Comprehensive Guide to Machine Learning & Python OOP

Overview

This repository serves as a comprehensive resource for understanding Machine Learning algorithms, Python Object-Oriented Programming (OOP), data preprocessing, and visualization techniques using industry-standard tools.

Topics Covered

🔹 Machine Learning Algorithms

✔ Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, SVM, KNN
✔ Unsupervised Learning: K-Means Clustering, PCA, DBSCAN
✔ Ensemble Methods: Random Forest, Gradient ✔ Deep Learning (Basic): Neural Networks, CNN, RNN (Intro)

🔹 Data Preprocessing Techniques

✔ Handling Missing Values (Mean/Mode Imputation, Interpolation)
✔ Feature Scaling: Min-Max Scaling, Standardization
✔ Categorical Encoding: One-Hot Encoding, Label Encoding
✔ Feature Selection: Correlation Analysis, Recursive Feature Elimination (RFE)

🔹 Visualization Techniques

✔ Seaborn & Matplotlib: Histograms, Pair Plots, Heatmaps
✔ Pandas Profiling: Automated EDA
✔ Plotly & Interactive Visuals: Scatter Plots, Line Graphs, 3D Plots

🔹 Python OOP in Machine Learning

✔ DataPreprocessor Class (Handles missing values, encoding, scaling)
✔ ModelTrainer Class (Fits and evaluates ML models)
✔ Visualizer Class (Generates charts & plots for analysis)
✔ Pipeline Implementation (Combining preprocessing, training, and evaluation)

Installation

To set up the environment, install dependencies with:

pip install -r requirements.txt

Future Enhancements

🚀 Implement Deep Learning models for advanced tasks
🚀 Add more real-world datasets for hands-on learning
🚀 Expand visualization techniques with interactive tools