ml_model.py

# using linear regression
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
preds_valid = model.predict(X_test)
linearreg =mean_absolute_error(y_test, preds_valid)
print(linearreg)
>>> 54.0539523895

# using xgboost regressor
from xgboost import XGBRegressor
model = XGBRegressor(n_estimators=1000, max_depth=7, eta=0.1, subsample=0.7, colsample_bytree=0.8)
model.fit(X_train, y_train)
preds_valid = model.predict(X_test)
xgboost_res = mean_absolute_error(y_test, preds_valid)
print(xgboost_res)
>>> 142.000435933435
# using ridge regressor
from sklearn.linear_model import Ridge
clf = Ridge(alpha=1.0)
clf.fit(X_train, y_train)
preds_valid = clf.predict(X_test)
ridge_reg = mean_absolute_error(y_test, preds_valid)
print(ridge_reg)
>>> 123.245023458340
# export the model with pickle
import pickle
pickle.dump(model, open( "model_lin.p", "wb" ))