2. INTRODUCTION
Machine Learning is used across many spheres
around the world. The healthcare industry is no
exception. Machine Learning can play an essential
role in predicting presence/absence of Locomotor
disorders, Heart diseases and more. Such
information, if predicted well in advance, can
provide important insights to doctors who can then
adapt their diagnosis and treatment per patient
basis.
3. ABOUT THE PROJECT
It is basically a web application which takes some
input from the users and based on that input it
predicts whether a person has a heart disease or not.
Its predictions are completely based on the trained
machine learning models.
4. Imported libraries
I imported several libraries for the project:
1.numpy: To work with arrays
2.pandas: To work with csv files and dataframes
3.matplotlib: To create charts using pyplot, define parameters
using rcParams and color them with cm.rainbow
4.warnings: To ignore all warnings which might be showing up in the
notebook due to past/future depreciation of a feature
5.train_test_split: To split the dataset into training and testing data
6.StandardScaler: To scale all the features, so that the Machine Learning
model better adapts to the dataset
5. 7.Flask :Flask is a lightweight WSGI web application
framework. It is designed to make getting started quick and
easy, with the ability to scale up to complex applications. It
began as a simple wrapper around Werkzeug and Jinja and
has become one of the most popular Python web application
frameworks.
7. Different algorithms used
1.Logistic regression : Logistic Regression is a Machine Learning algorithm
which is used for the classification problems, it is a predictive analysis
algorithm and based on the concept of probability.
2.Random forest classifier:Random Forest is a popular machine learning
algorithm that belongs to the supervised learning technique. It can be used
for both Classification and Regression problems in ML. It is based on the
concept of ensemble learning, which is a process of combining multiple
classifiers to solve a complex problem and to improve the performance of
the model.
8. 3. Decision Tree: Decision Tree is a Supervised
learning technique that can be used for both
classification and Regression problems, but mostly it is
preferred for solving Classification problems. It is a
tree-structured classifier, where internal nodes
represent the features of a dataset, branches
the decision rules and each leaf node represents the
outcome.
