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
• Usage
• Functions
  • Data Preprocessing
  • Model Training
  • Model Saving and Loading
• Contributing
• License

Installation

1. Clone the repository:

   git clone https://github.com/Saurav-Navdhare/BayesianClassifier.git
   cd BayesianClassifier

2. Install dependencies:

   go mod tidy

Usage

1. Data Preprocessing: Convert raw data into a format suitable for training.

   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.

   import "BayesianClassifier/model"
   
   trainSet, testSet := model.TrainTestSplit(df, 0.2)

3. Model Training: Train the model using the training set.

   classProbabilities, featureStats := model.CalculateProbabilities(trainSet)

4. Save Model: Save the trained model to a file.

   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.

   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.