I will apply machine learning to gene expression data

Looking to uncover patterns, classify conditions, or identify biomarkers in your gene expression data?

I'll apply machine learning techniques to RNA-Seq or microarray datasets to reveal meaningful biological insights. Whether you need clustering, classification, or feature selection, Ill deliver clean, interpretable, and reproducible results.

• Dimensionality reduction (PCA, t-SNE, UMAP)
• Clustering (k-means, hierarchical)
• Supervised modeling (SVM, Random Forest, Logistic Regression)
• Performance evaluation (accuracy, F1-score, confusion matrix)
• Ranked gene/features list and visualizations
• Source code and documentation included

I use industry-standard tools like scikit-learn, pandas, seaborn, matplotlib, and NumPy in a clear, modular structure ideal for research teams, thesis students, and biotech partners.

Lets turn your omics data into actionable, visual insights using modern ML workflows.

Please message me before ordering to discuss your dataset and goals.