https://github.com/r-mahesh45/gold-price-prediction-using-machine-learning
This project uses Random Forest and ARIMA models to predict daily gold prices with 97% accuracy. By cleaning and analyzing historical data (2016–2021), we created a model that provides actionable insights. Deployed with Streamlit, it offers real-time forecasting for investors and traders to stay ahead of the market.
https://github.com/r-mahesh45/gold-price-prediction-using-machine-learning
arima arima-forecasting eda pandas-python sarimax-model stationery
Last synced: 7 months ago
JSON representation
This project uses Random Forest and ARIMA models to predict daily gold prices with 97% accuracy. By cleaning and analyzing historical data (2016–2021), we created a model that provides actionable insights. Deployed with Streamlit, it offers real-time forecasting for investors and traders to stay ahead of the market.
- Host: GitHub
- URL: https://github.com/r-mahesh45/gold-price-prediction-using-machine-learning
- Owner: R-Mahesh45
- Created: 2024-04-11T09:50:11.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-04T11:14:40.000Z (9 months ago)
- Last Synced: 2025-01-30T07:16:12.992Z (9 months ago)
- Topics: arima, arima-forecasting, eda, pandas-python, sarimax-model, stationery
- Language: Jupyter Notebook
- Homepage:
- Size: 16 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Gold Price Prediction Model
This project involves the development of a **gold price prediction model** using a combination of **Random Forest** and **ARIMA models**. The model was trained on historical gold price data and is capable of forecasting day-wise gold prices with **97% accuracy**, significantly improving from an initial accuracy of **76%**.
## Project Overview
We developed a **gold price prediction model** using time series analysis, focusing on improving forecasting accuracy. The model leverages advanced techniques like **Random Forest** and **ARIMA** to predict gold prices on a daily basis. The project aims to provide accurate predictions for investors, traders, and analysts in the precious metals market.
## Key Features
- **Improved Prediction Accuracy**: Achieved an impressive **97% accuracy** after applying Random Forest and ARIMA models, up from **76%** in initial experiments.
- **Data Preprocessing**: Performed extensive data cleaning, manipulation, and exploratory data analysis (EDA) on time series data spanning from **2016 to 2021**.
- **Forecasting**: Deployed the prediction model using **Streamlit**, making it accessible for real-time day-wise gold price forecasting.
## Technical Details
- **Data Source**: Time series data (2016-2021) on historical gold prices.
- **Tools Used**:
- **Python** for data processing, modeling, and analysis
- **Random Forest** for machine learning-based predictions
- **ARIMA** for time series forecasting
- **Streamlit** for model deployment
## Steps Taken
1. **Data Cleaning**: Cleaned the raw data to handle missing values, outliers, and inconsistencies.
2. **Exploratory Data Analysis (EDA)**: Visualized and understood the data trends, patterns, and seasonal variations.
3. **Modeling**:
- Built **Random Forest** model for price prediction.
- Applied **ARIMA** model for capturing time-dependent patterns.
4. **Model Improvement**: Enhanced accuracy from 76% to 97% through hyperparameter tuning and model refinement.
5. **Deployment**: Used **Streamlit** to deploy the final model, making it accessible for real-time predictions.
## Installation
To run the project locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/your-username/gold-price-prediction.git
cd gold-price-prediction
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Run the Streamlit app to interact with the model:
```bash
streamlit run app.py
```
## Project Structure
```
gold-price-prediction/
│
├── data/ # Contains raw and processed data
│ └── gold_prices.csv # Gold price data from 2016 to 2021
│
├── models/ # Contains model scripts
│ ├── random_forest.py # Random Forest model code
│ ├── arima_model.py # ARIMA model code
│
├── app.py # Streamlit app for deployment
├── requirements.txt # Project dependencies
└── README.md # Project documentation
```
## Future Improvements
- Incorporate additional features such as geopolitical events, market trends, and currency fluctuations.
- Experiment with other models like LSTM for deep learning-based time series forecasting.