From d92eb30bb615a301e2c7696ba41383d324b3d31a Mon Sep 17 00:00:00 2001
From: shlear <116897538+shlear@users.noreply.github.com>
Date: Tue, 31 Jan 2023 14:22:22 +0300
Subject: [PATCH] =?UTF-8?q?=D0=A1=D0=BE=D0=B7=D0=B4=D0=B0=D0=BD=D0=BE=20?=
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MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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---
 01-intro/DataHandling.ipynb | 1666 ++---------------------------------
 1 file changed, 72 insertions(+), 1594 deletions(-)

diff --git a/01-intro/DataHandling.ipynb b/01-intro/DataHandling.ipynb
index 403fd1b..6bcfc91 100644
--- a/01-intro/DataHandling.ipynb
+++ b/01-intro/DataHandling.ipynb
@@ -13,43 +13,31 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "pfojW1Laghph"
+        "id": "n_nitmHugcSH"
       },
       "source": [
-        "During the practical sessions of the course we are going to use [Python programming language](https://www.python.org) in the [Google Colab environment](https://colab.research.google.com). Alternatively you can download some other python distribution, e.g. [anaconda](https://www.anaconda.com/) and run jupyter locally (see the [docs](https://jupyter.readthedocs.io/en/latest/running.html) for more info)."
+        "# Welcome"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 1,
+      "cell_type": "markdown",
       "metadata": {
-        "id": "WAa6UFGdwp2z",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "372c73c8-310c-4edd-b448-ef33d866360c"
+        "id": "pfojW1Laghph"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Python 3.8.16\n"
-          ]
-        }
-      ],
       "source": [
-        "!python --version"
+        "During the practical sessions of the course we are going to use [Python programming language](https://www.python.org) in the [Google Colab environment](https://colab.research.google.com). Alternatively you can download some other python distribution, e.g. [anaconda](https://www.anaconda.com/) and run jupyter locally (see the [docs](https://jupyter.readthedocs.io/en/latest/running.html) for more info)."
       ]
     },
     {
       "cell_type": "code",
-      "source": [],
+      "execution_count": null,
       "metadata": {
-        "id": "eeHtY_2sV5eO"
+        "id": "WAa6UFGdwp2z"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "!python --version"
+      ]
     },
     {
       "cell_type": "markdown",
@@ -70,15 +58,6 @@
         "Don't forget to follow [PEP-8](https://peps.python.org/pep-0008/). You may also check other[style guides](https://google.github.io/styleguide/pyguide.html)."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "n_nitmHugcSH"
-      },
-      "source": [
-        "# Welcome"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -205,7 +184,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
         "id": "5oQdc9MIJXVW"
       },
@@ -275,29 +254,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {
-        "id": "gcTokNL-JXWV",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "c4aa70ae-329f-4b7a-c1d3-3af55e516014"
+        "id": "gcTokNL-JXWV"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "a =  [1 2 3 4 5]\n",
-            "b =  [5 4 3 2 1]\n",
-            "a + 1 = [2 3 4 5 6]\n",
-            "a * 2 = [ 2  4  6  8 10]\n",
-            "a == 2 [False  True False False False]\n",
-            "a + b = [6 6 6 6 6]\n",
-            "a * b = [5 8 9 8 5]\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "import numpy as np\n",
         "\n",
@@ -983,256 +944,12 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "! pip install -q kaggle\n",
-        "\n",
-        "from google.colab import files\n",
-        "\n",
-        "files.upload()"
-      ],
-      "metadata": {
-        "id": "XrJUrAW6XMQj",
-        "outputId": "fb400dad-5dcc-4a2b-9ea9-1ca33c768ace",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 92
-        }
-      },
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "     <input type=\"file\" id=\"files-ec972f4e-6117-424c-a0e7-fbed0a03f20b\" name=\"files[]\" multiple disabled\n",
-              "        style=\"border:none\" />\n",
-              "     <output id=\"result-ec972f4e-6117-424c-a0e7-fbed0a03f20b\">\n",
-              "      Upload widget is only available when the cell has been executed in the\n",
-              "      current browser session. Please rerun this cell to enable.\n",
-              "      </output>\n",
-              "      <script>// Copyright 2017 Google LLC\n",
-              "//\n",
-              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-              "// you may not use this file except in compliance with the License.\n",
-              "// You may obtain a copy of the License at\n",
-              "//\n",
-              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
-              "//\n",
-              "// Unless required by applicable law or agreed to in writing, software\n",
-              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-              "// See the License for the specific language governing permissions and\n",
-              "// limitations under the License.\n",
-              "\n",
-              "/**\n",
-              " * @fileoverview Helpers for google.colab Python module.\n",
-              " */\n",
-              "(function(scope) {\n",
-              "function span(text, styleAttributes = {}) {\n",
