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https://github.com/philippe2023/stock-price-prediction-ml

Unsuvervised Machine Learning Project using Prophet and NeuralProphet for time-series forecasting.
https://github.com/philippe2023/stock-price-prediction-ml

jupyterlab neuralprophet prophet python3 streamlit

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Unsuvervised Machine Learning Project using Prophet and NeuralProphet for time-series forecasting.

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README 

        

# **Stock Price Prediction and Analysis App**



## **Overview**



This project is a **Stock Price Prediction and Analysis App** built using **Streamlit** for the frontend, **Prophet** and **NeuralProphet** for time-series forecasting, **Yahoo Finance** for fetching stock data, and **Google News** for fetching the latest market-related news.



The app allows users to visualize stock data, predict future stock prices, and retrieve recent news headlines about selected stocks. It also provides a dashboard where users can compare multiple stocks and track their returns over a chosen time frame.



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## **Features**



### 1. **Stock Data Visualization**

   - Allows users to select any stock from the S&P 100 list and view its historical stock data (Open, Close, High, Low, and Adjusted Close prices).

   - Visualizes stock data with interactive time-series graphs using Plotly.



### 2. **Stock Price Prediction**

   - Uses **Prophet** and **NeuralProphet** models for stock price prediction.

   - Users can select a stock and predict future prices for up to 4 years.

   - Displays forecast components such as trends and seasonality.



### 3. **Google News Stock Search**

   - Fetches and displays the latest news headlines related to the selected stock from Google News.

   - Users can keep track of the latest news and make better-informed stock predictions and decisions.



### 4. **Stock Dashboard**

   - A dashboard that allows users to select multiple stocks and compare their performance over a specific time period.

   - Displays the cumulative returns of the selected stocks in an easy-to-interpret line chart.



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## **Technologies Used**



- **Frontend**: 

   - **Streamlit**: For creating the interactive web app interface.

   - **Plotly**: For generating dynamic and interactive visualizations.



- **Backend**: 

   - **yfinance**: To fetch real-time and historical stock data from Yahoo Finance.

   - **Prophet**: A time-series forecasting model developed by Facebook, for predicting stock prices.

   - **NeuralProphet**: An advanced neural network-based time-series forecasting model.

   - **Google News**: For fetching the latest stock-related news headlines.



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## **Project Structure**



```

.

├── app

│   ├── tableau_dashboard.png        # Image for displaying Tableau dashboard

├── main.py                          # Main file to run the Streamlit app

├── google_news.py                   # File for fetching Google News

├── dashboard.py                     # Stock dashboard logic (merged into main.py)

└── README.md                        # This readme file

```



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## **Usage**



- **Home**: Displays information about the app's features.

- **Visualization**: Allows users to select a stock and visualize its historical data with interactive charts.

- **Prediction**: Choose a stock and predict future prices using either Prophet or NeuralProphet models.

- **Google News**: Search for the latest news headlines related to the selected stock.

- **Dashboard**: Compare the performance of multiple stocks over a selected time range and view their cumulative returns.



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## **Contributors**



- **Alessia Urzì** - Data Analyst

- **Sasha Crowe** - Data Analyst

- **Jean Philippe Auguste** - Data Analyst