diff --git a/0-pilot-project/MNIST-kNN.ipynb b/0-pilot-project/MNIST-kNN.ipynb
index 6eeb096..8c21503 100644
--- a/0-pilot-project/MNIST-kNN.ipynb
+++ b/0-pilot-project/MNIST-kNN.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "a53bc33d",
+   "id": "5a43420b",
    "metadata": {},
    "source": [
     "### Load MNIST dataset"
@@ -10,8 +10,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "id": "7f5485c6",
+   "execution_count": 1,
+   "id": "ea54ab53",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,8 +22,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "id": "a7c20b8c",
+   "execution_count": 2,
+   "id": "d138914e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,8 +34,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "id": "efa1777a",
+   "execution_count": 3,
+   "id": "16c08d7d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,8 +45,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "id": "f61e1e2f",
+   "execution_count": 4,
+   "id": "259df665",
    "metadata": {},
    "outputs": [
     {
@@ -55,7 +55,7 @@
        "sklearn.utils.Bunch"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48008a44",
+   "id": "c32f3027",
    "metadata": {},
    "source": [
     "Bunch objects are sometimes used as an output for functions and methods. They extend dictionaries by enabling values to be accessed by key, bunch[\"value_key\"], or by an attribute, bunch.value_key.\\\n",
@@ -82,8 +82,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "id": "cb7cd8b0",
+   "execution_count": 5,
+   "id": "0b2d3b38",
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +92,7 @@
        "dict_keys(['name', 'age'])"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -105,8 +105,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "id": "86c8c4c9",
+   "execution_count": 6,
+   "id": "026d00cb",
    "metadata": {},
    "outputs": [
     {
@@ -115,7 +115,7 @@
        "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be902d54",
+   "id": "0045da2b",
    "metadata": {},
    "source": [
     "Datasets loaded by Scikit-Learn generally have a similar dictionary structure, including the following:\\\n",
@@ -138,8 +138,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "id": "b6e38bcd",
+   "execution_count": 7,
+   "id": "5bfb594c",
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +148,7 @@
        "\"**Author**: Yann LeCun, Corinna Cortes, Christopher J.C. Burges  \\n**Source**: [MNIST Website](http://yann.lecun.com/exdb/mnist/) - Date unknown  \\n**Please cite**:  \\n\\nThe MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples  \\n\\nIt is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.  \\n\\nWith some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets.  \\n\\nThe MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.\\n\\nDownloaded from openml.org.\""
       ]
      },
-     "execution_count": 76,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e075b017",
+   "id": "322f80cf",
    "metadata": {},
    "source": [
     "### Prepare the MNIST dataset"
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3c2fbdfe",
+   "id": "5f1005a4",
    "metadata": {},
    "source": [
     "$f(X) = y$\n",
@@ -180,8 +180,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "id": "171d9760",
+   "execution_count": 8,
+   "id": "85354007",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,8 +190,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "id": "7f85bb27",
+   "execution_count": 9,
+   "id": "6e123fc5",
    "metadata": {},
    "outputs": [
     {
@@ -200,7 +200,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,8 +211,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
-   "id": "050a0699",
+   "execution_count": 10,
+   "id": "570b4ca6",
    "metadata": {},
    "outputs": [
     {
@@ -221,7 +221,7 @@
        "(70000, 784)"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -232,8 +232,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
-   "id": "8b2e374f",
+   "execution_count": 11,
+   "id": "ad03b0c1",
    "metadata": {},
    "outputs": [
     {
@@ -242,7 +242,7 @@
        "(70000,)"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a749472",
+   "id": "91c936b7",
    "metadata": {},
    "source": [
     "### Plot data"
@@ -261,8 +261,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
-   "id": "06947e31",
+   "execution_count": 12,
+   "id": "cb6b5d57",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,8 +273,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
-   "id": "fb335302",
+   "execution_count": 13,
+   "id": "8896a0b3",
    "metadata": {},
    "outputs": [
     {
@@ -296,8 +296,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "id": "88958ee2",
+   "execution_count": 14,
+   "id": "b108073e",
    "metadata": {},
    "outputs": [
     {
@@ -316,8 +316,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
-   "id": "1db59a90",
+   "execution_count": 15,
+   "id": "b4a8737d",
    "metadata": {},
    "outputs": [
     {
@@ -342,8 +342,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
-   "id": "2f694e64",
+   "execution_count": 16,
+   "id": "323f527a",
    "metadata": {},
    "outputs": [
     {
