Create model.py

model.py

@@ -0,0 +1,13 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+
+data=pd.read_csv('HeartDisease.csv')
+print(data)
+non_numeric_features = data.select_dtypes(include=['object']).columns.tolist()
+print(non_numeric_features)