Add Project — Predicting Commodity Prices Using Deep Learning

[keshripritesh]

🧾 Issue: Add Project — Predicting Commodity Prices Using Deep Learning

📘 Description

This project focuses on predicting commodity prices using deep learning models such as MLP, CNN, and RNN.
It demonstrates how macroeconomic factors like stock indices, interest rates, and GDP growth affect commodity price movements.

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🎯 Objectives

• Generate a synthetic dataset representing market and economic factors
• Perform Exploratory Data Analysis (EDA) on the dataset
• Implement and compare MLP, CNN, and RNN models for prediction
• Evaluate model accuracy and visualize performance

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🧠 Tech Stack

• Python
• Libraries: pandas, numpy, matplotlib, seaborn, tensorflow/keras

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📈 Expected Outcomes

• A clean and analyzed dataset (commodity_data.csv)
• Model comparison (MLP vs CNN vs RNN)
• Visualized results showing prediction performance
• Insights into which model performs best for price prediction

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