Logistic Regression in Python

LogReg.py

## import libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix


## load in training data
train = pd.read_excel(r'C:\Users\garym\Documents\PyWinAutoBlog\DD_data\DDData2.xlsx')

## run train test split (normally after doing data processing and analytics not shown)
X_train, X_test, y_train, y_test = train_test_split(train.drop('Ledderhose',axis=1), 
                                                    train['Ledderhose'], test_size=0.2, 
                                                    random_state=101)
## run logistic regression model 
logmodel = LogisticRegression()
logmodel.fit(X_train,y_train)

## run predictions
predictions = logmodel.predict(X_test)

## print reports 
print(classification_report(y_test,predictions))
print(confusion_matrix(y_test,predictions))