|
--- |
|
language: |
|
- en |
|
metrics: |
|
- accuracy |
|
tags: |
|
- health |
|
- classification |
|
--- |
|
|
|
# Model Name |
|
|
|
## Overview |
|
This repository contains the implementation of a machine learning model for predicting [mention the task or purpose of the model]. The model is trained using [describe the dataset used for training]. |
|
|
|
## Dataset |
|
The dataset used for training this model is sourced from [https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data]. It consists of [319795] instances and [18] features. The dataset was preprocessed using various techniques, including: |
|
- Handling missing values |
|
- Encoding categorical variables |
|
- Feature scaling or normalization |
|
|
|
## Model Architecture |
|
The model architecture includes the following algorithms: |
|
- Logistic Regression |
|
- K-Nearest Neighbors (KNN) |
|
- Naive Bayes |
|
- Decision Tree |
|
- Random Forest |
|
- Long Short-Term Memory (LSTM) |
|
- Convolutional Neural Network (CNN) |
|
|
|
## Cleaning Techniques |
|
During preprocessing, the following cleaning techniques were applied to the dataset: |
|
- Encoding categorical variables: Categorical variables were encoded using one-hot encoding. |
|
- Feature scaling or normalization: Numerical features were scaled or normalized to ensure uniformity across different features. |
|
|
|
|
|
|
|
## Usage |
|
To use the model, clone this repository and follow the instructions provided in the respective model's directory. Each algorithm has its implementation and usage instructions. |
|
|
|
## License |
|
[Specify the license under which the model and code are released, e.g., MIT License, Apache License 2.0, etc.] |
|
|
|
## Contact |
|
For questions or inquiries, please contact [your email or contact information]. |
|
|
