Bank_Customer_Satisfaction_CNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Bank_Customer_Satisfaction_CNN.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sp0RWmhVKSLh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "1f4871b4-30e3-421e-8169-1681ee503c97"
      },
      "source": [
        "!pip install tensorflow-gpu==2.0rc"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==2.0rc\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6a/12/8c64cc62149cc21c70c55018502831bbf4d42bd62bed196df7de6830d21b/tensorflow_gpu-2.0.0rc0-cp36-cp36m-manylinux2010_x86_64.whl (380.5MB)\n",
            "\u001b[K     |████████████████████████████████| 380.5MB 62kB/s \n",
            "\u001b[?25hCollecting tb-nightly<1.15.0a20190807,>=1.15.0a20190806 (from tensorflow-gpu==2.0rc)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bc/88/24b5fb7280e74c7cf65bde47c171547fd02afb3840cff41bcbe9270650f5/tb_nightly-1.15.0a20190806-py3-none-any.whl (4.3MB)\n",
            "\u001b[K     |████████████████████████████████| 4.3MB 31.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (0.8.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (1.0.8)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (0.1.7)\n",
            "Collecting tf-estimator-nightly<1.14.0.dev2019080602,>=1.14.0.dev2019080601 (from tensorflow-gpu==2.0rc)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/21/28/f2a27a62943d5f041e4a6fd404b2d21cb7c59b2242a4e73b03d9ba166552/tf_estimator_nightly-1.14.0.dev2019080601-py2.py3-none-any.whl (501kB)\n",
            "\u001b[K     |████████████████████████████████| 501kB 46.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (0.33.6)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (1.12.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (1.1.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (0.2.2)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (3.7.1)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (0.8.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (1.15.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (1.16.5)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (1.11.2)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0rc) (3.1.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow-gpu==2.0rc) (3.1.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow-gpu==2.0rc) (0.16.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow-gpu==2.0rc) (41.2.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu==2.0rc) (2.8.0)\n",
            "Installing collected packages: tb-nightly, tf-estimator-nightly, tensorflow-gpu\n",
            "Successfully installed tb-nightly-1.15.0a20190806 tensorflow-gpu-2.0.0rc0 tf-estimator-nightly-1.14.0.dev2019080601\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yS80tFUrKc8x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Conv1D , MaxPool1D , Dropout , BatchNormalization , Flatten , Dense"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVoCu7qMLHiy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7n5XwoHLXMv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "c01cc3e1-f14c-4383-effc-06193937ce63"
      },
      "source": [
        "!git clone https://github.com/laxmimerit/Data-Files-for-Feature-Selection.git"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Data-Files-for-Feature-Selection'...\n",
            "remote: Enumerating objects: 17, done.\u001b[K\n",
            "remote: Counting objects:   5% (1/17)\u001b[K\rremote: Counting objects:  11% (2/17)\u001b[K\rremote: Counting objects:  17% (3/17)\u001b[K\rremote: Counting objects:  23% (4/17)\u001b[K\rremote: Counting objects:  29% (5/17)\u001b[K\rremote: Counting objects:  35% (6/17)\u001b[K\rremote: Counting objects:  41% (7/17)\u001b[K\rremote: Counting objects:  47% (8/17)\u001b[K\rremote: Counting objects:  52% (9/17)\u001b[K\rremote: Counting objects:  58% (10/17)\u001b[K\rremote: Counting objects:  64% (11/17)\u001b[K\rremote: Counting objects:  70% (12/17)\u001b[K\rremote: Counting objects:  76% (13/17)\u001b[K\rremote: Counting objects:  82% (14/17)\u001b[K\rremote: Counting objects:  88% (15/17)\u001b[K\rremote: Counting objects:  94% (16/17)\u001b[K\rremote: Counting objects: 100% (17/17)\u001b[K\rremote: Counting objects: 100% (17/17), done.\u001b[K\n",
            "remote: Compressing objects: 100% (14/14), done.\u001b[K\n",
            "remote: Total 17 (delta 6), reused 10 (delta 2), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (17/17), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzixvfSlLh39",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv('/content/Data-Files-for-Feature-Selection/santander-train.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxgL68KzLuYm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "outputId": "0400cdff-51bd-40f8-fc27-dfb4f2114e88"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>var3</th>\n",
