{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {},

   "outputs": [],

   "source": [

    "import warnings\n",

    "import pandas as pd\n",

    "\n",

    "warnings.filterwarnings('ignore')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "metadata": {},

   "outputs": [],

   "source": [

    "# Load the noisy dataset.\n",

    "original = pd.read_csv('../../data/raw/original.csv')\n",

    "synthetic = pd.read_csv('../../data/raw/synthetic.csv')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "metadata": {},

   "outputs": [],

   "source": [

    "# Process the the datasets.\n",

    "original = original.drop_duplicates().dropna()\n",

    "synthetic = synthetic.drop_duplicates().dropna()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 4,

   "metadata": {},

   "outputs": [],

   "source": [

    "# Feature engineering\n",

    "original[['GENDER', 'LUNG_CANCER']] = original[['GENDER', 'LUNG_CANCER']].replace({'M': 1, 'F': 2, 'YES': 1, 'NO': 0})\n",

    "synthetic[['GENDER', 'LUNG_CANCER']] = synthetic[['GENDER', 'LUNG_CANCER']].replace({'M': 1, 'F': 2, 'YES': 1, 'NO': 0})"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 5,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Dataset processed.\n"

     ]

    }

   ],

   "source": [

    "# Saved dataset\n",

    "original.to_csv('../../data/processed/original.csv', index=False)\n",

    "synthetic.to_csv('../../data/processed/synthetic.csv', index=False)\n",

    "\n",

    "print('Dataset processed.')"

   ]

  }

 ],

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