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.

Hyper-tuned federated learning using flower framework.

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

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/

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

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

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

👩🏻‍🍳🍽️Restaurant Success Prediction using ML

Lending Club Data Analysis

One of the challenges faced by any IT company is about 30% of the candidates who accept the jobs offer do not join the company. This leads to huge loss of revenue and time as the companies initiate the recruitment process again to fill the workforce demand. This project builds a model can be used to predict the likelihood of a candidate joining …

📔 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.