Crowdfunding Data Analysis

Project Overview

This project analyzes a dataset of 1,000 crowdfunding campaigns to uncover market trends and identify factors contributing to the success or failure of campaigns. Using Excel, the project applies various analytical techniques including conditional formatting, pivot tables, statistical analysis, and goal-based success evaluation.

Files

• Crowdfunding_Data.xlsx: Contains the dataset of crowdfunding projects.
• Analysis_Report.docx: A report summarizing key findings and insights.
• README.md: This file.

Tasks and Methodology

1. Conditional Formatting:

   • Applied conditional formatting to visualize campaign outcomes (successful, failed, canceled, live).
   • Visualized percent funded using a color gradient.

2. Column Creation:

   • Created new columns for: Percent Funded, Average Donation, Category, Sub-Category, Date Created Conversion, and Date Ended Conversion.

3. Pivot Tables and Charts:

   • Created pivot tables and stacked-column charts for campaign outcomes by category and sub-category.
   • Generated line charts to show outcomes based on launch date.

4. Crowdfunding Goal Analysis:

   • Analyzed success rates based on crowdfunding goal ranges.
   • Created a line chart to illustrate the relationship between goal amount and success rates.

5. Statistical Analysis:

   • Evaluated summary statistics (mean, median, min, max, variance, and standard deviation) for backers of successful and unsuccessful campaigns.
   • Assessed variability and patterns in campaign performance.

Key Findings

• Analysis revealed key factors affecting campaign success, including funding goals and average donation amounts.
• Variability in backer numbers was higher in unsuccessful campaigns, indicating inconsistent support.

Limitations

• Dataset limitations include its small size and lack of detailed demographic data.
• Further analysis could explore the role of external factors such as social media engagement or geographic trends.

Future Improvements

• Additional visualizations like heatmaps for country-based trends.
• Incorporate machine learning techniques to predict campaign outcomes.

Tools Used

• Microsoft Excel

Contributors

• Sakina Jaffri - Data analysis, visualization, and report creation.