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