              "      <th>var15</th>\n",
              "      <th>imp_ent_var16_ult1</th>\n",
              "      <th>imp_op_var39_comer_ult1</th>\n",
              "      <th>imp_op_var39_comer_ult3</th>\n",
              "      <th>imp_op_var40_comer_ult1</th>\n",
              "      <th>imp_op_var40_comer_ult3</th>\n",
              "      <th>imp_op_var40_efect_ult1</th>\n",
              "      <th>imp_op_var40_efect_ult3</th>\n",
              "      <th>imp_op_var40_ult1</th>\n",
              "      <th>imp_op_var41_comer_ult1</th>\n",
              "      <th>imp_op_var41_comer_ult3</th>\n",
              "      <th>imp_op_var41_efect_ult1</th>\n",
              "      <th>imp_op_var41_efect_ult3</th>\n",
              "      <th>imp_op_var41_ult1</th>\n",
              "      <th>imp_op_var39_efect_ult1</th>\n",
              "      <th>imp_op_var39_efect_ult3</th>\n",
              "      <th>imp_op_var39_ult1</th>\n",
              "      <th>imp_sal_var16_ult1</th>\n",
              "      <th>ind_var1_0</th>\n",
              "      <th>ind_var1</th>\n",
              "      <th>ind_var2_0</th>\n",
              "      <th>ind_var2</th>\n",
              "      <th>ind_var5_0</th>\n",
              "      <th>ind_var5</th>\n",
              "      <th>ind_var6_0</th>\n",
              "      <th>ind_var6</th>\n",
              "      <th>ind_var8_0</th>\n",
              "      <th>ind_var8</th>\n",
              "      <th>ind_var12_0</th>\n",
              "      <th>ind_var12</th>\n",
              "      <th>ind_var13_0</th>\n",
              "      <th>ind_var13_corto_0</th>\n",
              "      <th>ind_var13_corto</th>\n",
              "      <th>ind_var13_largo_0</th>\n",
              "      <th>ind_var13_largo</th>\n",
              "      <th>ind_var13_medio_0</th>\n",
              "      <th>ind_var13_medio</th>\n",
              "      <th>ind_var13</th>\n",
              "      <th>...</th>\n",
              "      <th>saldo_medio_var5_ult1</th>\n",
              "      <th>saldo_medio_var5_ult3</th>\n",
              "      <th>saldo_medio_var8_hace2</th>\n",
              "      <th>saldo_medio_var8_hace3</th>\n",
              "      <th>saldo_medio_var8_ult1</th>\n",
              "      <th>saldo_medio_var8_ult3</th>\n",
              "      <th>saldo_medio_var12_hace2</th>\n",
              "      <th>saldo_medio_var12_hace3</th>\n",
              "      <th>saldo_medio_var12_ult1</th>\n",
              "      <th>saldo_medio_var12_ult3</th>\n",
              "      <th>saldo_medio_var13_corto_hace2</th>\n",
              "      <th>saldo_medio_var13_corto_hace3</th>\n",
              "      <th>saldo_medio_var13_corto_ult1</th>\n",
              "      <th>saldo_medio_var13_corto_ult3</th>\n",
              "      <th>saldo_medio_var13_largo_hace2</th>\n",
              "      <th>saldo_medio_var13_largo_hace3</th>\n",
              "      <th>saldo_medio_var13_largo_ult1</th>\n",
              "      <th>saldo_medio_var13_largo_ult3</th>\n",
              "      <th>saldo_medio_var13_medio_hace2</th>\n",
              "      <th>saldo_medio_var13_medio_hace3</th>\n",
              "      <th>saldo_medio_var13_medio_ult1</th>\n",
              "      <th>saldo_medio_var13_medio_ult3</th>\n",
              "      <th>saldo_medio_var17_hace2</th>\n",
              "      <th>saldo_medio_var17_hace3</th>\n",
              "      <th>saldo_medio_var17_ult1</th>\n",
              "      <th>saldo_medio_var17_ult3</th>\n",
              "      <th>saldo_medio_var29_hace2</th>\n",
              "      <th>saldo_medio_var29_hace3</th>\n",
              "      <th>saldo_medio_var29_ult1</th>\n",
              "      <th>saldo_medio_var29_ult3</th>\n",
              "      <th>saldo_medio_var33_hace2</th>\n",
              "      <th>saldo_medio_var33_hace3</th>\n",
              "      <th>saldo_medio_var33_ult1</th>\n",
              "      <th>saldo_medio_var33_ult3</th>\n",
              "      <th>saldo_medio_var44_hace2</th>\n",
              "      <th>saldo_medio_var44_hace3</th>\n",
              "      <th>saldo_medio_var44_ult1</th>\n",
              "      <th>saldo_medio_var44_ult3</th>\n",
              "      <th>var38</th>\n",
              "      <th>TARGET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39205.170000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>300.0</td>\n",
              "      <td>122.22</td>\n",
              "      <td>300.0</td>\n",
              "      <td>240.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49278.030000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67333.770000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>37</td>\n",
              "      <td>0.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>91.56</td>\n",
