|
--- |
|
tags: |
|
- autotrain |
|
- tabular |
|
- regression |
|
- tabular-regression |
|
datasets: |
|
- jwan2021/autotrain-data-us-housing-prices |
|
co2_eq_emissions: |
|
emissions: 0.12978708384729631 |
|
--- |
|
|
|
# Model Trained Using AutoTrain |
|
|
|
- Problem type: Single Column Regression |
|
- Model ID: 1771761513 |
|
- CO2 Emissions (in grams): 0.1298 |
|
|
|
## Validation Metrics |
|
|
|
- Loss: 100581.032 |
|
- R2: 0.922 |
|
- MSE: 10116543945.030 |
|
- MAE: 81586.656 |
|
- RMSLE: 0.101 |
|
|
|
## 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] |
|
data.columns = ["feat_" + str(col) for col in data.columns] |
|
|
|
predictions = model.predict(data) # or model.predict_proba(data) |
|
|
|
``` |
