During my university course, I worked on a project that involved predicting customer churn for a telecom company. I used Python for data preprocessing and exploratory data analysis, employing libraries like Pandas and Matplotlib. For modeling, I utilized scikit-learn to implement logistic regression and random forest algorithms. The project also involved using Jupyter Notebooks for interactive coding and visualizations, which helped in presenting the findings effectively.

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 the affected rows, but for larger datasets, I prefer imputation methods. These can include mean/median imputation for numerical data and mode imputation for categorical data. More advanced techniques like regression imputation or using machine learning algorithms like K-Nearest Neighbors (KNN) can also be employed, depending on the context and the importance of the missing data.

I have a solid foundation in various machine learning algorithms, ranging from supervised to unsupervised learning. In supervised learning, I've worked with regression models (linear, logistic), decision trees, and ensemble methods like Random Forest and Gradient Boosting. For unsupervised learning, I've applied clustering techniques such as K-means and hierarchical clustering. Additionally, I've used neural networks in projects involving image recognition, utilizing frameworks like TensorFlow and Keras.

Ensuring ethical data use is paramount. I adhere to principles of transparency, fairness, and privacy. This involves obtaining clear consent from data subjects when necessary, anonymizing data to protect identities, and avoiding algorithms that could lead to biased outcomes. I also stay updated with regulations like GDPR and apply them appropriately. Regular audits and reviews of data practices are essential to maintain ethical standards.

I have experience with various data visualization tools including Matplotlib, Seaborn, and Tableau. These tools are crucial for effectively communicating data insights to stakeholders who may not have a technical background. Visualizations help in understanding data distributions, identifying trends, and making data-driven decisions. They also play a key role in exploratory data analysis, allowing data scientists to quickly grasp complex patterns and anomalies in the data.