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.

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

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.

Testing out ClearML.

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

Here I analysed data and made pipeline out of it making model making/training/testing/selecting and hyperparameters selecting more user friendly and visualised, like an application. I have worked for this project ~2 weeks.

Predict the 10-year risk of coronary heart disease (CHD) using patient data from the Framingham Heart Study. The dataset contains over 4,240 records with 15 attributes, allowing for exploration, feature engineering, and the development of classification models

this project was inspired by Aurélien Géron's Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, where he performs detailed analysis on the Housing dataset. Motivated by that, I explored and applied similar machine learning techniques on the Student Habits and Academic Performance dataset to predict exam scores.

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

Tensorflow image classifier Keras Applications model comparison

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

Predicting the success of bank marketing campaigns using machine learning models (Random Forest, XGBoost) on customer and economic data. The project includes data preprocessing, model training, and evaluation with accuracy and ROC-AUC scores.

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

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.

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

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.