Streamlit Skills Showcase 🌐

Welcome to my Streamlit application! This project serves as a demonstration of the skills I've acquired in data science, data visualization, and web app development. By leveraging Streamlit, I've created an interactive application that showcases my proficiency in these areas.

Objective 🎯

The primary objective of this project is to present my skills and knowledge in a practical, interactive format. Through this Streamlit app, I aim to demonstrate my capabilities in data analysis, visualization, and the creation of user-friendly web applications.

Application Link 🚀

You can access the Streamlit application here.

Features 🛠️

The Streamlit application includes the following features:

• Interactive Data Visualizations: Explore various datasets through interactive charts and graphs.
• Data Analysis Tools: Utilize built-in tools for data manipulation and analysis.
• Machine Learning Models: Test and visualize machine learning models on provided datasets.
• User Input: Input your own data and parameters to see real-time updates and results.

Tools and Technologies 🛠️

For this project, I've utilized:

• Streamlit: For creating the interactive web application.
• Python: For data manipulation, analysis, and machine learning.
• Pandas: For data handling and preprocessing.
• Matplotlib/Seaborn/Plotly: For creating visualizations.
• Scikit-learn: For implementing machine learning models.

Structure 📁

This repository contains the following files and directories:

• Data/: Directory containing datasets used in the application.
  • Dictionary_MENSQ_descriptif_champs.csv
  • Weather_historical.csv
  • Weather_historical.xlsx
• pages/: Directory containing the pages of the Streamlit application.
  • 1_📈_Plotting_Demo.py: Demonstrates plotting and animation by generating random numbers and updating a line chart in real-time.
  • 2_🌍_Mapping_Demo.py: Demonstrates how to use st.pydeck_chart to display geospatial data with various layers such as bike rentals, BART stop exits, stop names, and outbound flow.
  • 3_📊_DataFrame_Demo.py: Shows how to use st.write to visualize Pandas DataFrames and create interactive Altair charts for gross agricultural production data from the UN Data Explorer.
  • 4_🚗_uber_pickups.py: Visualizes Uber pickups in NYC, including a bar chart of pickups by hour and an interactive map of pickup locations based on user-selected hours.
  • 5_🌞_Weather_France.py: Compares historical weather data for various cities in France, including precipitation and temperature metrics, with interactive line charts and data filtering options.
• Hello.py: The home directory of the Streamlit app.
• requirements.txt: File listing the dependencies required to run the application.
• README.md: This file, providing an overview of the project.

How to Run the Application 🚀

To run the Streamlit application locally:

1. Clone this repository to your local machine.
2. Navigate to the project directory.
3. Install the required dependencies using the command: pip install -r requirements.txt
4. Run the Streamlit app using the command: streamlit run Hello.py
5. Open your web browser and go to the local URL provided by Streamlit to interact with the application.

Skills Demonstrated 📝

Through this project, I aim to demonstrate the following skills:

• Data Analysis: Proficiency in cleaning, manipulating, and analyzing datasets.
• Data Visualization: Ability to create informative and interactive visualizations.
• Machine Learning: Implementation and evaluation of machine learning models.
• Web App Development: Development of user-friendly and interactive web applications using Streamlit.

Conclusion 📝

This Streamlit application is a testament to my skills in data science and web development. By interacting with the app, you can explore my capabilities and see the practical applications of my knowledge. I welcome any questions or feedback, and I hope you find the application insightful and engaging.

Feel free to explore the application and reach out with any questions or feedback!