-              "  const element = document.createElement('span');\n",
-              "  element.textContent = text;\n",
-              "  for (const key of Object.keys(styleAttributes)) {\n",
-              "    element.style[key] = styleAttributes[key];\n",
-              "  }\n",
-              "  return element;\n",
-              "}\n",
-              "\n",
-              "// Max number of bytes which will be uploaded at a time.\n",
-              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
-              "\n",
-              "function _uploadFiles(inputId, outputId) {\n",
-              "  const steps = uploadFilesStep(inputId, outputId);\n",
-              "  const outputElement = document.getElementById(outputId);\n",
-              "  // Cache steps on the outputElement to make it available for the next call\n",
-              "  // to uploadFilesContinue from Python.\n",
-              "  outputElement.steps = steps;\n",
-              "\n",
-              "  return _uploadFilesContinue(outputId);\n",
-              "}\n",
-              "\n",
-              "// This is roughly an async generator (not supported in the browser yet),\n",
-              "// where there are multiple asynchronous steps and the Python side is going\n",
-              "// to poll for completion of each step.\n",
-              "// This uses a Promise to block the python side on completion of each step,\n",
-              "// then passes the result of the previous step as the input to the next step.\n",
-              "function _uploadFilesContinue(outputId) {\n",
-              "  const outputElement = document.getElementById(outputId);\n",
-              "  const steps = outputElement.steps;\n",
-              "\n",
-              "  const next = steps.next(outputElement.lastPromiseValue);\n",
-              "  return Promise.resolve(next.value.promise).then((value) => {\n",
-              "    // Cache the last promise value to make it available to the next\n",
-              "    // step of the generator.\n",
-              "    outputElement.lastPromiseValue = value;\n",
-              "    return next.value.response;\n",
-              "  });\n",
-              "}\n",
-              "\n",
-              "/**\n",
-              " * Generator function which is called between each async step of the upload\n",
-              " * process.\n",
-              " * @param {string} inputId Element ID of the input file picker element.\n",
-              " * @param {string} outputId Element ID of the output display.\n",
-              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
-              " */\n",
-              "function* uploadFilesStep(inputId, outputId) {\n",
-              "  const inputElement = document.getElementById(inputId);\n",
-              "  inputElement.disabled = false;\n",
-              "\n",
-              "  const outputElement = document.getElementById(outputId);\n",
-              "  outputElement.innerHTML = '';\n",
-              "\n",
-              "  const pickedPromise = new Promise((resolve) => {\n",
-              "    inputElement.addEventListener('change', (e) => {\n",
-              "      resolve(e.target.files);\n",
-              "    });\n",
-              "  });\n",
-              "\n",
-              "  const cancel = document.createElement('button');\n",
-              "  inputElement.parentElement.appendChild(cancel);\n",
-              "  cancel.textContent = 'Cancel upload';\n",
-              "  const cancelPromise = new Promise((resolve) => {\n",
-              "    cancel.onclick = () => {\n",
-              "      resolve(null);\n",
-              "    };\n",
-              "  });\n",
-              "\n",
-              "  // Wait for the user to pick the files.\n",
-              "  const files = yield {\n",
-              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
-              "    response: {\n",
-              "      action: 'starting',\n",
-              "    }\n",
-              "  };\n",
-              "\n",
-              "  cancel.remove();\n",
-              "\n",
-              "  // Disable the input element since further picks are not allowed.\n",
-              "  inputElement.disabled = true;\n",
-              "\n",
-              "  if (!files) {\n",
-              "    return {\n",
-              "      response: {\n",
-              "        action: 'complete',\n",
-              "      }\n",
-              "    };\n",
-              "  }\n",
-              "\n",
-              "  for (const file of files) {\n",
-              "    const li = document.createElement('li');\n",
-              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
-              "    li.append(span(\n",
-              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
-              "        `last modified: ${\n",
-              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
-              "                                    'n/a'} - `));\n",
-              "    const percent = span('0% done');\n",
-              "    li.appendChild(percent);\n",
-              "\n",
-              "    outputElement.appendChild(li);\n",
-              "\n",
-              "    const fileDataPromise = new Promise((resolve) => {\n",
-              "      const reader = new FileReader();\n",
-              "      reader.onload = (e) => {\n",
-              "        resolve(e.target.result);\n",
