Scalable Machine Learning and Deep Learning, Final Project, 2023/2024
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Updated
Nov 12, 2024 - Python
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
Building a ML model to predict whether the customer will apply for the claim or not with deployment.
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.
Utilizing LazyPredict, Feature Engine, Feature Tools: Narrow down base models automating various aspects of the eda process. Blog post at link.
FeatEngX is an automated Feature Engineering Tool used by Data Engineers & AI Researchers for making feature selection process, data preprocessing, and engineering accurate.
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 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.
记录在学习数据挖掘和分析中的实践,持续更新...
Raga Classification Using Deep Learning This project focuses on Indian classical raga classification using deep learning. Unlike Western music genres, ragas are modal structures with intricate ornamentations and improvisations, requiring specialized feature extraction and model selection
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.
A predictive analytics project leveraging machine learning algorithms to forecast the likelihood of heart disease in patients. Heart Disease Prediction Using Machine Learning Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
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 project tackles the Kaggle Spaceship Titanic challenge. The solution includes comprehensive data preprocessing, feature engineering, exploratory data analysis (EDA), and modeling with LightGBM using stratified K-fold cross-validation.
this project develops a robust machine learning model to estimate house prices in the state.
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.
Bitcoin Sentiment Forecast is a Multimodal approach to Bitcoin price forecasting using NLP and Time Series Analysis
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