https://github.com/devroopsaha744/diabetes-prediction-using-logistic-regression-beginner-level-
This project is based on the diabetes prediction model using Logistic Regression.
https://github.com/devroopsaha744/diabetes-prediction-using-logistic-regression-beginner-level-
Last synced: 3 months ago
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This project is based on the diabetes prediction model using Logistic Regression.
- Host: GitHub
- URL: https://github.com/devroopsaha744/diabetes-prediction-using-logistic-regression-beginner-level-
- Owner: devroopsaha744
- Created: 2023-09-18T17:31:56.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-04T19:13:12.000Z (over 1 year ago)
- Last Synced: 2023-10-05T07:18:47.500Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 1.79 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Diabetes-Prediction-using-Logistic-Regression-beginner-level-
## Introduction
This project is based on the diabetes prediction model using Logistic Regression.
## Dataset
Here is the dataset I have used: [click here!](https://www.kaggle.com/datasets/mathchi/diabetes-data-set)
## Libraries used
[Numpy](https://numpy.org/)
[pandas](https://pandas.pydata.org/)
[Matplotlib](https://matplotlib.org/)
[seaborn](https://seaborn.pydata.org/)
[Scikit-learn](https://scikit-learn.org/stable/)
## ML algorithm used
Logistic Regression is a classification model mainly used for Binary classification of the Target Variable. It's use cases arises when the outcome is in the fomr of 'Yes'/'No' or 0/1, ie. when the Target variable is a categorical variable itself.
It uses Sigmoid fucntion which converts that maps any input between 0 or 1.
Linear regression method cannot be used for classification because outliers can affect the regression coefficients. That's why Logistic Regression is used, as the sigmoid fucntion transforms the best fitting line into to a S-type curve.