The ML Healthy Heart Predictor using Logistic Regression
##Logistic Regression: The popular machine learning technique logistic regression is used for classification problems, where the objective is to forecast the likelihood that an input belongs to a certain class. It is a common learning technique that is utilised in a number of industries, including marketing, healthcare, and finance. The logistic function, sometimes referred to as the sigmoid function, is the foundation of the logistic regression model and is used to convert the input characteristics into probabilities. Any real-valued input is mapped into the 0 to 1 range via the logistic function. This result is then translated into the likelihood that the input comes under a certain class. In logistic regression, input features are multiplied by corresponding weights, and the resulting products are summed together to generate a single value. This value is then transformed by the logistic function to obtain the predicted probability of the input belonging to a particular class. The weights are learned during the training process by minimizing a loss function, such as cross-entropy, which measures the difference of the predicted probabilities and the actual labels. Logistic regression is particularly useful for binary classification tasks, where there are only two possible classes. However, it can also be extended to handle multiclass classification tasks by using techniques such as one-vs-all or SoftMax. Equation of the straight line :
Logistic Regression y lies between 0 and 1 only divind the above equation
To be in range of -infinity to +infinity log is taken
The Logistic Regression Equation as Above.
##Streamlit: Streamlit is an open-source Python library used for creating interactive web applications for machine learning and data science projects. It provides a simple and intuitive way to build web applications using Python code, allowing data scientists and machine learning engineers to easily share their work and communicate their findings with others. Streamlit is designed to simplify the process of building web applications by providing a variety of built-in widgets that can be used to create interactive visualizations and user interfaces. These widgets include sliders, dropdowns, checkboxes, and text inputs, among others. Additionally, Streamlit makes it easy to display data visualizations and charts using popular Python libraries such as Matplotlib and Plotly. Streamlit is built on top of Flask, a popular web framework for Python, and allows developers to write code in a single Python file, eliminating the need for complex web development frameworks and tools. This makes it easy for data scientists and ml engineers to create and deploy web applications without requiring extensive web development knowledge or experience.
visit : https://all3n2601-healthy-heart-predictor-app-t3czlh.streamlit.app