ML Topics Reference Project This repository contains a small project designed as a reference for various core Machine Learning (ML) concepts.
The project incorporates code examples and demonstrations for the following topics:
Cross-Validation: Techniques for evaluating model performance on unseen data.
Regression Models: Predictive models for continuous target variables.
Hyperparameter Tuning: Optimizing model parameters for improved performance.
Correlation Matrix: Exploring relationships between features.
GridSearchCV: Automated hyperparameter search using Grid Search with Cross-Validation.
Data Preprocessing: Preparing data for machine learning algorithms.
Classification Models: Predictive models for categorical target variables.
Data Visualization: Techniques for exploring and understanding data.
Q-Q Plot: Assessing the normality of data distribution.
Data Pipelines: Building efficient and reusable data processing workflows.
Residuals Analysis: Evaluating model fit by examining residuals.
This project serves as a starting point for familiarization and experimentation with these fundamental ML concepts.
Note:
Specific code examples and usage will vary depending on the chosen libraries (e.g., scikit-learn). Feel free to explore the code, modify examples, and experiment with different techniques!