This project focuses on classifying diabetes diagnosis using various machine learning models. The dataset contains several features such as glucose level, blood pressure, skin thickness, insulin, BMI, and more.
The dataset used for this project is sourced from the file diabetes.csv. It contains both categorical and numerical features related to diabetes diagnosis.
- Visual exploration: The distribution of numerical features is visualized using histograms to gain insights into the data.
- Handling missing values: Zero values in features such as glucose, blood pressure, skin thickness, insulin, and BMI are replaced with the mean value.
- Train-test split: The dataset is split into training, test, and validation sets for model evaluation.
Several classification models are implemented and evaluated for their performance in predicting diabetes diagnosis. The models include Decision Tree, Logistic Regression, Naive Bayes, K-Nearest Neighbors, Random Forest, Support Vector Machines, and more.
- Model training: Models are trained using the training set.
- Model evaluation: Performance metrics such as accuracy, precision, recall, and F1-score are calculated and visualized using confusion matrices.
- Model comparison: A comparison of model performance is presented through bar plots.
The project consists of the following files:
- Notebook/Notebook.ipynb : Python script containing the code for breast cancer diagnosis classification.
- data/diabetes.csv : Dataset file containing breast cancer diagnosis and feature information.
- requirements.txt : Text file containing the list of required Python packages to run the project.
The following libraries are required to run the code:
- pandas
- numpy
- matplotlib
- seaborn
- sklearn
Install the dependencies using the following command:
pip install -r requirements.txtTo use this project, follow these steps:
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Clone the repository:
git clone https://github.com/jaywyawhare/Diabetes-Prediction.git
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Navigate to the project directory:
cd Diabetes-Diagnosis-Classification