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https://github.com/datasqlsantosh/gold-price-prediction-portfolio

Welcome to my Gold Price Prediction Portfolio! This project focuses on predicting the future prices of gold using machine learning, particularly the Random Forest algorithm. The prediction model is implemented in a web application using Python Flask for backend development and HTML for the frontend.
https://github.com/datasqlsantosh/gold-price-prediction-portfolio

boot css exce flask html jupyter-notebook matplotlib pycharm pycharm-ide python sklearn sql

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Welcome to my Gold Price Prediction Portfolio! This project focuses on predicting the future prices of gold using machine learning, particularly the Random Forest algorithm. The prediction model is implemented in a web application using Python Flask for backend development and HTML for the frontend.

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README 

        
# Gold Price Prediction Portfolio



Welcome to my Gold Price Prediction Portfolio! This project focuses on predicting the future prices of gold using machine learning, particularly the Random Forest algorithm. The prediction model is implemented in a web application using Python Flask for backend development and HTML for the frontend.



## Project Overview



- **Data Source:** Historical gold price data sourced from reputable financial datasets.



- **Prediction Model:** Utilizing the Random Forest algorithm for its ability to handle non-linear relationships and capture complex patterns in time-series data.



- **Web Application:** The prediction model is integrated into a web-based interface using Python Flask for backend development and HTML for the frontend.



## Folder Structure



- **`/notebooks`:** Jupyter notebooks containing the Python code for data preprocessing, model training, and evaluation.

- **`/flask-app`:** Flask application files including Python scripts and HTML templates.

- **`/data`:** Folder containing the historical gold price dataset.



## Model Training and Evaluation



1. **Data Preprocessing:**

   - Jupyter notebooks in `/notebooks` detail the steps taken to preprocess the historical gold price data.



2. **Model Training:**

   - Implementation of the Random Forest algorithm for training the prediction model.



3. **Evaluation Metrics:**

   - Assessment of model performance using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).



## Web Application



1. **Flask Backend:**

   - Flask scripts in `/flask-app` that define routes, handle predictions, and serve the HTML templates.



2. **HTML Frontend:**

   - HTML templates for user interaction and displaying predictions.



## How to Use



1. **Run the Flask App:**

   - Navigate to the `/flask-app` directory and run the Flask application to start the web server.



2. **Access the Web Interface:**

   - Open a web browser and visit the specified localhost address to access the gold price prediction interface.



3. **Enter Data for Prediction:**

   - Input relevant features into the web interface to receive a predicted gold price.



## Challenges Faced



Document any challenges encountered during the development of the web application and model, along with the solutions applied.



## Future Work



Suggest potential enhancements or additional features for the gold price prediction application. Encourage collaboration and further exploration in the field of predictive modeling.



## Contact



Feel free to reach out for questions, collaborations, or discussions related to this project.



**Note:** If you find this portfolio interesting, consider giving it a star! ⭐️