Safe-Heart-Classifier-Multi-Layer-Perceptron-from-Scratch

LINK : https://vice777-safe-heart-classifier-multilayer-perceptron--app-idf5su.streamlitapp.com/

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Description:

Classify the chances of having a Heart Attack based on your Heart's Condition.
In this end-to-end Machine Learning project-tutorial, I have created and trained Multi-Layer model from scratch, using NumPy.
Furthermore, the model with the best accuracy is embedded in the web-app developed using streamlit module for the purpose of classification of your Heart's Condition.

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Understanding the Problem Statement

This project uses the popular Heart Attack Analysis & Prediction Dataset for training the model and making predictions.

For the purpose of prediction and classification, the features given in the table below are used.
Detailed description about the features is provided within the table.

Features	Description
Age	Age of the patient
Sex	Sex of the patient
cp	Chest Pain type chest pain type: Value 1: Typical angina Value 2: Atypical angina Value 3: Non-anginal pain Value 4: Asymptomatic
trtbps	Resting Blood Pressure (in mm Hg)
chol	Cholestoral in mg/dl fetched via BMI Sensor
fbs	(Fasting blood sugar > 120 mg/dl) 1 = true 0 = false
restecg	Value 0: Normal Value 1: Having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) Value 2: Showing probable or definite left ventricular hypertrophy by Estes criteria
thalach	Maximum Heart Rate achieved
exang	Exercise induced angina 1 = yes 0 = no
Oldpeak	ST depression induced by exercise relative to rest
slp	Peak exercise ST segment Slop 0 = Downsloping 1 = Flat 2 = Upsloping
caa	The number of major vessels (0–3)
thall	A blood disorder called Thalassemia Value 1: fixed defect (no blood flow in some part of the heart) Value 2: normal blood flow Value 3: reversible defect (a blood flow is observed but it is not normal)
target	Percentage of deliverable volume 0= less chance of heart attack 1= more chance of heart attack

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Key Project Takeaways

This project provided hands-on experience in real-time data handling and working behind Neural Networks :

Data preprocessing and cleaning for training and testing the data

Building an efficient Neural Network (Multi-Layer Perceptron) from scratch using NumPy

Mathematics behind Activation Functions and Gradient Losses

Web-app development using Streamlit