Data imputation

Data imputation

Single imputation

Data imputation

Single imputation

 Robust, simple to implement and effective when outliers are present

Data imputation

Single imputation

 Robust, simple to implement and effective when outliers are present

• mean
• median
• mode

• constant
• hot-deck

Data imputation

Single imputation

Multiple imputation

 Robust, simple to implement and effective when outliers are present

• mean
• median
• mode

• constant
• hot-deck

Data imputation

Single imputation

Multiple imputation

 Robust, simple to implement and effective when outliers are present

• mean
• median
• mode

• constant
• hot-deck

Captures imputation variability, suitable for complex datasets

Data imputation

Single imputation

Multiple imputation

 Robust, simple to implement and effective when outliers are present

• mean
• median
• mode

• constant
• hot-deck

Captures imputation variability, suitable for complex datasets

• MICE

• Bayesian

Data normalization

Data normalization

Scaling

Encoding

Continuous var.

Categorical var.

Data normalization

Scaling

Encoding

Continuous var.

Categorical var.

• StandardScaler
• MinMaxScaler
• MaxAbsScaler
• Normalizer

• OneHotEncoder
• LabelEncoder
• OrdinalEncoder

Dataset

Dataset

Dataset

Clustering

Clustering

Our goals:

1. Identify natural groupings within the dataset;
2. Compare the performance of Spectral Clustering and KMeans technique. 

Clustering

Silhouette Score

The Silhoutte Score mesaures how similar a point is to its own cluster compare to other clusthers.

This method helps to identify the perfect value of k.

Clustering

Spectral Clustering

Spectral Clustering builds a similarity graph between the points dividing them in clusters using the eigenvectors of the Laplacian.

Clustering

Spectral Clustering

Pros

• ​Works well when data has complex structure;
• Can detect non-linear and non-convex cluster shapes.

Cons

• ​Computationally expensive and not ideal for large dataset;
• Requires careful tuning of parameters.

Clustering

Spectral Clustering: PCA visualization

Clustering

Spectral Clustering: tSNE visualization

Clustering

KMeans partitions data into k groups by minimizing the distance between points and their cluster's centroid. 

KMeans

Clustering

KMeans

Pros

• Fast, efficient and works well with large datasets;
• Very simple to implement and easy to interpet.

Cons

• ​Assumes spherical and equally sized cluster, limiting flexibility;
• More sensitive to outliers and to the initial placement of centroids.

Clustering

KMeans: PCA visualization

Clustering

KMeans: tSNE visualization

Clustering

Final thoughts

In summary, both methods identify very similar cluster, showing a consistent structure in the dataset.

However, the Spectral Clustering method provides a more accurate representation of the groupings in the data.