Finding interesting trends/behaviors while analyzing and perform clustering on the dataset. ( ⭐️ Star us on GitHub — it helps! )

I build this notebook for quick overview on Airplane crashes since 1908 dataset. For your convenience, please view it in kaggle.

I encourage you to fork this kernel/GitHub repo, play with the code and learn new trends and patterns. The EDA & Visualizations are included to allow developers to dive into analysis of dataset. Good luck!

This project requires Python 2.7 and the following Python libraries installed:

• NumPy
• matplotlib
• seaborn
• scikit-learn
• Pandas
• Random

You will also need to have the software installed to run and execute an iPython Notebook

An ipython notebook is used for data preprocessing, trend analysis and data visualiation. All core scripts are in file .ipynb" folder. All input data are in input folder and the detailed description of the data can be found in Kaggle.

• $ sudo apt-get install python3-pip python3-tk
• $ pip3 install jupyter
• Add ~/.local/bin to your path variable (in your .bashrc/.zshrc, see https://stackoverflow.com/questions/7360889/adding-a-directory-to-path-in-ubuntu)

In your project directory, start the notebook with:

• PYTHON=python3 jupyter notebook
• Click on New and start coding/exploring.

1. 15 valuable Visualizations.

2. Text Clustering with K-Means

3. Text Clustering with DBSCAN

4. Summary & Location WordCloud

5. Location Visualization on World Map

1. Setting Up The Environment
2. Data Cleaning And Pre-Processing
3. Data Visualisation
4. Text Clustering With K-Means
5. Text Clustering With DBSCAN
6. Important Conclusions & Learnings
7. Acknowledgments

The final Crash Location Visualization on World Map can be viewed as :

The K-Means Clustering can be viewed as :

The final clustering (DBSCAN) and Common Terms per Cluster in K-Means for dataset is present in the output folder as a .png file.

Rohit Kumar Singh (IIT Bombay)

Feel free to send us feedback on file an issue. Feature requests are always welcome. If you wish to contribute, please take a quick look at the kaggle.

Programming is all about "borrowing" code, because knife sharpens knife. Nonetheless, I want to give credit, where credit is due :

1. https://www.kaggle.com/garydee/who-not-to-fly-with

2. https://www.kaggle.com/ruslankl/airplane-crashes-data-visualization

3. https://www.kaggle.com/pprajapati/how-not-to-die-in-an-airplane-crash-an-eda

4. https://www.kaggle.com/elifnkaraca/text-clustering-using-kmeans-for-airplane-crashes

Written with StackEdit.