I asked this question: i want to create a modern data science project where i will analyze oil price and make predictions give me a coherent plan please

You gave me this answer: Certainly! Here’s a coherent plan to create a modern data science project for analyzing oil prices and making predictions:

1. Problem Statement: Define the specific problem you want to address. For example, you can focus on predicting future oil prices based on historical data and external factors.

2. Data Collection: Gather relevant datasets that contain historical oil prices along with various factors that potentially influence oil prices, such as economic indicators, geopolitical events, weather data, etc. Some reliable sources include government agencies, financial platforms, and market analysis websites.

3. Data Preprocessing: Clean and preprocess the collected data to handle missing values, outliers, and format inconsistencies. Conduct data exploration and perform statistical analysis to gain insights into the data distribution and detect potential patterns or correlations.

4. Feature Engineering: Extract meaningful features from the collected data that can help improve predictions. Consider creating lagged features by incorporating trends or previous price movements. Additionally, you may generate new features by combining existing ones or using domain knowledge.

5. Model Selection: Choose appropriate machine learning algorithms that can handle time-series analysis and regression tasks. You can consider models such as ARIMA, LSTM, Random Forests, or Gradient Boosting models. Experiment with various models and compare their performance using suitable evaluation metrics.

6. Model Training: Split the dataset into training and testing sets. Train the selected models using the training data, tuning hyperparameters if necessary. Implement techniques like cross-validation to ensure the robustness of the models.

7. Model Evaluation: Evaluate the trained models using the testing dataset. Measure their performance using suitable metrics like mean absolute error (MAE), root mean squared error (RMSE), or R-squared. Additionally, you can visualize the predicted vs. actual prices to assess accuracy.

8. Model Deployment: Deploy the trained model to make real-time predictions for future oil prices. This can involve creating a user-friendly interface or API that allows users to input relevant data points and obtain price predictions.

9. Monitoring and Maintenance: Continuously monitor the performance and accuracy of the deployed model. Update the model periodically by retraining it with new data to adapt to market changes or trends.

10. Communication and Visualization: Present your findings, insights, and predictions in a coherent and understandable manner. Create visualizations (e.g., line charts, scatter plots) to showcase historical price trends, correlations with other factors, and model predictions.

11. Documentation and Collaboration: Document your project, including data sources, preprocessing steps, model selection and hyperparameters, evaluation results, and insights gained. Share your project with others and collaborate with fellow data scientists for feedback and improvements.

Remember to follow ethical guidelines, respect data privacy, and ensure your project adheres to legal regulations.

now. Can you elaborate them like 1-a, 1-b, 2-a, 2-b, 2-c with smaller steps and give me a coherent plan?