Iris Flower Classification Dashboard 🌸

A premium machine learning web application built using Python, Scikit-Learn, and Streamlit to classify Iris flower species (Setosa, Versicolor, Virginica) in real-time based on their physical measurements.

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🚀 Features

• Real-Time Classification: Select sepal and petal measurements via interactive sliders to predict the species instantly.
• Vibrant Class Confidence Charts: Displays a live horizontal probability distribution bar chart showing the model's confidence for all three classes.
• Premium Species Imagery: Renders custom, high-resolution photographs of the classified species in real-time.
• Interactive Exploratory Data Analysis (EDA): Fully responsive scatter plots visualizing sepal and petal measurements from the dataset, highlighting the user's custom input point with a distinctive red star (⭐).
• Model Diagnostics: Detailed metrics showing the performance of the classification model (precision, recall, confusion matrix) and relative feature importances.
• Clean Code Architecture: Separated model training pipelines (train_model.py), exploratory analysis (eda_analysis.py), and the user interface (app.py).

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📈 Model Performance

Three machine learning classifiers were evaluated on the training set (80/20 split):

Classifier	Test Accuracy	Status
Support Vector Classifier (SVC)	96.67%	Selected & Deployed
Logistic Regression	93.33%	Backup
Random Forest	90.00%	Backup

The best-performing model (SVC) is serialized using joblib along with the scaling parameters.

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🛠️ Setup & Running Instructions

Follow these steps to run the application locally:

1. Clone the repository

git clone https://github.com/Omranjan110/CodeAlpha_ProjectName.git
cd CodeAlpha_ProjectName

2. Create and Activate a Virtual Environment

• Windows (PowerShell):

  python -m venv .venv
  .venv\Scripts\Activate.ps1

• Windows (CMD):

  python -m venv .venv
  .venv\Scripts\activate.bat

• macOS/Linux (Terminal):

  python3 -m venv .venv
  source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the Model Training Pipeline (Optional)

If you want to train the model from scratch:

python train_model.py

5. Launch the Web App

streamlit run app.py

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📦 File Structure

├── assets/                  # High-quality flower images for Setosa, Versicolor, Virginica
├── static_plots/            # Generated EDA visualization charts
├── app.py                   # Streamlit web application entry point
├── train_model.py           # Model training and serialization script
├── eda_analysis.py          # Exploratory Data Analysis script
├── requirements.txt         # Package dependencies
├── .gitignore               # Ignored files (venv, pycache, etc.)
└── README.md                # Project documentation