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Analyse de Cluster
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Contenu du cours

Analyse de Cluster

Analyse de Cluster

1. Clustering Fundamentals
Introduction to ClusteringClustering Vs ClassificationClustering Algorithms and Libraries
2. Core Concepts
Missing Values HandlingCategorical Features EncodingData NormalizationDistance MeasuresLinkagesChallenge: Preprocessing the Dataset
3. K-Means
What is K-Means Clustering?How K-Means Algorithm Works?Finding Optimal Number of Clusters Using WSSFinding Optimal Number of Clusters Using Silhouette ScoreImplementing on Dummy DatasetImplementing on Real DatasetChallenge: Implementing K-Means Clustering
4. Hierarchical Clustering
How Hierarchical Clustering Works?Optimal Number of ClustersImplementing on Dummy DatasetImplementing on Customers DatasetChallenge: Implementing Hierarchical Clustering
5. DBSCAN
Why DBSCAN?How DBSCAN Works?How to Assign Points to the Clusters?Implementing on Dummy DatasetImplementing on Real DatasetChallenge: Implementing DBSCAN
6. GMMs
Problem StatementWhat is Gaussian Distribution?How GMMs Work?Implementing GMM on Dummy DataImplementing GMM on Real DataChallenge: Implementing Gaussian Mixture ModelsConclusion

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Challenge: Preprocessing the Dataset

Tâche

Swipe to start coding

You are given a synthetic dataset stored in the data variable.

  • Replace missing values in the 'Age' column with the mean value of this column and store the result in this column.
  • Create an instance of an appropriate encoder, which will be used for the 'City' column and store it in the city_encoder variable. Make sure to specify the removal of the first column.
  • Encode the values in the 'City' column using city_encoder and store the result in the city_encoded variable.
  • Create an instance of an appropriate encoder, which will be used for the 'Income' column and store it in the income_encoder variable. Note that 'High' > 'Middle' > 'Low'.
  • Encode the values in the 'Income' column using income_encoder and store the result in the 'Income' column.

Solution

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Section 2. Chapitre 6
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book
Challenge: Preprocessing the Dataset

Tâche

Swipe to start coding

You are given a synthetic dataset stored in the data variable.

  • Replace missing values in the 'Age' column with the mean value of this column and store the result in this column.
  • Create an instance of an appropriate encoder, which will be used for the 'City' column and store it in the city_encoder variable. Make sure to specify the removal of the first column.
  • Encode the values in the 'City' column using city_encoder and store the result in the city_encoded variable.
  • Create an instance of an appropriate encoder, which will be used for the 'Income' column and store it in the income_encoder variable. Note that 'High' > 'Middle' > 'Low'.
  • Encode the values in the 'Income' column using income_encoder and store the result in the 'Income' column.

Solution

Switch to desktopPassez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous
Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 2. Chapitre 6
Switch to desktopPassez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous
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