Day 33 of Intellipaat x IIT Roorkee Data Analytics Course

Day 33 moved deeper into feature engineering and data preprocessing. The trainer explained how raw data is rarely ready for machine learning and why preparing it properly makes a huge difference in model performance. We learned about handling missing values, encoding categorical variables, and scaling numerical features.

The assignments were practical and required us to clean and transform datasets before training models. One task was to try different methods for filling missing values and then compare the results. Another was to use techniques like one-hot encoding and label encoding to prepare categorical columns. These exercises showed how preprocessing steps directly affect the accuracy of the models.

What stood out today was the importance of scaling. Models like logistic regression and KNN gave very different results when features were normalized, and this made the concept very clear. The trainer also explained why some algorithms are sensitive to scaling while others are not, which added depth to the understanding. It was a busy but productive day, and the more we practice, the clearer it becomes how much data preparation matters in analytics. This part feels very close to what data analysts and data scientists do in their daily work.

Will be back with the update on Day 35.