Solution for Kaggle "IEEE-CIS-Fraud-Detection" competition (top 26%)
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Updated
Sep 4, 2019
Solution for Kaggle "IEEE-CIS-Fraud-Detection" competition (top 26%)
Analytics Vidhya Jantahack Agriculture, Hackathon.
Australia rain prediction model with LGBM
Учебные проекты (Яндекс.Практикум)
Instacart’s data science team plays a big part in providing this delightful shopping experience. Currently they use transactional data to develop models that predict which products a user will buy again, try for the first time, or add to their cart next during a session.
This repo contains machine learning algorithms such as Linear, Logistic Regression, Random Forests, XGBoost, LGBM
Predicting Rossmann sales six weeks in advance. Feel free to access the Telegram Bot in the link below.
This contains all the machine learning projects.
Bank Customer Churn Prediction using Ensemble Model
This repository contains the code to build a prediction engine for London housing prices
Проекты, выполненные на курсе "Специалист по Data Science" в Яндекс.Практикум
Repository for the "Google Analytics Customer Revenue Prediction" Kaggle competition.
Code for my first ML competition on kaggle. The two codes are LSTM and LGBM prediction model with technical analysis features. To download dataset for the competition visit : https://www.kaggle.com/competitions/jpx-tokyo-stock-exchange-prediction
Predicting building energy consumption as part of the WiDS 2022 Datathon
Practicum Workshop
Kaggle competition, which challenge us to create algorithms for "Knowledge Tracing," the modeling of student knowledge over time. The goal is to accurately predict how students will perform on future interactions. We had pair our machine learning skills using Riiid’s EdNet data to get an AUC of 0.738 in private leaderboard .
Implement a Scoring Model
Binary classification of start as pulsar or non- pulsar based on observation results using CatBoost, XGBoost and LGBM.
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