A Live Analytics tool for the police
Features:

-Analyzing patrols -Resource allocation -Accidents -Crime rates -Weather

⭐ Quick Start ⭐

💻 Requirements

Python version

• Main supported version : 3.7
  
• Other supported versions : 3.8 & 3.9

📈 Usage

Once installed, run the following command from CLI to open the app in your default web browser:

streamlit_prophet deploy dashboard

Now you can train, evaluate and optimize forecasting models in a few clicks. All you have to do is to upload a time series dataset. This dataset should be a csv file that contains a date column, a target column and optionally some features, like on the example below:

Then, follow the guidelines in the sidebar to:

• Prepare data: Filter, aggregate, resample and/or clean your dataset.
• Choose model parameters: Default parameters are available but you can tune them. Look at the tooltips to understand how each parameter is impacting forecasts.
• Select evaluation method: Define the evaluation process, the metrics and the granularity to assess your model performance.
• Make a forecast: Make a forecast on future dates that are not included in your dataset, with the model previously trained.

Once you are satisfied, click on "save experiment" to download all plots and data locally.