@@ -363,8 +363,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
-   "id": "ac6db0a7",
+   "execution_count": 17,
+   "id": "507d99b3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,8 +374,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "id": "76885a2b",
+   "execution_count": 18,
+   "id": "72250688",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -388,8 +388,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "id": "1da99848",
+   "execution_count": 19,
+   "id": "b88a415e",
    "metadata": {},
    "outputs": [
     {
@@ -412,8 +412,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "id": "a26aa8a4",
+   "execution_count": 20,
+   "id": "dbb12d11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,8 +428,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
-   "id": "c21d180a",
+   "execution_count": 21,
+   "id": "9e02d526",
    "metadata": {},
    "outputs": [
     {
@@ -454,7 +454,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6b82d42",
+   "id": "d2d84b97",
    "metadata": {},
    "source": [
     "### Prepare data for machine learning"
@@ -462,8 +462,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
-   "id": "fb7f3887",
+   "execution_count": 22,
+   "id": "e16bb697",
    "metadata": {},
    "outputs": [
     {
@@ -472,7 +472,7 @@
        "70000"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -484,7 +484,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "07bb81c8",
+   "id": "3046819d",
    "metadata": {},
    "source": [
     "### Train classifier"
@@ -492,8 +492,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
-   "id": "a6edf780",
+   "execution_count": 23,
+   "id": "71d43c04",
    "metadata": {},
    "outputs": [
     {
@@ -638,13 +638,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
-   "id": "5bda0ee0",
+   "execution_count": 26,
+   "id": "a3e1c22a",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -664,9 +664,8 @@
     "plot_train = np.array([i for i in train_ranges for _ in kVals])\n",
     "plot_kVals = np.array([i for _ in train_ranges for i in kVals])\n",
     "\n",
-    "for i in range(n):\n",
-    "    ax.plot(plot_train, plot_kVals, np.array(accuracies))\n",
     "\n",
+    "ax.plot(plot_train, plot_kVals, np.array(accuracies))\n",
     "ax.set_xlabel(\"Train_size\")\n",
     "ax.set_ylabel(\"k\")\n",
     "ax.set_zlabel(\"accuracy\")\n",
@@ -675,8 +674,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "id": "67e23852",
+   "execution_count": 27,
+   "id": "eecb54d6",
    "metadata": {},
    "outputs": [
     {
@@ -700,8 +699,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
-   "id": "5663042f",
+   "execution_count": 28,
+   "id": "12e68bca",
    "metadata": {},
    "outputs": [
     {
@@ -719,8 +718,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "id": "8c8872c9",
+   "execution_count": 29,
+   "id": "d25b4012",
    "metadata": {},
    "outputs": [
     {
@@ -738,31 +737,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "id": "d5d660ce",
+   "execution_count": 31,
+   "id": "9c9a6afe",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'KNeighborsClassifier' object has no attribute 'decision_function'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-63-dc695bf5c8bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# see propability for all classes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m12121\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'KNeighborsClassifier' object has no attribute 'decision_function'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# see propability for all classes\n",
-    "classifier.decision_function([X[12121]])"
+    "# classifier.decision_function([X[12121]])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "id": "8475697c",
+   "execution_count": 32,
+   "id": "0c230c0f",
    "metadata": {},
    "outputs": [
     {
@@ -771,7 +758,7 @@
        "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -783,7 +770,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c75e0e9",
+   "id": "560efd6a",
    "metadata": {},
    "source": [
     "### Evaluation"
@@ -791,36 +778,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "id": "44afe895",
+   "execution_count": 33,
+   "id": "894a0b6f",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy Train 96.24666666666667\n"
+      "Accuracy Train 96.24854239546893\n"
      ]
     }
    ],
    "source": [
     "# trainings accuracy\n",
-    "wrong_images = X_train[(classifier.predict(X_train)-y_train) != 0]\n",
+    "wrong_images = X_train[(csict(X_train)-y_train) != 0]\n",
     "percentage = ((1-len(wrong_images)/len(X_train)) * 100)\n",
     "print(\"Accuracy Train \" + str(percentage))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "7ca14fcd",
+   "execution_count": 34,
+   "id": "61f770dc",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy Test 58.92989985693848\n"
+      "Accuracy Test 95.93781344032097\n"
      ]
     }
    ],
@@ -833,7 +820,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48176383",
+   "id": "6adaaccc",
    "metadata": {},
    "source": [
     "Accuracy is strongly influenced by the distribution of the classes in the test data."
