Data Science, Machine Learning Trainee
Classification
I analyzed a telecom company's dataset to identify customer churn patterns. This involved preprocessing, cleaning, and labeling data for use in supervised machine learning models. My work focused on ensuring that each data instance was accurately classified for training and validation. • Conducted feature engineering and label generation for churn status.
• Validated labeling consistency across datasets to prevent bias.
• Used Python libraries such as Pandas and Scikit-learn for labeling and model evaluation.
• Improved label quality to optimize prediction accuracy.
I analyzed a telecom company's dataset to identify customer churn patterns. This involved preprocessing, cleaning, and labeling data for use in supervised machine learning models. My work focused on ensuring that each data instance was accurately classified for training and validation. • Conducted feature engineering and label generation for churn status. • Validated labeling consistency across datasets to prevent bias. • Used Python libraries such as Pandas and Scikit-learn for labeling and model evaluation. • Improved label quality to optimize prediction accuracy.
2024 - 2024