Mosquito Species Classification System

Overview

This project implements a machine learning system for classifying mosquito species based on their genomic and morphological features. It uses a Random Forest classifier optimized with GridSearchCV to achieve high classification accuracy.

Purpose

Mosquito species identification is critical for:

• Disease vector surveillance and control
• Ecological studies
• Public health initiatives

This system provides an automated way to classify mosquito species using their genetic markers and physical characteristics.

Project Structure

mosquito_classification/
├── create_sample_data.py    # Generates synthetic genomic data
├── mosquito_classifier.py   # Main classification script
├── mosquito_genomic_data.csv # Dataset (generated)
└── README.md                # This documentation

Requirements

This project requires:

• Python 3.x
• pandas
• scikit-learn
• numpy

These packages are installed in a virtual environment to avoid conflicts with system packages.

Installation

1. Clone or download this repository
2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate
   

3. Install required packages:

   pip install pandas scikit-learn numpy
   

Usage

Generate Sample Data

If you don't have your own dataset, you can generate synthetic data:

python3 create_sample_data.py

This creates a file called mosquito_genomic_data.csv with 500 samples across 4 species.

Run Classification

To train and evaluate the classification model:

python3 mosquito_classifier.py

The script will:

1. Load the dataset
2. Split it into training and testing sets
3. Train a Random Forest model using grid search for hyperparameter optimization
4. Evaluate model performance
5. Display the most important features

Dataset Information

The dataset contains the following features:

• Gene expression data: 10 gene expression levels (gene_expr_1 to gene_expr_10)
• SNP data: 20 single nucleotide polymorphisms (snp_1 to snp_20)
• Morphological features:
  • body_length
  • wing_width
  • proboscis_length
  • leg_length
  • thorax_width
• Target variable: species (Anopheles_gambiae, Aedes_aegypti, Culex_pipiens, Anopheles_stephensi)

Model Details

The Random Forest classifier is optimized using GridSearchCV with the following parameters:

• n_estimators: [100, 200]
• max_depth: [None, 10, 20]
• min_samples_split: [2, 5]

The best parameters found are:

• max_depth: None
• min_samples_split: 2
• n_estimators: 200

Results

The model achieves:

• Cross-validation accuracy: 99%
• Test set accuracy: 100%

Top features for classification:

1. body_length
2. gene_expr_7
3. gene_expr_6
4. leg_length
5. gene_expr_4

Future Improvements

Potential enhancements include:

• Model persistence functionality
• Interactive prediction for new samples
• Additional visualization of results
• Support for more species
• Feature selection techniques
• Testing with real-world data

License

This project is provided as open-source software for educational and research purposes.