This repository contains a comprehensive data analysis of the Boonsong Lekagul Waterways dataset, leveraging the Altair Python visualization library. The analysis focuses on understanding water quality and environmental factors through interactive and insightful visualizations.
The primary objectives of this analysis are:
- Perform exploratory data analysis (EDA) to uncover initial patterns and anomalies.
- Apply feature engineering techniques to enhance data quality and prepare it for visualization.
- Develop insightful visualizations that highlight key environmental insights.
- Data cleaning and preprocessing
- Identification of trends, correlations, and potential outliers
- Preliminary insights generation
- Creation and transformation of features for enhanced analysis
- Handling missing values and data imputation
- Scaling, normalization, or categorization of data features as needed
- Detailed visualizations using Altair to represent data effectively
- Interactive charts highlighting significant environmental patterns
- Summary of key findings and actionable insights
Visualizations are dynamically generated and thus not included by default. To view all visuals:
-
Clone the repository:
git clone https://github.com/azizzoaib786/data-analysis-with-altair-python.git
-
Install necessary dependencies:
pip install -r requirements.txt
-
Run the notebook:
- Open
data-analysis-altair.ipynbin Jupyter Notebook or JupyterLab. - Execute all cells (
Run All).
- Open
- Python (3.7 or higher recommended)
- Altair
- pandas
- Jupyter Notebook/JupyterLab
- Dataset used: Boonsong Lekagul Waterways dataset (provided within the repository)
Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.
- Email: azizzoaib786@gmail.com
This project is available under the MIT License.