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README.md

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+
+---
+tags:
+- autotrain
+- tabular
+- regression
+- tabular-regression
+datasets:
+- Ammok/laptop_price_prediction
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Tabular regression
+
+## Validation Metrics
+
+- r2: 0.7770511763473569
+- mse: 7850730654540.005
+- mae: 1734575.7588461537
+- rmse: 2801915.5330844657
+- rmsle: 0.23713967369435024
+- loss: 2801915.5330844657
+
+## Best Params
+
+- learning_rate: 0.02229837095040035
+- reg_lambda: 2.510764141176911
+- reg_alpha: 0.001531565861357925
+- subsample: 0.8214234508684097
+- colsample_bytree: 0.3555990037002663
+- max_depth: 5
+- early_stopping_rounds: 355
+- n_estimators: 20000
+- 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
+
+```

model.joblib

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+size 6704233

target_encoders.joblib

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training_params.json

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+{"data_path": "Ammok/laptop_price_prediction", "model": "xgboost", "username": "NamithaMurali", "seed": 42, "train_split": "train", "valid_split": "test", "project_name": "autotrain-mlwn8-r9h09", "push_to_hub": true, "id_column": "id", "target_columns": ["Price"], "repo_id": "NamithaMurali/autotrain-mlwn8-r9h09", "categorical_columns": null, "numerical_columns": null, "task": "regression", "num_trials": 10, "time_limit": 600, "categorical_imputer": "most_frequent", "numerical_imputer": "median", "numeric_scaler": "robust"}