@@ -841,7 +828,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6de5b7f",
+   "id": "a412d401",
    "metadata": {},
    "source": [
     "#### Cross Validation\n",
@@ -850,15 +837,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "a44e4077",
+   "execution_count": 44,
+   "id": "f62a53ac",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.67647059 0.63636364 0.84848485]\n"
+      "[0.95112444 0.95077461 0.95322339]\n"
      ]
     }
    ],
@@ -871,17 +858,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "53e0618a",
+   "execution_count": 45,
+   "id": "b29ea8f6",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1 0 1 1 9 9 1 3 1 4 3 1 3 6 1 7 1 9 1 9 4 0 9 1 1 2 1 3 7 1 1 1 1 9 0 1 6\n",
-      " 0 7 6 1 8 1 9 1 9 1 1 1 3 1 0 7 1 4 8 0 9 4 1 4 1 6 0 6 5 6 1 1 0 1 7 1 6\n",
-      " 3 0 1 1 1 7 6 0 2 6 7 8 1 9 0 4 6 7 4 6 8 0 7 8 3 1]\n"
+      "[5 0 4 ... 4 0 1]\n"
      ]
     }
    ],
@@ -895,7 +880,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2cb9460c",
+   "id": "fd8a72db",
    "metadata": {},
    "source": [
     "#### Precision"
@@ -903,17 +888,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "81103895",
+   "execution_count": 46,
+   "id": "abe435e9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8456190476190476"
+       "0.9526968189412931"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -926,7 +911,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c262acc",
+   "id": "3233385e",
    "metadata": {},
    "source": [
     "#### Recall"
@@ -934,17 +919,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "cec0a2d6",
+   "execution_count": 47,
+   "id": "5c8a9c0b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.72"
+       "0.9517074795935365"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -957,7 +942,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6da80f22",
+   "id": "e491c479",
    "metadata": {},
    "source": [
     "#### F1 Score"
@@ -965,17 +950,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "4ccd91b5",
+   "execution_count": 48,
+   "id": "aed52544",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7283140672193305"
+       "0.9516064433898149"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -988,7 +973,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "829a2f80",
+   "id": "b2aefea8",
    "metadata": {},
    "source": [
     "#### Confusion Matrix"
@@ -996,24 +981,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "e23fc6af",
+   "execution_count": 49,
+   "id": "49c6b21d",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[12  1  0  0  0  0  0  0  0  0]\n",
-      " [ 0 14  0  0  0  0  0  0  0  0]\n",
-      " [ 0  2  2  0  0  0  0  1  0  1]\n",
-      " [ 0  4  0  7  0  0  0  0  0  0]\n",
-      " [ 0  4  0  0  6  0  1  0  0  0]\n",
-      " [ 0  4  0  0  0  1  0  0  0  0]\n",
-      " [ 0  2  0  0  0  0  9  0  0  0]\n",
-      " [ 0  2  0  0  0  0  0  8  0  0]\n",
-      " [ 0  2  0  0  0  0  0  0  5  1]\n",
-      " [ 0  1  0  0  1  0  1  0  0  8]]\n"
+      "[[5863    7    5    2    0   11   30    2    4    4]\n",
+      " [   0 6694   15    5   10    0    5   11    1    5]\n",
+      " [  66  149 5496   28   15    7   21  135   29   13]\n",
+      " [  13   45   32 5804    3   75    9   64   44   43]\n",
+      " [   6   90    1    1 5522    0   29   15    3  180]\n",
+      " [  24   42    3   86   14 5112   72    7   13   51]\n",
+      " [  38   22    2    0    4   30 5823    0    2    0]\n",
+      " [   7  124   13    2   24    1    0 6003    1   93]\n",
+      " [  28  147   22  125   35  131   38   27 5182  116]\n",
+      " [  25   33    5   70   49   10    3  116   11 5632]]\n"
      ]
     }
    ],
@@ -1026,34 +1011,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "cbfaa4c8",
+   "execution_count": 50,
+   "id": "373dcc20",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.92307692 0.07692308 0.         0.         0.         0.\n",
-      "  0.         0.         0.         0.        ]\n",
-      " [0.         1.         0.         0.         0.         0.\n",
-      "  0.         0.         0.         0.        ]\n",
-      " [0.         0.33333333 0.33333333 0.         0.         0.\n",
-      "  0.         0.16666667 0.         0.16666667]\n",
-      " [0.         0.36363636 0.         0.63636364 0.         0.\n",
-      "  0.         0.         0.         0.        ]\n",
-      " [0.         0.36363636 0.         0.         0.54545455 0.\n",
