Breaking the problem down into features

To break down the problems into features, we need to consider:

1. Data preparation: Load the dataset and inspect the data to understand its structure, missing values, and overall characteristics. Preprocess the data, which may involve handling missing values, data type conversions, and data cleaning.
2. Feature engineering: Select relevant features, extract features from text, and derive new features.
3. Text data preprocessing: Tokenize text, remove punctuation, and stop words. Convert text to numerical format using the Term Frequency-Inverse Document Frequency (TF-IDF) technique.
4. Apply clustering algorithm: Create a K-means clustering model and determine the optimal number of clusters using appropriate techniques like the elbow method and silhouette score.
5. Evaluate and visualize clustering results: Assess clustering performance and visualize the results using PCA in reduced dimensionality space.

We will...