--- a
+++ b/flask/model.py
@@ -0,0 +1,124 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Description:                                                                                                                   
+# The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.
+# 
+# Attributes:
+# 1. Glucose Level
+# 2. BMI
+# 3. Blood pressure
+# 4. Pregnancies
+# 5. Skin thickness
+# 6. Insulin
+# 7. Diabetes pedigree function
+# 8. Age
+# 9. Outcome
+
+# # Step 0: Import libraries and Dataset
+
+# In[1]:
+
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+import warnings
+warnings.filterwarnings('ignore')
+
+import pickle
+
+# In[2]:
+
+
+dataset = pd.read_csv('diabetes.csv')
+
+
+
+# # Step 3: Data Preprocessing
+
+# In[13]:
+
+
+dataset_X = dataset.iloc[:,[1, 4, 5, 7]].values
+dataset_Y = dataset.iloc[:,8].values
+
+
+# In[14]:
+
+
+dataset_X
+
+
+# In[15]:
+
+
+from sklearn.preprocessing import MinMaxScaler
+sc = MinMaxScaler(feature_range = (0,1))
+dataset_scaled = sc.fit_transform(dataset_X)
+
+
+# In[16]:
+
+
+dataset_scaled = pd.DataFrame(dataset_scaled)
+
+
+# In[17]:
+
+
+X = dataset_scaled
+Y = dataset_Y
+
+
+# In[18]:
+
+
+X
+
+
+# In[19]:
+
+
+Y
+
+
+# In[20]:
+
+
+from sklearn.model_selection import train_test_split
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.20, random_state = 42, stratify = dataset['Outcome'] )
+
+
+# # Step 4: Data Modelling
+
+# In[25]:
+
+
+from sklearn.svm import SVC
+svc = SVC(kernel = 'linear', random_state = 42)
+svc.fit(X_train, Y_train)
+
+
+# In[26]:
+
+
+svc.score(X_test, Y_test)
+
+
+# In[27]:
+
+
+Y_pred = svc.predict(X_test)
+
+
+
+
+
+pickle.dump(svc, open('model.pkl','wb'))
+model = pickle.load(open('model.pkl','rb'))
+#print(model.predict(sc.transform(np.array([[86, 66, 26.6, 31]]))))
+
+