[Mention The Feature]: Adding a tutorial page on Gold Price Prediction in project page website.

[J-B-Mugundh]

Is your feature request related to a problem? Please describe.

Currently, we dont have gold price predictor which will be useful for general people and investors

Describe the solution you'd like.

We aim to create a machine learning model for gold price prediction using historical financial data and feature engineering techniques. This project will forecast future gold prices based on various economic indicators such as currency exchange rates, interest rates, and global stock indices. The focus will be on data preprocessing, feature extraction, and building a robust regression model for accurate predictions.

Describe alternatives you've considered.

No response

Additional context.

Tasks: Set Up Development Environment Install required libraries: pandas, numpy, matplotlib, scikit-learn, statsmodels, seaborn. Set up a Python virtual environment for the project. Explore and Load Dataset Download historical gold price data and relevant financial indicators. Load and organize the dataset. Perform exploratory data analysis (EDA) on the dataset (e.g., trends, correlations between variables). Preprocess and Engineer Features Handle missing values and outliers in the dataset. Normalize or scale the feature values as required. Create new features based on the dataset (e.g., moving averages, rolling statistics, or lagged variables). Split data into training and test sets. Build Regression Model Train different regression models such as Random Forest Regressor, Linear Regression, and XGBoost. Use cross-validation to select the best model and tune hyperparameters. Compile the final model using the appropriate loss function and optimizer (e.g., mean_squared_error and Adam for neural networks). Train the Model Train the model using the preprocessed data. Use a validation set to monitor the model performance during training. Save the best-performing model using checkpoints. Model Evaluation and Optimization Evaluate the model on the test set using appropriate regression metrics (e.g., R-squared, MAE, RMSE). Visualize the actual vs. predicted gold prices. Experiment with additional feature engineering, regularization, and model fine-tuning. Model Deployment Save the trained model in a suitable format (e.g., .pkl or .h5). Create a deployment script using Flask or FastAPI to serve the model for real-time gold price prediction. Implement a web-based interface for users to input economic data and receive gold price predictions.

Show us the magic with screenshots

No response

Checklist

• [X] I have checked the Existing Issues
• [X] I have read the Contributing Guidelines
• [X] I want to work on this issue. (optional)

[vivekvardhan2810]

@J-B-Mugundh please change the title of the issue.

[J-B-Mugundh]

@J-B-Mugundh please change the title of the issue.

Updated bro!

[vivekvardhan2810]

@J-B-Mugundh for now I am assigning this issue.

Make sure to complete this.

And also please do contribute to other projects also.

[J-B-Mugundh]

@J-B-Mugundh for now I am assigning this issue.

Make sure to complete this.

And also please do contribute to other projects also.

Yeah sure bro. I'm actually contributing to this repo recently only. I've contributed to 12 other projects in the past. I'll surely do