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

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

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

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

SDK for Inquire model test database API, by Sevan

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'.

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.

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.

📚 A curated list of papers & technical articles on AI Quality & Safety

Model Tester is a utility for automatically testing model classes.

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

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

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.

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

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.

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.

Credit Card Fraud Detection Using Machine Learning

Automated Machine Learning Framework for predicting drinking water quality

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.