              "      <td>138.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64007.970000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>39</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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>...</td>\n",
              "      <td>40501.08</td>\n",
              "      <td>13501.47</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>85501.89</td>\n",
              "      <td>85501.89</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117310.979016</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 371 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   ID  var3  var15  ...  saldo_medio_var44_ult3          var38  TARGET\n",
              "0   1     2     23  ...                     0.0   39205.170000       0\n",
              "1   3     2     34  ...                     0.0   49278.030000       0\n",
              "2   4     2     23  ...                     0.0   67333.770000       0\n",
              "3   8     2     37  ...                     0.0   64007.970000       0\n",
              "4  10     2     39  ...                     0.0  117310.979016       0\n",
              "\n",
              "[5 rows x 371 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wCerI7VLwRN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = data.drop(['ID','TARGET'],axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhspJjRvMFTB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6d9781e3-cb42-4f35-9b4e-75cca585fe68"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(76020, 369)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbZhuJ9qMHst",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = data[\"TARGET\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbqYymRLMQBq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pz5IeRwMb-w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state = 0,stratify = y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-R6QgGwMtkV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.feature_selection import VarianceThreshold"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0YaSbBtNM3T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fil = VarianceThreshold(0.01)\n",
        "X_train = fil.fit_transform(X_train)\n",
        "X_test = fil.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vOo2r5DNcgc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_T = X_train.T\n",
        "X_test_T = X_test.T"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "al5f5_4jOMGL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_T = pd.DataFrame(X_train_T)\n",
        "X_test_T = pd.DataFrame(X_test_T)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1pxi6blOP2T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a24a5a88-4e19-4a66-a469-4448b8b3b289"
      },
      "source": [
        "X_train_T.duplicated().sum()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAR1y_E4OTJR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "duplicated_features = X_train_T.duplicated()\n",
        "features_to_keep = [not index for index in duplicated_features]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qycCrgvOaDN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cdfc53bf-8ced-4720-b699-3ef17d1b1c37"
      },
      "source": [
        "X_train = X_train_T[features_to_keep].T\n",
        "X_train.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60816, 256)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ibhltwwOebV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9369b991-5be3-42c1-cac9-a0a6220f7dfa"
      },
      "source": [
        "X_test = X_test_T[features_to_keep].T\n",
        "X_test.shape"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15204, 256)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pc-AWKPzOhB7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwYwlPa1OkWq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train.reshape(60816, 256,1)\n",
        "X_test = X_test.reshape(15204, 256, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiYZX4gVOn8g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train = y_train.to_numpy()\n",
        "y_test = y_test.to_numpy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_DsdhPG0Orb-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv1D(32, 3, activation='relu', input_shape = (256,1)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool1D(2))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Conv1D(64, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool1D(2))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv1D(128, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool1D(2))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1,activation = 'sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYQa-G9HO0Ou",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "outputId": "c1ef8bb1-8a81-4bf3-883c-2a23bc753db0"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_6 (Conv1D)            (None, 254, 32)           128       \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 254, 32)           128       \n",
