diff --git a/notebooks/examples/algorithm_comparison_classification.ipynb b/notebooks/examples/algorithm_comparison_classification.ipynb
index 8aac97478f3e4d1af3869047703ca9aef653120b..937c8b929898c910f95ddf5d02d2555379f7734e 100644
--- a/notebooks/examples/algorithm_comparison_classification.ipynb
+++ b/notebooks/examples/algorithm_comparison_classification.ipynb
@@ -2,25 +2,33 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "# Algorithm comparison for classification"
-   ]
+    "# A Comparison of different Classification Algorithms"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
+   "execution_count": 1,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\nikla\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
+      "c:\\users\\nikla\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\numpy\\.libs\\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll\n",
+      "c:\\users\\nikla\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\numpy\\.libs\\libopenblas.XWYDX2IKJW2NMTWSFYNGFUWKQU3LYTCZ.gfortran-win_amd64.dll\n",
+      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
+     ]
     }
-   },
-   "outputs": [],
+   ],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
@@ -29,363 +37,356 @@
     "from sklearn.cluster import KMeans\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.svm import SVC\n",
-    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
     "from sklearn.impute import  SimpleImputer"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "For this comparison we will be using the Titanic dataset.\n",
+    "We load our data and show the first 5 rows"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "source": [
+    "data = pd.read_csv(\"../data/Titanic/titanic.csv\")\n",
+    "\n",
+    "data.head()\n"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
+   "execution_count": 2,
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>PassengerId</th>\n",
-       "      <th>Survived</th>\n",
-       "      <th>Pclass</th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Sex</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SibSp</th>\n",
-       "      <th>Parch</th>\n",
-       "      <th>Ticket</th>\n",
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-       "      <td>1</td>\n",
-       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
-       "      <td>female</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>PC 17599</td>\n",
-       "      <td>71.2833</td>\n",
-       "      <td>C85</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Heikkinen, Miss. Laina</td>\n",
-       "      <td>female</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>STON/O2. 3101282</td>\n",
-       "      <td>7.9250</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>113803</td>\n",
-       "      <td>53.1000</td>\n",
-       "      <td>C123</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Allen, Mr. William Henry</td>\n",
-       "      <td>male</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>373450</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   PassengerId  Survived  Pclass  \\\n",
-       "0            1         0       3   \n",
-       "1            2         1       1   \n",
-       "2            3         1       3   \n",
-       "3            4         1       1   \n",
-       "4            5         0       3   \n",
-       "\n",
-       "                                                Name     Sex   Age  SibSp  \\\n",
-       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
-       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
-       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
-       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
-       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
-       "\n",
-       "   Parch            Ticket     Fare Cabin Embarked  \n",
-       "0      0         A/5 21171   7.2500   NaN        S  \n",
-       "1      0          PC 17599  71.2833   C85        C  \n",
-       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
-       "3      0            113803  53.1000  C123        S  \n",
-       "4      0            373450   8.0500   NaN        S  "
-      ]
+      "text/plain": "   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n\n                                                Name     Sex   Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male  22.0      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                             Heikkinen, Miss. Laina  female  26.0      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                           Allen, Mr. William Henry    male  35.0      0   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Braund, Mr. Owen Harris</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>A/5 21171</td>\n      <td>7.2500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>PC 17599</td>\n      <td>71.2833</td>\n      <td>C85</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Heikkinen, Miss. Laina</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>STON/O2. 3101282</td>\n      <td>7.9250</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113803</td>\n      <td>53.1000</td>\n      <td>C123</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Allen, Mr. William Henry</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>373450</td>\n      <td>8.0500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 221,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
