KasiMuthuveerappan/LoanTap-LogisticRegression

📔 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.

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mounishvatti/FedCustom

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.

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EagerAI/kerastuneR

R interface to Keras Tuner

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DHwass/XGBoost-on-Home-Data-KAGGLE

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.

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Mirfaisal72/Deep-Learning-Specialization-Coursera-

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

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amshrbo/nsfw-detection

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.

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nvnovitskiy/2021-AI-ML

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

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pyhtonman0101/Rossmann-Sales-Prediction-

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

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Sanchariii/Rice_crop_field_classification_using_satellite_data

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jahnvisikligar/Automobile-Fuel-Efficiency-Predictions

This repository is a reflection of final web deployment of ML models using Flask and cloud service Heroku. The models deployed are - Linear Regression, Decision Tree, Random Forest and Support Vector Machines.

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805karansaini/AQI_Pre_v2

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/

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Deeksha-Shet/Project-in-Python

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

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