This repository is totally focused on Feature Engineering Concepts in detail, I hope you'll find it helpful.

This comprehensive analysis delves into the crucial role of cash holdings in determining a firm's future performance and market dynamics.

Predicting popularity of movies using the IMDb movies dataset with multiple regression algorithms such as XGBoost, Gradient Boosting, Regularization Regressors, and Stacking Regressor; Performed extensive data cleaning, feature engineering, and used transformation techniques such as winsorization and log-transformation

This project explores the Framingham Heart disease dataset with the objective to predict its risk in 10 years. Various methods for handling missing values and outliers are explored as iterations. After analysing the dataset, important and necessary features are selected. Seven ML models are implemented, with evaluation on the basis of Test Recall.

Outlier_Exterminator is a Python tool for detecting and treating outliers using IQR, Z-Score, and Percentile methods. It supports trimming, capping, and Winsorization, demonstrated in a Jupyter Notebook.

- Fundamentos de Estadística matemática. - Conceptos clave de Machine Learning. - Desarrollo de modelos y Algoritmos. -Proceso EDA y preprocesamiento de datos. -Tratamiento de Outliers y NaN. -Estandarización y Codificación de características para un modelo. - Entrenamiento de Modelo de ML. -Desarrollo de una APP a partir de un modelo.

An end to end ML solution to predict customer churn, aiding businesses in identifying at-risk customers. This repository features a tuned LightGBM model, custom preprocessing, SMOTE for class balancing, and a user-friendly Streamlit app for predictions, emphasizing model optimization and deployment.