What's included

• Data preparation and cleaning

  Preparing and cleaning the dataset for analysis and modelling.

• Exploratory data analysis (EDA)

  Analysis to understand the data distribution, relationships, and key patterns.

• Feature engineering

  Creating and selecting features to improve model performance.

• Model development

  Developing and training machine learning models.

• Model evaluation

  Evaluating the performance of the machine learning model.

• Model tuning and optimisation

  Fine-tuning the model to improve its performance.

• Model deployment

  Deploying the model for use in a production environment.

• Model documentation

  Comprehensive documentation of the entire modelling process.

• Prediction results

  Predictions made by the model on new data.

• Consultation and support

  Ongoing support and consultation to ensure successful implementation and use of the model.