model-training-and-evaluation
There are 83 repositories under model-training-and-evaluation topic.
ksm26/Finetuning-Large-Language-Models
Unlock the potential of finetuning Large Language Models (LLMs). Learn from industry expert, and discover when to apply finetuning, data preparation techniques, and how to effectively train and evaluate LLMs.
SayamAlt/Company-Bankruptcy-Prediction
Successfully developed a machine learning model which can accurately predict whether a firm will become bankrupt or not, depending on various features such as net value growth rate, borrowing dependency, cash/total assets, etc.
Aayush711/Federated-Learning-Project
This repository contains a project showcasing Federated Learning using the EMNIST dataset. Federated Learning is a privacy-preserving machine learning approach that allows a model to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them.
bethropolis/myia
An Image classifier model and builder for binary image classification.
ehtisham-sadiq/Cirrhosis-Patient-Outcome-Prediction
Multi-class classification model to predict outcomes of cirrhosis patients using machine learning
ialexmp/Machine-Learning
This Machine Learning repository encompasses theory, hands-on labs, and two projects. Project 1 analyzes customer segmentation for marketing using clustering, while Project 2 applies supervised classification in marketing and sales.
KishorAlagappan/house-price-prediction-app
🏡 Empower property market decisions with a machine learning model predicting house prices using the Boston Housing dataset. 💸🏠💹
nafisalawalidris/Employee-Attrition-Control
The Employee Attrition Control project uses data analysis and predictive modeling to understand and address employee turnover. It provides insights and recommendations to reduce attrition and improve employee satisfaction and retention.
owaisahmadlone/deeplearning-code-raw-
This is my public repository with mostly experimental code I write while exploring or creating various deep(or not so deep) neural networks.
AllanOtieno254/House-Sales-Price-Prediction-2
This repository contains code for predicting house sales prices using machine learning models. It includes data preprocessing, model training, evaluation, and prediction on test data.
Alqama-svg/public_streamlit_ml_web_app
I deployed this bi-disease prediction model in python using Machine Laerning. Deployed this ML model as a web application on cloud streamlit. To see the model please visit
bhaveshGhanchi/FakeNewsPrediction
Fake News Prediction Model
JLeigh101/deep-learning-challenge
NU Bootcamp Module 21
patilkiran123/Marketing-Campaign-Analysis
Aditya Marketing is facing low response rates to their marketing campaigns. The objective of this project is to conduct thorough Exploratory Data Analysis, extracting insights through univariate and bivariate analysis. And Recommended strategic customer targeting tactics.
qtle3/multiple-linear-regression
A Python implementation of multiple linear regression to predict the profit of startups based on their spending in R&D, Administration, Marketing, and the state they operate in.
SayamAlt/Black-Friday-Sales-Prediction
Successfully established a machine learning regression model which can estimate the gross Black Friday sales for a particular customer, based on a distinct set of related and meaningful features, to a fair level of accuracy.
SayamAlt/Walmart-Weekly-Sales-Prediction-using-Machine-Learning
Successfully established a supervised machine learning model which can accurately forecast the total weekly sales amount obtained at Walmart stores, based on a certain set of features provided as input.
sergio11/headline_generation_lstm_transformers
Explore advanced neural networks for crafting captivating headlines! Compare LSTM 🔄 and Transformer 🔀 models through interactive notebooks 📓 and easy-to-use wrapper classes 🛠️. Ideal for content creators and data enthusiasts aiming to automate and enhance headline generation ✨.
sohbatSandhu/california-housing-price-prediction
California Housing Prediction - Full Machine Learning Project with deployment configurations and utilizing cloud databases for storage
srimallipudi/Movie-Recommendation-Service-using-Apache-Spark
This project implements a movie recommendation service with Apache Spark using collaborative filtering.
ssloth1/MNIST-Boat-Classification
ISeeYou is a model designed for binary image classification using the Boat-MNIST dataset. The dataset provides a simple hands-on benchmark to test small neural networks on the task of distinguishing between images containing watercraft and other images.
TheVinh-Ha-1710/Diabetes-Predictive-Model
This project aims to train a predictive model to diagnose diabetes on women patients.
Vivek02Sharma/Diabetes-Prediction-Project
Diabetes Prediction
yupeeee/WAH
a library so simple you will learn Within An Hour
jibbs1703/Loan-Approval-Prediction
This repository contains a Loan Approval Prediction Model. The model predicts the likelihood of loan approval based on applicant data. The model deployment is done using FastAPI to allow applicant data to be entered in order to obtain an approval prediction.
qtle3/random-forest-regressor
This project implements **Random Forest Regression** to predict the salary of an employee based on their position level. Using a dataset that includes position levels and corresponding salaries, this project demonstrates how an ensemble method like Random Forest can improve prediction accuracy by averaging multiple decision trees.
SayamAlt/Bank-Customer-Churn-Prediction-using-PySpark
Successfully established a machine learning model using PySpark which can accurately classify whether a bank customer will churn or not up to an accuracy of more than 86% on the test set.
SayamAlt/Cats-Dogs-and-Snakes-Image-Classification-using-CNNs
This project focuses on accurately classifying images of cats, dogs, and snakes using Convolutional Neural Networks (CNNs) in PyTorch. A custom CNN model was initially designed and trained, achieving strong classification performance. Additionally, state-of-the-art (SOTA) pre-trained image classification models such as AlexNet, ResNet50, and VGG16.
SayamAlt/Client-Term-Deposit-Subscription-Prediction-using-ANN
Successfully established an ANN model which can accurately predict whether a customer will subscribe to term deposits provided by the bank or not.
SayamAlt/Drug-Classification-using-ANN
This project implements a drug classification model using Artificial Neural Networks (ANN) built with PyTorch. The model classifies drugs into different categories based on patient features such as age, blood pressure, cholesterol levels, and more.
SayamAlt/Flowers-Classification-using-CNN
Successfully developed a CNN model which can precisely classify flowers upto an accuracy of more than 80% on the test set.
SayamAlt/Green-Energy-Production-Forecasting-using-LSTM
This project utilizes Long Short-Term Memory (LSTM) networks in PyTorch to forecast green energy production based on historical data. The model is designed to predict energy output from renewable sources like solar and wind by capturing time-dependent patterns in the data.
SayamAlt/Lung-Cancer-Detection-using-CNNs
This project focuses on detecting lung cancer from medical images using Convolutional Neural Networks (CNNs). It includes custom-built CNNs and fine-tuned pretrained models such as ResNet101, DenseNet121, AlexNet, and VGG16 to improve detection accuracy.
SayamAlt/Steel-Energy-Consumption-Prediction-using-PySpark
Successfully established a machine learning model using PySpark which can precisely predict the energy consumption of the steel industry, up to an r2 score of approximately 99.5%.
SayamAlt/Weather-Image-Recognition-using-CNNs
This project focuses on classifying distinct weather images using Convolutional Neural Networks (CNN) built from scratch and fine-tuning various state-of-the-art (SOTA) pre-trained models like AlexNet, ResNet50, VGG16, and MobileNet v3 Large. The models are trained and evaluated on a custom weather dataset, leveraging PyTorch for deep learning.
SayamAlt/Wind-Solar-Electricity-Production-Forecasting-using-LSTM
This project forecasts the total wind and solar electricity production using Long Short-Term Memory (LSTM) neural networks implemented in PyTorch. The model leverages time-series data to predict future renewable energy generation, helping to optimize energy management and grid stability.