In my current role as a Data Analyst at PredictiveSolutions, I often use NumPy and Pandas for data manipulation in various machine learning projects. One particular project required developing a recommendation system for a popular e-commerce website. The raw user data required extensive preprocessing before it could be utilized.
First, I used Pandas to clean and transform the data that had multiple irregularities like inconsistent formatting, missing values, and irrelevant variables. Pandas was particularly beneficial in effortlessly handling large datasets, dropping unnecessary features, and converting categorical variables into numerical ones. After going through this initial cleaning phase with Pandas, I used NumPy to handle array-based computations. For instance, I found the correlation between features, performed matrix operations, and reshaped arrays, which are actionable for recommender algorithms.
The data manipulation techniques significantly improved our machine learning model's performance, leading to more accurate and relevant product recommendations, which in turn increased the click-through rate by 15%.