Read dataset and check imbalance

article-identify-hardbounce-ml_2_read-and-check-imbalance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.insert(1, '../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functions import aux_functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hardbounce = pd.read_csv('../datasets/hardbounce_featureEngineering.csv',sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_vars=['emailDomain_cat','emailDomainPiece1','emailDomainPiece2']\n",
    "\n",
    "hardbounce_dummy = aux_functions.buildDummyVariables(hardbounce, cat_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flgHardBounce_n\n",
       "0    0.787886\n",
       "1    0.212114\n",
       "Name: __dummy__, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aux_functions.freqTable(\n",
    "    hardbounce_dummy['flgHardBounce_n']\n",
    "    ,\"\"\n",
    "    ,False\n",
    "    ,\"columns\"\n",
    ")"
   ]
  }
 ],
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