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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.