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

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

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

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 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.

A step-by-step approach to predict customer attrition using supervised machine learning algorithms in Python. This is best for Beginner who wants to start with easy machine learning projects.

记录在学习数据挖掘和分析中的实践，持续更新...

Data Exploration & Analytics on Bangalore South Crime Data

Developed a comprehensive data analysis project focusing on Walmart sales data. Imported raw sales data into a MySQL database and applied data transformation techniques to enrich and clean the dataset. Implemented various SQL queries to extract meaningful insights and generate actionable business recommendations.

All about Data intelligence

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

this project develops a robust machine learning model to estimate house prices in the state.

This repository contains a solution for Kaggle competition-Titanic-Machine Learning from Disaster

This project develops a machine learning model to predict customer churn for a California-based telecom company using data from 7043 customers. Our goal is to enhance customer retention strategies through detailed data analysis and feature engineering.

This is a capstone level classification ML project for predicting IPL team finishing position for an year based on Individual player's performance. The project includes web-scraping ESPN cricinfo website for ipl player statistics, pre-processing the data, and comparing different classification models and hyperparameter tuning them.

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.

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

In this project we will work with housing data for the city of Ames, Iowa, United States from 2006 to 2010. You can read more about why the data was collected here (https://doi.org/10.1080/10691898.2011.11889627). You can also read about the different columns in the data here (https://www.tandfonline.com/doi/abs/10.1080/10691898.2011.11889627).

Zomato Dataset: Restaurant & Food Exploratory Data Analysis (EDA) for data preprocessing and preparation for machine learning models.