KNN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "449a2049",
      "metadata": {
        "id": "449a2049"
      },
      "source": [
        "## <font color = blue>**1. What is KNN?**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c64c0b33",
      "metadata": {
        "id": "c64c0b33"
      },
      "source": [
        "K-NN or K Nearest Neighbour Algorithm is a Machine Learning algorithm that works on Classification models.\n",
        "Logistic regression was limited to solve the problems for Binary classification. However, K-NN is designed to solve the problem for Multi-class classification.\n",
        "\n",
        "K-NN uses a simple concept called K - nearest neighbour, where K is the hyperparameter. As per this concept, on the\n",
        "occurence of a new data point, K-NN algorithm tries to include the datapoint in the class having maximum number of\n",
        "datapoints closest to the new datapoint.\n",
        "\n",
        "Consider the following example to understand it more better:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "from IPython.display import Image"
      ],
      "metadata": {
        "id": "jZayisUrVCo-"
      },
      "id": "jZayisUrVCo-",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "tIWPdXmmVQNB",
        "outputId": "eb9193f9-baa7-4a82-c854-994e1f74ff42"
      },
      "id": "tIWPdXmmVQNB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-bae91bd7-efdc-4fab-b9c5-8b465e1c499c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-bae91bd7-efdc-4fab-b9c5-8b465e1c499c\">\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 KNN_graph.jpg to KNN_graph.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Image('KNN_graph.jpg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "WCW2SBSUVfys",
        "outputId": "2843655d-e6b5-4450-d418-c7424a4dce04"
      },
      "id": "WCW2SBSUVfys",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c3fd8f8d",
      "metadata": {
        "id": "c3fd8f8d"
      },
      "source": [
        "So, in the above example we have a  new datapoint occuring between Category Class A & category Class B.\n",
        "It is our job to classify it in one of the categories.\n",
        "\n",
        "Let us start first with locking in the value for hyperparameter 'k'. let us fix k=5.\n",
        "So when I say k=5, then one has to only look for 5 nearest neighbours\n",
        "\n",
        "Once fixed, the nearest neighbours are calculated by the concept of Euclidean distance. Euclidean distance is defined\n",
        "as the shortest distance between 2 points.\n",
        "\n",
        "Euclidean distance can be well understood from the following figure:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "OOPcp6srV_xt",
        "outputId": "971e46a1-e680-4636-a38d-4918b3688229"
      },
      "id": "OOPcp6srV_xt",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-94656cdc-550e-46a8-9deb-47192746a45e\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-94656cdc-550e-46a8-9deb-47192746a45e\">\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 KNN_Eucledian_dist.jpg to KNN_Eucledian_dist.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Image('KNN_Eucledian_dist.jpg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "U8aAj0ZtWqAs",
        "outputId": "232c0c35-5175-433f-abb4-a38cc2a1210b"
      },
      "id": "U8aAj0ZtWqAs",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "86cf702f",
      "metadata": {
        "id": "86cf702f"
      },
      "source": [
        "By using the concept of Euclidean distance we came to the following result."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "oVg8gFLxWz75",
        "outputId": "d537208e-1fd3-4d31-abf6-cd38702bca46"
      },
      "id": "oVg8gFLxWz75",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-24b6a03d-aa7b-4e21-b23c-6da9cdfb88a0\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-24b6a03d-aa7b-4e21-b23c-6da9cdfb88a0\">\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 KNN_Eucledian_dist_2.jpg to KNN_Eucledian_dist_2.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Image('KNN_Eucledian_dist_2.jpg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "SRQITn5_XHtf",
        "outputId": "4b7dddc9-fb5e-4e30-c6cd-ef05b33cdc8e"
      },
      "id": "SRQITn5_XHtf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9932e466",
      "metadata": {
        "id": "9932e466"
      },
      "source": [
        "As evident from the above figure, for k=5 we have 3 nearest neighbours belonging to category A\n",
        "and 2 nearest neighbours belonging to Category B. Since we have majority of close datapoints belonging to category A,\n",
        "the new data point belongs to Category A."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7369bef5",
      "metadata": {
        "id": "7369bef5"
      },
      "source": [
        "K-NN is a non-parametric algorithm, which means it does not make any assumption on underlying data.\n",
        "\n",
        "It is also called a lazy learner algorithm because it does not learn from the training set immediately\n",
        "instead it stores the dataset and at the time of classification, it performs an action on the dataset.\n",
        "\n",
        "One major drawback of K-NN is the fact that it does not perform well in case of dense data. Dense data\n",
        "is the data where the datapoints are closely stacked.\n",
        "\n",
        "K-NN however works well for Noisy data (data with too many Outliers) and small data."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99687bb9",
      "metadata": {
        "id": "99687bb9"
      },
      "source": [
        "# <font color = green>**Code for Underfitting**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = red>**1. Importing the libraries**</font>"
      ],
      "metadata": {
        "id": "gMOzWlZbYuTb"
      },
      "id": "gMOzWlZbYuTb"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us import the essential libraries first."
