🎲 Casino Analytics Dashboard - Part 1

Intro

Today I started my iGaming project: a useful repository to analyse data on customers on the iGaming sector… …Without even working there!!

Main Goals of the day:

• Writing the structure of the script to simulate users’ data
• Choosing parameters
• Create the dataset and save it as a .csv file

Step by Step

📍 Step 1: Import libraries (Numpy, Pandas…), and type the random seed for random generation.

📍 Step 2: Parameters selected: number of players, starting and ending date (a week in this case)

📍 Step 3: Wrote a function to simulate sessions, given the number of players and games selected.

📍 Step 4: Print the .csv file to use it soon.

Challenges / Insights

• Handling new libraries (e.g, datetime for time-series analysis).
• Choosing the method for the simulation (cycle).
• Selecting the best configuration and the key values to be evaluated from the sample.

Code Snippet Final

```python
np.random.seed(42)                                      # formula to generate random numbers

# Step 2: Selecting parameters
n_players = 500                                         # Number of players
start = datetime(2025, 9, 1)                            # Starting data: 1st September 2025
end = datetime(2025, 9, 7)                              # Ending data: 7th September 2025
```

Next Step

👉 Running the data_generator.py script to create/improve the .csv file.