-      "  0.09090909 0.         0.         0.        ]\n",
-      " [0.         0.8        0.         0.         0.         0.2\n",
-      "  0.         0.         0.         0.        ]\n",
-      " [0.         0.18181818 0.         0.         0.         0.\n",
-      "  0.81818182 0.         0.         0.        ]\n",
-      " [0.         0.2        0.         0.         0.         0.\n",
-      "  0.         0.8        0.         0.        ]\n",
-      " [0.         0.25       0.         0.         0.         0.\n",
-      "  0.         0.         0.625      0.125     ]\n",
-      " [0.         0.09090909 0.         0.         0.09090909 0.\n",
-      "  0.09090909 0.         0.         0.72727273]]\n"
+      "[[9.89035088e-01 1.18083671e-03 8.43454791e-04 3.37381916e-04\n",
+      "  0.00000000e+00 1.85560054e-03 5.06072874e-03 3.37381916e-04\n",
+      "  6.74763833e-04 6.74763833e-04]\n",
+      " [0.00000000e+00 9.92291728e-01 2.22353988e-03 7.41179958e-04\n",
+      "  1.48235992e-03 0.00000000e+00 7.41179958e-04 1.63059591e-03\n",
+      "  1.48235992e-04 7.41179958e-04]\n",
+      " [1.10756838e-02 2.50041953e-02 9.22302400e-01 4.69877496e-03\n",
+      "  2.51720087e-03 1.17469374e-03 3.52408122e-03 2.26548079e-02\n",
+      "  4.86658835e-03 2.18157409e-03]\n",
+      " [2.12002609e-03 7.33855186e-03 5.21852577e-03 9.46510111e-01\n",
+      "  4.89236791e-04 1.22309198e-02 1.46771037e-03 1.04370515e-02\n",
+      "  7.17547293e-03 7.01239400e-03]\n",
+      " [1.02616727e-03 1.53925090e-02 1.71027878e-04 1.71027878e-04\n",
+      "  9.44415940e-01 0.00000000e+00 4.95980845e-03 2.56541816e-03\n",
+      "  5.13083633e-04 3.07850180e-02]\n",
+      " [4.42477876e-03 7.74336283e-03 5.53097345e-04 1.58554572e-02\n",
+      "  2.58112094e-03 9.42477876e-01 1.32743363e-02 1.29056047e-03\n",
+      "  2.39675516e-03 9.40265487e-03]\n",
+      " [6.41783483e-03 3.71558858e-03 3.37780780e-04 0.00000000e+00\n",
+      "  6.75561561e-04 5.06671170e-03 9.83448742e-01 0.00000000e+00\n",
+      "  3.37780780e-04 0.00000000e+00]\n",
+      " [1.11678366e-03 1.97830249e-02 2.07402680e-03 3.19081047e-04\n",
+      "  3.82897256e-03 1.59540523e-04 0.00000000e+00 9.57721761e-01\n",
+      "  1.59540523e-04 1.48372687e-02]\n",
+      " [4.78550675e-03 2.51239104e-02 3.76004102e-03 2.13638694e-02\n",
+      "  5.98188344e-03 2.23893352e-02 6.49461630e-03 4.61459580e-03\n",
+      "  8.85660571e-01 1.98256708e-02]\n",
+      " [4.19885791e-03 5.54249244e-03 8.39771582e-04 1.17568021e-02\n",
+      "  8.22976150e-03 1.67954316e-03 5.03862949e-04 1.94827007e-02\n",
+      "  1.84749748e-03 9.45918710e-01]]\n"
      ]
     }
    ],
@@ -1064,8 +1059,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "a1bd37f7",
+   "execution_count": 51,
+   "id": "b239a737",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1075,13 +1070,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "bf5b4a57",
+   "execution_count": 52,
+   "id": "849572b4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x504 with 2 Axes>"
       ]
@@ -1103,25 +1098,9 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c7056a40",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f1e96198",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
-   "id": "ed4cade6",
+   "id": "ee19282c",
    "metadata": {},
    "source": [
     "## Train kNN Classifer\n",
@@ -1131,7 +1110,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d611976e",
+   "id": "a0c32f85",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/0-pilot-project/notes.md b/0-pilot-project/notes.md
index ee13f57..63f0d53 100644
--- a/0-pilot-project/notes.md
+++ b/0-pilot-project/notes.md
@@ -1,3 +1,13 @@
 # Notes for 0-pilot-project
 
+- Beste Ergebnisse bei hoher train_sz(60030) und k=3
+- Wie geht vernünftiges Plotten, z.B. nach accuracy normalizen oder accuracy einfärben
+- Wo ist der Unterschied zwischen sklearn.model_selection.train_test_split und manuellem pick mit list arguments
+
 ## Todos
+- Unterschied zwischen accuracy und precision score (steht vlt in Folien)
+- Classifier grafisch anzeigen lassen (https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py)
+- Schauen wie wir mit weniger Features arbeiten können
+- Zeitmessung von einzelnen classifier test loops und mitprinten bei jedem Durchlauf
+- Unterschiedliche Validierungsmethoden testen
+- Code anpassen, dass er nicht stirbt
\ No newline at end of file