Gold Price Forecasting App: Python & Streamlit SolutionGold Price Forecasting App: Python & Streamlit Solution
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Akshat Tripathi
• Feb 19
🥇 Gold Price Prediction – Machine Learning Web Application (Python | Streamlit)
I developed a Gold Price Prediction system that forecasts future gold prices using historical market data and machine learning algorithms. This project helps investors and analysts make data-driven decisions by identifying trends and patterns in gold price movements.
🔹 Project Features
📈 Predicts gold prices based on historical financial data
🤖 Machine learning model built using scikit-learn
📊 Data preprocessing, feature engineering, and model evaluation
📉 Performance metrics such as R² Score and Mean Squared Error
🌐 Interactive web application built using Streamlit
🔹 Technology Stack
Python – Core implementation
scikit-learn – Regression modeling
Matplotlib / Visualization tools – Trend analysis
Streamlit – Web application deployment
machinelearningdatasciencedatasciencemlscikit-learnData ScienceMatplotlib
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Tristan's avatar
Tristan Alejandro
• Feb 23
Curious — how did you handle regime shifts and non-stationarity in the gold price series? Did you use rolling retraining or walk-forward validation to avoid overfitting
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Akshat Tripathi
• Feb 24
Yes I actually used rolling retraining to handle the regime shifts and non stationary prices
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