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Upload folder using huggingface_hub

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  1. README.md +56 -0
  2. model.joblib +3 -0
  3. target_encoders.joblib +3 -0
  4. training_params.json +1 -0
README.md ADDED
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+
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+ ---
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+ tags:
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+ - autotrain
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+ - tabular
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+ - regression
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+ - tabular-regression
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+ datasets:
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+ - Ammok/laptop_price_prediction
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+ ---
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+
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+ # Model Trained Using AutoTrain
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+
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+ - Problem type: Tabular regression
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+
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+ ## Validation Metrics
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+
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+ - r2: 0.7770511763473569
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+ - mse: 7850730654540.005
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+ - mae: 1734575.7588461537
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+ - rmse: 2801915.5330844657
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+ - rmsle: 0.23713967369435024
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+ - loss: 2801915.5330844657
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+
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+ ## Best Params
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+
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+ - learning_rate: 0.02229837095040035
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+ - reg_lambda: 2.510764141176911
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+ - reg_alpha: 0.001531565861357925
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+ - subsample: 0.8214234508684097
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+ - colsample_bytree: 0.3555990037002663
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+ - max_depth: 5
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+ - early_stopping_rounds: 355
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+ - n_estimators: 20000
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+ - eval_metric: rmse
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+
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+ ## Usage
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+
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+ ```python
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+ import json
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+ import joblib
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+ import pandas as pd
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+
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+ model = joblib.load('model.joblib')
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+ config = json.load(open('config.json'))
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+
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+ features = config['features']
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+
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+ # data = pd.read_csv("data.csv")
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+ data = data[features]
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+
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+ predictions = model.predict(data) # or model.predict_proba(data)
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+
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+ # predictions can be converted to original labels using label_encoders.pkl
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+
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+ ```
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+ size 6704233
target_encoders.joblib ADDED
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training_params.json ADDED
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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"}