This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.

How to do a simple end-to-end machine learning classification project using the telco churn dataset

Classifying Criminal Offenses: Classification Application in Python Using scikit-learn

Solar Cells☀️ Project Using Random Forest🌴 Regressor

Similarity based email sorting for Google Mail using RandomForest classifiers

A final year project for the University of Exeter, using machine learning to study patterns in millions of chess games (~350 GB). Ranked 1st in the cohort for undergraduate projects (85%).

The aim of this project to predict whether the product from an e-commerce company will reach on time or not. This project also analyzes various factors that affect the delivery of the product as well as studies the customer behavior.

To predict the disease based on the symptoms

ML project focused on predicting Titanic passenger survival using various algorithms and extensive data analysis techniques. This project includes detailed data visualization and interpretation to uncover key factors affecting survival. By leveraging various ML models the analysis aims to achieve high predictive accuracy.

This project leverages machine learning to predict the quality of red wine based on various chemical properties. By analyzing key factors that influence wine quality, the model provides insights and predictions to assist in evaluating and selecting high-quality red wines.🍷

My portfolio website regarding data science projects. Some visualization and analysis projects reflect work for PITAPOLICY clients.

Data Science Project - Full Depth analysis AND Prediction Using Decision Tree and Random Forest

Educational notebooks reviewing machine learning models and concepts.

Predict and prevent customer churn in the telecom industry with this project. Leverage advanced analytics and ML on a diverse dataset to build a robust classification model. Gain a deep understanding of customer behavior and identify key factors influencing churn. Clone the repository, explore insights, and enhance customer retention startegies.

Skin Cancer Detection: Leveraging Hybrid Deep Learning Models and Traditional Machine Learning Classifiers

This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.

Comparing logistic regression, decision tree, random forest, k-nearest neighbors, and SVMs in regard to binary prediction performance metrics.

Applying statistical data science methods into loan default prediction task