House price estimation from visual and textual features using both machine learning and deep learning models

Kaggle LANL Earthquake Prediction challenge, Genetic Algorithm (DEAP) + CatboostRegressor, private score 2.425 (31 place)

Mohs Hardness Prediction Project | Ensemble Models with Neural Networks, LGBM, CAT, XGB using a Voting Mechanism. 🚀💎

Crypto & Stock* price prediction with regression models.

This repository will work around solving the problem of food demand forecasting using machine learning.

Interpreting wealth distribution via poverty map inference using multimodal data

Production prediction is one of the core problems in a company. The provided dataset is a set of nearby wells located in the United States and their 12 months cumulative production. The company data scientist needs to build a model from scratch to predict production.

Model that uses 10 different algorithms to predict the revenue of a movie before it's release

To develop a machine learning model that accurately predicts housing prices using the Boston Housing dataset by analyzing various house features, and it utilizes a CatBoost model to assist potential buyers or sellers in estimating housing prices.

A python script for basic data cleaning/manipulation and modelling based on the open source House Sales Advanced Regression Techniques(Kaggle)

This application is based on a CatBoost machine learning model. This basically takes four queries from the user (Upazila/Thana name, Network availability (3G/4G), District, and Zip code) and outputs the best operator for that location. This model was trained on the data I collected from Opensingnal application. I collected 22,360 data for 559 lo…

This project was developed as an individual endeavor to predict the price of used cars based on various car attributes. Using multiple regression models

Time series forecasting on power consumption pattern with Catboost and regression model

Example machine learning applications for the determination of the residual yield force of corroded steel bars tested under monotonic tensile loading. Data is collected from 26 experimental programs avaialbe in the literature.

😺 CatBoost Model Per Family

Developed a multi-class classification model to identify and classify faults according to specified categories. The model can be used to flag a device returning faulty data automatically.

The goal of the challenge is to predict, based on the analysis of the correlation of a year of consumption and weather training data, the electricity consumption of two given sites for a test year.

Prediction of the sale price of a vehicle using predictive models using gradient boosting

House Rate Predictor

This github repositiory contains the Flight Price Prediction project aims to develop a machine learning model to predict flight ticket prices based on various factors such as departure and arrival locations, dates, airlines, and other relevant features.