Update README.md

README.md

@@ -35,13 +35,13 @@ as well as technical answers, which was necessary for the interview questions. I
 dataset. For each job posting in the dataset, I had the model create a 'great', 'mediocre', and 'bad' user profile. An example of the few shot prompting for this was:
 
 
-'''python
+```python
 Job Title: Data Scientist
 Job Description: Analyze data and build predictive models.
 Applicant Profile: Experienced in Python, R, and ML models.
 Interview Question: Tell me about a machine learning project you are proud of.
 Optimal Answer: I developed a predictive model using Python and scikit-learn to forecast customer churn, achieving 85% accuracy by carefully preprocessing the data and tuning hyperparameters.
-'''
+```
 
 Finetuning Tasks: As you did for the second project check in,
 clearly define the training data you used, making sure to note any