      ],
      "metadata": {
        "id": "HUooUSorY3ih"
      },
      "id": "HUooUSorY3ih"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4680c64b",
      "metadata": {
        "id": "4680c64b"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = red>**2. Reading the dataset**</font>"
      ],
      "metadata": {
        "id": "yGBiF8P8ZRZz"
      },
      "id": "yGBiF8P8ZRZz"
    },
    {
      "cell_type": "markdown",
      "id": "29faad27",
      "metadata": {
        "id": "29faad27"
      },
      "source": [
        "Let us read the dataset now. This dataset comprises of different types of Fish and their corresponding sizes. We need to use KNN to classify fishes species based on their attributes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa13ec39",
      "metadata": {
        "id": "aa13ec39"
      },
      "outputs": [],
      "source": [
        "data=pd.read_csv(\"Fish_dataset.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "33c12c6a",
      "metadata": {
        "id": "33c12c6a",
        "outputId": "1d61ee6a-71f7-42ef-eb9b-8468715c7010"
      },
      "outputs": [
        {
          "data": {
            "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>Species</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Length1</th>\n",
              "      <th>Length2</th>\n",
              "      <th>Length3</th>\n",
              "      <th>Height</th>\n",
              "      <th>Width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bream</td>\n",
              "      <td>242.0</td>\n",
              "      <td>23.2</td>\n",
              "      <td>25.4</td>\n",
              "      <td>30.0</td>\n",
              "      <td>11.5200</td>\n",
              "      <td>4.0200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bream</td>\n",
              "      <td>290.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>26.3</td>\n",
              "      <td>31.2</td>\n",
              "      <td>12.4800</td>\n",
              "      <td>4.3056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bream</td>\n",
              "      <td>340.0</td>\n",
              "      <td>23.9</td>\n",
              "      <td>26.5</td>\n",
              "      <td>31.1</td>\n",
              "      <td>12.3778</td>\n",
              "      <td>4.6961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bream</td>\n",
              "      <td>363.0</td>\n",
              "      <td>26.3</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.5</td>\n",
              "      <td>12.7300</td>\n",
              "      <td>4.4555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bream</td>\n",
              "      <td>430.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>29.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>12.4440</td>\n",
              "      <td>5.1340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>12.2</td>\n",
              "      <td>11.5</td>\n",
              "      <td>12.2</td>\n",
              "      <td>13.4</td>\n",
              "      <td>2.0904</td>\n",
              "      <td>1.3936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>13.4</td>\n",
              "      <td>11.7</td>\n",
              "      <td>12.4</td>\n",
              "      <td>13.5</td>\n",
              "      <td>2.4300</td>\n",
              "      <td>1.2690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>12.2</td>\n",
              "      <td>12.1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.8</td>\n",
              "      <td>2.2770</td>\n",
              "      <td>1.2558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>19.7</td>\n",
              "      <td>13.2</td>\n",
              "      <td>14.3</td>\n",
              "      <td>15.2</td>\n",
              "      <td>2.8728</td>\n",
