This repository contains various Machine Learning models and experiments along with EDA(Exploratory Data Analysis) implemented in Python using Jupyter Notebooks (.ipynb). The goal is to understand, practice, and apply different ML algorithms to real-world datasets and recommend insights through EDA.
- Supervised Learning
- Regression (Linear, Polynomial, Decision Tree, Random Forest, etc.)
- Classification (Logistic Regression, KNN, SVM, Naive Bayes, etc.)
- Unsupervised Learning
- Clustering (K-Means, Hierarchical, DBSCAN)
- Dimensionality Reduction (PCA)
- Recommender System
- Exploratory Data Analysis (EDA)
- Feature Engineering & Data Preprocessing
- Model Evaluation & Metrics
- Python
- Jupyter Notebook
- Scikit-learn
- Pandas & NumPy
- Matplotlib & Seaborn
The aim of this repository is to build a strong foundation in machine learning concepts and develop a collection of ML models that can be used for academic learning, research, and real-world applications.