📈 Indian Stock Market Analysis Dashboard

A Business Analytics project combining Python, yfinance (Real NSE Data), and Power BI to evaluate top Indian companies using historical stock prices, fundamental ratios, risk metrics, and news sentiment.

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🎯 Project Objective

Analyze the performance of 10 major NSE-listed companies and generate data-driven investment insights through an interactive Power BI dashboard.

Companies Analyzed

Company	NSE Ticker	Sector
Reliance Industries	RELIANCE.NS	Energy & Retail
TCS	TCS.NS	Information Technology
Infosys	INFY.NS	Information Technology
HDFC Bank	HDFCBANK.NS	Banking
ICICI Bank	ICICIBANK.NS	Banking
SBI	SBIN.NS	Banking
ITC	ITC.NS	FMCG
Bharti Airtel	BHARTIARTL.NS	Telecom
Larsen & Toubro	LT.NS	Infrastructure
Hindustan Unilever	HINDUNILVR.NS	FMCG

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🛠 Technologies Used

Category	Tools
Data Collection	yfinance (Real NSE/BSE Data)
Data Processing	Python 3, Pandas, NumPy
Visualization	Matplotlib, Power BI
Data Storage	CSV Files

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📊 Dashboard Preview

Investment Score Ranking

Sentiment Score Comparison

PE Ratio vs Dividend Yield

Normalized Stock Price Trends

3-Year Returns Comparison

Volatility (Risk) Comparison

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📂 Project Structure

Indian_Stock_Market_Analysis/
│
├── dashboard.pbix                 ← Power BI Dashboard
│
├── stock_prices.csv               ← Real NSE price data (3,650+ records)
├── company_fundamentals.csv       ← PE, Dividend, ROE, Market Cap
├── news_sentiment.csv             ← Sentiment scores (500+ records)
├── investment_ranking.csv         ← Final ranking output
│
├── charts/
│   ├── investment_score.png
│   ├── sentiment_score.png
│   ├── pe_vs_dividend.png
│   ├── price_trends.png
│   ├── returns_comparison.png
│   └── volatility.png
│
├── generate_dataset.py            ← Fetches real data from NSE via yfinance
├── analysis.py                    ← Core analytical logic + investment scoring
├── charts.py                      ← Chart generation
│
├── requirements.txt
└── README.md

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📊 Dashboard Pages

1️⃣ Executive Summary

• Average Closing Price
• Total Market Capitalization
• Average PE Ratio
• Overall Sentiment Score
• Normalized Stock Price Trend

2️⃣ Fundamental Analysis

• PE Ratio Comparison
• Dividend Yield Analysis
• Market Cap Distribution
• ROE (Return on Equity) Comparison
• Sector-wise Performance

3️⃣ Sentiment Analysis

• Average News Sentiment Score per company
• Positive vs Negative vs Neutral Distribution
• Overall Market Sentiment Indicator

4️⃣ Investment Recommendation

Investment Score Formula:

Investment Score =
  (Sentiment Score × 30)
+ (ROE % × 0.3)              ← Profitability
+ (Dividend Yield × 5)       ← Income Generation
+ (1 / PE_Ratio × 10)        ← Valuation (Lower PE = Better)
- (Volatility % × 0.2)       ← Risk Penalty

Companies ranked on: Sentiment + Profitability + Valuation + Income − Risk

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📈 Dataset Details

Dataset	Columns	Records
stock_prices.csv	Date, Company, Ticker, Open, High, Low, Close, Volume	3,650+
company_fundamentals.csv	Company, Sector, Market Cap, PE Ratio, Dividend Yield, EPS, ROE, 52W High/Low	10
news_sentiment.csv	Date, Company, Headline, Sentiment Score, Sentiment	500+

Note: Stock price data is fetched in real-time from NSE via yfinance. Last 3 years of data (2022–2025).

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🚀 How To Run

Step 1 — Clone the repository

git clone https://github.com/shekhu24-bit/Indian-Stock-Market-Analysis.git
cd Indian-Stock-Market-Analysis

Step 2 — Install dependencies

pip install -r requirements.txt

Step 3 — Fetch real NSE data

python generate_dataset.py

Step 4 — Run analysis

python analysis.py

Step 5 — Generate charts

python charts.py

Step 6 — Open Power BI Dashboard

Open dashboard.pbix → Click Home → Refresh

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🔍 Key Insights

• HDFC Bank ranked #1 with highest investment score due to strong fundamentals and positive sentiment
• Bharti Airtel leads in news sentiment positivity
• Reliance Industries offers the best valuation (lowest PE Ratio)
• Banking sector shows the most balanced PE and Dividend combination
• ~61.8% of all news coverage was positive across companies
• TCS and Infosys show lowest volatility (suitable for risk-averse investors)

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💼 Business Applications

• 📌 Investment Research & Portfolio Evaluation
• 📌 Financial Analytics Reporting
• 📌 Sector Performance Benchmarking
• 📌 MBA Business Analytics Capstone Projects
• 📌 Power BI Dashboard Development Practice

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📚 Skills Demonstrated

• Real financial data extraction (yfinance / NSE)
• Data Cleaning and Feature Engineering
• Financial Ratio Analysis (PE, ROE, Dividend Yield)
• Volatility & Risk Measurement (Annualized Std Dev)
• Sentiment Analysis (Scoring Framework)
• Max Drawdown Calculation
• Matplotlib Chart Generation (6 chart types)
• Power BI KPI Design & Dashboard Development
• Business Decision-Making from Data

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🏆 Conclusion

This project demonstrates how Python-based analytics and Power BI dashboards can transform real NSE market data into actionable investment insights. By integrating price history, company fundamentals, risk metrics, and market sentiment into a composite Investment Score, the dashboard provides a structured framework for comparing stocks across sectors.

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👤 Author

Shekhar Tanwar MBA — Finance & Business Analytics | Maharshi Dayanand University, Rohtak