              "      <td>2.0672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>19.9</td>\n",
              "      <td>13.8</td>\n",
              "      <td>15.0</td>\n",
              "      <td>16.2</td>\n",
              "      <td>2.9322</td>\n",
              "      <td>1.8792</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>159 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Species  Weight  Length1  Length2  Length3   Height   Width\n",
              "0     Bream   242.0     23.2     25.4     30.0  11.5200  4.0200\n",
              "1     Bream   290.0     24.0     26.3     31.2  12.4800  4.3056\n",
              "2     Bream   340.0     23.9     26.5     31.1  12.3778  4.6961\n",
              "3     Bream   363.0     26.3     29.0     33.5  12.7300  4.4555\n",
              "4     Bream   430.0     26.5     29.0     34.0  12.4440  5.1340\n",
              "..      ...     ...      ...      ...      ...      ...     ...\n",
              "154   Smelt    12.2     11.5     12.2     13.4   2.0904  1.3936\n",
              "155   Smelt    13.4     11.7     12.4     13.5   2.4300  1.2690\n",
              "156   Smelt    12.2     12.1     13.0     13.8   2.2770  1.2558\n",
              "157   Smelt    19.7     13.2     14.3     15.2   2.8728  2.0672\n",
              "158   Smelt    19.9     13.8     15.0     16.2   2.9322  1.8792\n",
              "\n",
              "[159 rows x 7 columns]"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eef97d6a",
      "metadata": {
        "id": "eef97d6a"
      },
      "source": [
        "As evident above, we have different Fish species (e.g Bream, Smelt) and their corresponding size attributes.\n",
        "Since the dataset is big and we cannot see all the Species above, let us try to check all the categories of\n",
        "species with the help of unique function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b29334b7",
      "metadata": {
        "id": "b29334b7",
        "outputId": "b0d0c698-dd31-4b2e-caaa-fb010eb6de2a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Bream', 'Roach', 'Whitefish', 'Parkki', 'Perch', 'Pike', 'Smelt'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Species'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e91f97af",
      "metadata": {
        "id": "e91f97af"
      },
      "source": [
        "Here unique function will remove the duplicates and return the distinct category names of Species.\n",
        "From the array above we get to see 7 different categories."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c6b1d2ca",
      "metadata": {
        "id": "c6b1d2ca"
      },
      "source": [
        "## <font color = red>**3. Null value check**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let us check if we got any null values in the dataset."
      ],
      "metadata": {
        "id": "4rkiNAAZaC0M"
      },
      "id": "4rkiNAAZaC0M"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9278afb4",
      "metadata": {
        "id": "9278afb4",
        "outputId": "bc2779bb-e890-4391-ecd2-414399c79aa9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Species    0\n",
              "Weight     0\n",
              "Length1    0\n",
              "Length2    0\n",
              "Length3    0\n",
              "Height     0\n",
              "Width      0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No null value as such, let us check for any special characters."
      ],
      "metadata": {
        "id": "-__zcB66aVNa"
      },
      "id": "-__zcB66aVNa"
    },
    {
      "cell_type": "markdown",
      "id": "ae8e7d10",
      "metadata": {
        "id": "ae8e7d10"
      },
      "source": [
        "## <font color = red>**4. Special characters check**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We check the special characters in the data for every column using unique function()."
