diff --git a/main.py b/main.py
new file mode 100644
index 00000000..e69efb01
--- /dev/null
+++ b/main.py
@@ -0,0 +1,46 @@
+import pandas as pd
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+df = pd.read_csv('framingham.csv')
+df.head()
+
+df.dropna(axis=0, inplace=True)
+print(df.shape)
+
+df['TenYearCHD'].value_counts()
+
+
+plt.figure(figsize = (14, 10))
+sns.heatmap(df.corr(), cmap='Purples',annot=True, linecolor='Green', linewidths=1.0)
+plt.show()
+
+
+X = df.iloc[:,0:15]
+y = df.iloc[:,15:16]
+
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=21)
+from sklearn.linear_model import LogisticRegression
+logreg = LogisticRegression()
+print(X_train)
+print(y_train)
+
+logreg.fit(X_train, y_train)
+y_pred = logreg.predict(X_test)
+score = logreg.score(X_test, y_test)
+print("Prediction score of the trained model is:",score)
+
+
+from sklearn.metrics import confusion_matrix, classification_report
+cm = confusion_matrix(y_test, y_pred)
+print("Confusion Matrix of model is:\n",cm)
+
+print("Classification Report of model is:\n\n", classification_report(y_test, y_pred))
+conf_matrix = pd.DataFrame(data = cm,
+                           columns = ['Predicted:0', 'Predicted:1'],
+                           index =['Actual:0', 'Actual:1'])
+plt.figure(figsize = (10, 6))
+sns.heatmap(conf_matrix, annot = True, fmt = 'd', cmap = "Greens", linecolor="Blue", linewidths=1.5)
+plt.show()