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CodeSignal

Regression Models for Prediction

via CodeSignal

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Overview

Grasp the basics of using different regression models for predictive modeling. Learn how to establish polynomial, lasso and ridge regression models within Python.

Syllabus

  • Unit 1: Mastering Multiple Linear Regression with Python
    • Visualizing Multiple Linear Regression in 3D Space
    • Expanding Dimensions: Introducing More Features
    • Visualizing Regression with a New Feature Combination
    • Crafting a Predictor: From Data to 3D Visualization
    • Navigating the Cosmos with Multiple Linear Regression
  • Unit 2: Meeting Polynomial Regression
    • Visualizing Polynomial Regression
    • Elevating Polynomial Features to the Third Degree
    • Adding the Finishing Touches: Print Model Coefficients
    • Polynomial Degree Adjustment in Regression Model
    • Crafting the Polynomial Regression Model
  • Unit 3: Decoding the Language of Coefficients in Regression Models
    • Absolute Insights: Modifying Coefficients Display
    • Debugging Coefficients Fetching
    • Fetching the Coefficients of a Regression Model
    • California Housing and Regression Model Coefficients
  • Unit 4: Evaluating Model Accuracy: MSE, RMSE, and MAE in Regression Analysis
    • Measuring Model Accuracy with Metrics
    • Evaluating Metric Sensitivity
    • Calculating the Root Mean Squared Error
    • Calculating Prediction Accuracy Metrics from Scratch

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