      ],
      "metadata": {
        "id": "k-jViV_-ao4b"
      },
      "id": "k-jViV_-ao4b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6030555d",
      "metadata": {
        "id": "6030555d",
        "outputId": "ac2e5b27-1637-49cf-ec4c-ea59316a4354"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique values for coulmn: Species\n",
            "\n",
            "['Bream' 'Roach' 'Whitefish' 'Parkki' 'Perch' 'Pike' 'Smelt']\n",
            "\n",
            "Unique values for coulmn: Weight\n",
            "\n",
            "[ 242.   290.   340.   363.   430.   450.   500.   390.   475.   600.\n",
            "  700.   610.   650.   575.   685.   620.   680.   725.   720.   714.\n",
            "  850.  1000.   920.   955.   925.   975.   950.    40.    69.    78.\n",
            "   87.   120.     0.   110.   150.   145.   160.   140.   169.   161.\n",
            "  200.   180.   272.   270.   306.   540.   800.    55.    60.    90.\n",
            "  170.   273.   300.     5.9   32.    51.5   70.   100.    80.    85.\n",
            "  115.   125.   130.   135.   225.   188.   197.   218.   260.   265.\n",
            "  250.   320.   514.   556.   840.   690.   900.   820.  1015.  1100.\n",
            "  345.   456.   510.   567.   770.  1250.  1600.  1550.  1650.     6.7\n",
            "    7.5    7.     9.7    9.8    8.7   10.     9.9   12.2   13.4   19.7\n",
            "   19.9]\n",
            "\n",
            "Unique values for coulmn: Length1\n",
            "\n",
            "[23.2 24.  23.9 26.3 26.5 26.8 27.6 28.5 28.4 28.7 29.1 29.5 29.4 30.4\n",
            " 30.9 31.  31.3 31.4 31.5 31.8 31.9 32.  32.7 32.8 33.5 35.  36.2 37.4\n",
            " 38.  12.9 16.5 17.5 18.2 18.6 19.  19.1 19.4 20.4 20.5 21.  21.1 22.\n",
            " 22.1 23.6 25.  24.1 25.6 33.7 37.3 13.5 14.3 16.3 18.4 19.8 21.2 23.\n",
            "  7.5 12.5 13.8 15.  15.7 16.2 16.8 17.2 17.8 19.3 20.  20.7 21.5 22.6\n",
            " 23.5 25.2 25.4 25.9 26.9 27.8 30.5 32.5 34.  34.5 34.6 36.5 36.6 36.9\n",
            " 37.  37.1 39.  39.8 40.1 40.2 41.1 30.  31.7 34.8 35.5 36.  40.  42.\n",
            " 43.2 44.8 48.3 52.  56.  59.   9.3 10.  10.1 10.4 10.7 10.8 11.3 11.4\n",
            " 11.5 11.7 12.1 13.2]\n",
            "\n",
            "Unique values for coulmn: Length2\n",
            "\n",
            "[25.4 26.3 26.5 29.  29.7 30.  30.7 31.  31.5 32.  33.  33.5 34.  34.5\n",
            " 35.  36.  37.  38.5 39.5 41.  14.1 18.2 18.8 19.8 20.  20.5 20.8 21.\n",
            " 22.  22.5 24.  23.4 23.5 25.2 26.  27.  31.7 28.  36.4 40.  14.7 15.5\n",
            " 17.7 19.  20.7 21.5 23.  25.   8.4 13.7 15.  16.2 17.4 18.  18.7 19.6\n",
            " 21.3 22.7 24.6 25.6 27.3 27.5 28.7 32.8 36.5 39.  42.  43.  43.5 44.\n",
            " 32.3 37.3 38.  42.5 45.  46.  48.  51.7 56.  60.  63.4  9.8 10.5 10.6\n",
            " 11.  11.2 11.3 11.8 12.  12.2 12.4 13.  14.3]\n",
            "\n",
            "Unique values for coulmn: Length3\n",
            "\n",
            "[30.  31.2 31.1 33.5 34.  34.7 34.5 35.  35.1 36.2 36.4 37.3 37.2 38.3\n",
            " 38.5 38.6 38.7 39.5 39.2 39.7 40.6 40.5 40.9 41.5 41.6 42.6 44.1 44.\n",
            " 45.3 45.9 46.5 16.2 20.3 21.2 22.2 22.8 23.1 23.7 24.7 24.3 25.3 25.\n",
            " 27.2 26.7 26.8 27.9 29.2 30.6 28.7 29.3 30.8 39.6 43.5 16.5 17.4 19.8\n",
            " 21.3 22.4 23.2 24.1 25.8 28.  29.   8.8 14.7 16.  17.2 18.5 19.2 19.4\n",
            " 20.2 20.8 21.  22.5 23.5 24.  24.2 24.5 25.5 26.2 26.5 27.  28.9 29.4\n",
            " 30.1 31.6 36.5 39.  39.4 39.3 41.4 41.3 42.3 42.5 42.4 44.6 45.2 45.5\n",
            " 46.  46.6 34.8 37.8 38.8 39.8 41.  45.8 48.  48.7 51.2 55.1 59.7 64.\n",
