https://maliki-airquality.streamlit.app/

This project, submitted for the "Learn Data Analysis with Python" course from Dicoding, focuses on analyzing air quality data, particularly PM2.5 levels, from the Wanshouxigong station. The objective is to uncover trends, seasonal variations, and the impact of different weather conditions on air quality.

This analysis serves as a course submission for "Learn Data Analysis with Python" offered by Dicoding. It demonstrates the application of data analysis techniques and visualization skills learned in the course.

• Introduction
• Data Source
• Libraries Used
• Key Insights
• How to Run the Dashboard
• About Me

The goal of this project is to analyze air quality data, specifically PM2.5 pollutant levels, and understand their relationship with various environmental factors. The analysis includes identifying trends, seasonal patterns, and correlations with weather conditions.

The dataset used in this project includes air quality measurements from the Wanshouxigong station, with a focus on PM2.5 levels and other related environmental data.

• Streamlit
• Pandas
• Matplotlib
• Seaborn
• NumPy
• SciPy
• Statsmodels

• Seasonal variation in PM2.5 levels with higher concentrations in colder months.
• Correlation between PM2.5 levels and weather conditions like temperature and humidity.
• Trends and patterns revealed through time series analysis.

To run the Air Quality Analysis Dashboard, follow these steps:

1. Create and Activate a Python Environment:

   • If using Conda (ensure Conda is installed):

     conda create --name airquality-ds python=3.9
     conda activate airquality-ds
     

   • If using venv (standard Python environment tool):

     python -m venv airquality-ds
     source airquality-ds/bin/activate  # On Windows use `airquality-ds\Scripts\activate`
     

2. Install Required Packages:

   • The following packages are necessary for running the analysis and the dashboard:

     pip install pandas numpy scipy matplotlib seaborn streamlit statsmodels
     

     or you can do

     pip install -r requirements.txt
     

1. Navigate to the Project Directory where dashboard.py is located.

2. Run the Streamlit App:

   streamlit run dashboard.py
   

• The dataset used for this analysis is included in the project repository.
• A detailed Python notebook (maliki-dicoding-ds-airquality.ipynb) containing the data analysis and visualizations is also provided.

Since Dicoding recommended creating the good and tidy folder structures, as dashboard.py in dashboard folder, then the deployment for Streamlit App affected.

That was why I put the requirements.txt in the dashboard folder as well.

• Name: Reza Maliki Akbar
• Email Address: rezamaliki.akbar@gmail.com
• Dicoding ID: maliki_borneo

Find also the Python Notebook in here Kaggle - maliki_borneo - Air Quality