The "Gold Price Prediction" project focuses on predicting the prices of gold using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, Random Forest Regressor, and others, this project provides a comprehensive solution for accurate price estimation.
The "Gold Price Prediction" project aims to develop a model that can accurately predict the prices of gold based on various factors. This prediction task is of great significance in the financial sector, enabling investors and traders to make informed decisions. By employing machine learning algorithms and a curated dataset, this project offers a valuable tool for estimating gold prices.
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Data Collection and Processing: The project involves collecting a dataset containing features related to gold prices, such as historical price data, economic indicators, and market sentiment. Using Pandas, the collected data is cleaned, preprocessed, and transformed to ensure it is suitable for analysis. The dataset is included in the repository for easy access.
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Data Visualization: The project utilizes data visualization techniques to gain insights into the dataset. Matplotlib and Seaborn are employed to create visualizations such as time series plots, correlation matrices, and distribution plots. These visualizations provide a deeper understanding of the relationships between features and help identify trends, patterns, and outliers.
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Train-Test Split: To evaluate the performance of the regression model, the project employs the train-test split technique. The dataset is divided into training and testing subsets, ensuring that the model is trained on a portion of the data and evaluated on unseen data. This allows for an accurate assessment of the model's predictive capabilities.
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Regression Model using Random Forest: The project utilizes the Random Forest Regressor algorithm to build the regression model. Random Forest is an ensemble learning method that combines multiple decision trees to make predictions. It is well-suited for capturing complex relationships and handling high-dimensional data. The Scikit-learn library provides an implementation of Random Forest Regressor that is utilized in this project.
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Model Evaluation: The project evaluates the performance of the regression model using evaluation metrics such as mean squared error (MSE) and mean absolute error (MAE). These metrics quantify the differences between the predicted and actual gold prices, providing insights into the model's accuracy and precision. Additionally, visualizations such as line plots are created to compare the predicted prices against the actual prices.
To run this project locally, follow these steps:
- Clone the repository:
gh repo clone MYoussef885/Gold_Price_Prediction - Install the required libraries:
If you're using Google Colab, you don't need to pip install. Just follow the importing the dependencies section. - Launch Google Colab:
https://colab.research.google.com/ - Open the
Gold_Price_Prediction.ipynbfile and run the notebook cells sequentially.
The "Gold Price Prediction" project provides a practical solution for estimating gold prices based on various factors. By leveraging data collection, preprocessing, visualization, Random Forest regression modeling, and model evaluation, this project offers a comprehensive approach to addressing the price prediction task. The project also includes a curated dataset to facilitate seamless exploration and experimentation.
This project is licensed under the MIT license. See the LICENSE file for more information.
This project is made possible by the contributions of the open-source community and the powerful libraries it provides, including NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, and Random Forest Regressor. I extend my gratitude to the developers and maintainers of these libraries for their valuable work. In addition, the mentor Siddhardan, visit his channel here : https://www.youtube.com/@Siddhardhan