From the outset of my work with data, I have been especially passionate about creating innovative solutions that directly improve processes and enhance customer experience. This focus was key in one of my most notable projects: predicting contract durations for Interconnect, a telecommunications company.

Project Summary

The primary goal of this project was to develop a Machine Learning model capable of predicting the total contract duration (in months) for active Interconnect customers. This approach enables the company to proactively identify clients likely to end their contracts soon, allowing the marketing team to implement strategies aimed at extending customer retention and improving satisfaction.

Methodology and Results

The project began with an exhaustive exploratory data analysis and rigorous preprocessing of the company-provided data, which included customer plans, contracts, and personal details. Several regression models were implemented and evaluated to determine the best-performing model based on criteria such as training and testing RMSE.

The evaluated models included:

• Linear Regression:
  • Train RMSE: 1.81 | Test RMSE: 1.81
• RandomForestRegressor:
  • Train RMSE: 0.92 | Test RMSE: 1.42
• Two-Layer Neural Network with GridSearch:
  • Train RMSE: 2.47 | Test RMSE: 2.47

The RandomForestRegressor was selected as the most suitable model for predictions due to its balance between precision and generalization. To further enhance usability, a dashboard was created to visually represent predictions, including insights into contract duration distribution and identification of clients likely to end their contracts early.

Conclusions

• A Machine Learning model has been successfully developed to predict the total duration of customer contracts, providing actionable insights for retention strategies.
• The RandomForestRegressor demonstrated superior predictive performance (Test RMSE: 1.42), making it the most effective model for deployment.
• The dashboard enhances decision-making by visually representing key predictions, helping stakeholders act on valuable insights effectively.

Recommendations

1. Utilizing the Model: Interconnect's marketing team should integrate this model and dashboard into their workflow to proactively identify customers with shorter predicted contract durations and implement timely, personalized offers.
2. Ongoing Maintenance: It is recommended to monitor the model’s performance continuously and update it with new customer data to ensure accuracy and relevance over time.
3. Expanding Features: Incorporating additional variables, such as demographic data or service usage patterns, could further improve the model's predictive capability and lead to even more targeted interventions.

This project reflects my passion for transforming data into actionable strategies and my commitment to delivering solutions that tangibly impact business operations and goals. By focusing on contract duration predictions, Machine Learning can empower companies like Interconnect to elevate their customer retention strategies and optimize performance!