abbott_nn_poc.ipynb

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     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mJupyter server crashed. Unable to connect. \n",
      "\u001b[1;31mError code from Jupyter: 1\n",
      "\u001b[1;31musage: jupyter.py [-h] [--version] [--config-dir] [--data-dir] [--runtime-dir]\n",
      "\u001b[1;31m                  [--paths] [--json] [--debug]\n",
      "\u001b[1;31m                  [subcommand]\n",
      "\u001b[1;31m\n",
      "\u001b[1;31mJupyter: Interactive Computing\n",
      "\u001b[1;31m\n",
      "\u001b[1;31mpositional arguments:\n",
      "\u001b[1;31m  subcommand     the subcommand to launch\n",
      "\u001b[1;31m\n",
      "\u001b[1;31moptional arguments:\n",
      "\u001b[1;31m  -h, --help     show this help message and exit\n",
      "\u001b[1;31m  --version      show the versions of core jupyter packages and exit\n",
      "\u001b[1;31m  --config-dir   show Jupyter config dir\n",
      "\u001b[1;31m  --data-dir     show Jupyter data dir\n",
      "\u001b[1;31m  --runtime-dir  show Jupyter runtime dir\n",
      "\u001b[1;31m  --paths        show all Jupyter paths. Add --json for machine-readable\n",
      "\u001b[1;31m                 format.\n",
      "\u001b[1;31m  --json         output paths as machine-readable json\n",
      "\u001b[1;31m  --debug        output debug information about paths\n",
      "\u001b[1;31m\n",
      "\u001b[1;31mAvailable subcommands:\n",
      "\u001b[1;31m\n",
      "\u001b[1;31mJupyter command `jupyter-notebook` not found. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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     "ename": "",
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     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mJupyter server crashed. Unable to connect. \n",
      "\u001b[1;31mError code from Jupyter: 1\n",
      "\u001b[1;31musage: jupyter.py [-h] [--version] [--config-dir] [--data-dir] [--runtime-dir]\n",
      "\u001b[1;31m                  [--paths] [--json] [--debug]\n",
      "\u001b[1;31m                  [subcommand]\n",
      "\u001b[1;31m\n",
      "\u001b[1;31mJupyter: Interactive Computing\n",
      "\u001b[1;31m\n",
      "\u001b[1;31mpositional arguments:\n",
      "\u001b[1;31m  subcommand     the subcommand to launch\n",
      "\u001b[1;31m\n",
      "\u001b[1;31moptional arguments:\n",
      "\u001b[1;31m  -h, --help     show this help message and exit\n",
      "\u001b[1;31m  --version      show the versions of core jupyter packages and exit\n",
      "\u001b[1;31m  --config-dir   show Jupyter config dir\n",
      "\u001b[1;31m  --data-dir     show Jupyter data dir\n",
      "\u001b[1;31m  --runtime-dir  show Jupyter runtime dir\n",
      "\u001b[1;31m  --paths        show all Jupyter paths. Add --json for machine-readable\n",
      "\u001b[1;31m                 format.\n",
      "\u001b[1;31m  --json         output paths as machine-readable json\n",
      "\u001b[1;31m  --debug        output debug information about paths\n",
      "\u001b[1;31m\n",
      "\u001b[1;31mAvailable subcommands:\n",
      "\u001b[1;31m\n",
      "\u001b[1;31mJupyter command `jupyter-notebook` not found. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from datetime import datetime\n",
    "\n",
    "# This turns all the axes white in all the matplotlib plots. Comment this out if you dont want that\n",
    "COLOR = 'white'\n",
    "matplotlib.rcParams['text.color'] = COLOR\n",
    "matplotlib.rcParams['axes.labelcolor'] = COLOR\n",
    "matplotlib.rcParams['xtick.color'] = COLOR\n",
    "matplotlib.rcParams['ytick.color'] = COLOR\n",
    "\n",
    "df_transaction = pd.read_csv('../datasets/ieee-fraud-detection/train_transaction.csv')\n",
    "\n",
    "df_transaction.head()\n",
    "\n",
    "# Features that are used, isFraud is the target\n",
    "features = ['isFraud', 'TransactionDT',\n",
    "            'TransactionAmt','ProductCD', 'P_emaildomain','R_emaildomain']\n",
    "\n",
    "target = 'isFraud'\n",
    "# Categorical features\n",
    "cat = ['TransactionDT','ProductCD', 'P_emaildomain','R_emaildomain']\n",
    "# Numeric features\n",
    "num = ['TransactionAmt']\n",
    "\n",
    "#Drop rows with missing features\n",
    "df = df.dropna()\n",
    "y = df[target].values\n",
    "\n",
    "x_cat = df.filter(items = cat).values\n",
    "x_num = df.filter(items = num).values\n",
    "\n",
    "labelencoder_X = LabelEncoder()\n",
    "# Label encode every categorical column\n",
    "for i in range(len(cat)): \n",
    "    x_cat[:, i] = labelencoder_X.fit_transform(x_cat[:, i])\n",
    "\n",
    "# Build input vector X, the training data\n",
    "X = np.concatenate((x_cat, x_num), axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.2, random_state = 0)\n",
    "\n",
    "X_train = np.asarray(X_train).astype('float32')\n",
    "y_train = np.asarray(y_train).astype('float32').reshape((-1,1))\n",
    "X_val = np.asarray(X_val).astype('float32')\n",
    "y_val = np.asarray(y_val).astype('float32').reshape((-1,1))\n",
    "\n",
    "X_train.shape, y_train.shape\n",
    "\n",
    "# Initialize model\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
    "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
    "model.add(tf.keras.layers.Dense(1, activation=tf.nn.sigmoid))\n",
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(X_train, y_train, epochs=3)\n",
    "\n",
    "val_loss, val_acc = model.evaluate(X_val, y_val)\n",
    "print(val_loss, val_acc)"
   ]
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