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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.
Black Friday Dataset: E-commerce Exploratory Data Analysis (EDA) and Feature Engineering for data preprocessing and preparation for machine learning models.
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.
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.
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.
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.
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 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.
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
FeatEngX is an automated Feature Engineering Tool used by Data Engineers & AI Researchers for making feature selection process, data preprocessing, and engineering accurate.