This Jupyter notebook is focused on customer segmentation using the K-Means clustering algorithm.

1. Data Gathering and Preprocessing:

   • Imports necessary libraries like pandas, matplotlib, seaborn, numpy, and scikit-learn.
   • Loads a customer dataset from a CSV file hosted on GitHub.
   • Performs data cleaning by:
     • Removing null values in the 'Profession' column.
     • Removing inconsistent age entries (below 18).
     • Handling inconsistent values in numerical columns (e.g., removing 0 values from 'Annual Income').
   • Identifies and removes outliers from numerical columns using the IQR method.

2. Data Visualization:

   • Creates histograms to visualize the distribution of numerical features.
   • Generates a countplot to show the distribution of customers by gender.
   • Uses a pairplot to explore relationships between different features.
   • Creates a barplot to display the number of customers in different age groups.

3. Data Standardization:

   • Standardizes the numerical features using StandardScaler to have a mean of 0 and a standard deviation of 1. This is important for K-Means as it's sensitive to feature scaling.

4. One-Hot Encoding:

   • Applies one-hot encoding to the categorical features ('Gender' and 'Profession') to convert them into numerical representations suitable for the K-Means algorithm.
   • Concatenates the standardized numerical features and the one-hot encoded categorical features into a single DataFrame (df4).

5. Model Training and Evaluation:

   • Uses the Elbow method and the Silhouette score to determine the optimal number of clusters (k) for the K-Means algorithm. Both methods suggest k=2.
   • Trains a K-Means model with the optimal number of clusters.
   • (Visualization Code Commented Out): There's commented-out code that would have visualized the clusters, but it's not executed.
   • Saves the trained K-Means model to a pickle file (customer_clustering_model.pkl).
   • Loads the saved model from the pickle file and demonstrates how to use it to predict the cluster for new customer data.

Overall, the notebook provides a comprehensive example of how to perform customer segmentation using K-Means clustering, including data preprocessing, visualization, standardization, one-hot encoding, model training, evaluation, and saving/loading the model.