Welcome to the Iris Flower Classifier project! This repository contains a deep learning model based on TensorFlow's DNNClassifier to classify the famous Iris flower dataset 🌼. The goal is to train a powerful neural network 🧠 that can accurately distinguish between different Iris species, making it an excellent showcase of machine learning 🌿 in action.
The Iris dataset is a classic benchmark in the machine learning community. It includes 150 samples of Iris flowers, each belonging to one of three species: Setosa, Versicolor, or Virginica. The dataset is divided into four features - sepal length, sepal width, petal length, and petal width. Below is a table summarizing the dataset information:
| Species | Sepal Length (cm) | Sepal Width (cm) | Petal Length (cm) | Petal Width (cm) |
|---|---|---|---|---|
| Setosa | 5.1 | 3.5 | 1.4 | 0.2 |
| Versicolor | 7.0 | 3.2 | 4.7 | 1.4 |
| Virginica | 6.3 | 3.3 | 6.0 | 2.5 |
- Clone the repository:
git clone https://github.com/Asirwad/IrisFlowerClassifier-DNNClassifier.git - Install the required dependencies:
pip install tensorflow matplotlib pandas - Train the DNNClassifier and observe the classification magic! 🌟
The TensorFlow DNNClassifier in this project is built with multiple hidden layers, allowing it to learn intricate patterns from the Iris dataset. Feel free to customize the architecture and experiment with different hyperparameters to achieve the best performance 🚀.
| Output 1 | Output 2 |
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Contributions are welcome! Whether it's optimizing the model, improving the documentation, or enhancing the visualizations, your contributions can help this project flourish 🌱. Submit a pull request and let's grow together! 🌷
