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README.md

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+
+---
+tags:
+- autotrain
+- tabular
+- regression
+- tabular-regression
+datasets:
+- autotrain-vessel-eta/autotrain-data
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Tabular regression
+
+## Validation Metrics
+
+- r2: 0.2033836841583252
+- mse: 53092.500978994
+- mae: 150.96381290340423
+- rmse: 230.41810037189788
+- rmsle: 0.9569819523414094
+- loss: 230.41810037189788
+
+## Best Params
+
+- learning_rate: 0.0695392185390836
+- reg_lambda: 0.00017542491817558795
+- reg_alpha: 0.6577124531542021
+- subsample: 0.3632574815242663
+- colsample_bytree: 0.8007491192913739
+- max_depth: 4
+- early_stopping_rounds: 166
+- n_estimators: 15000
+- eval_metric: rmse
+
+## 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]
+
+predictions = model.predict(data)  # or model.predict_proba(data)
+
+# predictions can be converted to original labels using label_encoders.pkl
+
+```

autotrain-data/dataset_dict.json

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+{"splits": ["train", "validation"]}

autotrain-data/train/data-00000-of-00001.arrow

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autotrain-data/train/dataset_info.json

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+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "course": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "heading": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "latitude": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "longitude": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "speed": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "timestamp": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "destination_port_index": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "autotrain_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "autotrain_label": {
+      "dtype": "int64",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

autotrain-data/train/state.json

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+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "100187c468cd0ab1",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

autotrain-data/validation/data-00000-of-00001.arrow

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autotrain-data/validation/dataset_info.json

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+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "course": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "heading": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "latitude": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "longitude": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "speed": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "timestamp": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "destination_port_index": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "autotrain_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "autotrain_label": {
+      "dtype": "int64",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

autotrain-data/validation/state.json

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+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
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+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

model.joblib

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target_encoders.joblib

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training_params.json

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+{"data_path": "autotrain-vessel-eta/autotrain-data", "model": "xgboost", "username": "farrukhshahzad25", "seed": 42, "train_split": "train", "valid_split": "validation", "project_name": "autotrain-vessel-eta", "push_to_hub": true, "id_column": "autotrain_id", "target_columns": ["autotrain_label"], "categorical_columns": null, "numerical_columns": ["course", "heading", "latitude", "longitude", "speed", "timestamp", "destination_port_index"], "task": "regression", "num_trials": 10, "time_limit": 600, "categorical_imputer": null, "numerical_imputer": null, "numeric_scaler": null}