Genetic Algorithm .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3e190ce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f5be5ec5",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow.core.framework.types_pb2' has no attribute 'SerializedDType'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_36616/3759496548.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mActivation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDropout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimizers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mAdam\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mAccuracy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtyping\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0m_typing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmodule_util\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0m_module_util\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     38\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlazy_loader\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLazyLoader\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0m_LazyLoader\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;31m# Bring in subpackages.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     43\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdistribute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;31m# from tensorflow.python import keras\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;31m# pylint: disable=unused-import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mexperimental\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset_ops\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mAUTOTUNE\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset_ops\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\experimental\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m \u001b[1;31m# pylint: disable=unused-import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mservice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     97\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatching\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdense_to_ragged_batch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatching\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdense_to_sparse_batch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\experimental\\service\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    417\u001b[0m \"\"\"\n\u001b[0;32m    418\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 419\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_service_ops\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdistribute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    420\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_service_ops\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfrom_dataset_id\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    421\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_service_ops\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mregister_dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\experimental\\ops\\data_service_ops.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprotobuf\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdata_service_pb2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtf2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcompression_ops\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mservice\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_pywrap_server_lib\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mservice\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_pywrap_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\experimental\\ops\\compression_ops.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;31m# ==============================================================================\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;34m\"\"\"Ops for compressing and uncompressing dataset elements.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstructure\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mgen_experimental_dataset_ops\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mged_ops\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\util\\structure.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mwrapt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcomposite_tensor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\util\\nest.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     32\u001b[0m \"\"\"\n\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msparse_tensor\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0m_sparse_tensor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     35\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_pywrap_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\sparse_tensor.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtf2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcomposite_tensor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mconstant_op\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     25\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtypes_pb2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meager\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meager\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mexecute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     26\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mop_callbacks\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpywrap_tfe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meager\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensor_shape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mtf_export\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"dtypes.DType\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"DType\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m class DType(\n\u001b[0m\u001b[0;32m     43\u001b[0m     \u001b[0m_dtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDType\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m     \u001b[0mtrace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTraceType\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py\u001b[0m in \u001b[0;36mDType\u001b[1;34m()\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 202\u001b[1;33m   \u001b[1;32mdef\u001b[0m \u001b[0mexperimental_type_proto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mType\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtypes_pb2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSerializedDType\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    203\u001b[0m     \u001b[1;34m\"\"\"Returns the type of proto associated with DType serialization.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    204\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mtypes_pb2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSerializedDType\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow.core.framework.types_pb2' has no attribute 'SerializedDType'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import Dense, Activation, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.metrics import Accuracy\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6cc7cfc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "\n",
