README.md
| 1 | --- |
| 2 | tags: |
| 3 | - autotrain |
| 4 | - tabular |
| 5 | - regression |
| 6 | - tabular-regression |
| 7 | datasets: |
| 8 | - FreekyMeeky/autotrain-data-tm-pricepredictor |
| 9 | co2_eq_emissions: |
| 10 | emissions: 0.13802782283135043 |
| 11 | --- |
| 12 | |
| 13 | # Model Trained Using AutoTrain |
| 14 | |
| 15 | - Problem type: Single Column Regression |
| 16 | - Model ID: 98386147082 |
| 17 | - CO2 Emissions (in grams): 0.1380 |
| 18 | |
| 19 | ## Validation Metrics |
| 20 | |
| 21 | - Loss: 37.254 |
| 22 | - R2: 0.959 |
| 23 | - MSE: 1387.870 |
| 24 | - MAE: 18.787 |
| 25 | - RMSLE: 0.086 |
| 26 | |
| 27 | ## Usage |
| 28 | |
| 29 | ```python |
| 30 | import json |
| 31 | import joblib |
| 32 | import pandas as pd |
| 33 | |
| 34 | model = joblib.load('model.joblib') |
| 35 | config = json.load(open('config.json')) |
| 36 | |
| 37 | features = config['features'] |
| 38 | |
| 39 | # data = pd.read_csv("data.csv") |
| 40 | data = data[features] |
| 41 | data.columns = ["feat_" + str(col) for col in data.columns] |
| 42 | |
| 43 | predictions = model.predict(data) # or model.predict_proba(data) |
| 44 | |
| 45 | ``` |