Customer Churn Prediction with PySpark

This project demonstrates how to use PySpark for predicting customer churn. The dataset contains various parameters related to customers of a telecom company.

The main objective is to build a machine learning model using PySpark to predict whether a customer will churn based on various features such as tenure, monthly charges, total charges, and several categorical features.

Table of Contents

1. Introduction
2. Data Preparation
3. Feature Engineering
4. Model Building
5. Model Evaluation
6. Conclusion

Introduction

In this notebook, we will walk through the process of building a machine learning model with PySpark to predict customer churn. PySpark is the Python API for Apache Spark, which is a distributed computing framework that provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.

Data Preparation

We will start by loading the dataset and performing basic data cleaning and preparation. This includes handling missing values, encoding categorical features, and splitting the data into training and testing sets.

Feature Engineering

Feature engineering involves transforming the raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.

Model Building

We will use PySpark MLlib to build a machine learning model. MLlib is Spark's scalable machine learning library which provides many machine learning algorithms.

Model Evaluation

After building the model, we will evaluate its performance using appropriate metrics. This helps us understand how well our model is performing and if there are any improvements needed.

Conclusion

In the conclusion, we will summarize the findings and the performance of the model. We will also discuss potential improvements and future work.

How to Use This Repository

1. Clone the repository:

git clone https://github.com/yourusername/CustomerChurnPrediction_PySpark.git