Research Data Analytics
Research data analytics involves using data analysis techniques to extract meaningful insights and draw conclusions from datasets, ultimately supporting researchers in reaching informed decisions and advancing knowledge.
We offer comprehensive Data Analysis Services for supporting researchers and helping them to turn their research data into insights.
Some of the projects we have handled include:
Project 1: The Use of Machine Learning in the Creation of Healthcare Diabetic Level Detection Equipment: Ethical Considerations
Project 2: Spotting Fake News: A Data Science Approach
Project 3: Machine Learning for the Identification of DDoS Attack Anomalies in Network Function Virtualization Environments
Project 4: A Framework for Sustainable Transportation Planning Systems Based on GIS and Artificial Intelligence
Project 5: Utilizing Data from the US Electric Vehicle Population, Predictive Analysis of Electric Vehicle Ranges
Project 6: Financial Risk Management and Credit Risk Prediction: The goal of this study is to identify important determinants of the degree of credit risk by analyzing a data set of personal financial information, such as age, income, credit rating, loan size, and employment status.
Project 7: Fitness Tracker Analysis: Making individualized health and fitness projections is intended to help people who want to improve their physical well-being make better decisions. The goal is to create predictive models for calorie consumption by analyzing fitness data to find trends and connections.
Project 8: Statistical Analysis and Predictive Modeling for IBM Employee’s Monthly Income: This project uses the IBM employees' dataset to do exploratory study on the monthly salary levels and profile traits of the employees. Analyzing sample vs population monthly income statistics, doing hypothesis testing (t-tests, ANOVA, Chi-Square), and developing and assessing predictive models (simple and multiple regression) are the goals.
Project 9: Analysis of E-Commerce Shipment Data: The project's goals are to comprehend the elements influencing customer happiness and to find patterns and insights for bettering operations.
Project 10: Exploring Caloric Density and Predict Calories of Starbucks Beverages: Analyzing the nutritional value of Starbucks beverages with an emphasis on determining the variables affecting calorie counts is the goal of this project. The goals consist of In order to comprehend the caloric diversity among various beverages, evaluate the nutritional information of Starbucks beverages and examine the calories, fat content, sugars, and caffeine levels.
