https://github.com/sayande01/gold_price_prediction_using_time_series_forecasting

Forecasting gold prices with machine learning, employing Linear Regression and Naive models. Analyzing historical data to predict future prices, aiding decision-making in financial markets.
https://github.com/sayande01/gold_price_prediction_using_time_series_forecasting

linear-regression timeseries-forecasting

Last synced: 2 months ago
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Forecasting gold prices with machine learning, employing Linear Regression and Naive models. Analyzing historical data to predict future prices, aiding decision-making in financial markets.

• Host: GitHub
• URL: https://github.com/sayande01/gold_price_prediction_using_time_series_forecasting
• Owner: sayande01
• Created: 2024-05-12T15:42:17.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-12T15:44:56.000Z (about 1 year ago)
• Last Synced: 2025-02-13T02:39:04.949Z (4 months ago)
• Topics: linear-regression, timeseries-forecasting
• Language: Jupyter Notebook
• Homepage:
• Size: 929 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Title: Gold Price Prediction

Description:

This project delves into forecasting gold prices through the utilization of machine learning techniques, specifically employing Linear Regression and Naive Models. By analyzing historical gold price data, spanning a considerable timeframe, the aim is to develop accurate predictive models that provide insights into future price trends. Additionally, the Mean Absolute Percentage Error (MAPE) metric is employed to evaluate the performance of the Naive Forecast Model.

Objective:

1. Utilize historical gold price data to train a Linear Regression Model for forecasting future gold prices.

2. Implement a Naive Model as a benchmark for comparison against the Linear Regression Model.

3. Develop scripts to calculate the MAPE metric for evaluating the accuracy of the Naive Forecast Model.

4. Preprocess the dataset to ensure compatibility with the selected forecasting models.

5. Train the Linear Regression Model using appropriate features and target variables.

6. Evaluate the performance of the Linear Regression Model using relevant evaluation metrics.

7. Construct the Naive Forecast Model based on simple statistical techniques.

8. Generate predictions using both the Linear Regression and Naive Forecast Models for a future time period.

9. Calculate the MAPE for the Naive Forecast Model to assess its predictive accuracy.

10. Present the findings, including model performances and MAPE results, in a clear and comprehensible manner.