views.py

from django.shortcuts import render
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
def home(request):
    return render(request, 'home.html')
def predict(request):
    return render(request, 'predict.html')

def result(request):
    data = pd.read_csv("C:/Users/sujit/AIML Internship/diabetes_dataset/diabetes.csv")
    X = data.drop("Outcome", axis=1)
    Y = data['Outcome']
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)

    model = SVC()
    model.fit(X_train, Y_train)

    val1 = float(request.GET['n1'])
    val2 = float(request.GET['n2'])
    val3 = float(request.GET['n3'])
    val4 = float(request.GET['n4'])
    val5 = float(request.GET['n5'])
    val6 = float(request.GET['n6'])
    val7 = float(request.GET['n7'])
    val8 = float(request.GET['n8'])

    pred = model.predict([[val1, val2, val3, val4, val5, val6, val7, val8]])

    result2 = ""
    if pred==[1]:
        result1 = "Oopsie! Diabetes alert!"
    else:
        result1 = "Woohoo!!! No diabetes here!"

    return render(request, "predict.html", {"result2":result1})