Commit From AutoTrain

.gitattributes

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README.md

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+---
+tags:
+- autotrain
+- tabular
+- classification
+- tabular-classification
+datasets:
+- nvenhuizen14/autotrain-data-mofodb_classifications
+co2_eq_emissions:
+  emissions: 0.04250103814751933
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 66203136426
+- CO2 Emissions (in grams): 0.0425
+
+## Validation Metrics
+
+- Loss: 0.007
+- Accuracy: 0.997
+- Macro F1: 0.915
+- Micro F1: 0.997
+- Weighted F1: 0.996
+- Macro Precision: 0.926
+- Micro Precision: 0.997
+- Weighted Precision: 0.995
+- Macro Recall: 0.915
+- Micro Recall: 0.997
+- Weighted Recall: 0.997
+
+## Usage
+
+```python
+import json
+import joblib
+import pandas as pd
+
+model = joblib.load('model.joblib')
+config = json.load(open('config.json'))
+
+features = config['features']
+
+# data = pd.read_csv("data.csv")
+data = data[features]
+data.columns = ["feat_" + str(col) for col in data.columns]
+
+predictions = model.predict(data)  # or model.predict_proba(data)
+
+```

config.json

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+{"features": ["Full Description", "Group", "Account", "Institution"], "targets": ["target"], "model_type": "logistic_regression", "target_mapping": {"App Building": 0, "Car Insurance": 1, "Car Payment": 2, "Date-Night": 3, "Eating Out": 4, "Fees": 5, "Gaming": 6, "Gas": 7, "Gift": 8, "Groceries": 9, "HSA": 10, "Joyful Noise": 11, "Lunch&Snacks": 12, "Mikal's Paycheck": 13, "Misc": 14, "Misc Auto Expenses": 15, "Nick's Paycheck": 16, "Nicotine": 17, "Retirement": 18, "Shopping": 19, "Software": 20, "Spotify": 21, "Transfer": 22, "Vision": 23, "Vitamins&NooTropics": 24, "Xfinity Internet": 25, "Xfinity Mobile": 26, "car payment": 27, "credit building": 28, "fees": 29, "gaming": 30, "gift": 31, "kratom": 32, "mental": 33, "misc auto expenses": 34, "nicotine": 35, "shopping": 36, "x": 37}}

model.joblib

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