-              "      };\n",
-              "      reader.readAsArrayBuffer(file);\n",
-              "    });\n",
-              "    // Wait for the data to be ready.\n",
-              "    let fileData = yield {\n",
-              "      promise: fileDataPromise,\n",
-              "      response: {\n",
-              "        action: 'continue',\n",
-              "      }\n",
-              "    };\n",
-              "\n",
-              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
-              "    let position = 0;\n",
-              "    do {\n",
-              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
-              "      const chunk = new Uint8Array(fileData, position, length);\n",
-              "      position += length;\n",
-              "\n",
-              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
-              "      yield {\n",
-              "        response: {\n",
-              "          action: 'append',\n",
-              "          file: file.name,\n",
-              "          data: base64,\n",
-              "        },\n",
-              "      };\n",
-              "\n",
-              "      let percentDone = fileData.byteLength === 0 ?\n",
-              "          100 :\n",
-              "          Math.round((position / fileData.byteLength) * 100);\n",
-              "      percent.textContent = `${percentDone}% done`;\n",
-              "\n",
-              "    } while (position < fileData.byteLength);\n",
-              "  }\n",
-              "\n",
-              "  // All done.\n",
-              "  yield {\n",
-              "    response: {\n",
-              "      action: 'complete',\n",
-              "    }\n",
-              "  };\n",
-              "}\n",
-              "\n",
-              "scope.google = scope.google || {};\n",
-              "scope.google.colab = scope.google.colab || {};\n",
-              "scope.google.colab._files = {\n",
-              "  _uploadFiles,\n",
-              "  _uploadFilesContinue,\n",
-              "};\n",
-              "})(self);\n",
-              "</script> "
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Saving kaggle.json to kaggle.json\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "{'kaggle.json': b'{\"username\":\"egorbevz\",\"key\":\"60602a5f249baeadc5d7ef09fdbe23af\"}'}"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": null,
       "metadata": {
         "collapsed": true,
-        "id": "dBaZHbB1Dt5Z",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "67761bd4-f659-4077-98e9-770736e650db"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
-          ]
-        }
-      ],
+        "id": "dBaZHbB1Dt5Z"
+      },
+      "outputs": [],
       "source": [
         "!mkdir ~/.kaggle\n",
         "!cp kaggle.json ~/.kaggle/\n",
@@ -1241,44 +958,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "id": "ZLUjnvLAuCYU",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "17d0f16b-75f7-44ec-c387-c9312cb65c28"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "ref                                                            title                                                size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
-            "-------------------------------------------------------------  --------------------------------------------------  -----  -------------------  -------------  ---------  ---------------  \n",
-            "gauravduttakiit/tabular-playground-series-jan-2022             Tabular Playground Series - Jan 2022                230KB  2022-01-04 08:56:10             84          9  0.7647059        \n",
-            "carlmcbrideellis/gdp-20152019-finland-norway-and-sweden        GDP data for TPS competitions                        769B  2022-09-01 08:38:31            742         54  1.0              \n",
-            "lucamassaron/festivities-in-finland-norway-sweden-tsp-0122     Festivities in Finland, Norway, Sweden (TSP 01-22)    3KB  2022-01-23 23:41:23             62         21  0.7647059        \n",
-            "sergiosaharovskiy/tps2022novfeather                            TPS2022NOVFEATHER                                     1GB  2022-11-03 03:19:18             83         23  0.88235295       \n",
-            "lucamassaron/tabular-playground-series-sep-2021                Tabular Playground Series - Sep 2021                597MB  2022-04-04 22:29:02             21          6  0.8235294        \n",
-            "samuelcortinhas/gdp-of-european-countries                      GDP of European countries                            795B  2022-09-01 13:15:51            545         35  1.0              \n",
-            "mustafakeser4/tpsoct22-parquet                                 TPS-OCT-22 Parquet                                    3GB  2022-10-19 17:41:31             36         13  1.0              \n",
-            "samuelcortinhas/gdp-per-capita-finland-norway-sweden-201519    GDP per capita: Finland, Norway, Sweden (2015-19)    362B  2022-01-11 10:43:40            273         27  0.9411765        \n",
-            "criskiev/november21                                            Original train.csv for TPS Nov 2021                 225MB  2021-11-10 21:29:55            173         17  0.47058824       \n",
-            "hrshuvo/tabular-sep-21                                         tabular_sep_21                                      150MB  2021-09-18 12:30:02             15          8  0.64705884       \n",