            " 68.  10.8 11.6 12.  12.4 12.6 13.1 13.2 13.4 13.5 13.8 15.2]\n",
            "\n",
            "Unique values for coulmn: Height\n",
            "\n",
            "[11.52   12.48   12.3778 12.73   12.444  13.6024 14.1795 12.67   14.0049\n",
            " 14.2266 14.2628 14.3714 13.7592 13.9129 14.9544 15.438  14.8604 14.938\n",
            " 15.633  14.4738 15.1285 15.9936 15.5227 15.4686 16.2405 16.36   16.3618\n",
            " 16.517  16.8896 18.957  18.0369 18.084  18.7542 18.6354 17.6235  4.1472\n",
            "  5.2983  5.5756  5.6166  6.216   6.4752  6.1677  6.1146  5.8045  6.6339\n",
            "  7.0334  6.55    6.4     7.5344  6.9153  7.3968  7.0866  8.8768  8.568\n",
            "  9.485   8.3804  8.1454  8.778  10.744  11.7612 12.354   6.8475  6.5772\n",
            "  7.4052  8.3922  8.8928  8.5376  9.396   9.7364 10.3458 11.088  11.368\n",
            "  2.112   3.528   3.824   4.5924  4.588   5.2224  5.1992  5.6358  5.1376\n",
            "  5.082   5.6925  5.9175  6.384   6.11    5.64    5.875   5.5225  5.856\n",
            "  6.792   5.9532  5.2185  6.275   7.293   6.375   6.7334  6.4395  6.561\n",
            "  7.168   8.323   7.1672  7.0516  7.2828  7.8204  7.5852  7.6156 10.03\n",
            " 10.2565 11.4884 10.881  10.6091 10.835  10.5717 11.1366 12.4313 11.9286\n",
            " 11.73   12.3808 11.135  12.8002 11.9328 12.5125 12.604  12.4888  5.568\n",
            "  5.7078  5.9364  6.2884  7.29    6.396   7.28    6.825   7.786   6.96\n",
            "  7.792   7.68    8.9262 10.6863  9.6    10.812   1.7388  1.972   1.7284\n",
            "  2.196   2.0832  1.9782  2.2139  2.2044  2.0904  2.43    2.277   2.8728\n",
            "  2.9322]\n",
            "\n",
            "Unique values for coulmn: Width\n",
            "\n",
            "[4.02   4.3056 4.6961 4.4555 5.134  4.9274 5.2785 4.69   4.8438 4.9594\n",
            " 5.1042 4.8146 4.368  5.0728 5.1708 5.58   5.2854 5.1975 5.1338 5.7276\n",
            " 5.5695 5.3704 5.2801 6.1306 5.589  6.0532 6.09   5.8515 6.1984 6.603\n",
            " 6.3063 6.292  6.7497 6.7473 6.3705 2.268  2.8217 2.9044 3.1746 3.5742\n",
            " 3.3516 3.3957 3.2943 3.7544 3.5478 3.8203 3.325  3.8    3.8352 3.6312\n",
            " 4.1272 3.906  4.4968 4.7736 5.355  4.2476 4.2485 4.6816 6.562  6.5736\n",
            " 6.525  2.3265 2.3142 2.673  2.9181 3.2928 3.2944 3.4104 3.1571 3.6636\n",
            " 4.144  4.234  1.408  1.9992 2.432  2.6316 2.9415 3.3216 3.1234 3.0502\n",
            " 3.0368 2.772  3.555  3.3075 3.6675 3.534  3.4075 3.525  3.995  3.624\n",
            " 3.63   3.626  3.725  3.723  3.825  4.1658 3.6835 4.239  5.1373 4.335\n",
            " 4.5662 4.2042 4.6354 4.7716 6.018  6.3875 7.7957 6.864  6.7408 6.2646\n",
            " 6.3666 7.4934 6.003  7.3514 7.1064 7.225  7.4624 6.63   6.8684 7.2772\n",
            " 7.4165 8.142  7.5958 3.3756 4.158  4.3844 4.0198 4.5765 3.977  4.3225\n",
            " 4.459  5.1296 4.896  4.87   5.376  6.1712 6.9849 6.144  7.48   1.0476\n",
            " 1.16   1.1484 1.38   1.2772 1.2852 1.2838 1.1659 1.3936 1.269  1.2558\n",
            " 2.0672 1.8792]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for i in data.columns:\n",
        "    print(f\"Unique values for coulmn: {i}\\n\\n{data[i].unique()}\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "85848ef5",
      "metadata": {
        "id": "85848ef5"
      },
      "source": [
        "As evident from the output above we do not have any special characters (similar to ?) in the above dataset.\n",
        "So let us move on to spliting the dependent and independent variables here.\n",
        "\n",
