This project focuses on classifying Iris flowers into different species using machine learning techniques. The classification model is built using Jupyter Notebook and deployed using Streamlit. Demonstration of how to load IRIS dataset, visualize and build a KNN Classifier and Logistic Regression model on it and predict accuracy.
Before running the project, make sure you have the following prerequisites:
Python (version 3.6 or above) Jupyter Notebook (installed via Anaconda or standalone installation) Streamlit (installed via pip or conda) scikit-learn (installed via pip or conda) pandas (installed via pip or conda) numpy (installed via pip or conda)
Launch Jupyter Notebook:
Open the iris classification.ipynb file in the Jupyter Notebook interface.
Run the notebook cells to train the classification model and evaluate its performance.
After training the model, you can deploy it using Streamlit:
streamlit run projects.py Open your web browser and navigate to http://localhost:8501 to access the Streamlit application.
This project is free to use and does not contains any license.