-            "rhythmcam/pycaret-regression-auto-model                        PyCaret Regression Blend Model                       968B  2022-01-16 10:44:45             10          9  0.875            \n",
-            "jcaliz/tps-sep22-covid-data                                    TPS Sep22: Covid Data 🦠                              67KB  2022-09-01 16:34:50            373         21  1.0              \n",
-            "kaaveland/tpsdec2021parquet                                    tps-dec-2021-parquet                                152MB  2021-12-06 16:03:32             60          5  0.5625           \n",
-            "satoshiss/dataset-for-2022-tps-sep                             Dataset for 2022 TPS Sep                             37KB  2022-09-07 14:12:42             22          8  0.7058824        \n",
-            "alexryzhkov/tps-competitions-private-leaderboards              TPS competitions private leaderboards               414KB  2021-08-01 12:42:58             18          6  0.7352941        \n",
-            "kavehshahhosseini/tpsoctclassicfeatureimportance               TPS Oct 2021 - Classic Feature Importance             3KB  2021-10-22 09:47:56             10          8  0.8235294        \n",
-            "mathurinache/tabularplaygroundseriesnov2021augmented           tabular-playground-series-nov-2021-augmented          2GB  2021-11-21 21:08:29              5          6  0.3529412        \n",
-            "towhidultonmoy/tabular-playground-series-sep21-processed-data  Tabular Playground Series Sep-21 processed data     278MB  2021-09-16 18:13:07              3          2  0.47058824       \n",
-            "sandeepmajumdar/tpssep22-gdp-per-capita-20172021               TPSSEP22 GDP Per Capita & Growth Rate 2017-2021       1KB  2022-09-09 16:46:04             12          8  0.9411765        \n",
-            "aphilip/resampled-traincsv                                     SMOTE resampled May Tabular Playground Series         3MB  2021-05-25 18:40:45              7          2  0.3529412        \n"
-          ]
-        }
-      ],
+      "execution_count": null,
+      "metadata": {
+        "id": "ZLUjnvLAuCYU"
+      },
+      "outputs": [],
       "source": [
         "!kaggle datasets list -s tabular-playground-series"
       ]
@@ -1294,25 +978,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": null,
       "metadata": {
-        "id": "uX-OE6_kDPuO",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "c97d312f-b0a7-4267-bae3-a591de2d7c30"
+        "id": "uX-OE6_kDPuO"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Downloading tabular-playground-series-aug-2022.zip to /content\n",
-            "\r  0% 0.00/2.27M [00:00<?, ?B/s]\n",
-            "\r100% 2.27M/2.27M [00:00<00:00, 90.2MB/s]\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "!kaggle competitions download -c 'tabular-playground-series-aug-2022'"
       ]
@@ -1328,7 +998,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": null,
       "metadata": {
         "id": "NyJE7OdxESxz"
       },
@@ -1339,26 +1009,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": null,
       "metadata": {
-        "id": "tMiK_rJcEFLl",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "d59cfdf2-6bda-4777-d47e-750dd173815e"
+        "id": "tMiK_rJcEFLl"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Archive:  /content/tabular-playground-series-aug-2022.zip\n",
-            "  inflating: data/sample_submission.csv  \n",
-            "  inflating: data/test.csv           \n",
-            "  inflating: data/train.csv          \n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "!unzip /content/tabular-playground-series-aug-2022.zip -d data"
       ]
@@ -1374,7 +1029,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": null,
       "metadata": {
         "id": "G36oVo3RJXVc"
       },
@@ -1413,338 +1068,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "Zpm0KYxF3jGs",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 331
-        },
-        "outputId": "7b6178ba-1225-4120-a841-e305fde7448c"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "      product_code  loading attribute_0 attribute_1  attribute_2  attribute_3  \\\n",
-              "id                                                                              \n",
-              "20085            D    92.47  material_7  material_5            6            6   \n",
-              "3226             A    95.84  material_7  material_8            9            5   \n",
-              "3200             A   138.67  material_7  material_8            9            5   \n",
-              "10058            B   149.84  material_5  material_5            8            8   \n",
-              "25618            E    67.50  material_7  material_6            6            9   \n",
-              "\n",
-              "       measurement_0  measurement_1  measurement_2  measurement_3  ...  \\\n",
-              "id                                                                 ...   \n",
-              "20085              3             18              9         17.006  ...   \n",
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-              "    .colab-df-container {\n",
-              "      display:flex;\n",
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-              "      border-radius: 50%;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
