A project of using machine learning model (tree-based) to predict short-term instrument price up or down in high frequency trading.

Open solution to the TalkingData AdTracking Fraud Detection Challenge

🟣 LightGBM interview questions and answers to help you prepare for your next machine learning and data science interview in 2026.

This is Kaggle Competition for predicting next 28 data sales for products in 3 states of United States

Data Mining Final Project

Predicting the medical costs charged by health insurers

multi-variate deep time series forecasting ensemble models

Earthquake Prediction using Regression Models

Heroku web service client for sklearn2sql

Predicting the time remaning for the next Earthquake. Kaggle Competition

Determining whether two questions are asking the same thing can be challenging, as word choice and sentence structure can vary significantly. Traditional natural language processing techniques been found to have limited success in separating related question from duplicate questions. In this paper, we explore methods of determining semantic equi…

📈 Forecast retail sales using the M5 dataset to enhance inventory management and revenue planning with proven statistical and machine-learning models.

A logistic regression model with cross-validation strategy employed onto it to produce good enough results for India ML Hiring Hackathon 2019 and securing 410th rank.

A Light GBM and LSTM based model to predict whether a windows machine will soon be affected by a malware based on the specifications of the machine.

An ensemble stacking that enhances pre-trained ML models through meta-learning and deep neural architectures.

This project is a comprehensive machine learning dag pipeline designed to predict the potability of water. It integrates various stages of the machine learning lifecycle, from data extraction and cleaning to model training and evaluation.

This project predicts driver attrition at Ola using ensemble models (Random Forest, XGBoost, LightGBM). It includes data preprocessing, feature engineering, imbalance handling, and model tuning, evaluated with Precision, Recall, F1, and ROC-AUC to identify churn factors and improve retention.

Sauti East Africa request for a segmentation analysis on all of their user's behavior. Sauti wishes to better optimize their menu design and explore the feasibility of smart menus based on user predicted behavior.

Salary prediction web application created using machine learning algorithms

This project is a minor project in MSc[IT] . diabetes(Type-2) is a todays leading health concern in world. we to early prediction of diabetes and clustering based risk prediction using data analytics . In it we perform various supervised classification model to find optimized solution by comparing various model. stacking model performance is 96.4