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!