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

Predicting Big mart sales

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.

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

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.

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 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

Supervised ML Regression Project on Bike Demand Predicting

This project focuses on developing a Machine Learning model to predict housing prices in California.

House price prediction can help the developer determine the selling price of a house and can help the customer to arrange the right time to purchase a house. There are three factors that influence the price of a house which include physical conditions, concept and location.

Bike Sharing (Rentals) machine learning regression to predict total rentals by considering features of dataset

Student performance

Health insurance cost prediction according to patient parameters using machine learning algorithms

This is a personal project of mine. I decided to do a process of data storing, data manipulation, data analysis & visualizations, feature engineering, model creation/testing, model evaluation, and metrics visualizations.