Multi-Level Classification ML Project to Predict Churn in Telecom

In this Classification Machine Learning Project, you will build a multi-level classification model to predict churn category and reason in the telecom industry, enabling proactive measures to reduce customer churn and enhance retention.

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Project Description

Overview

An organization loses its customers to its competition for various reasons. What mainly attracts customers to go ahead with a shift is attributed to the price of the product and its quality. This is true for the Telecom industry as well. A churn of 10 to 60 percent in any company is a big number that can affect the company’s overall growth.

Churn is the percentage of customers who stop using a company's product or service during a particular period. The churn category refers to why a customer stops using a company's service, while churn reason refers to the specific reason for a customer to churn. In the telecom industry, multiclass classification predicts a customer's churn category and reason.

The main goal of multiclass and multilevel classification in telecom is to predict the churn category and churn reason of a customer, which can help a telecom company to take proactive measures to reduce customer churn, improve customer retention and enhance customer satisfaction. By understanding the reasons behind churn, a telecom company can develop targeted retention strategies to retain customers.

Given our assumptions about the data, we will build a prediction model based on the historical data. To simplify, here's the logic of what we'll build:

Exploratory Data Analysis: We will perform a detailed problem-specific EDA to understand the churn data. We'll use plotly interactive plots, folium, and Branca to visualize and explore the data.
Multi-Class Classification: Using multi-class classification, we'll build a model to predict the churn category. Specifically, we'll use the Random Forest algorithm to build the model. To handle the imbalanced data, we'll use SMOTE and undersampling techniques.
Multi-Label Classification: We'll then formulate the problem as a multi-label classification problem and predict both churn category and reason. We'll use a neural network-based algorithm with Keras to accomplish this.

Aim

This project aims to develop a predictive model using multi-level classification algorithms to identify telecom customers' churn category and churn reason.

Data Description

The telecom company from the US provided the data with 98230 customers over 73

unique features. The features are related to customer demographics, personal

information, and usage.

Tech Stack

Language: Python
Libraries: pandas, numpy, matplotlib, scikit-learn, xgboost, lightgbm, branca, folium, plotly, keras

Approach

Exploratory Data Analysis (EDA):
- Understand the features and their relationships with target variables
- Check for missing or invalid values, and outliers
- Visualize the data using interactive plots (e.g., Plotly, Folium, and Branca) to gain insights
Data Preprocessing:
- Encode categorical features using one-hot encoding or label encoding
- Split the dataset into training and testing sets
- Balance the data using techniques such as SMOTE and undersampling

Multi-level Classification:
- Predict churn category using multi-class classification using random forest
- Formulate the problem as a multi-label classification problem, predict both churn category and reason, and solve with Neural Networks.

Multi-Level Classification

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