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README.md

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-license: mit
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+# AMR prediction with LGBMClassifier models
+This repository contains a Python script for predicting antimicrobial resistance (AMR) using the LGBMClassifier model. The script reads input datasets from a directory, applies feature extraction techniques to obtain k-mer features, trains and tests the models using cross-validation, and outputs the results in text files.
+
+![Retrospectives](https://user-images.githubusercontent.com/43249674/224884310-71214a69-3f27-4628-ad21-bb34c6daac45.jpg)
+
+
+## Getting Started
+These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
+
+### Prerequisites
+This script requires the following Python libraries:
+
+    pandas
+    scikit-learn
+    numpy
+    tqdm
+    lightgbm
+    hyperopt
+    joblib
+    bayesian-optimization
+    skopt
+
+### Installing
+Clone the repository to your local machine and install the required libraries:
+
+
+```bash
+  $ git clone https://github.com/username/repo.git
+  $ cd repo
+  $ pip install -r requirements.txt
+```
+
+
+### Usage
+To use the script, execute the following command:
+
+css
+Copy code
+
+```bash
+  $ python main.py
+```
+
+## Code Structure
+The main script consists of several sections:
+
+    1 Import necessary libraries
+    2 Set seed for reproducibility
+    3 Define function to get list of models to evaluate
+    4 Load list of selected samples
+    5 Call function to get list of models
+    6 Initialize KFold cross-validation
+    7 Iterate over values of k to read the corresponding k-mer feature dataset
+    8 Iterate over the models list
+    9 Write results to text file
+
+## Data Description
+The input datasets are CSV files containing bacterial genomic sequences and their corresponding resistance profiles for selected antibiotics. The script reads these files from a directory and applies k-mer feature extraction techniques to obtain numerical feature vectors.
+
+## Models
+The script uses two models for AMR prediction: Random Forest and LGBMClassifier.
+
+## Output
+The script outputs the results of each model to a text file in the specified output directory. The results include accuracy, precision, recall, F1 score, and area under the ROC curve.
+
+## Authors
+Gabriel Sousa - gabrieltxs
+
+## License
+This project is licensed under the MIT License - see the LICENSE.md file for details.
+[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)