model-testing

Hameem1/Step-Detection-using-Machine-Learning

Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.

vaitybharati/Assignment-04-Simple-Linear-Regression-2

Assignment-04-Simple-Linear-Regression-2. Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.

Jithsaavvy/Sentiment-analysis-from-MLOps-paradigm

This project promulgates an automated end-to-end ML pipeline that trains a biLSTM network for sentiment analysis, experiment tracking, benchmarking by model testing and evaluation, model transitioning to production followed by deployment into cloud instance via CI/CD

koushikvikram/multimodal-image-retrieval

📝🔍🖼️ A deep learning application for retrieving images by searching with text.

SevanSSP/inquire-modeltest-sdk

SDK for Inquire model test database API, by Sevan

neonexus/fixted

Simple DB Fixtures for Sails.js v1 (fake data for testing).

brian-kipkoech-tanui/sagemaker-ML-workflow

Image Classifiers are used in the field of computer vision to identify the content of an image and it is used across a broad variety of industries, from advanced technologies like autonomous vehicles and augmented reality, to eCommerce platforms, and even in diagnostic medicine.

SajeebChakraborty/Artificial_Intelligence_Lab

Daymenion/mask-rcnn-training-with-coco-like-dataset-in-colab

mask rcnn training with coco-like dataset. You can use for trainnig your own coco.json (polygon) dataset in Google Colab.

gershonc/mlflow_showcase

Showcase of MLflow capabilities

vaitybharati/Assignment-04-Simple-Linear-Regression-1

Assignment-04-Simple-Linear-Regression-1. Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regression.

vaitybharati/P36.-Supervised-ML---Decision-Tree---C5.0-Entropy-Iris-Flower-

Supervised-ML-Decision-Tree-C5.0-Entropy-Iris-Flower-Using Entropy Criteria - Classification Model. Import Libraries and data set, EDA, Apply Label Encoding, Model Building - Building/Training Decision Tree Classifier (C5.0) using Entropy Criteria. Validation and Testing Decision Tree Classifier (C5.0) Model

ChaitanyaC22/Udacity-AWS-MLE-ND-Project2-Build-a-ML-Workflow-For-Scones-Unlimited-On-Amazon-SageMaker

The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.

Daymenion/Yolact-plus-training-with-custom-dataset-in-Google-Colab

Yolact++ training with custom dataset (coco.json format) in Google Colab

j-andrews7/STRprofiler

A python package, command-line tool, and Shiny application to compare short tandem repeat (STR) profiles.

joutvhu/model-tester

Model Tester is a utility for automatically testing model classes.

priyansh21112002/CIA-Country-Description

The aim is to gain insights into similarity between countries and regions of the world by experimenting with different cluster amounts.

priyansh21112002/Melbourne-House-Prices-Prediction

It involves prediction of House prices in Melbourne using Machine Learning. It involved concepts of Data extraction, Data Preprocessing, Data Visualisation, Data Aggregation, Model Creation and Testing. It comes under Supervised Learning.

rushikeshw791/Simple-linear-regression-2

Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.

SoufiyaneOuali/Employment-Analysis-Exploring-Economic-Factors-using-R-Language

The main objective is to understand the relationship between diffeent variable and testeing many Regression model and choosing the efficent one them predincting new points

swapnita-pandey/Credit-Card-Fraud-Detection

Credit Card Fraud Detection Using Machine Learning

mikethwolff/ML-Pipeline-NYC-Short-Term-Rental-Prices

This repository lists one of my projects and findings as part of my Machine Learning DevOps Engineer Nanodegree.

PatilSukanya/Assignment-04.-Simple-Linear-Regression-Q1

Used libraries and functions as follows:

qetdr/xAutoML-Project1

Automated Machine Learning Framework for predicting drinking water quality

shwetapardhi/Assignment-04-Simple-Linear-Regression-1

Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regressi

udaisharma99/Non-Linear-Harmonic-Oscillator

This project investigated the behavior of a nonlinear harmonic oscillator solver and explained the observed loss of accuracy under certain conditions. It extended a linear harmonic oscillator solver to a nonlinear counterpart using the model 'Method of Manufactured Solutions'.

iamluirio/lstm-sentiment-analysis

Goal is to identify an appropriate pipeline to obtain good performance on the dataset, using LSTM neural network approaches.

raghav-arora-1998/Kaggle-Classfication-Competition

Conducted predictive classification modelling and performance evaluation for several models used to predict the political affiliation (target variable) of random U.S citizens.

SayamAlt/Credit-Card-Approval-Prediction

Successfully developed a machine learning model which can accurately predict up to 100% accuracy whether a credit card application of a given applicant would be approved or not, based on several demographic features such as applicant age, total income, marital status, total years of work experience, etc.

SayamAlt/Superstore-Sales-Prediction

Successfully established a machine learning model that can accurately predict the sales of a superstore based on various features such as quantity, profit, discount, postal code, etc. The features are mainly associated with order details and customer demographics.