Srimedha - DataPrep

Importance of Data Preparation before Clustering:

Preparing the data before clustering, following exploratory data analysis (EDA), is really important for several reasons. Firstly, data preparation ensures that the data is in a suitable format for clustering algorithms, addressing issues such as missing values, outliers, and feature scaling. By addressing these issues upfront, the clustering algorithm can better identify meaningful patterns in the data.

Secondly, data preparation helps in selecting relevant features and reducing dimensionality, which can improve clustering performance and interpretability. Feature selection techniques, such as removing irrelevant or redundant features identified during EDA, help focus the clustering algorithm on the most informative aspects of the data. Dimensionality reduction methods, such as principal component analysis (PCA) or feature extraction, can further enhance clustering by simplifying the dataset while retaining important information.

Thirdly, data preparation enables the application of appropriate distance metrics or similarity measures, which are fundamental to many clustering algorithms. By preprocessing the data, such as scaling numerical features or encoding categorical variables, the distances between data points become more meaningful, leading to more accurate clustering results.

Overall, preparing the data after EDA and before clustering ensures that the dataset is in a suitable form, enhances clustering performance, and facilitates the extraction of meaningful insights from the data.

The data set looked like this before preparing it for clustering:

The data set looked like this after preparing it for clustering:

The text data is extracted.
Initializing a TF-IDF vectorizer object for transforming text data into numerical vectors based on term frequency-inverse document frequency (TF-IDF) weighting.
Assigning clusters using Euclidean distance for K-means clustering and Cosine similarity for Hierarchical clustering.

(The first image is the text data after extracting and the second image is the final dataset after clustering).

Once step-2 mentioned above is done, we will have the text data shown in the first image above to be converted into a sparse matrix. This is because these vectorizers convert the text data into a sparse matrix representation where rows correspond to documents and columns correspond to features (e.g., words or n-grams). The matrix elements represent the frequency of each feature in each document. This X has the unlabeled numeric data on which the clustering takes place and it looks like this:

K-Means Clustering