        "Remember our target column is Species which is 'y' whereas the columns 'weight', 'length1', 'length2', 'length3', 'height' and\n",
        "'width' are independent ones i.e. 'x'.\n",
        "So let us split the data accordingly."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0263dcd3",
      "metadata": {
        "id": "0263dcd3"
      },
      "source": [
        "## <font color = red>**5. Splitting the data**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us first look how our data looks like."
      ],
      "metadata": {
        "id": "j5mIDRBXczlE"
      },
      "id": "j5mIDRBXczlE"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "280dfcd1",
      "metadata": {
        "id": "280dfcd1",
        "outputId": "3660bd00-9431-4268-c264-93844f01713c"
      },
      "outputs": [
        {
          "data": {
            "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>Species</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Length1</th>\n",
              "      <th>Length2</th>\n",
              "      <th>Length3</th>\n",
              "      <th>Height</th>\n",
              "      <th>Width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bream</td>\n",
              "      <td>242.0</td>\n",
              "      <td>23.2</td>\n",
              "      <td>25.4</td>\n",
              "      <td>30.0</td>\n",
              "      <td>11.5200</td>\n",
              "      <td>4.0200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bream</td>\n",
              "      <td>290.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>26.3</td>\n",
              "      <td>31.2</td>\n",
              "      <td>12.4800</td>\n",
              "      <td>4.3056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bream</td>\n",
              "      <td>340.0</td>\n",
              "      <td>23.9</td>\n",
              "      <td>26.5</td>\n",
              "      <td>31.1</td>\n",
              "      <td>12.3778</td>\n",
              "      <td>4.6961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bream</td>\n",
              "      <td>363.0</td>\n",
              "      <td>26.3</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.5</td>\n",
              "      <td>12.7300</td>\n",
              "      <td>4.4555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bream</td>\n",
              "      <td>430.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>29.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>12.4440</td>\n",
              "      <td>5.1340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>12.2</td>\n",
              "      <td>11.5</td>\n",
              "      <td>12.2</td>\n",
              "      <td>13.4</td>\n",
              "      <td>2.0904</td>\n",
              "      <td>1.3936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>13.4</td>\n",
              "      <td>11.7</td>\n",
              "      <td>12.4</td>\n",
              "      <td>13.5</td>\n",
              "      <td>2.4300</td>\n",
              "      <td>1.2690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>12.2</td>\n",
              "      <td>12.1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.8</td>\n",
              "      <td>2.2770</td>\n",
              "      <td>1.2558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>19.7</td>\n",
              "      <td>13.2</td>\n",
              "      <td>14.3</td>\n",
              "      <td>15.2</td>\n",
              "      <td>2.8728</td>\n",
              "      <td>2.0672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>19.9</td>\n",
              "      <td>13.8</td>\n",
              "      <td>15.0</td>\n",
