Used data preprocessing pipelines to clean, validate, and standardize different data sets to be later utilized for machine learning models by utilizing Pandas and NumPy. This included handling missing data, outliers, and data uniformity.

Executed advanced feature engineering such as aggregation, encoding (one-hot & target encoding), scaling, and dimension reducing techniques (e.g., PCA) for optimizing model performance. Collaborated reusable scripts for automating feature selection to reduce manual preprocessing.

Processed unstructured text data for natural language processing (NLP), which included stemming, tokenization, and stop-word removal. Used text normalization and vectorization for compatibility with machine learning algorithms. Utilized this preprocessing to create a pipeline to transform raw text into structured input which reduced preprocessing time by Y%.

Conducted exploratory data analysis (EDA) to identify trends, correlations, and anomalies that are within the dataset. Visualized key insights to plot and image data using Matplotlib and Seaborn. Created insights by using statistical analysis to improve dataset usability.