Customer-Churn-Prediction-and-Analysis

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Customer-Churn-Prediction-and-Analysis

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Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library

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# Customer Churn Prediction and Analysis 😁😡  ![license](https://img.shields.io/github/license/Pegah-Ardehkhani/Customer-Churn-Prediction.svg) ![releases](https://img.shields.io/github/release/Pegah-Ardehkhani/Customer-Churn-Prediction.svg) <a href="https://colab.research.google.com/drive/14Qjd2CrysCLx4K95t-r_t8fRvXMjyDbj?usp=sharing" target="_parent\"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.org/github/Pegah-Ardehkhani/Customer-Churn-Prediction/blob/main/Telco%20Customer%20Churn%20Prediction%20and%20Analysis.ipynb)

> **`Note`**: Use [**nbviewer**](https://nbviewer.org/github/Pegah-Ardehkhani/Customer-Churn-Prediction/blob/main/Telco%20Customer%20Churn%20Prediction%20and%20Analysis.ipynb) (recommended) or google colab in order to view interactive plotly graphs. You can see all the codes and the outputs in nbviwer without running the whole code again.

## Dataset 📔

[Kaggle link: Telco Customer Churn](https://www.kaggle.com/datasets/blastchar/telco-customer-churn)

[Github link: Telco Customer Churn](https://github.com/IBM/telco-customer-churn-on-icp4d/tree/master/data)

## Objectives 🏆

In this project, these questions will be answered:

* [x] What's the % of Customers Churn and customers that keep in with the active services?
* [x] Is there any patterns in Customers Churn based on the gender?
* [x] Is there any patterns/preference in Customers Churn based on the type of service provided?
* [x] What's the most profitable service types?
* [x] Which features and services are most profitable?
* [x] Which features have the most impact on predicting customers churn?
* [x] Which model is the best for predicting churn?

## Project's Table of Contents ✍️
<details>
  <summary>Click to expand!</summary>

1. Problem statement
2. Import Libraries and Data
3. Handling Missing Values
4. Data Analysis and Visualization
5. Outlier Detection
6. Check for Rare Categories
7. Categorical Variables Encoding
8. Balance Data
9. Dataset Splitting
10. Feature Scaling
11. Modeling and Parameter Optimization
12. Feature Importance
13. Results
</details>

## Libraries 📚

**application** | **libraries**
--- | --- 
handle table-like data and matrices | pandas, numpy 
visualisation | plotly, seaborn, missingno 
classification models | sklearn, xgboost, mlens 
balance data | imblearn

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