Question

This is A PYTHON Question.

Developing a machine learning system for a given dataset.

Dataset

Diabetes dataset - https://www.kaggle.com/uciml/pima-indians-diabetes-database

Algorithms

Either one of the following:

1. K-Nearest Neighbors

2. Support Vector Machines

3. Neural Networks

Notes:

Explain your choice of algorithms and analyze the models developed.

Show what patterns/insights can be extracted from your chosen dataset and the selected algorithms.

Answer 1

Sorry that link is not working

I'm chosing support vector machine, because svm is very good at labeled data, so i choosed svm

if you have any problem in code, please let me know, you if you get desired output please upvote, any problem please mention in comment section

import pandas as pd
import requests
import csv
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn import svm
import numpy as np

payload = {
    '__RequestVerificationToken': '',
    'username': '',
    'password': '',
    'rememberme': 'false'
}

loginURL = 'https://www.kaggle.com/account/login'
dataURL = "https://www.kaggle.com/uciml/pima-indians-diabetes-database/downloads/diabetes.csv"

with requests.Session() as c:
    response = c.get(loginURL).text
    AFToken = response[response.index('antiForgeryToken')+19:response.index('isAnonymous: ')-12]
    #print("AntiForgeryToken={}".format(AFToken))
    payload['__RequestVerificationToken']=AFToken
    c.post(loginURL + "?isModal=true&returnUrl=/", data=payload)
    download = c.get(dataURL)
    decoded_content = download.content.decode('utf-8')
    cr = csv.reader(decoded_content.splitlines(), delimiter=',')
    my_list = list(cr)
    #for row in my_list:
    #    print(row)


df = pd.DataFrame(my_list)
def plot_decision_regions(X, y, classifier,test_idx=None, resolution=0.02):
    # setup marker generator and color map
    markers = ('s', 'x', 'v', '^', 'o')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])
    # plot the decision surface
    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                           np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z = Z.reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())
    # plot all samples
    X_test, y_test = X[test_idx, :], y[test_idx]
    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
                    alpha=0.8, c=cmap(idx),
                    marker=markers[idx], label=cl)
    # highlight test samples
    if test_idx:
        X_test, y_test = X[test_idx, :], y[test_idx]
        plt.scatter(X_test[:, 0], X_test[:, 1], c='',
                    alpha=1.0, linewidth=1, marker='v',
                    s=55, label='test set')

X = df[['Glucose','BMI']]
y = df['Outcome']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
X_train = np.array(X_train)
y_train = np.array(y_train)
X_test = np.array(X_test)
y_test = np.array(y_test)
machine1 = svm.SVC(kernel = 'linear')
machine1.fit(X_train,y_train)
y_pred = machine1.predict(X_test)
plot_decision_regions(X_train, y_train, machine1)
plt.show