diff --git a/Experiments.ipynb b/Experiments.ipynb
index e81ee88..8f7fe0e 100644
--- a/Experiments.ipynb
+++ b/Experiments.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "initial_id",
    "metadata": {
     "jupyter": {
@@ -11,12 +11,13 @@
    },
    "outputs": [],
    "source": [
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 14,
    "id": "67503952-9074-4cdb-9d7e-d9142f7c319c",
    "metadata": {},
    "outputs": [
@@ -42,128 +43,180 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>age</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>cp</th>\n",
        "      <th>trestbps</th>\n",
        "      <th>chol</th>\n",
-       "      <th>fbs</th>\n",
-       "      <th>restecg</th>\n",
        "      <th>thalach</th>\n",
-       "      <th>exang</th>\n",
        "      <th>oldpeak</th>\n",
-       "      <th>slope</th>\n",
-       "      <th>ca</th>\n",
-       "      <th>thal</th>\n",
-       "      <th>goal</th>\n",
+       "      <th>sex_0</th>\n",
+       "      <th>sex_1</th>\n",
+       "      <th>cp_1</th>\n",
+       "      <th>cp_2</th>\n",
+       "      <th>cp_3</th>\n",
+       "      <th>...</th>\n",
+       "      <th>slope_1</th>\n",
+       "      <th>slope_2</th>\n",
+       "      <th>slope_3</th>\n",
+       "      <th>thal_3.0</th>\n",
+       "      <th>thal_6.0</th>\n",
+       "      <th>thal_7.0</th>\n",
+       "      <th>ca_0.0</th>\n",
+       "      <th>ca_1.0</th>\n",
+       "      <th>ca_2.0</th>\n",
+       "      <th>ca_3.0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>63</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>145</td>\n",
-       "      <td>233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>150</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.708333</td>\n",
+       "      <td>0.481132</td>\n",
+       "      <td>0.244292</td>\n",
+       "      <td>0.603053</td>\n",
+       "      <td>0.370968</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>67</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>160</td>\n",
-       "      <td>286</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>108</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2</td>\n",
+       "      <td>0.791667</td>\n",
+       "      <td>0.622642</td>\n",
+       "      <td>0.365297</td>\n",
+       "      <td>0.282443</td>\n",
+       "      <td>0.241935</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>67</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>120</td>\n",
-       "      <td>229</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>129</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>0.791667</td>\n",
+       "      <td>0.245283</td>\n",
+       "      <td>0.235160</td>\n",
+       "      <td>0.442748</td>\n",
+       "      <td>0.419355</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>37</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>130</td>\n",
-       "      <td>250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>187</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.339623</td>\n",
+       "      <td>0.283105</td>\n",
+       "      <td>0.885496</td>\n",
+       "      <td>0.564516</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>41</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>130</td>\n",
-       "      <td>204</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>172</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.339623</td>\n",
+       "      <td>0.178082</td>\n",
+       "      <td>0.770992</td>\n",
+       "      <td>0.225806</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
-       "0   63    1   1       145   233    1        2      150      0      2.3      3   \n",
-       "1   67    1   4       160   286    0        2      108      1      1.5      2   \n",
-       "2   67    1   4       120   229    0        2      129      1      2.6      2   \n",
-       "3   37    1   3       130   250    0        0      187      0      3.5      3   \n",
-       "4   41    0   2       130   204    0        2      172      0      1.4      1   \n",
+       "        age  trestbps      chol   thalach   oldpeak  sex_0  sex_1   cp_1  \\\n",
+       "0  0.708333  0.481132  0.244292  0.603053  0.370968  False   True   True   \n",
+       "1  0.791667  0.622642  0.365297  0.282443  0.241935  False   True  False   \n",
+       "2  0.791667  0.245283  0.235160  0.442748  0.419355  False   True  False   \n",
+       "3  0.166667  0.339623  0.283105  0.885496  0.564516  False   True  False   \n",
+       "4  0.250000  0.339623  0.178082  0.770992  0.225806   True  False  False   \n",
        "\n",
-       "    ca  thal  goal  \n",
-       "0  0.0   6.0     0  \n",
-       "1  3.0   3.0     2  \n",
-       "2  2.0   7.0     1  \n",
-       "3  0.0   3.0     0  \n",
-       "4  0.0   3.0     0  "
+       "    cp_2   cp_3  ...  slope_1  slope_2  slope_3  thal_3.0  thal_6.0  thal_7.0  \\\n",
+       "0  False  False  ...    False    False     True     False      True     False   \n",
+       "1  False  False  ...    False     True    False      True     False     False   \n",
+       "2  False  False  ...    False     True    False     False     False      True   \n",
+       "3  False   True  ...    False    False     True      True     False     False   \n",
+       "4   True  False  ...     True    False    False      True     False     False   \n",