    "\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ed7ce084",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'torchvision'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_36616/3064430219.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0moptim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataLoader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTensorDataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtorchvision\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torchvision'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
    "from torchvision import datasets, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c3cf634a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
       "      <th>Online</th>\n",
       "      <th>CreditCard</th>\n",
       "      <th>Personal Loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>91107</td>\n",
       "      <td>4</td>\n",
       "      <td>1.6</td>\n",
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       "      <td>34</td>\n",
       "      <td>90089</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>94720</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>94112</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>91330</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  Mortgage  \\\n",
       "0   1   25           1      49     91107       4    1.6          1         0   \n",
       "1   2   45          19      34     90089       3    1.5          1         0   \n",
       "2   3   39          15      11     94720       1    1.0          1         0   \n",
       "3   4   35           9     100     94112       1    2.7          2         0   \n",
       "4   5   35           8      45     91330       4    1.0          2         0   \n",
       "\n",
       "   Securities Account  CD Account  Online  CreditCard  Personal Loan  \n",
       "0                   1           0       0           0              0  \n",
       "1                   1           0       0           0              0  \n",
       "2                   0           0       0           0              0  \n",
       "3                   0           0       0           0              0  \n",
       "4                   0           0       0           1              0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(r'C:\\Users\\navin\\Downloads\\Bank_Personal_Loan_Modelling.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e8ecb785",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Age  Experience  Income  ZIP Code  Family  CCAvg  Education  Mortgage  \\\n",
      "0      25           1      49     91107       4    1.6          1         0   \n",
      "1      45          19      34     90089       3    1.5          1         0   \n",
      "2      39          15      11     94720       1    1.0          1         0   \n",
      "3      35           9     100     94112       1    2.7          2         0   \n",
      "4      35           8      45     91330       4    1.0          2         0   \n",
      "...   ...         ...     ...       ...     ...    ...        ...       ...   \n",
      "4995   29           3      40     92697       1    1.9          3         0   \n",
      "4996   30           4      15     92037       4    0.4          1        85   \n",
      "4997   63          39      24     93023       2    0.3          3         0   \n",
      "4998   65          40      49     90034       3    0.5          2         0   \n",
      "4999   28           4      83     92612       3    0.8          1         0   \n",
      "\n",
      "      Securities Account  CD Account  Online  CreditCard  Personal Loan  \n",
      "0                      1           0       0           0              0  \n",
      "1                      1           0       0           0              0  \n",
      "2                      0           0       0           0              0  \n",
      "3                      0           0       0           0              0  \n",
      "4                      0           0       0           1              0  \n",
      "...                  ...         ...     ...         ...            ...  \n",
      "4995                   0           0       1           0              0  \n",
      "4996                   0           0       1           0              0  \n",
      "4997                   0           0       0           0              0  \n",
      "4998                   0           0       1           0              0  \n",
      "4999                   0           0       1           1              0  \n",
      "\n",
      "[5000 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "df = data.drop(columns=[\"ID\"],axis=1)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8bad0aad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   ID                  5000 non-null   int64  \n",
      " 1   Age                 5000 non-null   int64  \n",
      " 2   Experience          5000 non-null   int64  \n",
      " 3   Income              5000 non-null   int64  \n",
      " 4   ZIP Code            5000 non-null   int64  \n",
      " 5   Family              5000 non-null   int64  \n",
      " 6   CCAvg               5000 non-null   float64\n",
      " 7   Education           5000 non-null   int64  \n",
      " 8   Mortgage            5000 non-null   int64  \n",
      " 9   Securities Account  5000 non-null   int64  \n",
      " 10  CD Account          5000 non-null   int64  \n",
      " 11  Online              5000 non-null   int64  \n",
      " 12  CreditCard          5000 non-null   int64  \n",
      " 13  Personal Loan       5000 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 547.0 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bc02c7ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>45.338400</td>\n",
       "      <td>11.463166</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Experience</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>20.104600</td>\n",
       "      <td>11.467954</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>73.774200</td>\n",
       "      <td>46.033729</td>\n",
       "      <td>8.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIP Code</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>93152.503000</td>\n",
       "      <td>2121.852197</td>\n",
       "      <td>9307.0</td>\n",
       "      <td>91911.0</td>\n",
