📄 README.md

Event Analysis and Management 📊

This project is an interactive dashboard developed with Streamlit and Plotly for event analysis and management. The objective is to provide a comprehensive view of the financial performance, demand, and operational statistics of events, focusing on comparisons between different venues and periods.

⚠️ Important Notice: The dataset used in this dashboard is fictitious, created to simulate real event data (Thai house and River). The project was developed for portfolio purposes, demonstrating skills in Data Engineering (ETL), Data Analysis, and Interactive Web Application Development (Streamlit).

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🚀 Key Features

The dashboard is structured into tabs to facilitate navigation and in-depth analysis:

1. 💰 Financial View: Tracking the historical and monthly evolution of Forecasted and Actual values, in addition to the analysis of the Average Ticket per venue.
2. 📈 Demand & Frequency Analysis: Frequency and distribution charts for the volume of Quotes and Guests, segmented by year, month, day of the week, stage, and menu type.
3. 📋 Detailing & Statistics: Tables with Descriptive Statistics (mean, standard deviation, min/max) for financial and guest metrics, and detailed tables of events by status (Negotiation, Closed, Actualized).
4. Executive Reports (KPIs): Comparative summary performance panels (month-over-month), and detailed executive reports for each venue.

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⚙️ Technical Explanations and Architecture

The project uses a modularized architecture in Python, focused on clarity, reusability, and performance:

1. (Assumed) Folder Structure

The app.py code depends on modules following data engineering best practices:

• app.py: The main Streamlit file, responsible for the interface and visualization.
• etl_preprocessor.py: Contains the run_full_etl function for the extraction, transformation, and loading (ETL) of raw data.
• etl_utils.py: Contains helper classes such as DataProcess (pre-processing), FilterSelection (filter application), and dre (demonstration of results functions, possibly).
• charts.py: Contains the plotting functions (config_plot, plot_hist, plot_pie, plot_hztl, create_table, PainelEventos, GeradorRelatoriosEventos), responsible for creating and configuring the Plotly charts.

2. Processing and Optimization

• ETL (Extract, Transform, and Load): df = run_full_etl(...) is the starting point. The function loads the raw data and performs the necessary transformations so that the DataFrame (df) is ready for analysis.
• Data Caching (@st.cache_data): The load_data() function is decorated with Streamlit's @st.cache_data. This ensures that the complete ETL process (run_full_etl) is executed only the first time or when the underlying code is changed, drastically optimizing the dashboard loading time for users.
• Pre-processing and Filtering: The DataProcess class ensures initial cleaning and formatting, and the initial filtering restricts the data to events from 2022 onwards (.query('Ano evento > 2021')).
• Chart Customization: The Plotly library is used with default settings (plotly_white) and customized color scales, ensuring a clean and consistent aesthetic throughout the dashboard.

3. User Interaction (Streamlit Sidebar)

The sidebar (st.sidebar) is used for the main filters, ensuring the user can interact and segment the analysis quickly:

• Venue Selection: Allows selecting one or more venues (e.g., Thai house, River).
• Year Selection: Allows focusing the analysis on a specific year (the current year is the default).
• Stage Selection: Allows filtering events by status (e.g., Quote, Negotiation, Actualized).

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💻 How to Run the Project Locally

Prerequisites

Ensure you have Python installed. The project requires the following main libraries, in addition to the local modules (which you will need to create or simulate):

• streamlit
• pandas
• numpy
• plotly
• dateutil

1. Installing Dependencies

Create a virtual environment and install the dependencies. You can use a requirements.txt file (not provided, but recommended).

pip install streamlit pandas numpy plotly "python-dateutil"

2. Minimum Execution Structure

To run app.py, you must create the files etl_preprocessor.py, etl_utils.py, and charts.py and simulate the necessary classes/functions (such as run_full_etl, DataProcess, FilterSelection, plot_hist, etc.) that return valid DataFrames or Plotly objects, even if with simulated/empty data, to avoid import errors.

3. Running the Dashboard

With the auxiliary module files created/simulated, run the dashboard via Streamlit:

streamlit run app.py

The dashboard will automatically open in your default browser.

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💡 Next Steps (Future Development)

1. Conversion Funnel Implementation: Complete the Conversion tab (currently commented out) with metrics and a funnel chart (requires the implementation of funnel metrics in the ETL/Utils layer).
2. Database Integration: Migrate the data source from spreadsheets/local files to a robust Database (PostgreSQL, MySQL, etc.) for better scalability and integrity.
3. Performance Alerts: Add logic to notify the user ( via Streamlit info/warning boxes) when important KPIs are off-target.