The dataset used in this project contains a collection of health-related features along with the target variable indicating whether a person has diabetes or not. We will leverage Python's data preprocessing libraries to extract, clean, and preprocess the data.
The main steps of this project include:
1- Data Loading and Preprocessing:
Import the necessary libraries and load the dataset from a CSV file. Clean the data by handling missing values, encoding categorical features, and performing feature scaling if required.
2- Data Splitting:
Divide the dataset into training and testing sets. This division allows us to train the models on one subset and validate their performance on an independent subset.
3- Model Selection and Training: Choose a range of machine learning algorithms suitable for binary classification tasks. Train each model on the training data using their respective implementations from popular libraries like scikit-learn.
3- Model Evaluation:
Evaluate the trained models using various evaluation metrics to understand their strengths and weaknesses. Common metrics include accuracy, precision, recall, F1-score, and the confusion matrix.
4- Results Interpretation:
Analyze the performance metrics of each model to determine which algorithm provides the best predictions for diabetes occurrence. This analysis will help us identify the most suitable model for future predictions.
- Star this repository
- Fork this repository
- Clone your forked repository
- Add your scripts
- Commit and push
- Create a pull request
- Wait for pull request to merge
- Celebrate your first step into the open source world and contribute more
To run this project, you will to need execute step by step cells on Jupyter Notebook.
Make sure you have necessary libraries installed on your Jupyter if any error occur.
Change the path of your dataset according to your directory and name of file :
os.chdir(r' ')Now, you can evauate all the models and results by yourself.
This project is licensed under the MIT License - see the file for details.