To read the given data and perform Feature Encoding and Transformation process and save the data to a file.
STEP 1:Read the given Data.
STEP 2:Clean the Data Set using Data Cleaning Process.
STEP 3:Apply Feature Encoding for the feature in the data set.
STEP 4:Apply Feature Transformation for the feature in the data set.
STEP 5:Save the data to the file.
- Ordinal Encoding An ordinal encoding involves mapping each unique label to an integer value. This type of encoding is really only appropriate if there is a known relationship between the categories. This relationship does exist for some of the variables in our dataset, and ideally, this should be harnessed when preparing the data.
- Label Encoding Label encoding is a simple and straight forward approach. This converts each value in a categorical column into a numerical value. Each value in a categorical column is called Label.
- Binary Encoding Binary encoding converts a category into binary digits. Each binary digit creates one feature column. If there are n unique categories, then binary encoding results in the only log(base 2)ⁿ features.
- One Hot Encoding We use this categorical data encoding technique when the features are nominal(do not have any order). In one hot encoding, for each level of a categorical feature, we create a new variable. Each category is mapped with a binary variable containing either 0 or 1. Here, 0 represents the absence, and 1 represents the presence of that category.
• Log Transformation • Reciprocal Transformation • Square Root Transformation • Square Transformation
• Boxcox method • Yeojohnson method
import pandas as pd df=pd.read_csv("/content/Encoding Data.csv") df 
from sklearn.preprocessing import LabelEncoder,OrdinalEncoder pm=['Hot','Warm','Cold'] e1=OrdinalEncoder(categories=[pm]) e1.fit_transform(df[["ord_2"]]) 
df['bo2']=e1.fit_transform(df[["ord_2"]]) df 
df['bo2']=e1.fit_transform(df[["ord_2"]]) df 
le=LabelEncoder() dfc=df.copy() dfc['ord_2']=le.fit_transform(dfc['ord_2']) dfc 
from sklearn.preprocessing import OneHotEncoder ohe=OneHotEncoder(sparse=False) df2=df.copy() enc=pd.DataFrame(ohe.fit_transform(df2[['nom_0']])) df2=pd.concat([df2,enc],axis=1) df2 
pd.get_dummies(df2,columns=["nom_0"]) 
pip install --upgrade category_encoders 
from category_encoders import BinaryEncoder df=pd.read_csv("/content/data.csv") be=BinaryEncoder() nd=be.fit_transform(df['Ord_2']) fb=pd.concat([df,nd],axis=1) dfb=df.copy() dfb 
from category_encoders import TargetEncoder te=TargetEncoder() cc=df.copy() new=te.fit_transform(X=cc["City"],y=cc["Target"]) cc=pd.concat([cc,new],axis=1) cc 
import pandas as pd from scipy import stats import numpy as np df=pd.read_csv("/content/Data_to_Transform.csv") df 
df.skew() 
np.log(df["Highly Positive Skew"]) 
np.reciprocal(df["Moderate Positive Skew"]) 
np.sqrt(df["Highly Positive Skew"]) 
np.square(df["Highly Positive Skew"]) 
df["Highly Positive Skew_boxcox"],parameters=stats.boxcox(df["Highly Positive Skew"]) 
df["Moderate Negative Skew_yeojohnson"],parameters=stats.yeojohnson(df["Moderate Negative Skew"]) df.skew() 
df["Highly Negative Skew_yeojohnson"],parameters=stats.yeojohnson(df["Highly Negative Skew"]) df.skew() 
import matplotlib.pyplot as plt import seaborn as sns import statsmodels.api as sm import scipy.stats as stats sm.qqplot(df["Moderate Negative Skew"],line='45') plt.show() 
import matplotlib.pyplot as plt import seaborn as sns import statsmodels.api as sm import scipy.stats as stats sm.qqplot(df["Moderate Negative Skew"],line='45') plt.show() 
sm.qqplot(np.reciprocal(df["Moderate Negative Skew"]),line='45') 
from sklearn.preprocessing import QuantileTransformer qt=QuantileTransformer(output_distribution='normal',n_quantiles=891) df["Moderate Negative Skew"]=qt.fit_transform(df[["Moderate Negative Skew"]]) sm.qqplot(df["Moderate Negative Skew"],line='45') plt.show() 
Hence performing Feature Encoding and Transformation process is Successful.