#!/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]]))))