A simple Machine Learning classification project using the classic Iris Dataset. This project demonstrates how to train and evaluate ML models to classify Iris flower species based on their physical measurements.
The goal of this project is to predict the species of an Iris flower using four input features:
- Sepal Length
- Sepal Width
- Petal Length
- Petal Width
Based on these features, the model classifies the flower into one of the following species:
- Iris Setosa
- Iris Versicolor
- Iris Virginica
- Dataset Name: Iris Dataset
- Total Samples: 150
- Number of Classes: 3
- Features: 4 numerical attributes
Dataset file present in the repository: Iris.csv
- Data loading and preprocessing
- Exploratory Data Analysis (EDA)
- Machine Learning model training
- Model evaluation
- Simple deployment using Python
- Jupyter Notebook for experimentation
- Python
- NumPy
- Pandas
- Matplotlib & Seaborn
- Scikit-learn
- Jupyter Notebook
Clone the repository: git clone https://github.com/anushkaadak2684/Iris-Classification.git cd Iris-Classification
Create a virtual environment (optional): python -m venv .venv
Activate: Linux / macOS: source .venv/bin/activate Windows: .venv\Scripts\activate
Install dependencies: pip install -r requirements.txt
Run the Jupyter Notebook: jupyter notebook Iris-Dataset-Classification.ipynb (Model is saved as saved_model.sav, Scaler is saved as scaler.sav)
Run the deployment script: python app.py
Iris-Classification/
- static/
- bgimg.jpg
- templates/
- index.html
- Iris-Dataset-Classification.ipynb
- Iris.csv
- app.py
- requirements.txt
- .gitignore
- README.md
The trained model achieve high accuracy due to the clean and well-structured nature of the Iris dataset. Performance can be improved using hyperparameter tuning and ensemble methods.