This project involves predicting customer churn in a telecommunications company using machine learning techniques, exploring various features' impact, optimizing models, and identifying key factors influencing churn.

YOLOv8 image classifier model comparison

Using publicly available data for the national factors that impact supply and demand of homes in US, build a data science model to study the effect of these variables on home prices.

Create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.

Repository to store materials for our group project on predicting health insurance charges.

I worked through these machine learning algorithms to complete assignments in the European Center for Medium Range Weather Forecasting MOOC class

In the 10-day camp, we experienced the basics of machine learning by coding

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.

Tensorflow image classifier Keras Applications model comparison

This repository explores machine learning models applied to predict online shoppers' purchase intentions based on a comprehensive dataset, showcasing various classification algorithms and their performance in the e-commerce domain.

This is an end to end machine learning project using my personal shopping data collected over the past three years.

Predicting customer subscriptions for a bank's term deposit post marketing campaigns

A machine learning foundation project to predict defaulting credit card clients. Topics covered are EDA, feature engineering and selection, model evaluation.

Graduate Rotational Internship Program -TSF-The Spark Foundation(Data Science and Business Analysis Internship) #GRIPJULY21-Task#2:Prediction Using Unsupervised Machine Learning-In this task, we have to predict the optimum number of clusters from the iris dataset & represent it visually.