The Effect of Economic News on Gold Prices Analysis

[abhisheks008]

ML-Crate Repository (Proposing new issue)

🔴 Project Title : The Effect of Economic News on Gold Prices Analysis
🔴 Aim : The aim of this project is to analyze the gold prices based on the news articles given in the dataset.
🔴 Dataset : https://www.kaggle.com/datasets/fekihmea/the-effect-of-economic-news-on-gold-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. 😎

[avrk18]

Can I work on this under JWoC 2024?

[abhisheks008]

✅ 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.)

Mention the credentials please.
@avrk18

[hemant933]

Full name :Hemant chaudhary
GitHub Profile Link :github.com/hemant933
Participant ID (If not, then put NA) :
Approach for this Project : using 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.
What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) IWOC

[abhisheks008]

Assigned under IWOC @hemant933

[abhisheks008]

Unassigned as the open source event ended up.

[shivansh-2003]

Can You Please Assign this issue under SSOC. 2024 Season 3
Shivansh Mahajan
Github:- https://github.com/shivansh-2003
Participation ID:- NA
I will do EDA of the data set by various statistical methods like IQR , Study Distribution OF Feature and Correlation Matrix.
I would train the data in Various ML model to. arrive to the better Accuracy score. I would then feed the data for Feature engineering and then train it with different machine learning models KNN , Random forest , Decision Tree , SVM and Bossting Algorithms
. I am well versed with Machine Learning you can check out my linkedin :-https://www.linkedin.com/in/shivansh-mahajan-13227824a/ and Git repository .
can u assign me with this issue @abhisheks008
Participation Role:- SSOC Season 3

[abhisheks008]

Contributions will start from June 1, 2024. Till then please have some patience.

[fspzar123]

Hey,
Can You Please Assign this issue under SSOC 2024 Season 3
Filbert Shawn
Github:- https://github.com/fspzar123
I am pretty good with Neural Networks and CNN
Thanks.
Participation Role:- SSOC Season 3

[abhisheks008]

Hey, Can You Please Assign this issue under SSOC 2024 Season 3 Filbert Shawn Github:- https://github.com/fspzar123 I am pretty good with Neural Networks and CNN Thanks. Participation Role:- SSOC Season 3

Please share your approach in a detailed manner.

[fspzar123]

Step 1: Understand the Dataset
Dataset Description: Read through any accompanying documentation to understand what each column represents, the dataset's structure, and the data's source.
Column Details: Identify the key columns, such as date, gold prices, and any economic news variables (e.g., sentiment scores, and types of news events).
Step 2: Initial Data Exploration
Load the Data: Use a programming language like Python with libraries such as pandas to load the dataset.
Inspect the Data: Check the first few rows using df. head() and get a summary with df.info() and df. describe().
Check for Missing Values: Identify any missing values in the dataset using df.isnull().sum().
Step 3: Data Cleaning
Handle Missing Values: Decide how to handle missing data (e.g., imputation, removal).
Data Types: Ensure all columns have the correct data types (e.g., dates are in datetime format, numerical values are correctly formatted).
Step 4: Data Transformation
Feature Engineering: Create new features if necessary. For example, derive additional time-based features from the date column or calculate rolling averages of gold prices.
Sentiment Analysis: If the economic news data is in text form, you might need to perform sentiment analysis to quantify the sentiment.
Step 5: Exploratory Data Analysis (EDA)
Visualize Gold Prices: Plot the gold prices over time to identify trends or patterns.
Correlations: Examine the correlation between gold prices and economic news features.
Seasonality and Trends: Use time series analysis techniques to identify seasonality and trends in gold prices.
Step 6: Hypothesis Testing and Statistical Analysis
Hypothesis Formulation: Formulate hypotheses about the relationship between economic news and gold prices.
Statistical Tests: Conduct statistical tests to validate your hypotheses (e.g., t-tests, ANOVA).
Step 7: Modeling
Time Series Modeling: Use models like ARIMA, SARIMA, or Prophet to forecast gold prices.
Regression Analysis: Apply regression techniques to quantify the impact of economic news on gold prices.
Machine Learning Models: Consider using more complex models like LSTM or other machine learning algorithms if applicable.
Step 8: Interpretation and Insights
Model Evaluation: Evaluate the performance of your models using appropriate metrics (e.g., RMSE, MAE for regression models).
Insights: Draw insights from your analysis and models How does economic news affect gold prices? Are there specific types of news that have a stronger impact?
Step 9: Reporting
Visualizations: Create clear and informative visualizations to communicate your findings.
Summary: Write a report detailing your methodology, analysis, and key insights.

[abhisheks008]

Step 1: Understand the Dataset Dataset Description: Read through any accompanying documentation to understand what each column represents, the dataset's structure, and the data's source. Column Details: Identify the key columns, such as date, gold prices, and any economic news variables (e.g., sentiment scores, and types of news events). Step 2: Initial Data Exploration Load the Data: Use a programming language like Python with libraries such as pandas to load the dataset. Inspect the Data: Check the first few rows using df. head() and get a summary with df.info() and df. describe(). Check for Missing Values: Identify any missing values in the dataset using df.isnull().sum(). Step 3: Data Cleaning Handle Missing Values: Decide how to handle missing data (e.g., imputation, removal). Data Types: Ensure all columns have the correct data types (e.g., dates are in datetime format, numerical values are correctly formatted). Step 4: Data Transformation Feature Engineering: Create new features if necessary. For example, derive additional time-based features from the date column or calculate rolling averages of gold prices. Sentiment Analysis: If the economic news data is in text form, you might need to perform sentiment analysis to quantify the sentiment. Step 5: Exploratory Data Analysis (EDA) Visualize Gold Prices: Plot the gold prices over time to identify trends or patterns. Correlations: Examine the correlation between gold prices and economic news features. Seasonality and Trends: Use time series analysis techniques to identify seasonality and trends in gold prices. Step 6: Hypothesis Testing and Statistical Analysis Hypothesis Formulation: Formulate hypotheses about the relationship between economic news and gold prices. Statistical Tests: Conduct statistical tests to validate your hypotheses (e.g., t-tests, ANOVA). Step 7: Modeling Time Series Modeling: Use models like ARIMA, SARIMA, or Prophet to forecast gold prices. Regression Analysis: Apply regression techniques to quantify the impact of economic news on gold prices. Machine Learning Models: Consider using more complex models like LSTM or other machine learning algorithms if applicable. Step 8: Interpretation and Insights Model Evaluation: Evaluate the performance of your models using appropriate metrics (e.g., RMSE, MAE for regression models). Insights: Draw insights from your analysis and models How does economic news affect gold prices? Are there specific types of news that have a stronger impact? Step 9: Reporting Visualizations: Create clear and informative visualizations to communicate your findings. Summary: Write a report detailing your methodology, analysis, and key insights.

Implement 3-4 models for this project.

Assigned @fspzar123

[github-actions]

Hello @fspzar123! Your issue #507 has been closed. Thank you for your contribution!