+       "\n",
+       "   ca_0.0  ca_1.0  ca_2.0  ca_3.0  \n",
+       "0    True   False   False   False  \n",
+       "1   False   False   False    True  \n",
+       "2   False   False    True   False  \n",
+       "3    True   False   False   False  \n",
+       "4    True   False   False   False  \n",
+       "\n",
+       "[5 rows x 28 columns]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -171,31 +224,7 @@
    "source": [
     "df = pd.read_csv('./data/dataset_cleaned.csv')\n",
     "df.dropna(inplace=True)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "8fa945ef-34d4-4e4c-a1cd-f1e1e6da79e7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0],\n",
-       "       [1],\n",
-       "       [1],\n",
-       "       [0],\n",
-       "       [0]], dtype=int64)"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+    "\n",
     "# extract all columns except 'goal' --> X\n",
     "X = df.loc[:, df.columns != 'goal']\n",
     "# extract only the column 'goal' --> y\n",
@@ -206,12 +235,24 @@
     "y = y.reshape((len(y),1))\n",
     "\n",
     "# binarize y\n",
-    "y[y>0] = 1"
+    "y[y>0] = 1\n",
+    "\n",
+    "factor_columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'thal', 'ca']\n",
+    "numeric_columns = [column for column in X.columns if column not in factor_columns]\n",
+    "\n",
+    "# transform factors into onehot vectors\n",
+    "X = pd.get_dummies(X, columns=factor_columns)\n",
+    "\n",
+    "# min max scaling of numeric columns\n",
+    "scaler = MinMaxScaler()\n",
+    "X[numeric_columns] = scaler.fit_transform(X[numeric_columns])\n",
+    "\n",
+    "X.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 18,
    "id": "2bbee865-c000-43da-84d9-ce7e04874110",
    "metadata": {},
    "outputs": [],
@@ -221,7 +262,6 @@
     "        tf.keras.layers.InputLayer(shape=(n_features,)),\n",
     "        tf.keras.layers.Dense(30, activation='relu'),\n",
     "        tf.keras.layers.Dense(30, activation='relu'),\n",
-    "        tf.keras.layers.Dense(30, activation='relu'),\n",
     "        tf.keras.layers.Dense(1, activation='sigmoid')\n",
     "    ], name='test')\n",
     "    model.compile(optimizer=tf.keras.optimizers.Adam(), \n",
@@ -231,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 20,
    "id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933",
    "metadata": {},
    "outputs": [
@@ -239,27 +279,47 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training fold 0 for 5 epochs\n",
-      "Train samples:\t237\n",
-      "Test samples:\t60\n",
-      "Accuracy of fold 0: 0.6166666666666667\n",
-      "Training fold 1 for 5 epochs\n",
-      "Train samples:\t237\n",
-      "Test samples:\t60\n",
-      "Accuracy of fold 1: 0.75\n",
-      "Training fold 2 for 5 epochs\n",
-      "Train samples:\t238\n",
-      "Test samples:\t59\n",
-      "Accuracy of fold 2: 0.6949152542372882\n",
-      "Training fold 3 for 5 epochs\n",
-      "Train samples:\t238\n",
-      "Test samples:\t59\n",
-      "Accuracy of fold 3: 0.7457627118644068\n",
-      "Training fold 4 for 5 epochs\n",
-      "Train samples:\t238\n",
-      "Test samples:\t59\n",
-      "Accuracy of fold 4: 0.6610169491525424\n",
-      "Avg accuracy 0.6936723163841808\n"
+      "Training fold 0 for 20 epochs\n",
+      "Train samples:\t267\n",
+      "Test samples:\t30\n",
+      "Accuracy of fold 0: 0.8666666666666667\n",
+      "Training fold 1 for 20 epochs\n",
+      "Train samples:\t267\n",
+      "Test samples:\t30\n",
+      "Accuracy of fold 1: 0.8666666666666667\n",
+      "Training fold 2 for 20 epochs\n",
+      "Train samples:\t267\n",
+      "Test samples:\t30\n",
+      "Accuracy of fold 2: 0.8666666666666667\n",
+      "Training fold 3 for 20 epochs\n",
+      "Train samples:\t267\n",
+      "Test samples:\t30\n",
+      "Accuracy of fold 3: 0.9333333333333333\n",
+      "Training fold 4 for 20 epochs\n",
+      "Train samples:\t267\n",
+      "Test samples:\t30\n",
+      "Accuracy of fold 4: 0.8666666666666667\n",
+      "Training fold 5 for 20 epochs\n",
+      "Train samples:\t267\n",
+      "Test samples:\t30\n",
+      "Accuracy of fold 5: 0.8333333333333334\n",
+      "Training fold 6 for 20 epochs\n",
+      "Train samples:\t267\n",
+      "Test samples:\t30\n",
+      "Accuracy of fold 6: 0.8666666666666667\n",
+      "Training fold 7 for 20 epochs\n",
+      "Train samples:\t268\n",
+      "Test samples:\t29\n",
+      "Accuracy of fold 7: 0.896551724137931\n",
+      "Training fold 8 for 20 epochs\n",
+      "Train samples:\t268\n",
+      "Test samples:\t29\n",
+      "Accuracy of fold 8: 0.7931034482758621\n",
+      "Training fold 9 for 20 epochs\n",
+      "Train samples:\t268\n",
+      "Test samples:\t29\n",
+      "Accuracy of fold 9: 0.7931034482758621\n",
+      "Avg accuracy 0.8582758620689654\n"
      ]
     }
    ],
@@ -269,12 +329,13 @@
     "import tensorflow as tf\n",
     "\n",
     "# number of components extracted from the pca\n",
-    "n_features = 5 \n",
+    "n_features = 8\n",
     "\n",
-    "epochs = 5\n",
+    "epochs = 20\n",
+    "k_folds = 10\n",
     "\n",
     "# used to split the dataset into k folds\n",
-    "kf = KFold(n_splits=5)\n",
+    "kf = KFold(n_splits=k_folds)\n",
     "\n",
     "accuracies = []\n",
     "for i, (train_idx, test_idx) in enumerate(kf.split(X)):\n",