- An automobile club faces the challenge of automatically classifying destinations for tourists based on descriptions.
- In the first step, 12,000 different destinations are to be classified into 12 different classes (e.g. opening hours).
- Focus on 800 destinations manually labelled by the department.
- Preparation of the description texts in order to bring them into a readable format for a machine learning model (tokenisation, lemmatising, punctuation, word dictionary, ...) using suitable Python packages from the field of natural language processing.
- Use of an algorithm to classify the destinations and evaluation of the results
- Comparison of the results from the machine learning model with a simple heuristic ("strategic guessing")
- destinations into 12 different classes (Python script) was developed.
- Descriptive analyses to generate transparency of destinations for tourists and analysis for the client's department (Jupyter Notebook)
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