Pima Indians Diabetes Dataset

1. Introduction

The Pima Indians Diabetes Dataset involves predicting the onset of diabetes within 5 years in Pima Indians given medical details.

It is a binary (2-class) classification problem. The number of observations for each class is not balanced. There are 768 observations with 8 input variables and 1 output variable. Missing values are believed to be encoded with zero values. The variable names are as follows:

Number of times pregnant.
Plasma glucose concentration a 2 hours in an oral glucose tolerance test.
Diastolic blood pressure (mm Hg).
Triceps skinfold thickness (mm).
2-Hour serum insulin (mu U/ml).
Body mass index (weight in kg/(height in m)^2).
Diabetes pedigree function.
Age (years).
Class variable (0 or 1).

2. Loading the necessary libraries and datasets

import sys
assert sys.version_info >= (3, 5)

# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"

# Common imports
import numpy as np
import os
import pandas as pd

# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

#ML imports
from sklearn.linear_model import SGDClassifier

## Loading the dataset from github repo
url = 'https://raw.githubusercontent.com/hargurjeet/MachineLearning/Pima-Indians-Diabetes-Dataset/diabetes_dataset.csv'
df = pd.read_csv(url)

df.head()