diff --git a/examples/lgbm/1_lgbm.ipynb b/examples/lgbm/1_lgbm.ipynb
index ed3a664..83e0c21 100644
--- a/examples/lgbm/1_lgbm.ipynb
+++ b/examples/lgbm/1_lgbm.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>LGBMClassifier → PMML (using Nyoka) </font>\n",
+    "# LGBMClassifier → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: LGBMClassifier </font>\n",
-    "### <font color=purple> Data Set used: Iris, Auto-mpg </font>\n",
+    "### Exporter: LGBMClassifier \n",
     "\n",
+    "### Data Set used: Iris, Auto-mpg\n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with model using LGBMClassifier\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### LGBMClassifier Model building (using pipeline) for Iris data set"
    ]
   },
@@ -78,7 +75,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -102,7 +98,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### LGBMRegressor Model building (using pipeline) for Auto-mpg data set"
    ]
   },
@@ -147,7 +142,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -159,13 +153,6 @@
    "source": [
     "lgb_to_pmml(pipeline_obj,feature_names,target_name,\"lgbmr_pmml.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/lgbm/2_lgbm_With_Scaling.ipynb b/examples/lgbm/2_lgbm_With_Scaling.ipynb
index 12b4c10..e458c1c 100644
--- a/examples/lgbm/2_lgbm_With_Scaling.ipynb
+++ b/examples/lgbm/2_lgbm_With_Scaling.ipynb
@@ -4,16 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<font size = 30>LGBM → PMML (using Nyoka) </font>\n",
+    "# LGBM → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "<font color=purple> Exporter: LGBM Classifier </font>\n",
-    "<font color=purple> Data Set used: Iris </font>\n",
+    "### Exporter: LGBM Classifier\n",
+    "### Data Set used: Iris \n",
     "\n",
     "\n",
-    "<font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with preprocessing \n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +19,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Pre-processing, Model building (using pipeline) for Iris data set"
    ]
   },
@@ -79,7 +75,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -97,13 +92,6 @@
     "from nyoka import lgb_to_pmml\n",
     "lgb_to_pmml(pipeline_obj,features,target,\"lgbmc_pmml_preprocess.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/lgbm/3_lgbm_With_PreProcess .ipynb b/examples/lgbm/3_lgbm_With_PreProcess .ipynb
index cdcea27..24e3799 100644
--- a/examples/lgbm/3_lgbm_With_PreProcess .ipynb	
+++ b/examples/lgbm/3_lgbm_With_PreProcess .ipynb	
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<font size = 30>LGBM → PMML (using Nyoka) </font>\n",
+    "# LGBM → PMML \n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "<font color=purple> Exporter: LGBMRegressor </font>\n",
-    "<font color=purple> Data Set used: Auto </font>\n",
+    "### Exporter: LGBMRegressor\n",
     "\n",
+    "### Data Set used: Auto\n",
     "\n",
-    "<font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with preprocessing \n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Pre-processing, Model building (using pipeline) for Auto data set"
    ]
   },
@@ -84,7 +81,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -102,13 +98,6 @@
     "from nyoka import lgb_to_pmml\n",
     "lgb_to_pmml(pipeline_obj,feature_names,target_name,\"lgbmr_pmml_preprocess.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/skl/1_SVM.ipynb b/examples/skl/1_SVM.ipynb
index 2613ea3..9b84720 100644
--- a/examples/skl/1_SVM.ipynb
+++ b/examples/skl/1_SVM.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>scikit-learn → PMML (using Nyoka) </font>\n",
+    "# scikit-learn → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: Support Vector Classifier </font>\n",
-    "### <font color=purple> Data Set used: Iris </font>\n",
+    "### Exporter: Support Vector Classifier\n",
     "\n",
+    "### Data Set used: Iris \n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with model using sklearn SVC\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Model building (using pipeline) for Iris data set"
    ]
   },
@@ -76,7 +73,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -125,15 +121,6 @@
     "\n",
     "skl_to_pmml(pipeline_obj,features,target,\"svc_pmml.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -152,7 +139,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/examples/skl/3_RF_With_pre-processing.ipynb b/examples/skl/3_RF_With_pre-processing.ipynb
index f9e3777..0bf4e7d 100644
--- a/examples/skl/3_RF_With_pre-processing.ipynb
