Yoda Response: Certainly, Avi. During my work on the Cryptocurrency Prediction project, I had to wrangle a large dataset using pandas in Python. The data was in a CSV format and contained various inconsistencies, such as missing values and incorrect data types.

One of the main challenges I faced was handling missing values, which could significantly impact the model's performance. To address this, I used pandas functions like `fillna()` to replace missing values with the mean or median of the column, depending on the context. Additionally, I used `dropna()` to remove rows or columns with a significant number of missing values.

Another challenge was ensuring that the data types were consistent and appropriate for analysis. I used pandas functions like `astype()` to convert data types and `pd.to_datetime()` to handle date and time data correctly.

By using pandas' powerful data manipulation capabilities, I was able to clean and preprocess the data effectively, making it suitable for training the machine learning models. This experience underscored the importance of data wrangling in the machine learning pipeline and how pandas can be a valuable tool in this process.