8sknGB8f97BGRM5ilwj1DSEEPv/5z+PBgwcAgGeffRa/+tWvEAgEPK4ZkTN4hk19IxAI4Pz58zhw4AAOHDiA8+fPM1hTX+EZNvWVf/u3f8NXv/pVAMC//uu/4q//+q89rhGRc1y9GpPP55HNZt3cBVFDV69e9boKtMdMTk5CURTXyne1S2RlZQWrq6tu7oL6xOrqKra3tx0p6+TJkxgdHXWkrF6zvb3Nz1SPWl1dxcrKiqv7cLVLRE5YWFpacmsX1CcCgQCWlpYwMTHhdVV62o0bN3Du3DmwJ7P3dCPe8aIjEZFPMGATEfkEAzYRkU8wYBMR+QQDNhGRTzBgU19JJpNIJpNeV6NnVatVzM/Pe12NnjM/Pw9VVb2uRksM2EQOUlW1Z6fDV6tVXL58GcePH0cgEEAgEGj45SbX6x+9SlVVFAoFZDIZhMPhhtvl83mEw2GEw2Hk83nDutOnT2NychLVatXt6u4K805SX/F6duPdu3c93X8jqqoiGo0iHo9jZGQEtVoNa2triEQiAOrbTQiBarWKoaEhVCoVDA4OelFtW1KpFABgdna24TYrKyu4ceOGNvP6zTffxIcffoiLFy8CAEKhEOLxOKLRKLLZLILBoPsV7wDPsIkcoqoqMpmM19WwtLi4iFAohJGREQBAMBjUUs/Ozs5aztCTQbqXgzWw82XT7It6e3sbkUgE8XgcwWAQwWAQsVgMly5dQqlU0rYbGRnB4cOHsbi42I1qd4QBm/pGtVrFysqK9rPY/DyfzyMQCCAcDmvT4KvVqvZTGQAymQwCgQCmpqawubmplW3VNWBelkqltJ/a+uVe96tXq1XMzMzg5MmTlutTqRQikYjtadWqqmJlZUU7xkwmY+hKsNPu+m3n5+e19evr6x0eZWPvvfcegJ10u9IzzzwDALh//75h27GxMczMzPRu14hw0cTEhJiYmHBzF9QnAIilpaVdlaEoigAg5J+1/vnGxoYQQohyuSwAiFgspu3XvE2tVhOxWEwAEO+//74QQohKpWIoW1+Wfpn5uRBCJBIJkUgkdnVs0tLSUl35reRyOQFAlMvlunWyrEQiIQCIYrFouV5PURSRTqeFEDvtoiiKUBRF1Go1bX2rdte/dnl5WQghxO3bty3rYJdV2wshtPfSantFUQzLZD1zuVzb++9GvGPApp7gRMCW5bQKoHa2KRaLAoBIpVK7LstJnQRsGYytyOW1Wk0LtPJLSr9ekkG1UqloyzY2NgQALfDK17Vqq+XlZcttOv1ya9T27Syv1Wp177td3Yh37BIhshAKhQAAMzMzHtdk95pdjJOCwaDWd9usS0BmCtT3ax87dgzATmKqdsjtzV1LdurrFnmxsVffdwZsIgKwE4SLxSLy+Tyi0ajluOSFhYW6ZTLImYfKtSK3Fzu/9A0PJzXLTx2LxRzdl9sYsIma8NsHerdCoRByuRzy+bw2XE5PBj+rM/BO20p/cdcNVnWWFz+ff/55V/ftNAZsIgsyiLz00kse12T3ZOC1O5NPURQsLy9bdk3IfOVbW1vaMlnu2NhYW/VKp9MAgGw2q5XhxkzMF198EYCxzvJGzXKdWSKRcLQOTmHApr5hHlqmfy4Dgj5omc8S5bA2VVWRzWahKIrh57Q8g5TBvFAoaOumpqYAGM/mZODxeljfkSNHANQHbHn8VmfL4+PjlkHrzJkzUBQFc3Nz2uvW1tYQi8W0u/zYbfezZ88C2OmzHhgYQCAQwNDQkBb45XA//VjpRvTlm49zeHgY6XQa169fh6qqUFUV169fRzqdxvDwsGFbeeZ94sSJlvv0hJtXNDlKhOyCA6NEoBuiZ/Ww2ka/rFgsaiMl0um0NkxNKpfL2no57EsOS5OjJuTokkQioS3zelifHJIoh9gJYd1WVszD3mR56XRae93y8rKhrey2uxA7bSpHscRiMcPQw0QiIWKxmGUd9Jq933pyeKOiKOL27duWZckRL/pRMHZ1I97xFmHUE7y8RZgcneDiR8Exnd4iTJ7tT09Pt/U6VVU9n6YdDoeRy+W6sq9kMomBgYG22wngLcKIyCHRaBR37twxdOPY4XWwLhQKiMfjXdlXqVRCqVRCNBrtyv46wYBNe5q537tfyXHWc3NztvqEe8H6+joOHjyo5T9x0+bmJhYWFrC4uOj5l1QzvgjY5twERE4ZGhqy/H8/GhwcRDabxa1bt7yuii2jo6PaBVO35fN5XLlypecTXfkiYF++fBmRSKTtgfm9pFQqGWZ0yVEFdlnlJ5aP+fl55PN5XyRg7zXCxQkbvSgYDHbUP9vvpqenez5YAz4J2NeuXfO6CrtmzgrW7vheIQQqlYr2vFaraUHm9OnTyGQyvkjATkSd80XA7geHDh0ynMk1my7biP4MQN/PFgqFtDwQjaYUE5H/9WTA1ufbDYfDDaeuNsql204+Xvl6mdPXfCskJ/L1bm9vIxwOI5lMNrxKv9vJFYODg3jjjTeQz+fr7nril3YiohbcHOTd6UByRVFELBbTBuPLNIz66jbLpWs3H28qldIG6tdqtbo0lE7l65UD9uVDUZS6gfl2J1eY20FPpoa0m3O4l9oJDqVX7XedTJyh7tiT+bBlcNPn5JWBSP+H2iqXrlVgMy+DaUaTnBFmdx/tqNVqolgsasFOJoBvV7OAbbXeL+3EgG0PA3bv2pMBu9ndIfTL9WeH5ofV9lbL5L7MU2vt7qNT6XS65XTbRtoN2H5pp0av54MPPz323NT0RtOEzctbTSe2Wm9etrm5iZmZGW24YCqVMgx5cmvKsqqqGBgY6KjcZnWS5SYSCe2mpH5pp0AggNdffx3f+MY3Oi5jL/jxj3+Md955Bzdv3vS6KmTyzjvvYHh42NWp6ftdK7lLNjc3Ox5cf+TIEeRyOZRKJSwsLGh3mTCPU93NPqzIuzY77Sc/+QkAWN5s1Q/t9MILL7SdonOvefjwIYD2U5mS+959913X99