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File size: 1,651 Bytes
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data:
# url empty for now so script will default to local file; modify later as needed
url: "https://storage.googleapis.com/coffee-quality-data/preprocessed_data.csv"
local_path: "data/raw/raw_data.csv"
preprocessed_path: "data/preprocessed/preprocessed_data.csv"
target: "Total.Cup.Points"
input_columns:
- Number.of.Bags
- Category.One.Defects
- Category.Two.Defects
- Aroma
- Flavor
- Aftertaste
- Acidity
- Body
- Balance
- Uniformity
- Clean.Cup
- Sweetness
- Cupper.Points
- Moisture
- Quakers
- altitude_low_meters
- altitude_high_meters
- altitude_mean_meters
- Species
- Owner
- Country.of.Origin
- Mill
- ICO.Number
- Company
- Altitude
- Region
- Producer
- Bag.Weight
- In.Country.Partner
- Harvest.Year
- Grading.Date
- Owner.1
- Variety
- Processing.Method
- Color
- Expiration
- Certification.Body
- Certification.Address
- Certification.Contact
- unit_of_measurement
# model details to be added later during train.py work
train:
test_size: 0.2
random_state: 42
model_params:
n_estimators: 100
random_state: 42
n_jobs: -1
paths:
X_train: "data/cleaned/X_train.csv"
X_test: "data/cleaned/X_test.csv"
y_train: "data/cleaned/y_train.csv"
y_test: "data/cleaned/y_test.csv"
artifacts:
model: "artifacts/model.joblib"
preprocessor: "artifacts/preprocessor.joblib"
metrics: "artifacts/metrics.json"
# The above snippet was generated by chatGPT 5.1 at 10:20p at 11/20/25.
api_url:
# FastAPI: "http://127.0.0.1:8000/predict_named"
FastAPI: "https://coffee-api-354131048216.us-central1.run.app/predict_named"
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