app.py

import streamlit as st
st.set_page_config(
    page_title="Stock Prediction Model",
    page_icon="📈",
)
    
st.markdown(
    """# 📈 **Stock Prediction Model**
### **Predicting Stocks with ML**

## 🏗️ **How It's Built**
Stock Prediction Model is built with these core frameworks and modules:
- **Streamlit** - To create the web app UI and interactivity 
- **LSTM** - To build the Long Short Term Memory model
- **Plotly** - To create interactive financial charts
- **Keras** - To Train the model layer by layer.
- **Tensorflow** - To link the layers with the model.

### The app workflow is:

1. User feeds the CSV file.
2. Historical data is fetched with CSV file.
3. LSTM model is trained on the data 
4. Model makes multi-day price forecasts
5. Results are plotted with Plotly
## 🎯 **Key Features**
- **Financial charts** - Interactive historical and forecast charts
- **LSTM forecasting** - Make statistically robust predictions
- **Backtesting** - Evaluate model performance
- **Responsive design** - Works on all devices
## 🚀 **Getting Started**
### **Local Installation**
1. Clone the repo
```bash
git clone https://github.com/Rhythm-shahane/Trend-Pulse.git
```
2. Install requirements
```bash
pip install -r requirements.txt
```
3. Change directory
```bash
cd app.py
```
4. Run the app
```bash
streamlit run app.py
```
## 📈 **Future Roadmap**
Some potential features for future releases:
- **More advanced forecasting models like Transformer**
- **Quantitative trading strategies**
- **Portfolio optimization and tracking**
- **Additional fundamental data**
- **User account system**
- **Real Time Data Fetching**
## **⚖️ Disclaimer**
**This is not financial advice! Use forecast data to inform your own investment research. No guarantee of trading performance.**
"""
)