Classification-on-iris-data(https://classification-on-iris-data.vercel.app/)

This project aims to classify iris flowers into three species—setosa, versicolor, and virginica—based on their sepal and petal measurements. The classification model is built using Python and machine learning libraries, such as scikit-learn and TensorFlow.

Project Overview

The goal of this project is to develop a machine learning model that can accurately classify iris flowers based on their physical characteristics. The project involves data preprocessing, exploratory data analysis (EDA), model training, evaluation, and prediction.

Dataset

The Iris dataset consists of 150 samples with four features:

1. Sepal Length (cm)
2. Sepal Width (cm)
3. Petal Length (cm)
4. Petal Width (cm)

Each sample is labeled as one of three classes:

1. Setosa
2. Versicolor
3. Virginica

Requirements

1. Python 3.8+
2. scikit-learn
3. TensorFlow
4. NumPy
5. Pandas
6. Matplotlib
7. seaborn

Model Training

The model is trained using different algorithms such as:

1. Logistic Regression
2. Decision Trees
3. Random Forest
4. Support Vector Machine (SVM)
5. Neural Networks (using TensorFlow)

Results

The best-performing model achieved an accuracy of X% on the test set. Further details can be found in the results section of the notebook.

Contributing

Contributions are welcome! Please fork the repository and create a pull request for any improvements or fixes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.