-   ],
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "data = pd.read_csv(\"../data/Titanic/titanic.csv\")\n",
+    "## Preprocessing\n",
     "\n",
-    "data.head()\n"
-   ]
+    "We encode the \"Sex\" column  by mapping \"male\" to 0 and \"female\" to 1.\n",
+    "A Feature and Target array is created."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "execution_count": 3,
    "outputs": [],
    "source": [
     "data[\"Sex\"] = data[\"Sex\"].map({\"male\":0, \"female\":1})\n",
     "\n",
     "features = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\"]\n",
     "target = [\"Survived\"]"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We check if any empty values exist."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "source": [
+    "data.isnull().sum()"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
+   "execution_count": 4,
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "PassengerId      0\n",
-       "Survived         0\n",
-       "Pclass           0\n",
-       "Name             0\n",
-       "Sex              0\n",
-       "Age            177\n",
-       "SibSp            0\n",
-       "Parch            0\n",
-       "Ticket           0\n",
-       "Fare             0\n",
-       "Cabin          687\n",
-       "Embarked         2\n",
-       "dtype: int64"
-      ]
+      "text/plain": "PassengerId      0\nSurvived         0\nPclass           0\nName             0\nSex              0\nAge            177\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          687\nEmbarked         2\ndtype: int64"
      },
-     "execution_count": 223,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "data.isnull().sum()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We use the SimpleImputer to fill in the missing values with the mean value."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "imp = SimpleImputer(strategy=\"mean\")\n",
+    "\n",
+    "data[\"Age\"] = imp.fit_transform(data[[\"Age\"]])"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [],
+   }
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "imp = SimpleImputer(strategy=\"mean\")\n",
+    "## Creating the models\n",
     "\n",
-    "data[\"Age\"] = imp.fit_transform(data[[\"Age\"]])"
-   ]
+    "We first split our data into train and test data."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(data[features], data[target], test_size=0.5, random_state=42)\n",
+    "\n",
+    "y_train = np.array(y_train).ravel()\n",
+    "y_test = np.array(y_test).ravel()"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [],
+   "execution_count": 6,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(data[features], data[target], test_size=0.5, random_state=42)\n",
-    "\n",
-    "y_train = np.array(y_train).ravel()\n",
-    "y_test = np.array(y_test).ravel()"
-   ]
+    "Here we create all models we wish to test.\n",
+    "We will be comparing them with their default values. However by fine-tuning their hyperparameters better resoults could be achieved.\n",
+    "We only set the n_clusters parameter for k_means because we already know how many classes exist"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 7,
+   "outputs": [],
+   "source": [
+    "models = []\n",
+    "models.append(KNeighborsClassifier())\n",
+    "models.append(DecisionTreeClassifier())\n",
+    "models.append(RandomForestClassifier())\n",
+    "models.append(GaussianNB())\n",
+    "models.append(KMeans(n_clusters=2))\n",
+    "models.append(LogisticRegression())\n",
+    "models.append(SVC())"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [],
+   }
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "nearest_neighbors = KNeighborsClassifier()\n",
-    "decision_tree = DecisionTreeClassifier()\n",
-    "random_forest = RandomForestClassifier()\n",
-    "naive_bayes = GaussianNB()\n",
-    "k_means = KMeans(n_clusters=3, init='k-means++')\n",
-    "logistic_reg = LogisticRegression()\n",
-    "support_vector = SVC()"
-   ]
+    "Now all models are trained with the training dataset"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 8,
+   "outputs": [],
+   "source": [
+    "for m in models:\n",
+    "    m.fit(X_train, y_train)"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SVC()"
-      ]
-     },
-     "execution_count": 227,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   }
