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https://github.com/saurav-navdhare/bayesianclassifier
This repository contains golang code of Bayesian Learning on a diabetes dataset
https://github.com/saurav-navdhare/bayesianclassifier
bayesian-learning go golang
Last synced: 28 days ago
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This repository contains golang code of Bayesian Learning on a diabetes dataset
- Host: GitHub
- URL: https://github.com/saurav-navdhare/bayesianclassifier
- Owner: Saurav-Navdhare
- License: mit
- Created: 2024-10-30T12:31:26.000Z (3 months ago)
- Default Branch: master
- Last Pushed: 2024-10-30T12:50:54.000Z (3 months ago)
- Last Synced: 2024-12-20T21:13:39.535Z (28 days ago)
- Topics: bayesian-learning, go, golang
- Language: Go
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Project Title
This project implements a machine learning model in Go, focusing on data preprocessing, training, and evaluation. The model handles binary classification tasks and includes functions for splitting data, calculating probabilities, and saving/loading model parameters.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Functions](#functions)
- [Data Preprocessing](#data-preprocessing)
- [Model Training](#model-training)
- [Model Saving and Loading](#model-saving-and-loading)
- [Contributing](#contributing)
- [License](#license)
## Installation
1. Clone the repository:
```sh
git clone https://github.com/Saurav-Navdhare/BayesianClassifier.git
cd BayesianClassifier
```
2. Install dependencies:
```sh
go mod tidy
```
## Usage
1. **Data Preprocessing**: Convert raw data into a format suitable for training.
```go
import "BayesianClassifier/utils"
data := [][]string{
{"Yes", "No", "Male"},
{"No", "Yes", "Female"},
}
headers := []string{"Feature1", "Feature2", "Gender"}
binaryData := utils.BinaryLabelling(data)
df := utils.ConvertToDF(binaryData, headers)
```
2. **Train-Test Split**: Split the data into training and test sets.
```go
import "BayesianClassifier/model"
trainSet, testSet := model.TrainTestSplit(df, 0.2)
```
3. **Model Training**: Train the model using the training set.
```go
classProbabilities, featureStats := model.CalculateProbabilities(trainSet)
```
4. **Save Model**: Save the trained model to a file.
```go
model := model.Model{
ClassProbabilities: classProbabilities,
FeatureStats: featureStats,
}
model.SaveModel("model.json", model)
```
5. **Load Model**: Load the model from a file and print the parameters.
```go
loadedModel, err := model.LoadModel("model.json")
if err != nil {
log.Fatal(err)
}
```
## Functions
### Data Preprocessing
- `BinaryLabelling`: Converts categorical data into binary labels.
- `ConvertToDF`: Converts a 2D slice of integers into a DataFrame-like map.
### Model Training
- `TrainTestSplit`: Splits the dataset into training and test sets.
- `CalculateProbabilities`: Calculates class probabilities and feature statistics.
### Model Saving and Loading
- `SaveModel`: Saves the model parameters to a JSON file.
- `LoadModel`: Loads the model parameters from a JSON file and prints them.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.