/Customer-Segmentation-FrameWork-V1

Segmentation FrameWork

Primary LanguageJupyter Notebook

Customer-Segmentation-using-KNN

Machine learning, Python

Introduction

  • Title: K Nearest Neighbor
  • Objective: To build a KNN classifier to predict the classification of unknown cases within the customer base of a telecommunications provider.

Data Description

  • Segmentation: The customer base is segmented into four groups based on service usage patterns.
  • Target Variable: The custcat field, which includes four values corresponding to the customer groups:
    • Basic Service
    • E-Service
    • Plus Service
    • Total Service

Methodology

  1. Data Preparation:

    • Import relevant libraries for data manipulation (pandas, numpy) and visualization (matplotlib).
    • Load the customer data from a CSV file into a pandas DataFrame.
    • Perform initial data exploration to understand the dataset's structure.

  2. Exploratory Data Analysis:

    • The notebook likely contains statistical summaries and visualizations to explore the customer data and understand the distribution across different segments.

  3. Model Development:

    • Implement the KNN algorithm to create a predictive model.
    • Configure the model to identify the nearest neighbors and classify the customers accordingly.