In my previous role, I spearheaded a project to predict customer churn using machine learning. I began by defining the problem and gathering relevant data from various sources. I then cleaned and preprocessed the data, selecting features that would be most predictive. Using Python and scikit-learn, I built and tuned several models, ultimately selecting a Random Forest model due to its high accuracy. I presented my findings to the management team, which led to targeted marketing strategies that reduced churn by 15%.

Handling missing data is crucial for maintaining data integrity. I typically start by identifying the extent and pattern of missingness. For small amounts, I might remove those entries, but for larger amounts, I use imputation techniques like mean, median, or mode imputation for numerical and categorical data, respectively. More advanced methods include using machine learning algorithms to predict missing values or employing multiple imputation to account for uncertainty in imputed values.

I am proficient in a range of algorithms including Linear Regression, Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. For classification problems, I prefer using Random Forests due to their robustness and ability to handle overfitting. They also provide feature importance scores, which are valuable for understanding the data. However, the choice of algorithm depends on the specific problem and data characteristics.

Ethical considerations are paramount in data science. I ensure compliance with data protection regulations like GDPR and ensure data is anonymized where necessary. I also advocate for transparency in data collection and usage, clearly communicating the purpose and limitations of data analysis to stakeholders. Additionally, I regularly review and update data handling practices to align with evolving ethical standards and legal requirements.

Overfitting occurs when a model learns the noise in the training data to the extent that it negatively impacts the performance of the model on new data. To prevent overfitting, I use several strategies including cross-validation to assess the model's performance on unseen data, regularization techniques like L1 and L2 to add penalties for complex models, and ensuring the model is not too complex by using simpler algorithms or reducing the number of features.