---
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].