This repository serves as a complete, end-to-end guide and codebase for the machine learning lifecycle. It covers every step of the process, starting from raw data ingestion and Exploratory Data Analysis (EDA), moving through model building and evaluation, and finishing with putting the model into production (Deployment).
Whether you are looking to understand data preprocessing techniques, explore classic ML algorithms (like Logistic Regression, Random Forests, etc.), or learn how to serve a model via an API, this repo has you covered.
This repository is structured to follow a standard industry machine learning pipeline:
- Exploratory Data Analysis (EDA): Visualizing distributions, handling missing values, and finding correlations using Pandas, Matplotlib, and Seaborn.
- Data Preprocessing & Feature Engineering: Scaling, encoding categorical variables, handling outliers, and building scikit-learn pipelines.
- Model Training: Training supervised and unsupervised learning models (Regression, Classification, Clustering).
- Model Evaluation: Hyperparameter tuning (GridSearchCV, RandomizedSearchCV) and evaluating metrics (Accuracy, F1-Score, RMSE, ROC-AUC).
- Deployment: Saving models (Pickle/Joblib) and serving them using a REST API (Flask/FastAPI).