diff --git a/sqlflow_models/__init__.py b/sqlflow_models/__init__.py
index f999f69..57afd28 100755
--- a/sqlflow_models/__init__.py
+++ b/sqlflow_models/__init__.py
@@ -7,6 +7,7 @@
 from .dnnclassifier_functional_api_example import dnnclassifier_functional_model
 from .rnn_based_time_series import RNNBasedTimeSeriesModel
 from .auto_estimator import AutoClassifier, AutoRegressor
+from .score_card import ScoreCard
 from .native_keras import RawDNNClassifier
 from .custom_model_example import CustomClassifier
 from .gcn import GCN
diff --git a/sqlflow_models/score_card.py b/sqlflow_models/score_card.py
new file mode 100644
index 0000000..914de64
--- /dev/null
+++ b/sqlflow_models/score_card.py
@@ -0,0 +1,103 @@
+#!/bin/env python
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.python.data import make_one_shot_iterator
+from tensorflow.keras.losses import kld
+from tensorflow.keras.optimizers import SGD
+import numpy as np
+import pandas as pd
+import scipy.stats.stats as stats
+import sklearn
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import roc_auc_score, auc
+import pickle
+
+
+def optimizer():
+    return None
+
+
+def loss():
+    return None
+
+
+class ScoreCard(keras.Model):
+
+    def __init__(self, feature_columns=None):
+        super(ScoreCard, self).__init__(name='ScoreCard')
+
+        self._factor = 20/np.log(2)
+        self._offset = 600 - 20*np.log(20) / np.log(2)
+        self._bins = dict()
+
+    def _pf_bin(self, y, x, n=10):
+        # population frequency bucket
+        bad_num = y.sum()
+        good_num = y.count() - y.sum()
+        d1 = pd.DataFrame({'x': x,'y': y,'bucket': pd.qcut(x, n, duplicates='drop')})
+        d2 = d1.groupby('bucket',as_index=True)
+        d3 = pd.DataFrame(d2.x.min(),columns=['min_bin']) 
+
+        d3["min"] = d2.min().x
+        d3["max"] = d2.max().x
+        d3["badcostum"] = d2.sum().y
+        d3["goodcostum"] = d2.count().y - d2.sum().y
+        d3["total"] = d2.count().y
+        d3["bad_rate"] = d2.sum().y/d2.count().y
+        d3["woe"] = np.log(d3["badcostum"]/d3["goodcostum"]*good_num/bad_num)
+        iv = ((d3["badcostum"]/bad_num-d3["goodcostum"]/good_num)*d3["woe"])
+        d3["iv"] = iv
+        woe = list(d3["woe"].round(6))
+        cut = list(d3["max"].round(6))
+        cut.insert(0, float("-inf"))
+        cut[-1] = float("inf")
+        return d3, cut, woe, iv
+
+    def _to_dataframe(self, dataset):
+        x_df = pd.DataFrame()
+        y_df = pd.DataFrame()
+        for _, minibatch in enumerate(dataset):
+            data, label = minibatch
+            dx = {}
+            dy = {}
+            for name, value in data.items():
+                dx[name] = value.numpy()[0][0]
+            x_df = x_df.append(dx, ignore_index=True)
+            dy['label'] = label.numpy()[0]
+            y_df = y_df.append(dy, ignore_index=True)
+        return x_df, y_df
+
+    def _replace_woe(self, x, cut, woe):
+        return pd.cut(x, cut, labels=pd.Categorical(woe))
+
+    def _woe_encoder(self, x, y):
+        x_train_dict = {}
+        for col in x.columns:
+            dfx, cut, woe, iv = self._pf_bin(y, x[col])
+            self._bins[col] = (dfx, cut, woe, iv)
+            # replacing by the WOE encode
+            x_train_dict[col] = self._replace_woe(x[col], cut, woe)
+        return pd.DataFrame.from_dict(x_train_dict)
+
+    def sqlflow_train_loop(self, dataset, epochs=1, verbose=0):
+        x_df, y_df = self._to_dataframe(dataset)
+        x = self._woe_encoder(x_df, y_df['label'])
+        x.to_csv("/tmp/train_woe.csv")
+        lr = LogisticRegression()
+
+        x_train, x_test, y_train, y_test = train_test_split(x, y_df['label'])
+        lr.fit(x_train, y_train)
+        prob = lr.predict_proba(x_test)[:, 1]
+        auc_score = roc_auc_score(y_test, prob)
+        print("AUC: {}\n".format(auc_score))
+
+        # print the score card
+        print("THE SCORE CARD:")
+        coe = lr.coef_
+        for i, col_name in enumerate(x_df.columns):
+            bin_cols = self._bins[col_name][0].index.to_list()
+            for j, w in enumerate(self._bins[col_name][2]):
+                print(col_name, bin_cols[j],
+                      round(coe[0][i] * w * self._factor, 0))