The house price was estimated based on the user's choices. Xgboost was used for training the model and Streamlit was used for deployment.

Brief introduction to CreateML and Model creation with small example of CatDog model + Detection Project in Swift.

Online peer-to-peer (P2P) lending markets enable individual consumers to borrow from, and lend money to, one another directly. We study the borrower-, loan- and group- related determinants of performance predictability in an online P2P lending market by conceptualizing financial and social strength to predict borrower rate and whether the loan w…

Machine Learning internship tasks from Bharat Intern.

Creating a machine learning model to detect factors that contribute to cyber attacks in an effort to limit the vulnerability of the average consumer.

This GitHub repository contains code for predicting the country destination of new Airbnb users using machine learning techniques on the "Airbnb New User Bookings" dataset from a Kaggle competition.

This project analyzes NYC restaurant inspections, leveraging business acceleration data to predict inspection grades using machine learning models. The Random Forest model performed best, demonstrating high accuracy and identifying key features influencing food safety compliance and inspection outcomes.

A comprehensive suite of Python-based machine learning models for predictive analytics, employing different evolutionary algorithms for data analysis across various topics.

Pursued an Introductory Machine Learning course in Python on Kaggle in my free time, where I practiced on a dataset and built a small model on Kaggle.

This has been a machine learning quest to classify cancer types using gene expression data, utilizing powerful tools and techniques to preprocess, train and evaluate models. The ultimate goal, to save lives through early diagnosis with high accuracy and precision.

This repository contains the code and resources for a flight booking predictive model. The model is trained on customer booking data and is designed to predict the likelihood of a customer making a booking. The model is evaluated on its accuracy and f1 score. The findings are summarized in a presentation slide for management

Machine Learning algorithm used to distinguish between spam and ham texts.

Health-insurance-cross-sell-prediction