Welcome to the Iris Flower Classification project! This project aims to build a machine learning model for classifying iris flowers based on their features. The project is part of the Bharat Intern Virtual Internship program, focusing on hands-on experience in machine learning and neural networks.
- code.ipynb: Jupyter Notebook containing the main code for the project.
- README.md: Project documentation providing an overview, instructions, and details about the project.
- images/: Directory containing images used in the documentation.
- Python 3
- Jupyter Notebook
- Required Python packages (install using
pip install -r requirements.txt)
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Clone the repository:
git clone https://github.com/your-username/iris-classification.git cd iris-classification -
Install dependencies:
pip install -r requirements.txt
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Open the Jupyter Notebook:
jupyter notebook code.ipynb
- Data Loading and Preprocessing: The Iris dataset is loaded and preprocessed to prepare it for model training.
- Neural Network Architecture: A complex neural network is defined using Keras Tuner for hyperparameter tuning.
- Hyperparameter Tuning: The model is tuned using RandomSearch for optimal hyperparameter values.
- Model Training and Evaluation: The best model is selected and evaluated on the test set. Accuracy and performance metrics are calculated.
- Visualizations: Various visualizations, including training history, confusion matrix, and distribution charts, provide insights into the data and model performance.
The model achieved an accuracy of [Your Accuracy]% on the test set, demonstrating its effectiveness in classifying iris flowers.
Special thanks to Bharat Intern for providing this hands-on learning opportunity in machine learning and data science.
A Syed Khwaja