Depression Analysis Project

Overview

This project aims to analyze depression-related data using Python, SQL, Excel, and Power BI to uncover insights and visualize patterns. It combines data cleaning, exploratory analysis, and interactive dashboards to support data-driven decisions.

Project Components

• Python (Jupyter Notebook): Data cleaning, exploratory data analysis (EDA), and visualizations using Pandas, NumPy, Matplotlib, and Seaborn.
• SQL: Querying datasets to extract meaningful insights.
• Excel: Data preprocessing and organizing datasets for analysis.
• Power BI: Interactive dashboard showing trends, patterns, and key metrics related to depression.

Dataset

The project uses survey and clinical datasets related to depression indicators.

Objective

• Identify key patterns in depression-related data.
• Provide actionable insights through visualizations.
• Demonstrate proficiency in data analysis tools and techniques.

Outcome

• Cleaned and processed datasets.
• Detailed EDA and visualizations.
• SQL queries for insights.
• Power BI interactive dashboards.

Tools & Technologies

• Python, Jupyter Notebook
• SQL
• Excel
• Power BI
• Pandas, NumPy, Matplotlib, Seaborn