       "      <td>93437.0</td>\n",
       "      <td>94608.0</td>\n",
       "      <td>96651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>2.396400</td>\n",
       "      <td>1.147663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCAvg</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.937938</td>\n",
       "      <td>1.747659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.881000</td>\n",
       "      <td>0.839869</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mortgage</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>56.498800</td>\n",
       "      <td>101.713802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Securities Account</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.104400</td>\n",
       "      <td>0.305809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD Account</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.060400</td>\n",
       "      <td>0.238250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.596800</td>\n",
       "      <td>0.490589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreditCard</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>0.455637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal Loan</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>0.294621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean          std     min      25%  \\\n",
       "Age                 5000.0     45.338400    11.463166    23.0     35.0   \n",
       "Experience          5000.0     20.104600    11.467954    -3.0     10.0   \n",
       "Income              5000.0     73.774200    46.033729     8.0     39.0   \n",
       "ZIP Code            5000.0  93152.503000  2121.852197  9307.0  91911.0   \n",
       "Family              5000.0      2.396400     1.147663     1.0      1.0   \n",
       "CCAvg               5000.0      1.937938     1.747659     0.0      0.7   \n",
       "Education           5000.0      1.881000     0.839869     1.0      1.0   \n",
       "Mortgage            5000.0     56.498800   101.713802     0.0      0.0   \n",
       "Securities Account  5000.0      0.104400     0.305809     0.0      0.0   \n",
       "CD Account          5000.0      0.060400     0.238250     0.0      0.0   \n",
       "Online              5000.0      0.596800     0.490589     0.0      0.0   \n",
       "CreditCard          5000.0      0.294000     0.455637     0.0      0.0   \n",
       "Personal Loan       5000.0      0.096000     0.294621     0.0      0.0   \n",
       "\n",
       "                        50%      75%      max  \n",
       "Age                    45.0     55.0     67.0  \n",
       "Experience             20.0     30.0     43.0  \n",
       "Income                 64.0     98.0    224.0  \n",
       "ZIP Code            93437.0  94608.0  96651.0  \n",
       "Family                  2.0      3.0      4.0  \n",
       "CCAvg                   1.5      2.5     10.0  \n",
       "Education               2.0      3.0      3.0  \n",
       "Mortgage                0.0    101.0    635.0  \n",
       "Securities Account      0.0      0.0      1.0  \n",
       "CD Account              0.0      0.0      1.0  \n",
       "Online                  1.0      1.0      1.0  \n",
       "CreditCard              0.0      1.0      1.0  \n",
       "Personal Loan           0.0      0.0      1.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "198a6149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                   0\n",
       "Experience            0\n",
       "Income                0\n",
       "ZIP Code              0\n",
       "Family                0\n",
       "CCAvg                 0\n",
       "Education             0\n",
       "Mortgage              0\n",
       "Securities Account    0\n",
       "CD Account            0\n",
       "Online                0\n",
       "CreditCard            0\n",
       "Personal Loan         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "acbb80c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customers having credit cards is 29.4 %\n"
     ]
    }
   ],
   "source": [
    "Percent=(len(df[df['CreditCard']==1])/len(df))*100\n",
    "\n",
    "print('Customers having credit cards is', Percent, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "12ee8a4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customers who accepted a loan 9.6 %\n"
     ]
    }
   ],
   "source": [
    "accepted_customers= df[df['Personal Loan']==1]\n",
    "accepted_percent=(len(accepted_customers)/len(df))*100\n",
    "\n",
    "print('Customers who accepted a loan', accepted_percent, '%')\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f96f73a5",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5bd743ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,:-1].values\n",
    "y = df.iloc[:,-1].values\n",
    "     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dbca2903",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x, y, test_size=0.25, random_state=69)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "064929d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3750, 12), (1250, 12), (3750,), (1250,))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf067336",
   "metadata": {},
   "source": [
    "### Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "233f094d",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9f2bcf2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = torch.from_numpy(x_train).to(torch.float32)\n",
    "train_y = torch.from_numpy(y_train).to(torch.float32)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f9b5a9b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x = torch.from_numpy(x_test).to(torch.float32)\n",
    "test_y = torch.from_numpy(y_test).to(torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5130a278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([3750, 12]), torch.Size([3750]))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape, train_y.shape\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3f4301cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = TensorDataset(train_x,train_y)\n",
    "train_dataloader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "62fbe2f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = TensorDataset(test_x,test_y)\n",
    "test_dataloader = DataLoader(dataset=test_data, batch_size=batch_size, shuffle=True)\n",
    "     \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f72c763",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ddb46c0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetwork(torch.nn.Module):\n",
    "    \n",
    "    def _init_(self):\n",
    "        super(NeuralNetwork,self)._init_()\n",
    "        \n",
    "        self.layer1 = torch.nn.Linear(12,10)\n",
    "        self.layer2 = torch.nn.Linear(10,1)\n",
    "        self.sigmoid = torch.nn.Sigmoid()\n",
