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].
