added Gradient Boosting Tree Classifier

README.md

@@ -152,11 +152,16 @@ The exact procedure for creating the matrix can be found in the notebook [demogr
 
 The following two hypotheses were applied in this project:
 
-1. Using ECG data, a classifier can classify the four disease groupings with an accuracy of 80%.
+**Hypotheses 1**:
+1. Using ECG data, a classifier can classify the four diagnostic groupings with an accuracy of at least 80%.
 
     Result: 
 - For the first hypothesis, an accuracy of 83 % was achieved with the XGBoost classifier. The detailed procedure can be found in the following notebook: [ml_xgboost.ipynb](notebooks/ml_xgboost.ipynb)
+- Also a 82 % accuracy was achieved with a Gradient Boosting Tree Classifier. The detailed procedure can be found in the following notebook: [ml_grad_boost_tree.ipynb](notebooks/ml_grad_boost_tree.ipynb)
 
+With those Classifiers, the hypothesis can be proven, that a classifier is able to classify the diagnostic Groups with a accuracy of at least 80%.
+
+**Hypotheses 2**:
 
 2. Sinus bradycardia occurs significantly more frequently in the 60 to 70 age group than in other age groups.
 

notebooks/ml_xgboost.ipynb

@@ -1,8 +1,15 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extreme Gradient Boosting (XGBoost) Training and Analysis"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,6 +21,8 @@
     "import xgboost as xgb\n",
     "from sklearn.model_selection import GridSearchCV\n",
     "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
     "import seaborn as sns"
    ]
   },
@@ -26,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -323,12 +332,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "load the best model"
+    "load the best model to get the best hyperparameters from it"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -353,14 +362,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[23:05:40] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: \n",
+      "[20:16:51] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: \n",
       "Parameters: { \"best_iteration\", \"best_ntree_limit\", \"scikit_learn\" } might not be used.\n",
       "\n",
       "  This could be a false alarm, with some parameters getting used by language bindings but\n",
@@ -474,8 +483,8 @@
       "[97]\ttrain-merror:0.00029\teval-merror:0.18265\n",
       "[98]\ttrain-merror:0.00029\teval-merror:0.18265\n",
       "[99]\ttrain-merror:0.00029\teval-merror:0.18265\n",
-      "CPU times: total: 15.5 s\n",
-      "Wall time: 1.2 s\n"
+      "CPU times: total: 17.6 s\n",
+      "Wall time: 1.36 s\n"
      ]
     }
    ],
@@ -497,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -537,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {