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Browse files- BagReg_RSCV.joblib +2 -2
- DTree_RSCV.joblib +2 -2
- RndmFrstReg_RSCV.joblib +2 -2
- adaboost_RSCV.joblib +2 -2
- app.py +4 -4
- gradboost_RSCV.joblib +2 -2
- pipeline.joblib +2 -2
BagReg_RSCV.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:dd6a4d0e904bc121d6e54b79014e1e709e42fb64024e8cb4f840643afd74034d
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size 1105562
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DTree_RSCV.joblib
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size 176156
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RndmFrstReg_RSCV.joblib
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oid sha256:e3f8c4e28dbb9c6b868301ef0f57145eaf0aaead994cbb7f7cd5cd3e42b56af4
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size 6698627
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adaboost_RSCV.joblib
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oid sha256:8905d0822812cd2de264a732a45a6836550003e879718df22f1afac6f9705d35
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size 117962
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app.py
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@@ -55,11 +55,11 @@ def predict():
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'Age_Of_Store':float(data['Age_Of_Store'])
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}],columns=required_features)
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features_scaled = pipeline.transform(feature_for_prediction)
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logging.debug(f"Features scaled: {features_scaled}")
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prediction_gradboost = model_gradbosot.predict(
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prediction_randFrst = model_rndmfrst.predict(
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logging.debug(f"Prediction gradmodel: {prediction_gradboost}")
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logging.debug(f"Prediction RandmFrst: {prediction_randFrst}")
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'Age_Of_Store':float(data['Age_Of_Store'])
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}],columns=required_features)
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#features_scaled = pipeline.transform(feature_for_prediction)
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#logging.debug(f"Features scaled: {features_scaled}")
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prediction_gradboost = model_gradbosot.predict(feature_for_prediction)[0]
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prediction_randFrst = model_rndmfrst.predict(feature_for_prediction)[0]
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logging.debug(f"Prediction gradmodel: {prediction_gradboost}")
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logging.debug(f"Prediction RandmFrst: {prediction_randFrst}")
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gradboost_RSCV.joblib
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oid sha256:10748635102403eb14e3f861c5efe84bada390ed9d13a475b41b423f91982ad0
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size 437486
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pipeline.joblib
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oid sha256:6b9e2ec55de8d4310ac906ed2027c19c4ab909cc5e1d0661de186a52d82c0d79
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size 6421
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