random-forest-model

Predictive modeling framework for early detection of fish kill risks using multi-parameter water quality time-series data from IoT-based aquaculture monitoring systems.

An end-to-end credit risk classifier built on the German Credit dataset. The project covers EDA, a leak-free preprocessing pipeline, benchmarked and tuned models, repeated cross-validation, a cost-sensitive decision threshold, SHAP explainability, and a Streamlit app that returns a risk probability with a per-applicant explanation.

This multi-phase project identifies key satisfaction drivers and provides actionable insights to improve customer experience using statistical analysis and machine learning models, including logistic regression, decision trees, random forests, and XGBoost.

A machine learning project aimed at predicting failures in an industrial milling machine using a random forest model.

Email_Spam_Detection is a machine learning project that detects spam emails using a Random Forest model. Features a Flask backend (deployed via Render) and a simple HTML/CSS frontend. Easily deployable for both local and public use.

In this project, we build a predictive model to determine whether a passenger would have survived the Titanic disaster.