Project : Cardiovascular Risk Factor Data

Dataset

• DataSet is taken from Kaggle - https://www.kaggle.com/datasets/mamta1999/cardiovascular-risk-data
• DataSet provided information on over 4,000 patients and included 15 attributes, each representing a potential risk factor for CHD. These attributes included demographic, behavioral, and medical risk factors.

Variables Description

Demographic:

• Sex: male or female ("M" or "F") Age: Age of the patient (Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous)
• Education: The level of education of the patient (categorical values - 1,2,3,4)

Behavioral:

• is_smoking: whether or not the patient is a current smoker ("YES" or "NO")
• Cigs Per Day: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.)

Medical (history):

• BP Meds: whether or not the patient was on blood pressure medication (Nominal)
• Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal)
• Prevalent Hyp: whether or not the patient was hypertensive (Nominal)
• Diabetes: whether or not the patient had diabetes (Nominal)

Medical (current):

• Tot Chol: total cholesterol level (Continuous)
• Sys BP: systolic blood pressure (Continuous)
• Dia BP: diastolic blood pressure (Continuous)
• BMI: Body Mass Index (Continuous)
• Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.)
• Glucose: glucose level (Continuous)

Predict variable (desired target):

• 10-year risk of coronary heart disease CHD(binary: “1”, means “Yes”, “0” means “No”)

SKills

• Python - 3.11
• Pandas
• Numpy
• Matplotlib
• scikit-learn
• Treating missing values in dataset
• Data visualization
• Data Preprocessing
• Data Modeling
• Framework - Flask
• Frontend - HTML, CSS

Installation

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import confusion_matrix, auc, roc_curve
from flask import Flask, render_template, request
import pickle

Deployment