DNA Classification Using Machine Learning 🧬

This project presents a methodical approach to classifying DNA sequences leveraging machine learning techniques 🤖. It includes the journey from raw data preprocessing to the evaluation of several classification algorithms, culminating in identifying the most effective model for this task.

Overview 📖

The DNA Classification Project is rooted in bioinformatics, aiming to classify DNA sequences accurately 🔍. It undertakes a detailed exploration of various machine learning algorithms to ascertain the best fit for classifying DNA sequences.

Contents 📚

Step 1: Importing the Dataset 📥

• Introduction to and importation of the dataset that comprises DNA sequences.

Step 2: Preprocessing the Dataset 🛠

• The dataset undergoes several preprocessing steps to transform raw DNA sequences into a format amenable to machine learning algorithms. This includes encoding sequences, dealing with missing values, and normalizing data.

Step 3: Training and Testing the Classification Algorithms 🏋️‍♂️

• Algorithms Explored:
  • K-Nearest Neighbors (KNN) 🚶‍♂️
  • Support Vector Machine (SVM) ⚔
    • Variants with different kernels are tested, including linear, polynomial, and radial basis function (RBF).
  • Decision Trees 🌳
  • Random Forest 🌲
  • Naive Bayes 🔮
  • MultiLayer Perceptron 🧠
  • AdaBoost Classifier 🚀

Step 4: Model Evaluation 📊

• The models are evaluated based on accuracy, precision, recall, and F1 score metrics. This step involves a critical assessment of each model's performance to identify the best-performing model.
• Conclusion: The notebook concludes by endorsing the Support Vector Machine with a 'linear' kernel as the most efficient model, achieving an F1_score of 0.96 on the test data.

Conclusion 🏁

This project's findings underscore the efficacy of machine learning in the realm of DNA sequence classification, with the Support Vector Machine (linear kernel) standing out for its superior performance.