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

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.

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.

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

Ensamble Voting for Financial Time Series

Machine Learning Playground

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.

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.

ML_SUPERVISED_LEARNING_SALES_PRIDICTION_PROJECT

No description provided.

Demand forecasting using ML regression models and inventory optimization with PuLP

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.

Job-A-thon ML challenge

JOB-A-THON - January 2023

Predicting building energy consumption as part of the WiDS 2022 Datathon

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.

The Students Performance Analysis project, is an insightful exploration of the factors that impact students' academic performance. Using a dataset containing various student attributes, the project aims to uncover patterns and relationships that influence their success in examinations. This analysis is particularly valuable for educators, policymak

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

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.

Sales forecasting plays an important role in business development. Regardless of the size of a business or the number of salespeople, accurate sales forecasting can have a significant impact on all aspects of sales management, including planning, budgeting, and determining sales.

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.

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

The focus of this project is on Data Cleaning, Data Analysis, Data Extraction for story-telling, Gaining an insight from Pycaret Regression tool over best ML algorithms,and finally comparing 4 top models for selecting the best.