💡[FEATURE]: Telecom Churn Prediction

[Nidhi1314]

Is your feature request related to a problem? Please describe.: This feature request is related to improving the accuracy and efficiency of telecom churn prediction models. Telecom companies face challenges in retaining customers, and accurate prediction models help in identifying potential churners early.

Describe the solution you'd like: I would like to add a new feature to the existing telecom churn prediction model that incorporates more advanced machine learning algorithms, such as gradient boosting machines (GBM) or deep learning models like neural networks. These algorithms can enhance the model's predictive power and capture complex patterns in customer behaviour, leading to better churn predictions.

Describe alternatives you've considered: Some alternatives that could be considered include:

Ensemble methods like Random Forest or AdaBoost for improving model performance. Feature engineering techniques to create new predictive variables that capture more nuanced aspects of customer churn behavior. Exploring other data sources or external datasets that could provide additional insights into customer churn dynamics.

Additional context: The current project uses logistic regression and random forest algorithms for predictive modelling. Adding more advanced algorithms could further enhance the model's accuracy and predictive capabilities, leading to more effective churn prediction and management strategies for telecom companies.

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[Nidhi1314]

@Akshat111111 please assign this issue to me under gssoc. I strongly believe that this enhancement allows for targeted customer retention strategies, mitigating financial derivative losses tied to subscriber base fluctuations.

[Akshat111111]

With it also add analysis of any telecom stock