The dataset, generated by ChatGPT, consists of five tables: Employees, Performance, Turnover, Costs, and Training. The objective of the analysis is to identify factors influencing salary costs, employee performance, turnover types, and workforce efficiency, while exploring potential relationships and inefficiencies across departments.

The dataset used for this analysis was downloaded from Kaggle.com and contains data on various predisposing factors associated with heart disease. The objective of this project was to explore the relationships between multiple factors (such as age, smoking, alcohol consumption, family history, and blood pressure) and their contribution to the risk of developing heart disease.

The housing dataset is a synthetic dataset created by ChatGPT to simulate a real estate market. It contains information about various properties, including their physical attributes, location, and market metrics. The data is structured to be relational, making it ideal for statistical modeling, visualization, and exploratory data analysis.

Generated by ChatGPT, the analysis is based on data that represents a realistic simulation of patient care and management in a healthcare facility, covering admissions, billing, medications, and appointments.

The Retail Demographics report provides insights into customer behavior, preferences, and satisfaction within a retail environment.

I prompted ChatGPT to generate datasets containing three tables: Borrowers, Loans, and Market Risks. The goal was to create a realistic data structure for analyzing lending and market risk factors, enabling me to assess relationships between borrower profiles, loan characteristics, and market conditions.

The dataset was generated by ChatGPT and simulates a real world solar company that sells solar for home and productive use. The dataset consists of 4 tables, clients, sales, payments and aftersales. I utilized joins to analyze data across these different tables effectively.

I generated the call center dataset using ChatGPT. It consists 6 tables each representing a month between January and June. Each table has 8 columns and 500 rows. Analysis was comparative in nature and focused on how the metrics varied across the 6 months.

The dataset, generated using ChatGPT, simulates a real-world employee performance scenario. It comprises 3 tables: employees, customers, and sales all linked through the Employee ID key. The analysis enabled me to practice and refine my skills in using joins with R, allowing for efficient querying and exploration of the datasets.

This analysis pertains to a dataset containing 5000 synthetic records of various financial transactions across multiple categories. The analysis provides insights of digital payment behaviors and trends.

The analysis offers insights into how daily technology usage, including social media and screen time, impacts mental health. It captures various behavioral patterns and their correlations with mental health indicators.

Analysis of of the following categories, tumor type, location, size, grade, patient age and gender and how they relate to each other

The analysis was conducted using a dataset containing the following nutrient columns, food type, food category, protein, carbs, fats, calories, fiber and saturated fat. I conducted an analysis using R to explore nutrient composition by food types and categories as well as the relationship to each other. saturated fa