This project focuses on analyzing customer churn in a telecommunications company. Churn, in this context, refers to the rate at which customers leave or stop using a service over a given time period. Understanding churn is crucial for businesses as it helps them identify factors that contribute to customer attrition and develop strategies to retain them.
The dataset used for this analysis contains information about various customers, including their demographic details, services subscribed, contract details, and churn status.
- customerID
- gender
- SeniorCitizen
- Partner
- Dependents
- tenure
- PhoneService
- MultipleLines
- InternetService ... (and more)
The project is organized as follows:
Python code for loading, exploring, and preparing the dataset. This includes extracting relevant information, handling missing values, and converting data types.
Visualizations depicting distribution and relationships among various attributes.
Exploratory Data Analysis (EDA) providing insights into customer churn behavior.
- Logistic Regression
- Decision Tree
- Random Forest
- Linear Regression
Evaluation metrics for each model. Recommendations based on the analysis.
The code is written in Python and utilizes libraries such as pandas, numpy, matplotlib, seaborn, and scikit-learn for data manipulation, visualization, and model building.
This project provides valuable insights into customer churn behavior and presents machine learning models to predict and mitigate it. The analysis can be used to formulate targeted marketing strategies and improve customer retention efforts.
To run the project locally, follow these steps:
- Clone the repository:
git clone https://github.com/yourusername/customer-churn-project.git - Install Dependencies: Navigate to the project directory and run
pip install -r requirements.txt. - Open Jupyter Notebook: Launch Jupyter Notebook and open
notebooks/analysis.ipynb.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent