CHEARS

Overview:

The goal of this project is to develop a predictive model that can accurately identify customers who are likely to churn or cancel their subscriptions in a telecommunications company.

Customer churn, or customer attrition, is a critical concern for businesses, as it directly impacts revenue and customer retention.

By analyzing various customer attributes and usage patterns, we aim to create a model that can predict potential churners, allowing the company to take proactive measures to retain those customers.

Dataset:

The dataset used in this project consists of customer information and usage data, including features such as account length, area code, international plan, voice mail plan, call durations, charges, and customer service calls. The dataset contains 667 customer records and 20 columns (features), with the target variable being "Churn" which indicates whether a customer churned (True) or not (False).

Project Steps:

1. Exploratory Data Analysis (EDA): Conduct a thorough analysis of the dataset to gain insights into the distribution, summary statistics, and relationships between different features. Visualizations and statistical measures will be used to explore patterns and correlations in the data.

2.  Data Preprocessing: Clean and preprocess the dataset to handle missing values, categorical variables, and feature scaling. This involves encoding categorical variables into numerical representations and normalizing or scaling numerical features if required.

3.  Model Selection and Training: Choose a suitable machine learning algorithm(s) based on the nature of the problem and the dataset. In this project, logistic regression, decision tree, and potentially other algorithms can be considered. Train the selected model on the preprocessed training data.

4.  Model Evaluation: Evaluate the trained model's performance using appropriate evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix. This step helps assess how well the model generalizes to unseen data and its ability to predict churn accurately.

5.  Model Optimization: Fine-tune the model by adjusting hyperparameters, performing feature selection, or trying different algorithms to improve its performance. This iterative process aims to maximize the model's predictive accuracy.

6.  Deployment and Predictions: Once a satisfactory model is achieved, deploy it to make predictions on new, unseen data. The model can be used to predict whether a customer is likely to churn or not based on their attributes and usage patterns.

Benefits:

The developed churn prediction model can provide valuable insights to the telecommunications company. By identifying customers who are at a high risk of churning, the company can proactively implement customer retention strategies. This may include targeted marketing campaigns, personalized offers, improved customer service, or incentives to encourage customer loyalty. Ultimately, reducing customer churn can lead to increased customer satisfaction, improved profitability, and a more sustainable business.

 By completing this project, I aim to contribute to the company's customer retention efforts and provide actionable insights to optimize their operations and customer experience.