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "nearest_neighbors.fit(X_train, y_train)\n",
-    "decision_tree.fit(X_train, y_train)\n",
-    "random_forest.fit(X_train, y_train)\n",
-    "naive_bayes.fit(X_train, y_train)\n",
-    "k_means.fit(X_train, y_train)\n",
-    "logistic_reg.fit(X_train, y_train)\n",
-    "support_vector.fit(X_train, y_train)"
-   ]
+    "## Predictions\n",
+    "With our trained models we can now try tro predict with the test dataset"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 9,
+   "outputs": [],
+   "source": [
+    "predictions = []\n",
+    "for m in models:\n",
+    "    predictions.append(m.predict(X_test))"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [],
+   }
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "nearest_neighbors_pred = nearest_neighbors.predict(X_test)\n",
-    "decision_tree_pred = decision_tree.predict(X_test)\n",
-    "random_forest_pred = random_forest.predict(X_test)\n",
-    "naive_bayes_pred = naive_bayes.predict(X_test)\n",
-    "k_means_pred = k_means.predict(X_test)\n",
-    "logistic_reg_pred = logistic_reg.predict(X_test)\n",
-    "support_vector_pred = support_vector.predict(X_test)"
-   ]
+    "get accuracy of our predictions compared to the true values and confusionmatrix"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 10,
+   "outputs": [],
+   "source": [
+    "accuracy = []\n",
+    "confusion_matrix_list = []\n",
+    "for p in predictions:\n",
+    "    accuracy.append(accuracy_score(y_test, p))\n",
+    "    confusion_matrix_list.append(confusion_matrix(y_test, p))"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [],
+   }
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "nearest_neighbors_acc = accuracy_score(y_test, nearest_neighbors_pred)\n",
-    "decision_tree_acc = accuracy_score(y_test, decision_tree_pred)\n",
-    "random_forest_acc = accuracy_score(y_test, random_forest_pred)\n",
-    "naive_bayes_acc = accuracy_score(y_test, naive_bayes_pred)\n",
-    "k_means_acc = accuracy_score(y_test, k_means_pred)\n",
-    "logistic_reg_acc = accuracy_score(y_test, logistic_reg_pred)\n",
-    "support_vector_acc = accuracy_score(y_test, support_vector_pred)"
-   ]
+    "## Visualization of results\n",
+    "\n",
+    "First we will look at the confusion matrix for each algorithm."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 11,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1440x1440 with 14 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 2, figsize=(20,20))\n",
+    "\n",
+    "titles = ['K Nearest Neighbors',\n",
+    "          'Decision Tree',\n",
+    "          'Random Forest',\n",
+    "          'Naive Bayes',\n",
+    "          'K-Means',\n",
+    "          'Logistic Regression',\n",
+    "          'Support Vector Machine']\n",
+    "\n",
+    "group_names = ['True Neg', 'False Pos', 'False Neg', 'True Pos']\n",
+    "\n",
+    "\n",
+    "for i, cm in enumerate(confusion_matrix_list):\n",
+    "    group_counts = [\"{0:0.0f}\".format(value) for value in cm.flatten()]\n",
+    "    group_percentages = [\"{0:.2%}\".format(value) for value in cm.flatten()/np.sum(cm)]\n",
+    "    labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,group_counts,group_percentages)]\n",
+    "    labels = np.asarray(labels).reshape(2,2)\n",
+    "    sns.heatmap(cm/np.sum(cm), ax=ax[i%4][int(i/4)], cmap='Blues', annot=labels, fmt='')\n",
+    "    ax[i%4][int(i/4)].set_title(titles[i])\n",
+    "\n",
+    "\n",
+    "fig.delaxes(ax[3][1])"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "And now we compare the accuracies of each algorithm"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "outputs": [
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 1152x1152 with 1 Axes>",
+      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -396,25 +397,30 @@
    "source": [
     "\n",
     "x_axis = [\"K-Nearest Neighbors\",\"Decision Tree\", \"Random Forest\", \"Naive Bayes\", \"K-Means\",\"Logistic Regression\",\"Support Vector Machine\"]\n",
-    "heights = [nearest_neighbors_acc,decision_tree_acc,random_forest_acc,naive_bayes_acc,k_means_acc,logistic_reg_acc,support_vector_acc]\n",
-    "fig, ax = plt.subplots()\n",
+    "fig, ax = plt.subplots(figsize=(16,16))\n",
     "plt.title(\"Accuracy metric comparison\")\n",
     "plt.grid()\n",
-    "plt.bar(x=x_axis,height=heights)\n",
+    "plt.bar(x=x_axis,height=accuracy)\n",
     "\n",
     "fig.autofmt_xdate()\n",
     "\n",
     "plt.show()"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
    "source": [
     "This comparison shows that for this dataset using Random Forest gives us the best result. A comparison like this is however not necessarily ideal since different algorithms excel at different problems."
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   }
  ],
  "metadata": {
@@ -438,4 +444,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
\ No newline at end of file