Interconnect seeks to forecast customer churn by analyzing package choices and contracts. If a customer plans to leave, they're offered unique codes and special packages to foster loyalty.

Predicting the churn in the last month using the data (features) from the first three months and identify customers at high risk of churn and the main indicators of churn.

Applied various algorithm models to solve a binary classification problem of predicting if a patient will suffer from a disease. Project done for Machine Learning course of Data Science Ms

Applied numerous algorithm models to solve a binary classification problem of predicting if any given prospective customer converts to a sale, through the company’s online sales channel.

upGrad's Telecom Churn Case Study hosted on Kaggle platform

Analyze the data of Visa applicants, build a predictive model to facilitate the process of visa approvals, and based on important factors that significantly influence the Visa status recommend a suitable profile for the applicants for whom the visa should be certified or denied.

Minimal implementation of Adaboost classifier using weighted decision stumps without sklearn.

I applied the bagging and boosting methods using the decision tree as the base predictor on the sklearn’s breast cancer data set. I experiment with different parameters and report the results obtained.

The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the datsaset

Fall 2020 - Computational Medicine - course project

Credit Card Clients (CCC) Default Prediction Using various Machine learning Algorithms

This is about how to make Diabetes Prediction with Machine Learning. We are developing a machine learning model capable of predicting whether someone may have diabetes based on health data and specific parameters. Using the right machine learning algorithms, we will process this data to provide valuable predictions for patients and medical

Cancer Prediction using Adaboost

This mini-project involves experimenting with a variety of classification and regression models, exploring different techniques to understand their behaviors and applications in predictive analytics.

This project is aimed at predicting the case of customer's default payments. This dataset (30000,25) contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan is used to build a classification model.

Machine learning model to predict the sign of the VIX Index for the next day.

Practicum by Yandex Project 6: This is a Machine Learning project to develop a model that would analyze subscribers' behavior, and build a phone plan recommendation system to recommend the right plan.

This project explores the predictive modeling workflow using the Kaggle competition "Titanic - Machine Learning from Disaster." It emphasizes key stages like data analysis and model evaluation, aiming to identify the optimal model. Through a real-world approach, we enhance our understanding of the workflow and emphasize rigorous model evaluation.

Advanced Machine Learning