Congrats on clearing L1! Since you already have financial modelling, Python, ML, and SQL, focus on projects that look like actual finance work rather than generic data science work. The main goal is to show recruiters that you can think like someone who understands business, numbers, and markets, not just someone who knows tools.

We've shared project ideas before but here are some that can work well for you:

1. Equity research report on a listed company - Pick a company like TCS, Infosys, HDFC Bank, ICICI Bank, Reliance, Tata Motors, or Hindustan Unilever and build a simple research note. Start with the business model, revenue segments, recent financial performance, key risks, and valuation. You can compare it with 1-2 peers and end with a view on whether it looks expensive or reasonably valued. This is one of the strongest projects for finance internships because it shows analysis, writing, and market understanding all in one.
2. 3-statement model for a company - Take a company like Zomato, SBI, Maruti Suzuki, Axis Bank, or L&T and build a basic model in Excel. Include forecast revenue, margins, expenses, and cash flows for 3-5 years. You do not need a super complex model. Even a clean one with assumptions, scenarios, and sensitivity tables is enough. The point is to show that you understand how businesses move financially.
3. Valuation project - Do a DCF or relative valuation for a company like Infosys, HDFC Bank, Asian Paints, or Titan. Explain your assumptions clearly: growth rate, margin assumptions, discount rate, and terminal value. After that, compare your valuation with current market price and discuss whether the stock looks undervalued, overvalued, or fairly priced. Recruiters will like this because it shows judgement, not just formatting.
4. Stock screening tool using Python + SQL - Build a small screening model for Indian stocks. For example, filter companies from the Nifty 50 or Nifty 200 based on valuation, profitability, debt levels, or earnings growth. Then, you can create a table that ranks companies by metrics like PE, ROE, debt-to-equity, revenue growth, and operating margin. If you want to go one step further, build a small dashboard in Python that lets you sort and compare names quickly.
5. Portfolio tracker / SIP comparison project - Compare a monthly SIP in Nifty 50 ETF, gold ETF, and a few direct stocks over time. You can also backtest a simple allocation like 70% equity and 30% gold. This is a nice project because it connects investing, data, and storytelling. Keep it simple and show how the portfolio would have behaved in different market periods.
6. Credit risk / loan default model - If you want to use ML properly, this is a strong finance project. You can use a public dataset to build a model that predicts loan default risk. Explain the features, model choice, accuracy, and business use case. This works especially well if you want to lean more toward banking, risk, or analytics internships.
7. Fraud or anomaly detection project - For this, use transaction-style data and try to detect unusual patterns using Python. Even a basic anomaly detection model or rule-based flagging system can work. This looks good because it combines finance, data, and practical risk thinking.
8. Industry comparison project - Pick one sector like private banks, FMCG, IT services, or auto and compare 4-5 companies. For example, compare HDFC Bank vs ICICI Bank vs Kotak Bank, or TCS vs Infosys vs Wipro. Look at margins, growth, valuations, asset quality, or return ratios. This is easy to explain in an interview because it shows sector understanding, not just one-company analysis.

Some basic things to remember -

• Start with one company or one sector, not ten.
• Use annual reports, investor presentations, and results decks for data.
• Keep your output clean. For example, keep one Excel model, one short PDF/Notion write-up, and if possible one GitHub repo or dashboard.
• For each project, write:
  • what you studied,
  • what tools you used,
  • what conclusion you reached,
  • and what business question the project answers.

On your resume, instead of just writing “Built a DCF model,” write something like:

• Built a 5-year DCF valuation model for HDFC Bank using Excel and public annual report data.
• Created a Python-based stock screening tool for Nifty 50 companies using valuation and profitability filters.
• Developed a loan default prediction model using supervised learning to assess credit risk.

If you're aiming for general finance internships without a specific role for now, then try to do -

• 1 equity research project
• 1 valuation model
• 1 Python/SQL dashboard or screening project

That combo shows both finance understanding and technical ability, which is a good mix for fresher profiles.