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README.md

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@@ -16,6 +16,7 @@ Explore, visualize and model SpaceX Falcon 9 launch data using a complete data s
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1. Data wrangling
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2. Exploratory data analysis
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3. Implementing and evaluating ML models
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4. Summary and conclusions
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## Sample visualizations
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## What I learned throughout the project
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(TBD)
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- Structure and workflow of a data science pipeline.
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- Data collection and data wrangling methods.
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- Using Pandas for data manipulation and cleaning.
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- Using Matplotlib for exploratory data analysis and visualization.
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- Applying statistical analysis and machine learning to uncover hidden patterns.
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- Creating well-documented Github repositories with Jupyter notebooks.
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## What I learned throughout the project
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## Next steps/future work
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- Expand the dataset to help the models generalize better.
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- Incorporate new features to reveal factors that haven't been considered yet.
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- Integrate additional data sources like weather conditions.
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- Structure and workflow of a data science pipeline
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- Data collection and data wrangling methods
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- Using Pandas for data manipulation and cleaning
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- Using Matplotlib for exploratory data analysis and visualization
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- Applying statistical analysis and machine learning to uncover hidden patterns
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- Creating well-documented Github repositories with Jupyter notebooks
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## Status
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✅ Educational project - completed
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🗓️ Date: 24.05.2025
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✅ Educational project - completed at 24.05.2025.
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🗃️ In progress: creating the documentation and putting it all together
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✅ Creating the documentation and putting it all together - completed at 31.05.2025.

SpaceX-Falcon9-IBM-DS.ipynb

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