Python data mining and exploratory analysis project covering salary expectations, automobile data, and diabetes risk factors using Jupyter Notebook and Python.
This project applies data mining and exploratory data analysis techniques across multiple datasets to uncover patterns, relationships, and useful insights.
Analyzed salary expectations based on years of work experience.
Explored automobile data to understand pricing, performance, and related factors.
Explored health related data to identify patterns and risk factors associated with diabetes.
These are the individual notebooks included under this Data Mining project.
Raw datasets often contain hidden patterns that are difficult to identify through manual review. Without proper analysis, users may miss important relationships, trends, and risk indicators.
I used Python and Jupyter Notebook to perform exploratory data analysis and data mining across different datasets. The goal was to understand the data structure, identify meaningful relationships, and generate insights from salary, automobile, and diabetes related datasets.
This project demonstrates how Python can support structured analysis, pattern discovery, risk factor review, and insight generation before building dashboards, reports, or predictive analytics workflows.
The complete Jupyter Notebooks are available on GitHub.
I can help with data analysis, SQL reporting, Power BI dashboards, Excel dashboards, and automation workflows.
