Scalable Machine Learning and Deep Learning, Final Project, 2023/2024

Python package code repo for Implementation of syntactic n-grams (sn-gram) extraction

The ShadBot package has the ability to conduct trade online, perform backtests for offline trades, and review and analyze them, as well as optimize trades. Also, this package has the ability to optimize using artificial intelligence and predict future price values using machine learning algorithms

I developed a sophisticated ML model using LLMs to predict user preferences in chatbot interactions.implemented a comprehensive data preprocessing pipeline,including feature extraction and encoding,to optimize performance. conducted extensive hyperparameter tuning and evaluation, enhancing accuracy and in AI-driven conversational systems.

Building a ML model to predict whether the customer will apply for the claim or not with deployment.

Utilizing LazyPredict, Feature Engine, Feature Tools: Narrow down base models automating various aspects of the eda process. Blog post at link.

Energy Demand Forecasting with Machine Learning, An advanced AI model for natural gas consumption prediction using ensemble learning (Gradient Boosting, Random Forest, Ridge Regression) on multivariate time series data. Includes feature engineering, Fourier transforms, and econometric analysis.

This repository contains a comprehensive analysis of Beijing housing data, including data cleaning, categorical transformation, outlier removal, feature engineering, and advanced visualizations. The analysis focuses on understanding price trends, the impact of location, and district-level insights from 2010 onwards.

End-to-end Machine Learning project for customer credit risk prediction using PostgreSQL, XGBoost, FastAPI, and GitHub.

This GitHub repository contains code for predicting the country destination of new Airbnb users using machine learning techniques on the "Airbnb New User Bookings" dataset from a Kaggle competition.

This project analyzes customer data to understand why customers leave the company(churn pattern), identify the factors driving customer attrition, and build a Machine Learning model to predict customers likely to leave the company.

Predicting Movie Rental Duration

This project predicts lung cancer risks using machine learning models like Random Forest, Logistic Regression, and SVM. It analyzes patient data with features such as age, smoking habits, and symptoms. Data preprocessing, visualization, and performance evaluation ensure accurate predictions for early diagnosis.

This project works on data of different laptop features according to various specifications of laptop brands. I have done feature engineering on data and have build different chine Learning models to achieve maximum accuracy and chosen best ML algorithm for best predictions. This project is build to predict price of laptop as per specifications.

Market microstructure and high-frequency trading.

Analysis and feature engineering of the Online Retail Transactions dataset to uncover customer behaviour, product trends, and optimise pricing. Includes interactive dashboards for actionable insights.

A python library to improve ML algorithms by building physically constrained lag features from residence time distributions

FeatEngX is an automated Feature Engineering Tool used by Data Engineers & AI Researchers for making feature selection process, data preprocessing, and engineering accurate.

记录在学习数据挖掘和分析中的实践，持续更新...