Day 40/60—Understanding Train & Test Split 📊 Today I worked on splitting my dataset into training and testing sets, and the difference between the two scores reminded me why proper validation matters in data science. #PythonForDataScience #MachineLearning #PublicHealth

Day 39 / 60 — Python for Data Science 📊 This chart compares the actual number of deaths with what the model predicted. The closer the points fall along an upward line, the better the model’s predictions. #MachineLearning #PythonForDataScience #DataAnalytics #HealthcareAnalytics

Day 38 / 60 — Python for Data Science 📊🐍 Today I practiced model validation using `train_test_split`. The model returned an R² score of 0.708, which means it explains about 70% of the relationship between cases and deaths. #DiAnalyst #PythonForDataScience #machinelearning

Day 37 / 60 — Python for Data Science 📊 Today I focused on feature engineering. After retraining the model, the R² score remained around 0.80, showing consistent performance even after introducing a new feature (total cases). #DiAnalyst #PythonForDataScience #DataAnalytics