satkr7

import pandas as pd
pip install datawig
import datawig

data = pd.read_csv("train.csv")

df_train, df_test = datawig.utils.random_split(data)

#Initialize a SimpleImputer model
imputer = datawig.SimpleImputer(

satkr7

import pandas as pd

# load height-weight dataset downloaded from kaggle
data = pd.read_csv("weight-height.csv")

#equal width binning
data["ewb"] = pd.cut(data["Height"], bins=10)
#equal frequency binning
data["efb"] = pd.qcut(data["Height"], q=10)

satkr7

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder 
from category_encoders import BinaryEncoder  
from category_encoders import TargetEncoder 

#Label Encoder
le = LabelEncoder() 
df['columnName'] = le.fit_transform(df['columnName'])

satkr7

#Install the below libaries before importing
import pandas as pd
from pandas_profiling import ProfileReport

#EDA using pandas-profiling
profile = ProfileReport(pd.read_csv('titanic.csv'), explorative=True)

#Saving results to a HTML file
profile.to_file("output.html")

satkr7

import numpy as np
import pandas as pd
import os
import gc
import re
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from bs4 import BeautifulSoup

def preprocess(x):

satkr7

# -*- coding: utf-8 -*-
"""

@author: satyam.kumar
"""

'''
Import necessary packages
'''