alo7lika
commented
2 days ago Proposed Solution The proposed solution for the Gold Price Prediction project involves a comprehensive workflow that includes data acquisition, preprocessing, feature selection, model training, and evaluation. Initially, historical gold price data will be sourced from reliable financial databases or APIs. The dataset will be enhanced with relevant economic indicators that may impact gold prices.
Data preprocessing steps will include cleaning the dataset, dealing with missing values through interpolation or imputation, and normalizing the numerical features to improve model training. Exploratory data analysis (EDA) will help visualize trends, seasonal patterns, and correlations between variables, guiding feature selection for the predictive model.
For the model, various algorithms will be considered, including:
Linear Regression: For establishing a baseline predictive performance. Random Forest: To capture nonlinear relationships and interactions between features. LSTM Networks: For leveraging the sequential nature of time series data and capturing temporal dependencies. Once the models are trained, performance will be evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Additionally, backtesting strategies will be implemented to assess how the model would have performed in real market scenarios.
ADD LABELS GSSOC EXT 24 AND hacktoberfest ASSIGN ME THIS WORK
Gold Price Prediction is a project that involves forecasting future gold prices using historical data and various machine learning or time series analysis techniques. The goal of this project is to build a model that can accurately predict the price of gold, which is influenced by a range of economic, financial, and geopolitical factors.
Please assign to me under GSSOC24.