Fzo0RkjtxW02edyKUbCASgqipCoRCuXbuGYrFouDWQW/l6VVV1/ANXrVbx9ttvQ1EULc0l4O92IiITN/tbOunDlqMUFEXRRibIUQfA49EL+rtY6x/lctmwTva56i9cygtowM6FMbmfcrlsuPlms33Ytby8bEjlWC6XLe/IbGeUiP4Y9H3JcsSH1egTv7QTwIuOdvCiY+/akzfhHR4eRrlcxuHDh/Hcc89hamoKX/nKV7S7YFy5cgXAzrjjcrmsJVmPxWIol8sYHh425IQYGBgw/AsYc0a89tprWF1dRSAQwOrqquFnfrN92PX000/j1KlTCAQCSCaT+OijjzqaNBMIBAzHIBO+BwIB3Lp1C/F4HLlcrm56rV/aiYha67mLjrQ3eZkP2086zYdN7mM+bCIi0jBgE+1xe/EC8fz8vC9z7jBgd6hZulP9g3qfqqquvldul78b1WoVly9fxvHjx7W/2UY5bfz09729vY2pqSktlbE5t83p06d9md2SAbtDwpRHudGDep85WZbfyu+UqqqIRqO4cOECRkdHUavVsLy8jNnZWcugLXQpfiuVSs/+fauqilKphGvXrqFWq+Gb3/wmTp06ZcinHwqFEI/HfZfdkgGb9jRVVZHJZHxb/m4sLi4iFAppt+AKBoMYHx8HAMzOzmJlZaXuNXIUUi8n+7979642Ekt/TOY7Vo2MjODw4cNaamI/YMAm39Kn4dWnfpWsfrqbl6VSKe3MSy6vVqvI5/PaBzyTyWg/rfWpfjstH9h9Ot3dqlarmJmZsZwVC+zUOxKJWAZtK63ei3ZS+e42VW+jYbNWs4vHYP28nQAABCpJREFUxsYwMzPjm64RBmzyrcnJSXz88cfaT/V8Pm/4iau/Q49ULpcNz2XOFeBxN9fQ0BDC4TDy+TwKhQIuXryIWq0GADh69KgWtDstvxfcu3cPAPDFL37Rcv309DQSiQQikYitm/a2ei+i0ah2m79CoQBFUVAul5HP5/G9731PK6darSIajeLw4cMQQuCNN97AqVOndnXjYFkHq7s8yeOX7dHz3JyV042ZP9Qf0OZMRzn7VT+zc2NjQwDQ8nLLcs1/5uZldrYRYmdGKQDDLM9Oy++UUzMdzTnN9eTyWq2mZWLUpzs2v87J98LJlMb6+imKYplpUs7s1b+nndqTMx2J7FhdXQVg7Es9duwYgJ3JJW4IhUIAYMij4lezs7MttwkGg1r/brNuAyffC7m9uWvJTn0befvttxGPxw231ZPkMr+8pwzY5EsLCwt1y+SHTz8agHZncHAQxWKxrotDz8n3Qm4vHBpxtbKyAkVRtAurfseATb4kLyxZnfW5kbq2m+X3mlAohFwuh3w+j1QqVbfejfei0X1c21EqlfDTn/4UFy9e3HVZvYIBm3xJ5hzZ2trSlsmzP7dyRcsgYnXxym9k4LU7BlkmX7PqmnDyvXAqVW+1WsWtW7cMF31LpRKmpqYst5eJy3odAzb50pkzZ6AoCubm5rQzu7W1NcRiMUM+cHmGJ4Ot/q718sOrP0M0BwY5rE1VVWSzWSiKYhg21mn5Xg/rkzeaMAds2ZZWZ8vj4+OWgc3Oe6EvT+5Tv2+5/uzZswB2+qxlRsqhoSEt8Mvhfs1GjciRJjMzM4a+8K997Wt1X7ZySOGJEycaltdT3LyiyVEiZBc6yIddqVREOp3WRhpY3XOyXC5rIx1kHnJ5h3c5qkGO/kgkEoYc4IDx7vLpdNqx8u3kP7fi1CgRmcN8Y2NDWyaPWf+wYnU/0lbvhVW5jfZVLpe1USyxWMyQVz2RSIhYLNb0nqjyHqRWD/1oFyEej2Yx55HvRDfiHdOrUk/otfSqbt3Pc7ecTK8qz/bNt3prRVVVyxEX3RQOh5HL5XZdTjKZxMDAQNttYIXpVYnINdFoFHfu3DF049jhdbAuFAqIx+O7LqdUKqFUKiEajTpQq+5gwCYyMU+p7ldynPXc3NyuZhJ20/r6Og4ePLjrYXqbm5tYWFjA4uKi519A7WDAJjLR3xpN//9+NDg4iGw2i1u3bnldFVtGR0e1C6a7kc/nceXKlZ5OYmVlv9cVIOo1vdZv7bZgMOhIH66f+PV4eYZNROQTDNhERD7BgE1E5BMM2EREPuH6RcfV1VV861vfcns31Afu3buHAwcOeF2NniYT7cuUptQ7VldXXctjo3FzGuVbb73VcIooH3zwwUe/Pd566y03Q6q7U9OJiMg57MMmIvIJBmwiIp9gwCYi8gkGbCIin/g/Ql2ikOpVUasAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1653 samples, validate on 414 samples\n",
      "Epoch 1/10\n",
      "1653/1653 [==============================] - 0s 134us/step - loss: 1.0409 - acc: 0.7024 - val_loss: 0.6045 - val_acc: 0.7198\n",
      "Epoch 2/10\n",
      "1653/1653 [==============================] - 0s 83us/step - loss: 0.6329 - acc: 0.7423 - val_loss: 0.5838 - val_acc: 0.7150\n",
      "Epoch 3/10\n",
      "1653/1653 [==============================] - 0s 97us/step - loss: 0.5593 - acc: 0.7495 - val_loss: 0.5221 - val_acc: 0.7343\n",
      "Epoch 4/10\n",
      "1653/1653 [==============================] - 0s 92us/step - loss: 0.5143 - acc: 0.7750 - val_loss: 0.5088 - val_acc: 0.7464\n",
