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+# **This README describes the workflow for `Meron_ML Taxon Classifier_GSoC 2026_Prop.ipynb`.**
+
+# ML Taxon Classifier Proposal – GSoC 2026
+
+This file describes my step-by-step plan for building a machine-learning classifier to assign malaria mosquito samples to major taxa using raw sequencing reads.
+
+---
+
+## 1. Filter Samples
+
+I will start by using the MalariaGEN AG3 metadata to filter samples from **East African countries**.  
+This will give me the subset of samples I want to analyze, focusing on countries like Ethiopia, Kenya, Tanzania, Uganda, etc.
+
+---
+
+## 2. Access ENA FASTQ Links
+
+Using the `sample_id` column of the filtered metadata, I will retrieve **ENA run accessions** and generate links to the raw FASTQ files.  
+I will not download these files locally but instead use **Colab** to access them directly via HTTP/FTP links.
+
+---
+
+## 3. Fetch FASTQ Files in Colab
+
+In Colab, I will use `!wget` to fetch FASTQ files directly into the Colab environment without storing them on my local machine.  
+This ensures that the analysis can be done even in resource-limited environments.
+
+---
+
+## 4. Filter FASTQ Reads
+
+I will filter the raw FASTQ reads to **retain only high-quality reads**.  
+This step ensures that downstream k-mer extraction and ML models are trained on reliable data.
+
+---
+
+## 5. Extract K-mer Features
+
+From the filtered FASTQ reads, I will extract **k-mer counts** for each sample.  
+These k-mer features will form the **input matrix** for ML classification.
+
+---
+
+## 6. Split Dataset
+
+I will split the k-mer feature matrix into **training and testing datasets**.  
+The `taxon` column in the metadata will serve as the **target label**.
+
+---
+
+## 7. Train Machine Learning Classifiers
+
+I will train several ML models on the training dataset, including:
+
+- Random Forest  
+- Logistic Regression  
+- Support Vector Machine  
+- XGBoost  
+- LightGBM  
+- K-Nearest Neighbors  
+- Decision Tree  
+- Gradient Boosting  
+
+I will evaluate the models on the testing set and choose the best-performing one for further work.
+
+---
+
+## 8. Demonstration Notebook
+
+Alongside this proposal, I have included a **Colab-ready notebook** (`Meron_ML Taxon Classifier_GSoC 2026_Prop.ipynb`) that:
+
+- Filters East African samples  
+- Shows placeholder ENA FASTQ links  
+- Demonstrates k-mer feature extraction on toy data  
+- Splits dataset and trains toy classifiers  
+
+This notebook is intended to **demonstrate my workflow and coding approach safely**, without needing to download large FASTQ files.