1. Data Collection

Collect the historical data for each charging station, including the outcomes (success or failure) of the previous 10 bookings.

2. Feature Selection

Identify relevant features influencing the success of a charging session, such as time of day, day of the week, station usage patterns, and any reported issues.

3. Data Preprocessing

Clean and preprocess the historical data, handling missing values and outliers appropriately.

4. Model Development

Choose a suitable machine learning algorithm for binary classification to predict the success or failure of the next charging session.

- Train the model using the preprocessed historical data.

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5. Model Evaluation

- Evaluate the model's performance using metrics such as accuracy, precision, recall, and F1 score.

- Fine-tune the model to achieve optimal results.

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6. Interrogation with dashboard

- Integrate the prediction model with the existing dashboard to provide real-time insights into the probability of success for each charging station.

Ensure that the prediction model provides real-time results to support timely decision-making.

Real-time Processing

Design the model to scale efficiently as the number of charging stations and data volume increases.

Scalability

Implement robust security measures to protect the confidentiality and integrity of the charging station data.

Security