    "        self.relu = torch.nn.ReLU()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.layer1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.sigmoid(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9c021349",
   "metadata": {},
   "outputs": [],
   "source": [
    "neural_network = NeuralNetwork()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a8f9d7",
   "metadata": {},
   "source": [
    "### Genetic Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e1857c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from random import randint\n",
    "%matplotlib inline \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "def split(df,label):\n",
    "    X_tr, X_te, Y_tr, Y_te = train_test_split(df, label, test_size=0.25, random_state=42)\n",
    "    return X_tr, X_te, Y_tr, Y_te\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "classifiers = ['LinearSVM', 'RadialSVM', \n",
    "               'Logistic',  'RandomForest', \n",
    "               'AdaBoost',  'DecisionTree', \n",
    "               'KNeighbors','GradientBoosting']\n",
    "\n",
    "models = [svm.SVC(kernel='linear'),\n",
    "          svm.SVC(kernel='rbf'),\n",
    "          LogisticRegression(max_iter = 1000),\n",
    "          RandomForestClassifier(n_estimators=200, random_state=0),\n",
    "          AdaBoostClassifier(random_state = 0),\n",
    "          DecisionTreeClassifier(random_state=0),\n",
    "          KNeighborsClassifier(),\n",
    "          GradientBoostingClassifier(random_state=0)]\n",
    "\n",
    "\n",
    "def acc_score(df,label):\n",
    "    Score = pd.DataFrame({\"Classifier\":classifiers})\n",
    "    j = 0\n",
    "    acc = []\n",
    "    X_train,X_test,Y_train,Y_test = split(df,label)\n",
    "    for i in models:\n",
    "        model = i\n",
    "        model.fit(X_train,Y_train)\n",
    "        predictions = model.predict(X_test)\n",
    "        acc.append(accuracy_score(Y_test,predictions))\n",
    "        j = j+1     \n",
    "    Score[\"Accuracy\"] = acc\n",
    "    Score.sort_values(by=\"Accuracy\", ascending=False,inplace = True)\n",
    "    Score.reset_index(drop=True, inplace=True)\n",
    "    return Score\n",
    "\n",
    "def plot(score,x,y,c = \"b\"):\n",
    "    gen = [1,2,3,4,5]\n",
    "    plt.figure(figsize=(6,4))\n",
    "    ax = sns.pointplot(x=gen, y=score,color = c )\n",
    "    ax.set(xlabel=\"Generation\", ylabel=\"Accuracy\")\n",
    "    ax.set(ylim=(x,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7c8a3479",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initilization_of_population(size,n_feat):\n",
    "    population = []\n",
    "    for i in range(size):\n",
    "        chromosome = np.ones(n_feat,dtype=np.bool)     \n",
    "        chromosome[:int(0.3*n_feat)]=False             \n",
    "        np.random.shuffle(chromosome)\n",
    "        population.append(chromosome)\n",
    "    return population\n",
    "\n",
    "\n",
    "def fitness_score(population):\n",
    "    scores = []\n",
    "    for chromosome in population:\n",
    "        logmodel.fit(X_train.iloc[:,chromosome],Y_train)         \n",
    "        predictions = logmodel.predict(X_test.iloc[:,chromosome])\n",
    "        scores.append(accuracy_score(Y_test,predictions))\n",
    "    scores, population = np.array(scores), np.array(population) \n",
    "    inds = np.argsort(scores)                                    \n",
    "    return list(scores[inds][::-1]), list(population[inds,:][::-1]) \n",
    "\n",
    "\n",
    "def selection(pop_after_fit,n_parents):\n",
    "    population_nextgen = []\n",
    "    for i in range(n_parents):\n",
    "        population_nextgen.append(pop_after_fit[i])\n",
    "    return population_nextgen\n",
    "\n",
    "\n",
    "def crossover(pop_after_sel):\n",
    "    pop_nextgen = pop_after_sel\n",
    "    for i in range(0,len(pop_after_sel),2):\n",
    "        new_par = []\n",
    "        child_1 , child_2 = pop_nextgen[i] , pop_nextgen[i+1]\n",
    "        new_par = np.concatenate((child_1[:len(child_1)//2],child_2[len(child_1)//2:]))\n",
    "        pop_nextgen.append(new_par)\n",
    "    return pop_nextgen\n",
    "\n",
    "\n",
    "def mutation(pop_after_cross,mutation_rate,n_feat):   \n",
    "    mutation_range = int(mutation_rate*n_feat)\n",
    "    pop_next_gen = []\n",
    "    for n in range(0,len(pop_after_cross)):\n",
    "        chromo = pop_after_cross[n]\n",
    "        rand_posi = [] \n",
    "        for i in range(0,mutation_range):\n",
    "            pos = randint(0,n_feat-1)\n",
    "            rand_posi.append(pos)\n",
    "        for j in rand_posi:\n",
    "            chromo[j] = not chromo[j]  \n",
    "        pop_next_gen.append(chromo)\n",
    "    return pop_next_gen\n",
    "\n",
    "def generations(df,label,size,n_feat,n_parents,mutation_rate,n_gen,X_train,\n",
    "                                   X_test, Y_train, Y_test):\n",
    "    best_chromo= []\n",
    "    best_score= []\n",
    "    population_nextgen=initilization_of_population(size,n_feat)\n",
    "    for i in range(n_gen):\n",
    "        scores, pop_after_fit = fitness_score(population_nextgen)\n",
    "        print('Best score in generation',i+1,':',scores[:1])  #2\n",
    "        pop_after_sel = selection(pop_after_fit,n_parents)\n",
    "        pop_after_cross = crossover(pop_after_sel)\n",
    "        population_nextgen = mutation(pop_after_cross,mutation_rate,n_feat)\n",
    "        best_chromo.append(pop_after_fit[0])\n",
    "        best_score.append(scores[0])\n",
    "    return best_chromo,best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d5301ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_bc = pd.read_csv(r'C:\\Users\\navin\\Downloads\\Bank_Personal_Loan_Modelling.csv')\n",
    "label_bc = data_bc[\"Personal Loan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ecfc5bd7",
   "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>Classifier</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LinearSVM</td>\n",
       "      <td>0.9072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Logistic</td>\n",
       "      <td>0.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>0.8936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RadialSVM</td>\n",
       "      <td>0.8928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Classifier  Accuracy\n",
       "0      RandomForest    1.0000\n",
       "1          AdaBoost    1.0000\n",
       "2      DecisionTree    1.0000\n",
       "3  GradientBoosting    1.0000\n",
       "4         LinearSVM    0.9072\n",
       "5          Logistic    0.9000\n",
       "6        KNeighbors    0.8936\n",
       "7         RadialSVM    0.8928"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score1 = acc_score(data_bc,label_bc)\n",
    "score1"
   ]
  }
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