<jupyter_start><jupyter_text>1. EVBRP - constant value2. EVICR - constant value3. EVBCA - constant value<jupyter_code>train = train[["IMEI", "sp", "EVVSP", "EVGPO", "EVCFN", "EVTRQ",
               "EVBBV", "EVDR1", "EVACP", "EVPWA_MIN",
               "EVBMA_Min", "EVIRT_Max", "EVPSS", "EVOAS",
               "EVODO", "EVMCV_MAX", "EVBOV_AVG", "EVACO_Z", "EVSMA_MAX"]]
train.tail()
test = test[["IMEI", "sp", "EVVSP", "EVGPO", "EVCFN", "EVTRQ",
            "EVBBV", "EVDR1", "EVACP", "EVPWA_MIN",
            "EVBMA_Min", "EVIRT_Max", "EVPSS", "EVOAS",
            "EVODO", "EVMCV_MAX", "EVBOV_AVG", "EVACO_Z", "EVSMA_MAX"]]
test.head()
x_train = train.drop(columns = ["EVSMA_MAX"])
y_train = train["EVSMA_MAX"]

x_test = test.drop(columns = ["EVSMA_MAX"])
y_test = test["EVSMA_MAX"]
x_test.head()
x_train.head()
y_train.head()
y_test.head()
xgb_train = xgboost.DMatrix(x_train, label=y_train)
xgb_test = xgboost.DMatrix(x_test, label=y_test)
params = {
    "eta": 0.002,
    "max_depth": 3,
    "objective": "reg:linear",
    "subsample": 0.5
}
model_train = xgboost.train(params, xgb_train, 10000, evals = [(xgb_test, "test")], verbose_eval=1000)
xgboost.plot_importance(model_train)
shap.summary_plot(shap_values, train)
data.IMEI.unique()
data[data["tp"] == 42].EVIND.corr(data[data["tp"] == 42].EVSMA_MAX)<jupyter_output><empty_output>
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