Frequency Encoding in Feature Engineering with Python

Feature Engineering python- In this video we will be feature encoding techniques and How to do frequency encoding also known as count or frequency encoding. we will discuss it with examples using python. Even if you use any other language such as Rstudio or scala , this video will be extremely helpful.

I this technique we simply replace our categories by the count or occurrence of that particular category.

I would encourage you to checkout my complete feature engineering playlist which will help you to learn and understand other feature engineering techniques also.

Feature Engineering playlist : https://youtube.com/playlist?list=PLyB8AGpv661FvHtb9jbNYSsnSANV4bkFG

pandas playlist : https://youtube.com/playlist?list=PLyB8AGpv661FAEgt1cNQKq_KeVpfFK21T

Source code for this video:
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#!/usr/bin/env python
# coding: utf-8

import pandas as pd
from sklearn.model_selection import train_test_split

data = pd.read_csv('houseprice.csv', usecols=['MSZoning','Street','LotShape','Utilities','LandSlope','SalePrice'])

data.head()
data.isnull().mean()

X_train,X_test,y_train,y_test = train_test_split(data[['MSZoning','Street','LotShape','Utilities','LandSlope']],
data['SalePrice'], test_size =.3, random_state =111)

X_train.head()
X_train.shape
X_test.shape
y_train.shape
# In[32]:


y_train.head()


# In[33]:


Ms = X_train['MSZoning'].value_counts().to_dict()

Ms

cat_vars = ['MSZoning','Street','LotShape','Utilities','LandSlope']

encoder_dictionary ={}
for var in cat_vars:
encoder_dictionary[var] = (X_train[var].value_counts()/len(X_train)).to_dict()

encoder_dictionary


for var in cat_vars:
X_train[var] = X_train[var].map(encoder_dictionary[var])

X_train.head()


---------- End Source Code--------------------------------------------------

Related Tags:
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