In today's competitive business landscape, retaining customers and ensuring their satisfaction are paramount to the success of any company. Customer churn, or customer attrition, is a critical concern for businesses, as it directly impacts revenue and growth. Understanding why customers leave and predicting which ones are at risk of churning is crucial. It's always easier to retain a customer than to gain a new one!
This data science project is dedicated to addressing the challenges of customer churn for a telecommunications company. The primary objectives include:
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Comprehensive Data Exploration and Preprocessing: We've delved deep into the available data to uncover hidden insights and ensure data quality. Refer to EDA-1.ipynb and preprocessing-2.ipynb for detailed exploration and preprocessing steps.
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Machine Learning Model Building: To predict customer churn accurately, we've built, trained, and rigorously evaluated various machine learning models. The journey through model development and selection is documented in ML-model-3.ipynb.
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Hyperparameter Optimization: Fine-tuning models is essential to achieving peak performance. We've implemented hyperparameter optimization techniques to maximize predictive accuracy.
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Results Visualization: Communicating the results effectively is key. We've used data visualization techniques to showcase model performance and insights.
Understanding why customers churn and predicting potential churners enables businesses to implement targeted retention strategies, reduce revenue loss, and enhance overall customer satisfaction.
Before diving into this project, please ensure that you meet the following requirements:
- Python (version >= 3.6): You should have Python installed on your system to run the project code.
- Jupyter Notebook: Jupyter Notebook for code presentation and execution.
- Telco Customer Churn: IBM Dataset
- For a comprehensive data description, refer data-description.txt.
- The dataset can be found in Telco_customer_churn.xlsx.
- Source: Kaggle Dataset
Our project leverages the following technologies:
- Python: The primary programming language for data analysis and machine learning.
- Jupyter Notebook: Interactive environment for presenting and running code.
- Scikit-Learn: The main machine learning library for model development and evaluation.
- Pandas: Data manipulation library for data preprocessing and analysis.
- Matplotlib & Seaborn : Data visualisation tools
Made by Hrishikesh Reddy Papasani
Connect on LinkedIn: LinkedIn Profile
Contact at [email protected]