      "Epoch 5/10\n",
      "1653/1653 [==============================] - 0s 80us/step - loss: 0.4883 - acc: 0.7828 - val_loss: 0.5263 - val_acc: 0.7295\n",
      "Epoch 6/10\n",
      "1653/1653 [==============================] - 0s 86us/step - loss: 0.4763 - acc: 0.7816 - val_loss: 0.4859 - val_acc: 0.7609\n",
      "Epoch 7/10\n",
      "1653/1653 [==============================] - 0s 66us/step - loss: 0.4663 - acc: 0.7852 - val_loss: 0.4900 - val_acc: 0.7560\n",
      "Epoch 8/10\n",
      "1653/1653 [==============================] - 0s 88us/step - loss: 0.4515 - acc: 0.7955 - val_loss: 0.4909 - val_acc: 0.7633\n",
      "Epoch 9/10\n",
      "1653/1653 [==============================] - 0s 82us/step - loss: 0.4522 - acc: 0.7992 - val_loss: 0.4692 - val_acc: 0.7826\n",
      "Epoch 10/10\n",
      "1653/1653 [==============================] - 0s 79us/step - loss: 0.4471 - acc: 0.8004 - val_loss: 0.4762 - val_acc: 0.7657\n"
     ]
    }
   ],
   "source": [
    "history3 = model3.fit(X2_train, y2_train, batch_size=16, epochs=10, verbose=1, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "517/517 [==============================] - 0s 27us/step\n",
      "Test Score: 0.4535879584991263\n",
      "Test Accuracy: 0.8046421408653259\n"
     ]
    }
   ],
   "source": [
    "score = model3.evaluate(X2_test, y2_test, verbose=1)\n",
    "\n",
    "print(\"Test Score:\", score[0])\n",
    "print(\"Test Accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history3.history['acc'])\n",
    "plt.plot(history3.history['val_acc'])\n",
    "\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history3.history['loss'])\n",
    "plt.plot(history3.history['val_loss'])\n",
    "\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a model with multiple Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_all = data_df.drop('status', axis=1)\n",
    "\n",
    "y_all = data_df['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_1 = deep_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_2 = deep_inputs1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_3 = input2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_layer = Embedding(vocab_size, 9, weights=[embedding_matrix], trainable=False)(deep_inputs)\n",
    "LSTM_Layer_1 = LSTM(128)(embedding_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_layer1 = Embedding(vocab_size1, 16, weights=[embedding_matrix1], trainable=False)(deep_inputs1)\n",
    "LSTM1_Layer_1 = LSTM(128)(embedding_layer1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_layer_1 = Dense(10, activation='relu')(input2)\n",
    "dense_layer_2 = Dense(10, activation='relu')(dense_layer_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_layer = Concatenate()([LSTM_Layer_1, LSTM1_Layer_1, dense_layer_2])\n",
    "dense_layer_3 = Dense(10, activation='relu')(concat_layer)\n",
    "output = Dense(2, activation='softmax')(dense_layer_3)\n",