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-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-026bb38b-9b98-452b-b19b-05418e6fbcab button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
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-              "          const element = document.querySelector('#df-026bb38b-9b98-452b-b19b-05418e6fbcab');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
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-              "  </div>\n",
-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 24
-        }
-      ],
+      "execution_count": null,
+      "metadata": {
+        "id": "Zpm0KYxF3jGs"
+      },
+      "outputs": [],
       "source": [
         "data.sample(5)"
       ]
@@ -1868,251 +1196,11 @@
     },
     {
       "cell_type": "code",
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-      "outputs": [
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-              "   product_code  loading attribute_0 attribute_1  attribute_2  attribute_3  \\\n",
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-              "id                                                              ...   \n",
-              "2              14              3              3         18.213  ...   \n",
-              "3              12              1              5         18.057  ...   \n",
-              "\n",
-              "    measurement_9  measurement_10  measurement_11  measurement_12  \\\n",
-              "id                                                                  \n",
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-              "3          12.715          15.607             NaN          13.798   \n",
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-              "id                                                                   \n",
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-              "3           16.711          18.631          14.094          17.946   \n",
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-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-12a8a337-1150-40ce-9f71-264190391994');\n",
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-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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-              "  </div>\n",
-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 18
-        }
-      ],
+      "execution_count": null,
+      "metadata": {
+        "id": "lI0C2cG8g3To"
+      },
+      "outputs": [],
       "source": [
         "data.loc[2:3]"
       ]
@@ -2148,512 +1236,11 @@
     },
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       "cell_type": "code",
-      "execution_count": 17,
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-          "height": 519
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-        "outputId": "bd26b003-0eae-4276-b36b-716a93d51b38"
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-      "outputs": [
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-          "data": {
-            "text/plain": [
-              "      product_code  loading attribute_0 attribute_1  attribute_2  attribute_3  \\\n",
-              "id                                                                              \n",
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-              "5101              12             12              4         19.267  ...   \n",
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-              "\n",
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-              "id                                                                     \n",
-              "5101          12.261          14.961          18.640          11.267   \n",
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-              "...              ...             ...             ...             ...   \n",
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-              "10345         11.634          14.884          18.053             NaN   \n",
-              "10349         12.076          15.741          19.937          10.956   \n",
-              "\n",
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-              "id                                                                      \n",
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-              "    <tr>\n",
-              "      <th>10349</th>\n",
-              "      <td>B</td>\n",
-              "      <td>82.07</td>\n",
-              "      <td>material_5</td>\n",
-              "      <td>material_5</td>\n",
-              "      <td>8</td>\n",
-              "      <td>8</td>\n",
-              "      <td>2</td>\n",
-              "      <td>4</td>\n",
-              "      <td>11</td>\n",
-              "      <td>17.506</td>\n",
-              "      <td>...</td>\n",
-              "      <td>12.076</td>\n",
-              "      <td>15.741</td>\n",
-              "      <td>19.937</td>\n",
-              "      <td>10.956</td>\n",
-              "      <td>14.832</td>\n",
-              "      <td>17.106</td>\n",
-              "      <td>16.910</td>\n",
-              "      <td>15.281</td>\n",
