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. 🚀💎

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

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

Interpreting wealth distribution via poverty map inference using multimodal data

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.

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.

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

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

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…

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

😺 CatBoost Model Per Family

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.

House Rate Predictor

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.

Telecom Customer Churn Prediction with 9 Different Alghoritms

Machine Learning Regression Problem: Predicting the Car Orders Price (IDR) given car orders dataset

Build a machine learning model that predicts the Envision Racing drivers’ lap times.

You are provided hourly rental data along with weather data. For this competition, the training set is comprised of the first 20 days of each month, while the test set is the 21th to the end of the month. You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the renta…

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.