Sake

• Sake is your go-to, modular machine learning framework for Budget Execution data analysis built in Python with Scikit, XGBoost, PyTorch, and TensorFlow. Designed for rapid experimentation, visualization, and benchmarking of both classification and regression models, it provides a structured yet extensible workflow that’s equally useful for teaching, prototyping, and real-world application development.

🔬 Data Source

• File A (Account Balances) published monthly by agencies on USASpending
• Required by the DATA Act.
• Pulled automatically from data in the Governmentwide Treasury Account Symbol Adjusted Trial Balance System (GTAS)
• Contains Budgetary resources, obligation, and outlay data for all the relevant Treasury Account Symbols (TAS) in a reporting agency.
• It includes both award and non-award spending (grouped together), and crosswalks with the SF 133 report.

🚀 Features

🔄 Unified Evaluation Pipeline

Easily run multiple models through a single function train_and_evaluate(), which handles:

• Model training
• Accuracy computation
• Confusion matrix generation (for classifiers)
• Performance reporting (classification or regression metrics)

🧠 Dual Model Support

Out-of-the-box support for both:

• Classification models such as Logistic Regression, SVM, Random Forest, XGBoost
• Regression models such as Linear Regression, Ridge, SVR, Gradient Boosting

📊 Visual Performance Reports

• Heatmaps of confusion matrices
• Auto-generated classification_report with precision, recall, F1-score
• Regression summary with metrics like MAE, MSE, R²
• Tabular performance summary across all models

📁 Custom Dataset Integration

• Use default Scikit-Learn datasets or plug in your own CSV
• Built-in support for label encoding and numeric feature conversion
• Easy integration with Pandas for pre-processing pipelines

🧠 Deep Learning Ready

• Expandable with PyTorch and TensorFlow architectures
• Importable modules for CNNs, RNNs, and Transformers

🧪 Educational & Research Utility

• Ideal for teaching ML fundamentals in a comparative format
• Benchmarking for internal ML pipelines and research reproducibility

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🧠 Classification Models

Model	Module
Logistic Regression	sklearn.linear_model
Support Vector Machine	sklearn.svm
Decision Tree	sklearn.tree
Random Forest	sklearn.ensemble
k-Nearest Neighbors	sklearn.neighbors
Gaussian Naive Bayes	sklearn.naive_bayes
XGBoost Classifier	xgboost.XGBClassifier

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📉 Regression Models

Model	Module
Linear Regression	sklearn.linear_model.LinearRegression
Ridge Regression	sklearn.linear_model.Ridge
Support Vector Regressor	sklearn.svm.SVR
Decision Tree Regressor	sklearn.tree.DecisionTreeRegressor
Random Forest Regressor	sklearn.ensemble.RandomForestRegressor
Gradient Boosting Regressor	sklearn.ensemble.GradientBoostingRegressor
k-NN Regressor	sklearn.neighbors.KNeighborsRegressor

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📦 Dependencies

Package	Description	Link
numpy	Numerical computing library	numpy.org
pandas	Data manipulation and DataFrames	pandas.pydata.org
matplotlib	Plotting and visualization	matplotlib.org
seaborn	Statistical data visualization	seaborn.pydata.org
scikit-learn	ML modeling and metrics	scikit-learn.org
xgboost	Gradient boosting framework (optional)	xgboost.readthedocs.io
torch	PyTorch deep learning library	pytorch.org
tensorflow	End-to-end ML platform	tensorflow.org
openai	OpenAI’s Python API client	openai-python
requests	HTTP requests for API and web access	requests.readthedocs.io
PySimpleGUI	GUI framework for desktop apps	pysimplegui.readthedocs.io
typing	Type hinting standard library	typing Docs
pyodbc	ODBC database connector	pyodbc GitHub
fitz	PDF document parser via PyMuPDF	pymupdf
pillow	Image processing library	python-pillow.org
openpyxl	Excel file processing	openpyxl Docs
soundfile	Read/write sound file formats	pysoundfile
sounddevice	Audio I/O interface	sounddevice Docs
loguru	Structured, elegant logging	loguru GitHub
statsmodels	Statistical tests and regression diagnostics	statsmodels.org
dotenv	Load environment variables from .env	python-dotenv GitHub
python-dotenv	Same as above (modern usage)	python-dotenv

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🧪 How to Run

git clone https://github.com/your-username/balance-projector.git
cd balance-projector
pip install -r requirements.txt
jupyter notebook balances.ipynb

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📁 Customize Dataset

Replace dataset ingestion cell with:

import pandas as pd
df = pd.read_csv("your_dataset.csv")
X = df.drop("target_column", axis=1)
y = df["target_column"]

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📊 Outputs

• R², MAE, MSE for each model
• Bar plots of performance scores
• Visual predicted vs. actual scatter charts
• Residual error analysis

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🔮 Roadmap

• Add time series models (Prophet, ARIMA)
• Integrate GridSearchCV for model tuning
• SHAP-based interpretability
• Flask/FastAPI API for deploying forecasts
• LLM summarization of forecast outcomes

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🤝 Contributing

1. 🍴 Fork the project
2. 🔧 Create a branch: git checkout -b feat/new-feature
3. ✅ Commit and push changes
4. 📬 Submit a pull request

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📜 License

This project is licensed under the MIT License.

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