+++ b/examples/skl/3_RF_With_pre-processing.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>scikit-learn → PMML (using Nyoka) </font>\n",
+    "# scikit-learn → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: Random Forest </font>\n",
-    "### <font color=purple> Data Set used: Iris </font>\n",
     "\n",
+    "### Exporter: Random Forest\n",
+    "### Data Set used: Iris\n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**:\n",
     "- Build the Pipeline with preprocessing (using DataFrameMapper)\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Pre-processing, Model building (using pipeline) for Iris data set"
    ]
   },
@@ -81,7 +78,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
diff --git a/examples/skl/4_GB_With_pre-processing.ipynb b/examples/skl/4_GB_With_pre-processing.ipynb
index 36015f2..42f9cd2 100644
--- a/examples/skl/4_GB_With_pre-processing.ipynb
+++ b/examples/skl/4_GB_With_pre-processing.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>scikit-learn → PMML (using Nyoka) </font>\n",
+    "# scikit-learn → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: Gradient Boosting </font>\n",
-    "### <font color=purple> Data Set used: Titanic </font>\n",
     "\n",
+    "### Exporter: Gradient Boosting\n",
+    "### Data Set used: Titanic\n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with preprocessing (using DataFrameMapper)\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Pre-processing, Model building (using pipeline) for Titanic data set"
    ]
   },
@@ -104,7 +101,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
diff --git a/examples/skl/5_Decision_Tree_With_Tf-Idf.ipynb b/examples/skl/5_Decision_Tree_With_Tf-Idf.ipynb
index 002829f..92de212 100644
--- a/examples/skl/5_Decision_Tree_With_Tf-Idf.ipynb
+++ b/examples/skl/5_Decision_Tree_With_Tf-Idf.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>scikit-learn → PMML (using Nyoka) </font>\n",
+    "# scikit-learn → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: Decision Tree Regressor</font>\n",
-    "### <font color=purple> Data Set used: auto_mpg </font>\n",
     "\n",
+    "### Exporter: Decision Tree Regressor\n",
+    "### Data Set used: auto_mpg\n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**:\n",
     "- Build the Pipeline with model and pre-processing (tf-idf vectorizer) using sklearn Decision Tree Regressor\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Model building (using pipeline) for Iris data set"
    ]
   },
@@ -81,7 +78,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
diff --git a/examples/statsmodels/exponential_smoothing/exponential_smoothing.pmml b/examples/statsmodels/exponential_smoothing/exponential_smoothing.pmml
deleted file mode 100644
index 37d9c6b..0000000
--- a/examples/statsmodels/exponential_smoothing/exponential_smoothing.pmml
+++ /dev/null
@@ -1,52 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<PMML xmlns="http://www.dmg.org/PMML-4_3" version="4.4">
-    <Header copyright="Copyright (c) 2017 PB&amp;RB, exported to PMML by Nyoka (c) 2018 Software AG" description="Exponential Smoothing Model">
-        <Application name="Rattle/PMML" version="1.3"/>
-        <Timestamp>2018-08-29 10:06:30.663691</Timestamp>
-    </Header>
-    <DataDictionary numberOfFields="1">
-        <DataField name="datetime_index" optype="continuous" dataType="dateTime"/>
-        <DataField name="n_visitors" optype="continuous" dataType="double"/>
-    </DataDictionary>
-    <TimeSeriesModel modelName="simple exponential smoothing" functionName="timeSeries" bestFit="ExponentialSmoothing">
-        <MiningSchema>
-            <MiningField name="datetime_index" usageType="order"/>
-            <MiningField name="n_visitors" usageType="target"/>
-        </MiningSchema>
-        <TimeSeries startTime="0" endTime="23">
-            <TimeValue index="0" value="41.7275"/>
-            <TimeValue index="1" value="24.0418"/>
-            <TimeValue index="2" value="32.3281"/>
-            <TimeValue index="3" value="37.3287"/>
-            <TimeValue index="4" value="46.2132"/>
-            <TimeValue index="5" value="29.3463"/>
-            <TimeValue index="6" value="36.4829"/>
-            <TimeValue index="7" value="42.9777"/>
-            <TimeValue index="8" value="48.9015"/>
-            <TimeValue index="9" value="31.1802"/>
-            <TimeValue index="10" value="37.7179"/>
-            <TimeValue index="11" value="40.4202"/>
-            <TimeValue index="12" value="51.2069"/>
-            <TimeValue index="13" value="31.8872"/>
-            <TimeValue index="14" value="40.9783"/>
