In my role as a Junior Data Scientist at DataFrame.ai, Python was integral to our processes dealing with data handling, processing, and analysis. A significant project in which I used Python was a customer segmentation analysis for an e-commerce client.
To fetch and import data, I used Python's Requests and Pandas libraries. This includes CSVs, APIs, and databases. The data needed preprocessing, such as handling missing data, outlier detection, and data type conversions, which was seamlessly handled using Pandas and NumPy.
The segmentation involved clustering algorithms from scikit-learn, Python's machine learning library. We parameter-tuned our model using GridSearchCV, optimizing for better segmentation results.
The final customer segments greatly assisted our client's marketing team to create targeted campaigns, subsequently leading to a reported increase of 15% in their campaign conversion rate. The project underscored Python's capabilities in handling, processing, and modeling data effectively and efficiently.