D<ee>p learning [dev library]

Spotify Music Classifier with Machine Learning Using Spotify API

Our project employs machine learning to pinpoint phishing URLs with 97.4% accuracy, leveraging HTTPS and website traffic as critical indicators. Insights into features like AnchorURL enhance cybersecurity strategies, showcasing the power of AI in combating online threats.

A lightweight gradient boosted decision tree package.

India is one of the countries with the highest air pollution country. Generally, air pollution is assessed by PM value or air quality index value. For my further analysis, I have selected PM-2.5 value to determine the air quality prediction and the India-Bangalore region. Also, the data was collected through web scraping with the help of Beautif…

The study focuses on modeling and predicting H5N1 bird flu outbreaks in the United States at the county level, utilizing diverse statistical techniques and machine learning models.

This project leverages machine learning to forecast currency exchange rates to help optimize expenses in the face of fluctuating currencies.

Used Linear Regression, Decision Tree, Random Forest and XGBoost, AdaBoost, LightGBM and CatBoost ML Algorithm to Predict Bangalore housing price.

Used XGBoost Gradient Boosting Decision Tree Supervised ML Algorithm

In this project i am trying to use NLP, ML concepts on Amazon reviews using various ML based model like XGBoost, Decision tree classifier and random forest

Logical Rhythm is annual Machine learning competition hosted on Kaggle under Avishkar (Official Technical Fest of MNNIT)

Python code for Machine Learning Algorithms

Salary Prediction API using Flask predicts salaries for freshers joining organizations based on factors like past experience, company switches, courses completed, and academic marks. This Flask-based API allows users to input their details and receive a salary prediction. With no user interface, it's designed for integration into other applications

We have used our skill of machine learning along with our passion for cricket to predict the performance of players in the upcoming matches using ML Algorithms like random-forest and XG Boost

Applying boosting techniques such as GradientBoostingClassifier, GradientBoostingRegressor (eXtreme Gradient Boosting (XGBClassifier and XGBRegressor functions). This repo contains class examples and the project.

The Food Price Estimation project focuses on providing estimates of food prices to capture local price fluctuations in regions where people are vulnerable to localized price surges. The project utilizes a machine-learning algorithm designed to predict ongoing subnational price surveys, demonstrating accuracy comparable to direct price measurements.

A curated list of gradient boosting research papers with implementations.

Python code for common Machine Learning Algorithms

Project uses ML models (XGBoost, Regression, RandomForest, Neural Networks) to predict bank customer churn. XGBoost led with 86.73% accuracy. Utilized Tableau for visualization, hosted online via Docker, Neon, Flask.

This is XgBoost based Model Which takes inputs from user and gives review from inputs