-            <TimeValue index="15" value="43.7725"/>
-            <TimeValue index="16" value="55.5586"/>
-            <TimeValue index="17" value="33.8509"/>
-            <TimeValue index="18" value="42.0764"/>
-            <TimeValue index="19" value="45.6423"/>
-            <TimeValue index="20" value="59.7668"/>
-            <TimeValue index="21" value="35.1919"/>
-            <TimeValue index="22" value="44.3197"/>
-            <TimeValue index="23" value="47.9137"/>
-        </TimeSeries>
-        <ExponentialSmoothing>
-            <Level alpha="3.91951250045e-09" initialLevelValue="50.5692199285" smoothedValue="72.4799147391"/>
-            <Trend_ExpoSmooth gamma="2.36727613847e-16" initialTrendValue="0.912945617091" smoothedValue="0.912945617091"/>
-            <Seasonality_ExpoSmooth type="multiplicative" period="4" delta="4.35633789858e-07">
-                <Array n="4">
-0.835342901906 0.502963233327 0.624380923318 0.677218727883
-                </Array>
-            </Seasonality_ExpoSmooth>
-        </ExponentialSmoothing>
-    </TimeSeriesModel>
-</PMML>
diff --git a/examples/xgboost/1_xgboost.ipynb b/examples/xgboost/1_xgboost.ipynb
index 878fc01..c82d51a 100644
--- a/examples/xgboost/1_xgboost.ipynb
+++ b/examples/xgboost/1_xgboost.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>Xgboost → PMML (using Nyoka) </font>\n",
+    "# Xgboost → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: XGBoost  </font>\n",
-    "### <font color=purple> Data Set used: Iris, Auto-mpg </font>\n",
     "\n",
+    "### Exporter: XGBoost \n",
+    "### Data Set used: Iris, Auto-mpg\n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with model using XGBoost\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### XGBoost Classifier Model building (using pipeline) for Iris data set"
    ]
   },
@@ -78,7 +75,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -102,7 +98,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### XGBoost Regressor Model building (using pipeline) for Auto-mpg data set"
    ]
   },
@@ -147,7 +142,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -159,13 +153,6 @@
    "source": [
     "xgboost_to_pmml(pipeline_obj,feature_names,target_name,\"xgbr_pmml.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/xgboost/2_xgboost_With_Scaling.ipynb b/examples/xgboost/2_xgboost_With_Scaling.ipynb
index 2a5f058..643db65 100644
--- a/examples/xgboost/2_xgboost_With_Scaling.ipynb
+++ b/examples/xgboost/2_xgboost_With_Scaling.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<font size = 30>XGBoost → PMML (using Nyoka) </font>\n",
+    "# XGBoost → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "<font color=purple> Exporter: XGboost Classifier </font>\n",
-    "<font color=purple> Data Set used: Iris </font>\n",
     "\n",
+    "### Exporter: XGboost Classifier\n",
+    "### Data Set used: Iris\n",
     "\n",
-    "<font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**:\n",
     "- Build the Pipeline with preprocessing \n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Pre-processing, Model building (using pipeline) for Iris data set"
    ]
   },
@@ -79,7 +76,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -97,13 +93,6 @@
     "from nyoka import xgboost_to_pmml\n",
     "xgboost_to_pmml(pipeline_obj,features,target,\"xgbc_pmml_preprocess.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/xgboost/3_xgboost_With_PreProcess .ipynb b/examples/xgboost/3_xgboost_With_PreProcess .ipynb
index 8c2aecf..2d4879f 100644
--- a/examples/xgboost/3_xgboost_With_PreProcess .ipynb	
+++ b/examples/xgboost/3_xgboost_With_PreProcess .ipynb	
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<font size = 30>XGBoost → PMML (using Nyoka) </font>\n",
+    "# XGBoost → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "<font color=purple> Exporter: XGboost Regressor </font>\n",
-    "<font color=purple> Data Set used: Auto </font>\n",
     "\n",
+    "### Exporter: XGboost Regressor\n",
+    "### Data Set used: Auto\n",
     "\n",
-    "<font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**:\n",
     "- Build the Pipeline with preprocessing \n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Pre-processing, Model building (using pipeline) for Auto data set"
    ]
   },
@@ -85,7 +82,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -103,13 +99,6 @@
     "from nyoka import xgboost_to_pmml\n",
     "xgboost_to_pmml(pipeline_obj,feature_names,target_name,\"xgbr_pmml_preprocess.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {