Gold Price Prediction Model

This project predicts gold prices using a Random Forest Regressor. It also provides a simple web interface for visualization using Streamlit.

Features

• Data Analysis: Provides an exploratory data analysis (EDA) including data description, missing value identification, and correlation matrix.
• Prediction Model: Implements a machine learning model to predict gold prices using a Random Forest Regressor.
• Web App: An interactive web interface for displaying predictions and model performance.

Technologies Used

• Python
• Numpy: Numerical operations
• Pandas: Data manipulation and analysis
• Matplotlib: Plotting and visualization
• Seaborn: Statistical data visualization
• Scikit-learn: Machine learning algorithms (RandomForestRegressor, train_test_split, r2_score)
• Streamlit: Web app framework
• **PIL (Python Imaging Library)**Pillow: Image handling in Streamlit for loading images

Installation

1. Clone the repository:

   git clone https://github.com/FahithKRM/ML_Gold-Price-Prediction.git

   cd gold-price-prediction

2. Create and activate a virtual environment:

   python -m venv venv

   # On macOS/Linux :
   source venv/bin/activate

    # On Windows : 
   venv\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

   

4. Run the Streamlit app:

   streamlit run app.py

   

Dataset

The dataset used for this project contains gold price data. You can replace the gold_price_data.csv file with your dataset if needed. Dataset Link : https://www.kaggle.com/datasets/altruistdelhite04/gold-price-data

Usage

• The app allows you to visualize the dataset and check the model's performance.
• You can upload your dataset or modify the code to accommodate new features for gold price prediction.

Model

The model used is a Random Forest Regressor, and the R2 score for model performance is calculated.

License

This project is licensed under the MIT License - see the LICENSE file for details.