This project performs an in-depth analysis of Kickstarter crowdfunding campaigns to identify trends, success factors, and funding patterns. Using Python, Pandas, Matplotlib, and Power BI, the analysis uncovers key insights into successful vs. failed campaigns, helping creators optimize their fundraising strategies.
- Analyzed 380,000+ Kickstarter campaigns across various categories, funding goals, and success rates.
- Identified top-performing categories, with Technology and Games projects raising the highest funds.
- Success rate trends: Campaigns with a well-defined goal, shorter duration, and strong social media presence had 30-40% higher success rates.
- Location impact: US-based projects secured 60%+ of total Kickstarter funding, with New York and California being top contributors.
- Visualization: Created interactive Power BI dashboards for real-time data exploration.
✔ Exploratory Data Analysis (EDA) using Python & Pandas
✔ Data Visualization with Matplotlib, Seaborn & Power BI
✔ Success Prediction based on campaign duration, goal amount, and backer engagement
✔ Time-Series Analysis of funding trends over the years
To replicate this analysis:
# Clone the repository
git clone https://github.com/saivivek55/Kickstarter-Platform-Analysis.git
cd Kickstarter-Platform-Analysis
# Install required libraries
pip install pandas matplotlib seaborn jupyter notebook
# Run Jupyter Notebook
jupyter notebook📊 Power BI Dashboard -
The Power BI dashboard provides interactive visualizations of Kickstarter trends, including:
Campaign success rates by category, country, and funding goal
Backer engagement trends & funding timelines
Predictive insights for campaign optimization
🛠️ Tech Stack
Python: Data analysis & visualization (Pandas, Matplotlib, Seaborn)
Power BI, Tableau: Interactive dashboards for funding insights
Jupyter Notebook: Exploratory Data Analysis (EDA)
📄 License This project is licensed under the Apache License 2.0 – see the LICENSE file for details.