            "_________________________________________________________________\n",
            "max_pooling1d_6 (MaxPooling1 (None, 127, 32)           0         \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 127, 32)           0         \n",
            "_________________________________________________________________\n",
            "conv1d_7 (Conv1D)            (None, 125, 64)           6208      \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 125, 64)           256       \n",
            "_________________________________________________________________\n",
            "max_pooling1d_7 (MaxPooling1 (None, 62, 64)            0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 62, 64)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_8 (Conv1D)            (None, 60, 128)           24704     \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 60, 128)           512       \n",
            "_________________________________________________________________\n",
            "max_pooling1d_8 (MaxPooling1 (None, 30, 128)           0         \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 30, 128)           0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 3840)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               983296    \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 257       \n",
            "=================================================================\n",
            "Total params: 1,015,489\n",
            "Trainable params: 1,015,041\n",
            "Non-trainable params: 448\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCsU3U2GPIlU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.optimizers import Adam"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9kWkSL-PBNb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=Adam(lr=0.00005), loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mo5B6hpIPNP8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "8413889a-f8ce-4330-d767-724a019cad7a"
      },
      "source": [
        "history = model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test), verbose=1)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60816 samples, validate on 15204 samples\n",
            "Epoch 1/10\n",
            "60816/60816 [==============================] - 107s 2ms/sample - loss: 0.2055 - accuracy: 0.9559 - val_loss: 0.1810 - val_accuracy: 0.9537\n",
            "Epoch 2/10\n",
            "60816/60816 [==============================] - 105s 2ms/sample - loss: 0.1748 - accuracy: 0.9594 - val_loss: 0.1729 - val_accuracy: 0.9604\n",
            "Epoch 3/10\n",
            "60816/60816 [==============================] - 106s 2ms/sample - loss: 0.1686 - accuracy: 0.9601 - val_loss: 0.1720 - val_accuracy: 0.9603\n",
            "Epoch 4/10\n",
            "60816/60816 [==============================] - 105s 2ms/sample - loss: 0.1633 - accuracy: 0.9604 - val_loss: 0.1675 - val_accuracy: 0.9603\n",
            "Epoch 5/10\n",
            "60816/60816 [==============================] - 106s 2ms/sample - loss: 0.1616 - accuracy: 0.9604 - val_loss: 0.1681 - val_accuracy: 0.9604\n",
            "Epoch 6/10\n",
            "60816/60816 [==============================] - 105s 2ms/sample - loss: 0.1617 - accuracy: 0.9604 - val_loss: 0.1639 - val_accuracy: 0.9604\n",
            "Epoch 7/10\n",
            "60816/60816 [==============================] - 106s 2ms/sample - loss: 0.1577 - accuracy: 0.9604 - val_loss: 0.1650 - val_accuracy: 0.9603\n",
            "Epoch 8/10\n",
            "60816/60816 [==============================] - 104s 2ms/sample - loss: 0.1574 - accuracy: 0.9605 - val_loss: 0.1619 - val_accuracy: 0.9604\n",
            "Epoch 9/10\n",
            "60816/60816 [==============================] - 105s 2ms/sample - loss: 0.1558 - accuracy: 0.9604 - val_loss: 0.1607 - val_accuracy: 0.9604\n",
            "Epoch 10/10\n",
            "60816/60816 [==============================] - 107s 2ms/sample - loss: 0.1546 - accuracy: 0.9604 - val_loss: 0.1629 - val_accuracy: 0.9603\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_CKB5BpPVZn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "21942555-ccc8-4540-9a6a-1cea63a7d9ff"
      },
      "source": [
        "epoch_range = range(1, 11)\n",
        "plt.plot(epoch_range, history.history['accuracy'])\n",
        "plt.plot(epoch_range, history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Val'], loc='upper left')\n",
        "plt.show()\n",
        "plt.plot(epoch_range, history.history['loss'])\n",
        "plt.plot(epoch_range, history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTX68RibTm-J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}