-              "      <td>926.387</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>786 rows × 25 columns</p>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b524ac74-8652-45be-92c6-4182d45d93fc')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-b524ac74-8652-45be-92c6-4182d45d93fc button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-b524ac74-8652-45be-92c6-4182d45d93fc');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 17
-        }
-      ],
+      "execution_count": null,
+      "metadata": {
+        "id": "aMkc1lqHPjAo"
+      },
+      "outputs": [],
       "source": [
         "data.loc[(data['loading'] < 90) & (data['product_code'] == np.random.choice(data.product_code.unique()))]"
       ]
@@ -2815,51 +1402,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {
-        "id": "uE53osRgJXWs",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "fbd7c386-305d-4e9d-825c-92ec89d5276f"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Max measurement_17:  1312.794\n",
-            "\n",
-            "The study with the max measurement_17:\n",
-            " product_code               A\n",
-            "loading                109.5\n",
-            "attribute_0       material_7\n",
-            "attribute_1       material_8\n",
-            "attribute_2                9\n",
-            "attribute_3                5\n",
-            "measurement_0              9\n",
-            "measurement_1              6\n",
-            "measurement_2              5\n",
-            "measurement_3         18.111\n",
-            "measurement_4         11.886\n",
-            "measurement_5         17.354\n",
-            "measurement_6         18.558\n",
-            "measurement_7          11.54\n",
-            "measurement_8         19.887\n",
-            "measurement_9         11.557\n",
-            "measurement_10        15.965\n",
-            "measurement_11        19.604\n",
-            "measurement_12        14.091\n",
-            "measurement_13        15.674\n",
-            "measurement_14        13.327\n",
-            "measurement_15        13.535\n",
-            "measurement_16        15.408\n",
-            "measurement_17           NaN\n",
-            "failure                    0\n",
-            "Name: 9, dtype: object\n"
-          ]
-        }
-      ],
+      "execution_count": null,
+      "metadata": {
+        "id": "uE53osRgJXWs"
+      },
+      "outputs": [],
       "source": [
         "# calling np.max on a pure pandas column:\n",
         "column_name = 'measurement_17'\n",
@@ -2988,53 +1535,11 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "float_cols = [c for c in data.columns if data[c].dtype == float]\n",
-        "print (float_cols)"
-      ],
-      "metadata": {
-        "id": "xt8tv21yxNLq",
-        "outputId": "e4ff9048-cb0c-4a7a-d5ed-b6bc2fb84802",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "execution_count": 28,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "['loading', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {
-        "id": "fz5WDA4YJXXI",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 423
-        },
-        "outputId": "3e21aa99-eb61-4dad-dd4c-590d3461617e"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 720x360 with 8 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ],
+      "execution_count": null,
+      "metadata": {
+        "id": "fz5WDA4YJXXI"
+      },
+      "outputs": [],
       "source": [
         "# histogram - showing data density\n",
         "float_cols = [c for c in data.columns if data[c].dtype == float]\n",
@@ -3053,29 +1558,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": null,
       "metadata": {
-        "id": "d4F8dkG_l7sw",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 281
-        },
-        "outputId": "b7c3d0f6-8d12-44d3-8c2b-ed2da56ab1ed"
+        "id": "d4F8dkG_l7sw"
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ],
+      "outputs": [],
       "source": [
         "# or you can use inbuilt methods and combine it with pyplot\n",
         "data.failure.hist()\n",
@@ -3179,39 +1666,30 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": null,
       "metadata": {
-        "id": "JhbbBk93JXXV",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "6f68c126-c760-4207-e70d-efc14cc5c417"
+        "id": "JhbbBk93JXXV"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Test accuracy: 0.744824990590892\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "from sklearn.neighbors import KNeighborsClassifier\n",
         "from sklearn.metrics import accuracy_score\n",
         "\n",
         "x = data[float_cols].copy()\n",
-        "x.fillna(x.mean(), inplace = True)\n",
         "y = data[\"failure\"]\n",
+        "\n",
         "model = KNeighborsClassifier(n_neighbors=5)\n",
+        "\n",
         "# split the data into train(90%) and test(10%)\n",
-        "train_ids = np.random.choice(range(len(data)), size = int(0.9 * len(data)), replace = False)\n",
-        "test_ids = np.array([x for x in range(len(data)) if x not in train_ids])\n",
+        "train_ids = ...\n",
+        "test_ids = ...\n",
+        "\n",
         "# fit the model\n",
-        "model.fit(x.iloc[train_ids], y.iloc[train_ids] )\n",
+        "model.fit(...,... )\n",
+        "\n",
         "# make the prediction\n",
-        "test_predictions = model.predict(x.iloc[test_ids])\n",
-        "print(\"Test accuracy:\", accuracy_score(y.iloc[test_ids], test_predictions))\n"
+        "test_predictions = model.predict(...)\n",
+        "print(\"Test accuracy:\", accuracy_score(..., test_predictions))\n"
       ]
     },
     {