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stock-market-analysis

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Stock market analysis using ML

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# 📊 Stock Market Analysis & Prediction

---

## 🚀 Overview

This project performs a complete analysis of stock market data and builds machine learning models to understand and predict price movements.

It includes:

- Data Cleaning & Preprocessing  
- Exploratory Data Analysis (EDA)  
- Statistical Insights  
- Feature Engineering  
- Machine Learning Models  
- Interactive Visualizations

🎯 Goal:
> Understand stock behavior and build models to predict price trends.

---

## 🎯 Key Features

- 📥 Data Pipeline – Load, clean, and prepare stock data  
- 📊 EDA – Identify trends, volatility, and patterns  
- 📈 Technical Analysis – Moving averages & returns  
- 🤖 Machine Learning – Regression & classification models  
- 📉 Visualization – Graphs for trend and insights

---
```
## 📂 Project Structure

stock-market-analysis/
│
├── data/
│ ├── raw_data.csv
│ ├── cleaned_data.csv
│ ├── feature_data.csv
│
├── notebooks/
│ ├── data_cleaning.ipynb
│ ├── feature_engineering.ipynb
│ ├── visualization.ipynb
│ ├── model_building.ipynb
│
├── outputs/
│ ├── charts/
│ ├── reports/
│
├── models/
│ ├── regression_model.pkl
│ ├── classification_model.pkl
│
├── README.md

```

## 🔍 Workflow

### 1️⃣ Data Cleaning
- Handle missing values  
- Convert data types  
- Remove inconsistencies

---

### 2️⃣ Feature Engineering
- Daily Returns  
- Moving Averages (MA20, MA50)  
- Volatility

---

### 3️⃣ Exploratory Data Analysis (EDA)
- Price trends over time  
- Volume analysis  
- Correlation heatmaps

---

### 4️⃣ Visualization
- Line plots (Price trends)  
- Moving average graphs  
- Distribution plots

---

### 5️⃣ Machine Learning

#### 📌 Regression
- Linear Regression → Predict stock price

#### 📌 Classification
- Logistic Regression / Random Forest  
→ Predict price movement (Up/Down)

---

## 📊 Sample Visualizations

---

## ▶️ How to Run

### Step 1: Install dependencies

```bash
pip install pandas numpy matplotlib seaborn scikit-learn

python data_cleaning.py
python feature_engineering.py
python visualization.py
python model.py
```
📈 Model Evaluation
Mean Squared Error (MSE)
R² Score
Accuracy (for classification)

💡 Key Insights
Stock prices show strong trends over time
Moving averages smooth volatility
Returns highlight daily fluctuations
Volume impacts price movement

⚠️ Troubleshooting
❌ Data not loading

Ensure dataset is inside:
```
data/
```

❌ Module not found
```
pip install required-library
```

📚 References
NSE / Stock Market Data
Pandas, NumPy, Scikit-learn documentation

👨‍💻 Author
Shashank Singh Jalal

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