diff --git a/src/model/train/treinamento.py b/src/model/train/treinamento.py
index 7d86cd2..fa17fbd 100644
--- a/src/model/train/treinamento.py
+++ b/src/model/train/treinamento.py
@@ -43,7 +43,11 @@
   exclude_columns=['idJogador']
 )
 
-training_df = training_set.load_df().toPandas()
+training_df = training_set.load_df()
+
+# COMMAND ----------
+
+training_df.write.mode("overwrite").format("delta").saveAsTable("gold.gamersclub.abt_subs")
 
 # COMMAND ----------
 
diff --git a/src/model/train/treinamento_2.py b/src/model/train/treinamento_2.py
new file mode 100644
index 0000000..853376d
--- /dev/null
+++ b/src/model/train/treinamento_2.py
@@ -0,0 +1,143 @@
+# Databricks notebook source
+# MAGIC %pip install feature-engine scikit-plot
+# MAGIC
+# MAGIC dbutils.library.restartPython()
+
+# COMMAND ----------
+
+from sklearn import tree
+from sklearn import ensemble
+from sklearn import model_selection
+from sklearn import metrics
+from sklearn import pipeline
+
+from feature_engine import imputation
+
+df = spark.table("gold.gamersclub.abt_subs").toPandas()
+df.head()
+
+# COMMAND ----------
+
+target = "flSub"
+to_remove = ["dtRef", target]
+
+features = list(set(df.columns.tolist()) - set(to_remove))
+
+num_features = df[features].dtypes[df[features].dtypes != 'object'].index.tolist()
+cat_feature = list(set(features)-set(num_features))
+
+# COMMAND ----------
+
+X_train, X_test, y_train, y_test = model_selection.train_test_split(df[features],
+                                                                    df[target],
+                                                                    test_size=0.2,
+                                                                    random_state=42)
+
+# COMMAND ----------
+
+X_train.isna().sum().sort_values(ascending=False)
+
+# COMMAND ----------
+
+to_imput_0 = [
+    'qtdChurnMes',
+    'daysSinceFirstSub',
+    'qtdMedalhasMes',
+    'qtdChurn',
+    'qtdAssinaturasMes',
+    'qtdMedalhas',
+    'qtdAssinaturas',
+    'evolucao_level_pct',
+    'qtAcertosPernaEsquerda_pct',
+    'qtAcertosPeito_pct',
+    'qtAcertosBracoEsquerdo_pct',
+    'qtAcertosBracoDireito_pct',
+    'qtAcertosCabeca_pct',
+    'qtAcertosEstomagoo_pct',
+    'qtAcertosPernaDireita_pct',
+]
+
+imputer_0 = imputation.ArbitraryNumberImputer(arbitrary_number=0, variables=to_imput_0)
+
+# COMMAND ----------
+
+clf = ensemble.RandomForestClassifier(n_estimators=300, min_samples_leaf=50, n_jobs=-1)
+
+model_pipeline = pipeline.Pipeline(
+    [("imputer", imputer_0),
+     ("model", clf)]
+)
+
+# COMMAND ----------
+
+model_pipeline.fit(X_train, y_train)
+
+# COMMAND ----------
+
+y_train_proba = model_pipeline.predict_proba(X_train)
+proba_train = y_train_proba[:, 1]
+
+y_test_proba = model_pipeline.predict_proba(X_test)
+proba_1 = y_test_proba[:, 1]
+proba_1
+
+metrics.roc_auc_score(y_test, proba_1)
+
+# COMMAND ----------
+
+import numpy as np
+import pandas as pd
+from scipy import optimize as opt
+
+m_cost = np.array(
+    [[0,-5],
+     [-10,25]]
+)
+
+
+def calc_money(m_cost, m_conf):
+    '''Calcula o valor de garana dado duas matrizes'''
+    return np.multiply(m_cost, m_conf).sum()
+
+
+def otimiza_corte(m_cost, proba, y):
+    '''Otimiza dee forma exaustiva todas a probabilidades para o melhor ponto de corte'''
+    
+    data = {"proba":[], "cost":[]}
+    
+    for p in np.unique(proba):
+        m_conf = metrics.confusion_matrix(y, proba > p)
+        data["cost"].append(calc_money(m_cost, m_conf))
+        data["proba"].append(p)
+
+    data = pd.DataFrame(data)
+    max_cost = data["cost"].max()
+    return data[data["cost"]==max_cost].values[0].tolist()
+
+otimiza_corte(m_cost, proba_train, y_train)
+
+# COMMAND ----------
+
+def otimiza_opt(p, m_cost, proba, y):
+    '''Função para ser utilizar no otimizador'''
+    m_conf = metrics.confusion_matrix(y, proba > p)
+    return -1 * calc_money(m_cost, m_conf)
+
+opt.minimize(
+    fun=otimiza_opt,
+    x0=0.07,
+    args=(m_cost, proba_train, y_train),
+    method='Nelder-Mead',
+    options={"maxiter":1000}
+)
+
+# COMMAND ----------
+
+def f(x):
+    return x[0] ** 2 + x[1]
+
+opt.minimize(fun=f, x0=[5, 5], method='Nelder-Mead', b)
+
+# COMMAND ----------
+
+8.785e+15