Machine Learning | Dummy Variable Trap | One Hot Encoding | Dummy Encoding | Linear Regression

Publié le: 05 août 2019
sur la chaîne: technologyCult

5,294

Machine Learning | Dummy Variable Trap | One Hot Encoding | Dummy Encoding | Linear Regression

Python for Machine Learning
Topic to be covered - Dummy Variable Trap

1. Definition of Dummy Variable Trap
2. Learn about the scenario of Dummy Variable Trap is generated (X.T.dot(X))
3. Mathematical Reasoning behind Dummy Variable Trap
4. Solving Linear Regression Problem involving Dummy Variable Trap Issue.

Link for Dummy Encoding -    • Dummy Variables | Get Dummies to transform...

Coding Starts Here
===============
import pandas as pd
import numpy as np

df = pd.read_csv('expenses.csv')
y = df['expenses']

Case 1 - (intercept , weekdays with one-hot encoding)

df1 = pd.get_dummies(df['weekdays'])

X = pd.concat([df1,df['intercept']],axis=1)

print(np.linalg.det(X.T.dot(X)))
print(np.linalg.inv(X.T.dot(X)))

Case 2 - (intercept , weekdays with dummy encoding)

df1 = pd.get_dummies(df['weekdays'],drop_first=True)

X = pd.concat([df1,df['intercept']],axis=1)

print(np.linalg.det(X.T.dot(X)))
print(np.linalg.inv(X.T.dot(X)))

Case 3 - (intercept , weekdays and gender with one-hot encoding)

df1 = pd.get_dummies(df[['weekdays','gender']])

X = pd.concat([df1,df['intercept']],axis=1)

matr = X.T.dot(X)
print(np.linalg.det(X.T.dot(X)))
print(np.linalg.inv(X.T.dot(X)))

Case 4 - (intercept , weekdays and gender with dummy encoding)

df1 = pd.get_dummies(df[['weekdays','gender']],drop_first=True)

X = pd.concat([df1,df['intercept']],axis=1)

matr = X.T.dot(X)
print(np.linalg.det(X.T.dot(X)))
print(np.linalg.inv(X.T.dot(X)))

Case 5 - (drop intercept and weekdays and gender with one-hot encoding)

df1 = pd.get_dummies(df[['weekdays','gender']])

X = df1

matr = X.T.dot(X)
print(np.linalg.det(X.T.dot(X)))
print(np.linalg.inv(X.T.dot(X)))

Case 6 - (drop intercept and weekdays and gender with dummy encoding)

df1 = pd.get_dummies(df[['weekdays','gender']],drop_first=True)

X = df1

matr = X.T.dot(X)
print(np.linalg.det(X.T.dot(X)))
print(np.linalg.inv(X.T.dot(X)))

import pandas as pd
import numpy as np

df = pd.read_csv('expenses.csv')
y = df['expenses']

from sklearn.preprocessing import MinMaxScaler

mm = MinMaxScaler(feature_range=(0,1))

age_mm = mm.fit_transform(df.iloc[:,3:4])

df['age_mm'] = age_mm

df_weekdays_gender = pd.get_dummies(df[['weekdays','gender']],drop_first=True)

X = pd.concat([df_weekdays_gender,df['age_mm'],df['intercept']],axis=1)

beta = np.linalg.solve(X.T.dot(X),X.T.dot(y))

yhat = X.dot(beta)

comparision = pd.DataFrame()
comparision['Actual'] = y
comparision['Predicted'] = yhat

print(comparision)

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