A data-driven tool to predict the reaction order of homogeneous gas-phase reactions. Includes machine learning experiments on the NIST Chemical Kinetics Database.

Investigate the impact of general news headlines on Stock Indices

This project aims to predict life expectancy based on various socio-economic, health, and environmental factors. A Random Regression Model is used for prediction, leveraging machine learning techniques to analyze historical data and generate insights.

Life Expectancy Prediction using Random Forest Regression Model for improving patient's health by giving them health photos

Performing analyses on New York City Airbnb and developing business intelligence for both the hosts and the guests

An AI-powered web application that predicts optimal seed parameters (size, sowing depth, spacing) for different crops in Maharashtra, India, based on regional and environmental conditions.

An end-to-end Machine Learning project that predicts house prices based on property features like area, location attributes, and amenities. This project covers everything from data preprocessing and model training to web deployment using Flask.

Aplikasi untk mengklasifikasi golongan ukt menggunakan random forest regression

Predicting The Energy Output Of Wind Turbine Based On Weather Condition DEMO LINK : https://youtu.be/ICfu49Ud2HU

Sauti East Africa request for a segmentation analysis on all of their user's behavior. Sauti wishes to better optimize their menu design and explore the feasibility of smart menus based on user predicted behavior.

Smart paddy drying system utilizing Edge AI. Deploys optimized Random Forest algorithms on Raspberry Pi nodes to predict post-harvest outcomes locally, utilizing a decoupled web architecture (Flask/SQLAlchemy) for secure data aggregation and remote monitoring.

EDA & Linear Regression Models

Predict NYC taxi fares with machine learning.

Modeling and predicting carseats sales using decision trees and random forests, with feature importance analysis, test performance evaluation, and reproducible visualizations. This project was done in Python.

Welcome to My Collection of Data Science Projects

I participated in a Kaggle competition where I predicted the prices of AirBnb rental listings in NYC using supervised machine learning techniques such as linear regression, trees and bootstrap (random forest and boosting).

Predicting cement strength

Tutorial: Random Forest Regression in R

A Machine Learning exploration evaluating various models to predict Airbnb prices, culminating in an optimized Gradient Boosting Regressor.

Flask Web Application