Add final project files

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

KiDatabase.csv

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+size 23869637

README.md

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+# Serotonin Receptor (5-HT) Binding Affinity Prediction Dataset
+
+This dataset was curated from the PDSP Ki Database to support training machine learning models that predict binding affinity (Ki in nM) of ligands to serotonin (5-HT) receptors.
+
+## Files Included
+
+- `curated_ki_database.csv`: Cleaned Ki dataset filtered for 5-HT targets
+- `fingerprints_with_ki.csv`: Molecules represented as 2048-bit Morgan (ECFP4) fingerprints
+- `rf_model.pkl`: Trained scikit-learn RandomForestRegressor model
+- `test_predictions.csv`: Predictions vs. actual Ki values on the test set
+- `train_model.ipynb`: Full Jupyter notebook with training code and evaluation
+
+## Modeling Approach
+
+- Molecules were standardized using [MolVS](https://molvs.readthedocs.io).
+- SMILES strings were converted to 2048-bit Morgan fingerprints (ECFP4) using RDKit.
+- A Random Forest Regressor was trained using scikit-learn.
+- The model was evaluated on a held-out 20% test set.
+
+### 📈 Model Performance
+
+- **R² Score**: `your_R2_here`
+- **RMSE**: `your_RMSE_here` nM
+
+## Source
+
+- PDSP Ki Database: https://pdsp.unc.edu/databases/kidb.php
+
+## Author
+
+Sara Hantgan  
+University of Michigan | BIOINF 595 Final Project  
+Winter 2025

curated_ki_database.csv

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fingerprints_with_ki.csv

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rf_model.pkl

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test_predictions.csv

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