Description:
Taking in a CSV file with the patient's Age, Gender, Chest Pain, Resting BP, Chol, RestECG, Max HR, Old peak, Thal, AHD, we had to seperate and filter through which would be meaningful features. We took in Age, Rest BP, Chol, RestECG, MaxHR, Old peak, and AHD as our features and discarded the rest. We then split the data and used KNN, Logistic Regression, and Decision Tree to give us three different results. After the initial three results we then used one hot encoding and it boosted our results.
For the second problem, we loaded in the data to predict debt. Our features we used were income, limit, rating, number of cards, age, education, and if they were married. We then preprocessed the data and split it. We then ran linear regression on the data.
Stack: Python, Pandas, Numpy, SkLearn