R interface to Keras Tuner

Nudity, violence and drugs detection using nudeNet for nudity, for violence and drugs detection I hyper-tuned mobilenet model on my own collected dataset, the final results is a python flask API that takes an image or a set of images, will return a score on how much it's suitable for work.

Predicting train delays using historical SNCF data.

This project implements hyper-tuned federated learning using the Flower framework, combining FedAvg, Logistic Regression, and a 2-layer CNN. It enables decentralized model training across devices, optimizing performance while ensuring data privacy and improving accuracy on both simple and complex tasks.

In this project, XGBoost is applied to forecast real estate prices using the Boston Housing Dataset. The primary aim is to create an effective predictive model, assess its accuracy through metrics like Mean Absolute Error (MAE), and refine its performance by tuning hyperparameters with HYPEROPT.

AQI Predictor V2 use multiple Supervised Machine Learning with Hyper tuning. ML algorithms used Linear Regressor, Lasso Regressor, Decision Tree Regressor, Random Forest Regressor, XGboost Regressor. The Model deployed on web and can predict AQI visit https://aqipredictor.up.railway.app/

This repository contains work that has been done on various concepts of Python like linear regression, logistic regression, decision tree, Random forest, KNN, and K-means algorithm

This repository consists of Time series forecasting projects

A Supervised Machine Learning project in which I forecast upcoming six weeks of sales in advance using different ML Model For Rossmann Stores

Выполненные лабораторные работы по курсу: "Искусственный интеллект и машинное обучение".

This repository contains the Assignments and Projects of Deep Learning Specialization Course on Coursera.

In this project, we aim to predict weather conditions using machine learning models.

👩🏻‍🍳🍽️Restaurant Success Prediction using ML

Hypertuning- classification

A machine learning project that analyzes supply chain logistics data to identify patterns associated with disruption risk. The analysis combines exploratory data analysis, predictive modeling, and visualization to highlight key operational drivers of delays.

📔 This repository delves into Logistic Regression for loan approval prediction at LoanTap. It covers data preprocessing, model development, evaluation metrics, and strategic business recommendations. Explore model optimization techniques such as confusion matrix, precision, recall, Roc curve and F1 score to effectively mitigate default risks.

Lending Club Data Analysis

ML Projects and Practices