Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/vyvy-vi/iris-flower-classifier
Logistic Regression on Iris Flower Dataset with Numpy and pandas and predicted weights used to serve results to a website via FastAPI
https://github.com/vyvy-vi/iris-flower-classifier
Last synced: about 10 hours ago
JSON representation
Logistic Regression on Iris Flower Dataset with Numpy and pandas and predicted weights used to serve results to a website via FastAPI
- Host: GitHub
- URL: https://github.com/vyvy-vi/iris-flower-classifier
- Owner: Vyvy-vi
- License: mit
- Created: 2023-08-26T15:07:18.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-06T20:57:06.000Z (9 months ago)
- Last Synced: 2024-11-28T20:12:51.787Z (about 2 months ago)
- Language: Jupyter Notebook
- Homepage: http://iris-flower-ml.vercel.app/
- Size: 25.3 MB
- Stars: 0
- Watchers: 1
- Forks: 2
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Iris Flower Classifier
This project is an implementation of Logistic Regression with Gradient Descent and the One-vs-Rest strategy for Multiclass Classification, built from scratch with NumPy and Pandas. It leverages the [Iris Dataset](https://archive.ics.uci.edu/dataset/53/iris) from [UCI Machine Learning repository](https://archive.ics.uci.edu/) to perform precise species classification based on the sepal and petal characteristics of iris flowers.
The trained model's weights are then employed to serve predictions through a website powered by FastAPI and HTMX.
## Preview
## Live Version
This page is currently deployed. [View the live website.](https://iris-flower-ml.vercel.app/)
## Implementation details
### Features given in the dataset (input features):
- Petal length
- Petal width
- Sepal length
- Sepal width
### Classes Classified (target labels):
- Iris-setosa
- Iris-versicolor
- Iris-virginica
### Model used:
- `m` - training examples, `w` - weights vector, `b` - bias
- Logistic Regression Model:
$$f_{w,b}(x) = g(\textbf{w} . \textbf{x} + b)$$
where `g` is the sigmoid function given by:
$$g(z) = \frac{1}{e^{-z}}$$
- Cost function for logistic regression:
$$\textbf{J}(\textbf{w}, b) = \frac{1}{m} \sum_{i = 0}^{m - 1} \left( loss(f_{\textbf{w}, b}(\textbf{x}^{(i)}), y^{(i)}) \right)$$
where the loss function is the cost for a single data point and is given by:
$$loss(f_{\textbf{w},b}(x^{(i)}),y^{(i)}) = \left(−y^{(i)}\log(f_{\textbf{w},b}(\textbf{x}^{(i)})\right) − \left((1 − y^{(i)})\log(1 - f_{\textbf{w}, b}(\textbf{x}^{(i)}))\right)$$
where $f_{\textbf{w},b}(\textbf(x)^{(i)})$ is the model's prediction and $y^{(i)}$ is the actual label
$$f_{\textbf{w},b}(\textbf{x}^{(i)}) = g(\textbf{w} . \textbf{x}^{(i)} + b)$$
## Setup
- Clone this project:
```
git clone https://github.com/Vyvy-vi/iris-flower-classifier/
```
- [Install Python3](https://www.python.org/downloads/)
- Install dependencies
```
pip install -r requirements.txt
```
- Run Jupyter Notebook
```
jupyter notebook
```
- Run application
```
python3 main.py
```
## Usage
- Training the Model: To train the model and generate the weights and bias, run
the classification-logistic-regression-from-scratch.ipynb Jupyter notebook.
(run the `jupyter notebook` command)
- Running the Web Application: Execute the web application using `python main.py`. This starts the web server, making the prediction service available at http://localhost:8000.
- Making Predictions: Input sepal and petal measurements via the web interface and receive predictions for the iris flower species.
## Feedback and Bugs
If you have feedback or a bug report, please feel free to [open a GitHub issue](https://github.com/Vyvy-vi/iris-flower-classifier/issues/new/choose)!
## License
This software is licensed under [The MIT License](LICENSE).
Copyright 2023 Vyom Jain.