This mutual fund analysis project focuses on identifying top 30 schemes with high return and low risk using Python, Excel, and Power BI.
🛠️ Tools Used: Python (Pandas, Sklearn), Excel, Power BI
📁 Dataset: More Than 2500 Mutual Fund Schemes (Top 30 Filtered)
To identify top-performing, low-risk mutual fund schemes using data-driven techniques and present insights through a dynamic, professional Power BI dashboard.
I started by importing and exploring a dataset of over 2500 mutual fund schemes.
🔗 Mutual_Funds.csv
🔗 Python Script
- Removed unnecessary columns
- Handled missing values
- Standardized numeric formats (returns, expense ratios)
- Statistical summaries using Pandas: mean, median, mode, min, max, std deviation
- Analyzed fund distributions across return rates, risk levels, and fund age
- Used
MinMaxScalerfromsklearn.preprocessingto normalize numeric fields - Compared returns and expense ratios on a common scale
Custom scoring formula based on:
- High 3-Year Returns
- Low Expense Ratio
- Moderate Fund Age
- Consistent 1-Year Return > 0
Extracted the Top 30 Mutual Funds with best return-low risk balance
🔗 Top 30 Mutual Funds (Excel)
After processing the data using Python and Excel, I built an interactive dashboard in Power BI.
🔗 Power BI Dashboard File (.pbix)
🔗 Dashboard Preview Image
- Filter by Fund Type, Category, Sub-category, AMC Name, Risk Level, Fund Rating
- 💼 Total Investment by Fund Type: AUM across Equity, Debt, Hybrid, etc.
- 🔁 SIP vs Lumpsum Summary Cards: Monthly SIP trends and minimum lump sum amounts
- 🧾 Expense Ratio Comparison: By Investment Strategy and Sub-Category
- 📈 3-Year Returns (Donut Chart): Category-wise long-term returns
- 🏆 Top Performing AMCs: Average return and AUM
- 👤 Fund Manager AUM Comparison: Largest fund managers by assets
- 🧠 Insight Cards: Auto-generated insights with simple explanations
| Insight Category | Summary |
|---|---|
| 💼 Investment Trends | Equity Funds lead with ₹1.35M Cr total size |
| 👤 Fund Manager | Vivek Sharma manages highest AUM: ₹7.3M Cr |
| 📉 Cost vs Return | Index Funds have lowest expense ratio: 0.26% |
| 🏦 Best Return (1Y) | Bank of India Mutual Fund: 14.4% |
| 🔄 SIP vs Lumpsum | Avg. SIP: ₹528.50/month, Lumpsum Min: ₹3.05K |
| ⏳ 3-Year Returns | Equity Funds: 37.84%, Hybrid: 14.25% |
Through this project and dashboard, you can clearly see the power of investing in mutual funds when guided by data-driven insights.
By analyzing returns, expense ratios, risk levels, and fund manager performance, I’ve shown how even basic financial knowledge, supported by visual tools, can help improve financial decisions.
💡 This dashboard isn't just about numbers—it's about empowering people to make smarter, low-risk investments and take control of their financial future.
Early and informed mutual fund investment leads to long-term wealth creation.
By combining:
- Python for filtering,
- Excel for cleaning,
- Power BI for storytelling,
I created a tool that helps both beginners and experts make data-driven, low-risk, high-reward decisions.
| Tool | Purpose |
|---|---|
| Python | Data cleaning, scoring, filtering top 30 funds |
| Excel | Formatting, validation, supporting data |
| Power BI | Interactive dashboard and visual storytelling |
| File | Description |
|---|---|
| Mutual_Funds.csv | Main dataset |
| top_30_mutual_funds.xlsx | Final top 30 filtered mutual funds |
| Mutual Fund Dashboard.pbix | Power BI dashboard |
| Mutual Fund Dashboard.png | Dashboard image preview |
✅ Feel free to fork, explore, and contribute!
Thank you for exploring my Mutual Fund Analysis project!
I’m always open to suggestions, improvements, or collaboration ideas.
📩 Feel free to connect with me on LinkedIn
📧 Or drop an email: [email protected]
Your feedback helps me grow and build better data-driven solutions. Let’s connect and discuss ideas!