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24 days ago

"Explore the medical report analysis of cardiac patients in this project. Analyze and visualize the dataset to predict chances of heart attack using ML models. Learn data analysis with Python from beginner to advanced level. Download the dataset from Kaggle and start exploring now!"

# Project Title - Cardiac Patients Medical Report Analysis

image source: istockphoto.com

Heart attack is one of the common cause of premature death all over the world. In India alone more than 3 million people die due to heart attack and heart stroke annually. There are many facts that affect the health and efficiency of heart. With proper knowledge and precautions, the challenge and complication can be managed.

In this project medical report of several heart patients been analyzed. With the exploratory analysis, some facts can be checked and the processed dataset can also be used in ML models to predict chances of heart attack in new patients.

The vital part of Data Analysis is understanding the dataset. Asking relevant questions is the primary key to good analytics. So, here we will understand the dataset by going through the columns thoroughly.

Analysis and visualization of the data will be done in four steps

1. Interaction: Will have a closer look at the dataset and it's components.
2. Prepare: Cleaning and preparation of data for visualization
3. Analyze/Ask : In this step, relevant questions will be asked
4. Visualization: Will find the answers to the Analyze/Ask section.

• The dataset is available at Kaggle.
• This project work is a part of an excellent course on Data Analysis with Python: Zero to Pandas offered by Jovian. The course focuses on beginner to advanced level data analysis using python libraries like Pandas, Numpy, Matplotlib, and Seaborn.

#### Import requried libraries

``!pip install jovian opendatasets --upgrade --quiet``
``````# Import required libraries

import matplotlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import opendatasets as od
import os
%matplotlib inline

sns.set_style('darkgrid')
matplotlib.rcParams['font.size'] = 14
matplotlib.rcParams['figure.figsize'] = (9, 5)
matplotlib.rcParams['figure.facecolor'] = '#00000000'``````

Let us save and upload our work to Jovian before continuing.