IRIS Flower Prediction App

This repository contains a machine learning project focused on predicting the species of an Iris flower based on its sepal and petal dimensions. The project uses the famous Iris dataset and implements a Streamlit web app for users to interactively make predictions.

Project Structure

IRIS-Project/
│
├── data/
│   └── IRIS.csv
│
├── notebooks/
│   └── IRIS_Classification.ipynb
│
├── models/
│   └── IRIS_Model.pkl
│
├── app/
│   ├── streamlit_app.py
│   ├── images/
│   │   └── iris-flowers.jpg
│   └── utils/
│       ├── __init__.py
│       ├── model_loader.py
│       └── predictor.py
│
└── requirements.txt

Project Overview

This project involves:

• Data Exploration: Analyzing the Iris dataset to understand its structure and distributions.
• Model Training: Training a machine learning model using a Support Vector Classifier to predict the species of the flower.
• Model Deployment: A Streamlit-based web application that allows users to input the sepal and petal dimensions of an Iris flower and get a predicted species.

Features

• Interactive inputs for flower dimensions (sepal length, sepal width, petal length, petal width).
• Displays the predicted species along with a representative image of the flower.
• Clean and simple user interface powered by Streamlit.

Running the App

1. Install Dependencies

Make sure you have Python installed. Then, install the required dependencies by running:

pip install -r requirements.txt

2. Run the Streamlit App

To run the Streamlit app, navigate to the app/ directory and run the following command:

streamlit run app/streamlit_app.py

This will launch the app in your browser where you can input the sepal and petal dimensions to get a prediction.

3. Interact with the App

Once the app is running, you can:

• Adjust the sliders to set the dimensions of the sepal and petal.
• Click the "Prediction" button to view the predicted species and its corresponding image.

Model Training

To retrain the model, refer to the notebooks/iris_classification.ipynb notebook, which walks through:

• Data loading and preprocessing.
• Model training using a Support Vector Classifier.
• Saving the trained model as IRIS_Model.pkl.

Files Explained

• streamlit_app.py: The main code for the web app.
• model_loader.py: Contains the load_model function to load the saved model (IRIS_Model.pkl).
• predictor.py: Contains the get_prediction function that takes the model and input data to return the predicted species.
• images/: Contains the images for each predicted species, as well as a general image for IRIS flowers.

Dataset

The Iris dataset (IRIS.csv) is used in this project. It contains 150 observations of Iris flowers, each with:

• Sepal length (cm)
• Sepal width (cm)
• Petal length (cm)
• Petal width (cm)
• Species (Setosa, Versicolor, Virginica)

Dependencies

All dependencies are listed in requirements.txt. These include:

• Numpy: For numerical computations.
• Pandas: For data manipulation.
• Matplotlib: For plotting graphs and charts.
• Seaborn: For data visualization during exploratory data analysis.
• Scikit-learn: For training the machine learning model.
• Streamlit: For building the interactive web app.