Gold Price Analysis and Prediction

[abhisheks008]

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Gold Price Analysis and Prediction
🔴 Aim : The aim of this project is to predict the future Gold price and also make an analysis of the dataset.
🔴 Dataset : https://www.kaggle.com/datasets/gvyshnya/gold-future-prices
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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✅ To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Participant ID (If not, then put NA) :
• Approach for this Project :
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[Bayyana-kiran]

Full name : SAI KIRAN B L S
GitHub Profile Link : https://github.com/Bayyana-kiran
Participant ID (If not, then put NA) : NA
Approach for this Project : The project commences with rigorous data preprocessing, addressing missing values, outliers, and sorting the 'date' column chronologically. Exploratory Data Analysis (EDA) visualizes gold price trends, distribution, and variable relationships. Model implementation employs Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, with evaluation metrics guiding model selection and hyperparameter tuning. The chosen algorithm is utilized to predict future gold prices, visually compared to actual prices for transparent insights.
What is your participant role : IWOC2024

CAN U ASSIGN ME FOR THIS ISSUE?

[abhisheks008]

Complete your previous issue first @Bayyana-kiran

[Bayyana-kiran]

Can u assign me @abhisheks008 ?

[abhisheks008]

Issue assigned to you @Bayyana-kiran

[Bayyana-kiran]

Hey @abhisheks008 Created a PR kindly check!

Sorry for the Delay