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README.md

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+# Genom Classify
+
+## Project Overview
+This project processes genomic data from bacterial and archaeal organisms retrieved from the NCBI database, cleans the data, and trains Random Forest models using varying k-mer sizes and chunk sizes. The pipeline evaluates model performance and saves results for further analysis.
+
+## Setup Instructions
+
+### 1. Create a Conda Environment
+Create a new Conda environment named `genom_classify` with Python 3.12:
+```bash
+conda create -n genom_classify python=3.12 -y
+```
+
+### 2. Activate the Environment
+Activate the environment:
+```bash
+conda activate genom_classify
+```
+
+### 3. Install Dependencies
+Install the required dependencies listed in the `requirements.txt` file:
+```bash
+pip install -r requirements.txt
+```
+
+## Running the Project
+
+### Step 1: Data Processing
+To process the genomic data, run the following command from the root of the project:
+```bash
+python data_processing.py
+```
+
+This script performs the following tasks:
+- Retrieves more than 100 bacterial and archaeal organisms' genomes from the NCBI database.
+- Filters out genomes with invalid sequences (e.g., sequences containing letters other than A, C, G, or T).
+- Randomly selects 10 organisms from the filtered data and saves them to memory for use in the modeling pipeline.
+
+### Step 2: Model Training and Evaluation
+Run the main pipeline using:
+```bash
+python main.py
+```
+
+This script trains Random Forest models using varying configurations of:
+- **k-mer sizes**: [2, 3, 4, 5, 6, 7, 8]
+- **chunk sizes**: [500, 1000, 1500, 2000, 2500, 3000, 3500, 4000]
+
+For each combination of parameters, the pipeline:
+1. Trains a Random Forest model.
+2. Saves the trained model to the `models/` directory.
+3. Generates test gene CSV files in the `test_genes/` directory.
+4. Outputs a `grid_search_results.csv` file with accuracy metrics for each configuration.
+
+### Recommendation
+Running the full pipeline with all k-mer sizes and chunk sizes takes a significant amount of time. For initial testing, modify `main.py` to restrict:
+- **k-mer sizes**: [2, 3, 4]
+- **chunk sizes**: [500, 1000, 1500]
+
+This will significantly reduce runtime while allowing you to validate the pipeline functionality.
+
+## Project Structure
+- `data_processing.py`: Handles data retrieval, filtering, and selection.
+- `main.py`: Main script for model training and evaluation.
+- `models/`: Directory where trained models are saved.
+- `test_genes/`: Directory where test gene CSV files are saved.
+- `grid_search_results.csv`: Contains accuracy metrics for each model configuration.
+- `requirements.txt`: Lists all Python dependencies required for the project.
+
+## Future Steps
+Once the pipeline is validated and running efficiently, you can expand the parameter grid or experiment with different machine learning models and hyperparameters to improve performance.
+