- Introduction
- Diabetes Diagnosis using Naive Bayes
- Iris Classification with Keras
- Notebook
- License
- Acknowledgements
This repository contains two Python scripts for machine learning projects related to medical diagnosis and classification. The first script utilizes the Naive Bayes algorithm for the diagnosis of diabetes, while the second script employs a Keras model for the classification of Iris flowers.
The diabetes diagnosis script (Diabetes.py
) is designed to predict whether an individual is likely to have diabetes or not. It is implemented using the Naive Bayes algorithm, a probabilistic classifier based on Bayes' theorem. The dataset used for training and testing is given.
- Python 3.x
- scikit-learn
To install the required dependencies, run:
pip install scikit-learn
- Clone the repository:
git clone https://github.com/ankikadey/Machine-Learning-With-Python.git
cd Machine-Learning-With-Python
cd Diabetes-Detection-Using-NB
- Run the diabetes diagnosis script:
python Diabetes.py
The Iris classification script (Iris-with-keras.ipynb
) is focused on classifying Iris flowers into different species using a deep learning model built with the Keras library. The dataset used is the well-known Iris dataset, which is included in scikit-learn.
- Python 3.x
- TensorFlow
- Keras
To install the required dependencies, run:
pip install tensorflow keras
- Clone the repository:
git clone https://github.com/ankikadey/Machine-Learning-With-Python.git
cd Machine-Learning-With-Python
cd Iris Classification Using Neural Network
- Run the Iris Classification script:
python Iris-with-keras.py
You can view the Jupyter Notebooks in this project using the following links: