The objective of the project is to perform advance regression techniques to predict the house price in Boston.

Predicting Big mart sales

Predictive analytics project about time series forecasting for dicoding "Machine Learning Terapan" submission. The dataset was taken from yahoo finance website.

Ensamble Voting for Financial Time Series

Predicting the recovery rates on non-performing loans (NLP) using a private database from a debt collection agency. Freelance project.

A powerful stacked ensemble model for income prediction, combining GradientBoosting, AdaBoost, Bagging, Linear Regression, and Decision Trees. Achieves an impressive R² of 0.8761 on the RoS_sample_submission dataset.

Machine Learning Playground

This project builds a complete ML pipeline to model medical insurance costs based on demographic and lifestyle features. The workflow includes exploratory data analysis (EDA), preprocessing, multiple regression models, hyperparameter tuning, ensemble stacking, log-transform modeling, SHAP interpretability, and full residual diagnostics.

This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.

This is a web app where a user can signup to the website first and then login to access the website. Then, he/she can give their age, select his/her gender, bmi, number of children, select whether he/she is a smoker or not, and select his/her region. Gradient Boosting Regressor is used in this project which gives the best accuracy of 89.798.

Job-A-thon ML challenge

ML_SUPERVISED_LEARNING_SALES_PRIDICTION_PROJECT

This research is based on previous research related to Optimization of Airbnb Dynamic Pricing. This research analytical purposes was to create a model that was as flexible as possible by determining price at the scale of the smallest possible rental period at daily basis.

Demand forecasting using ML regression models and inventory optimization with PuLP

Klasicni Regresori ver2. Klasicni Regresori: DecisionTreeRegressor, KNeighborsRegressor, RandomForestRegressor, Ridge, GradientBoostingRegressor, XGBRegressor. MultioutputRegressor. ANSAMBL. STACKING.

This project aims to analyze and forecast the total funding amounts of startups using various regression and time series modeling techniques. Initially, we preprocess the dataset, which includes features such as funding amounts, company size, and number of funding rounds. The data is then scaled and split into training and testing sets.

Predicting building energy consumption as part of the WiDS 2022 Datathon

JOB-A-THON - January 2023

Gradient Boosting Regressor.