My final project was about using machine learning to tell if a mass in a mammogram is benign (not cancer) or malignant (cancer). I started by unzipping the project files and opening the FinalProject-SkillShare.ipynb notebook. Then I followed the challenge steps and built the models we learned in the course.

I used the dataset from the project to test different classification models. First, I looked at the data and cleaned it up. Then I trained each model and checked how well it worked. I compared the models by their accuracy and other results to see which one did best. I saw that each model works a little differently, and that even small changes, like using different settings, can change the results.

This project showed me how machine learning can help with real problems like health care. In a medical problem, mistakes matter a lot, so it is not enough to look only at accuracy. When I finished, I opened the solution notebook and compared it with my own work. This helped me learn better ways to improve my models and the best steps to follow for classification tasks.

Extra Ideas You Can Add (if they match your work)

• Data steps: Mention that you handled missing values or scaled the data before training.
• Model names: List the models you used, such as Logistic Regression, Decision Tree, Random Forest, SVM, KNN, or Naive Bayes.
• Best model: Say which model gave the best result and its accuracy score.
• Other checks: Mention precision, recall, or a confusion matrix, since missing a malignant case is a serious mistake.
• What you learned: Add one sentence about what you would try next, like tuning settings or testing more models.