    "model = Model(inputs=[input_1, input_2, input_3], outputs=output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_4\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 9)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            (None, 16)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_3 (InputLayer)            (None, 165)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_3 (Embedding)         (None, 9, 9)         3470976     input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_4 (Embedding)         (None, 16, 16)       9713408     input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 10)           1660        input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm_3 (LSTM)                   (None, 128)          70656       embedding_3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "lstm_4 (LSTM)                   (None, 128)          74240       embedding_4[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_7 (Dense)                 (None, 10)           110         dense_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 266)          0           lstm_3[0][0]                     \n",
      "                                                                 lstm_4[0][0]                     \n",
      "                                                                 dense_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 10)           2670        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 2)            22          dense_8[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 13,333,742\n",
      "Trainable params: 149,358\n",
      "Non-trainable params: 13,184,384\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "plot_model(model, to_file='model_plot10.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "chat_sample = chat_df.sample(frac =.5).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_chat = chat_sample[['chatEmbedding']].copy()\n",
    "y_chat = chat_sample[['status']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "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>chatEmbedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[0.012461126, 0.026670266, 0.0038820878, 0.04...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[[-0.043281972, -0.014264904, -0.041443884, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[[-0.03131336, 0.014859889, 0.017185342, 0.007...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[[-0.00550922, -0.014139641, -0.02140875, -0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[[0.021142248, 0.01665027, -0.048583306, -0.02...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       chatEmbedding\n",
       "0  [[0.012461126, 0.026670266, 0.0038820878, 0.04...\n",
       "1  [[-0.043281972, -0.014264904, -0.041443884, 0....\n",
       "2  [[-0.03131336, 0.014859889, 0.017185342, 0.007...\n",
       "3  [[-0.00550922, -0.014139641, -0.02140875, -0.0...\n",
       "4  [[0.021142248, 0.01665027, -0.048583306, -0.02..."
