Logistic Regression in Python: A Step by Step Implementation
This video will teach you how to implement logistic regression in Python using the scikit-learn library. You will learn how to prepare your data, train your model, make predictions, and model deployment.
Did you know that logistic regression is one of the most popular machine-learning algorithms? It's used in a variety of applications, such as predicting customer churn, diagnosing diseases, and detecting fraud. In this video, I'll teach you how to implement logistic regression in Python using the scikit-learn library.
I covered the following:
Introduction to Logistic Regression
Data Preparation for Logistic Regression
Encoding categorical variables using LabelEncoder for scikit-learn
Standardize and normalize the data points using StandardScaler
Split the data into training and testing sets using train_test_split
Train the model using LogisticRegression class scikit-learn library
Predict the test dataset
Evaluate the model performance using precision, recall, f1-score from sckit-learn metrics module
Save and Deploy the model using the Pickle library
Test the model using the new test dataset
Learn about Pickle here: • Pickle Tutorial - How to save data into Pi...
Learn more about Linear Regression with a real-world example: • Machine Learning Tutorials
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