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_chat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_matrix_chat = chat_sample.chatEmbedding[0]\n",
    "from itertools import repeat\n",
    "for i in range(2583):\n",
    "    embedding_matrix_chat = np.vstack((embedding_matrix_chat, chat_sample.chatEmbedding[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_chat = embedding_matrix_chat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41344"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_chat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983],\n",
       "       ...,\n",
       "       [ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983],\n",
       "       [ 0.01246113,  0.02667027,  0.00388209, ..., -0.00864174,\n",
       "         0.02887807,  0.03490983]], dtype=float32)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_chat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_chat = y_chat.loc[y_chat.index.repeat(16)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# label_encoder object knows how to understand word labels.\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "\n",
    "# Encode labels in column 'species'.\n",
    "y_chat = label_encoder.fit_transform(y_chat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41344, 9)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_chat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(192832,)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_chat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = np.random.choice(np.arange(len(X_chat)), 2584, replace=False)\n",
    "x_sample = X_chat[idx]\n",
    "y_sample = y_chat[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2584, 9)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2584,)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_chat, X_test_chat, y_train_chat, y_test_chat = train_test_split(x_sample, y_sample, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_transaction = chat_sample[['chatEmbedding']].copy()\n",
    "y_transaction = chat_sample[['status']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(607088, 16)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(607088,)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx1 = np.random.choice(np.arange(len(X1)), 2584, replace=False)\n",
    "x_trans = X1[idx1]\n",
    "y_trans = y1[idx1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_trans, X_test_trans, y_train_trans, y_test_trans = train_test_split(x_trans, y_trans, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1653 samples, validate on 414 samples\n",
      "Epoch 1/10\n",
      "1653/1653 [==============================] - 1s 661us/step - loss: 0.6379 - acc: 0.7175 - val_loss: 0.5514 - val_acc: 0.7657\n",
      "Epoch 2/10\n",
      "1653/1653 [==============================] - 0s 252us/step - loss: 0.5659 - acc: 0.7429 - val_loss: 0.5554 - val_acc: 0.7609\n",
      "Epoch 3/10\n",
      "1653/1653 [==============================] - 0s 254us/step - loss: 0.5560 - acc: 0.7453 - val_loss: 0.5413 - val_acc: 0.7633\n",
      "Epoch 4/10\n",
      "1653/1653 [==============================] - 0s 251us/step - loss: 0.5516 - acc: 0.7477 - val_loss: 0.5403 - val_acc: 0.7560\n",
      "Epoch 5/10\n",
      "1653/1653 [==============================] - 0s 252us/step - loss: 0.5486 - acc: 0.7489 - val_loss: 0.5358 - val_acc: 0.7585\n",
      "Epoch 6/10\n",
      "1653/1653 [==============================] - 0s 250us/step - loss: 0.5448 - acc: 0.7459 - val_loss: 0.5343 - val_acc: 0.7536\n",
      "Epoch 7/10\n",
      "1653/1653 [==============================] - 0s 256us/step - loss: 0.5400 - acc: 0.7471 - val_loss: 0.5447 - val_acc: 0.7343\n",
      "Epoch 8/10\n",
      "1653/1653 [==============================] - 0s 259us/step - loss: 0.5384 - acc: 0.7483 - val_loss: 0.5297 - val_acc: 0.7488\n",
      "Epoch 9/10\n",
      "1653/1653 [==============================] - 0s 258us/step - loss: 0.5311 - acc: 0.7495 - val_loss: 0.5313 - val_acc: 0.7440\n",
      "Epoch 10/10\n",
      "1653/1653 [==============================] - 0s 264us/step - loss: 0.5284 - acc: 0.7502 - val_loss: 0.5282 - val_acc: 0.7319\n"
     ]
    }
   ],
   "source": [
    "history4 = model.fit(x=[X_train_chat, X_train_trans, X2_train], y=y2_train, batch_size=128, epochs=10, verbose=1, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "517/517 [==============================] - 0s 150us/step\n",
      "Test Score: 0.511516957523975\n",
      "Test Accuracy: 0.7562862634658813\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x=[X_test_chat, X_test_trans, X2_test], y=y2_test, verbose=1)\n",
    "\n",
    "print(\"Test Score:\", score[0])\n",
    "print(\"Test Accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history4.history['acc'])\n",
    "plt.plot(history4.history['val_acc'])\n",
    "\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history4.history['loss'])\n",
    "plt.plot(history4.history['val_loss'])\n",
    "\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}