              "      <td>16.2</td>\n",
              "      <td>2.9322</td>\n",
              "      <td>1.8792</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>159 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Species  Weight  Length1  Length2  Length3   Height   Width\n",
              "0     Bream   242.0     23.2     25.4     30.0  11.5200  4.0200\n",
              "1     Bream   290.0     24.0     26.3     31.2  12.4800  4.3056\n",
              "2     Bream   340.0     23.9     26.5     31.1  12.3778  4.6961\n",
              "3     Bream   363.0     26.3     29.0     33.5  12.7300  4.4555\n",
              "4     Bream   430.0     26.5     29.0     34.0  12.4440  5.1340\n",
              "..      ...     ...      ...      ...      ...      ...     ...\n",
              "154   Smelt    12.2     11.5     12.2     13.4   2.0904  1.3936\n",
              "155   Smelt    13.4     11.7     12.4     13.5   2.4300  1.2690\n",
              "156   Smelt    12.2     12.1     13.0     13.8   2.2770  1.2558\n",
              "157   Smelt    19.7     13.2     14.3     15.2   2.8728  2.0672\n",
              "158   Smelt    19.9     13.8     15.0     16.2   2.9322  1.8792\n",
              "\n",
              "[159 rows x 7 columns]"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2b41fa10",
      "metadata": {
        "id": "2b41fa10"
      },
      "source": [
        "While splitting we are either doing slicing or providing an index.\n",
        "In the data above, the independent columns start from index '1' to the last index '6'.\n",
        "\n",
        "So while splitting the data for independent columns (X) we have to provide a colon ':' first which indicates 'for all rows of X' and then we have to provide a range (slicing) starting from index 1 to the end of the data.\n",
        "\n",
        "To mention the last column one can either use '1:6' or '1:' both means till the end of the data.\n",
        "While splitting the data  for dependent column 'Y' we  provide the index position of the Column Species which is '0'.\n",
        "\n",
        "The splitting done would be then viewed as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "80f5c88e",
      "metadata": {
        "id": "80f5c88e"
      },
      "outputs": [],
      "source": [
        "X= data.values[:,1:]\n",
        "Y= data.values[:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bc1f9c06",
      "metadata": {
        "id": "bc1f9c06",
        "outputId": "158bc1ea-1f01-471a-c78d-d9edf41894e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[242.0, 23.2, 25.4, 30.0, 11.52, 4.02],\n",
              "       [290.0, 24.0, 26.3, 31.2, 12.48, 4.3056],\n",
              "       [340.0, 23.9, 26.5, 31.1, 12.3778, 4.6961],\n",
              "       [363.0, 26.3, 29.0, 33.5, 12.73, 4.4555],\n",
              "       [430.0, 26.5, 29.0, 34.0, 12.444, 5.134],\n",
              "       [450.0, 26.8, 29.7, 34.7, 13.6024, 4.9274],\n",
              "       [500.0, 26.8, 29.7, 34.5, 14.1795, 5.2785],\n",
              "       [390.0, 27.6, 30.0, 35.0, 12.67, 4.69],\n",
              "       [450.0, 27.6, 30.0, 35.1, 14.0049, 4.8438],\n",
              "       [500.0, 28.5, 30.7, 36.2, 14.2266, 4.9594],\n",
              "       [475.0, 28.4, 31.0, 36.2, 14.2628, 5.1042],\n",
              "       [500.0, 28.7, 31.0, 36.2, 14.3714, 4.8146],\n",
              "       [500.0, 29.1, 31.5, 36.4, 13.7592, 4.368],\n",
              "       [340.0, 29.5, 32.0, 37.3, 13.9129, 5.0728],\n",
              "       [600.0, 29.4, 32.0, 37.2, 14.9544, 5.1708],\n",
              "       [600.0, 29.4, 32.0, 37.2, 15.438, 5.58],\n",
              "       [700.0, 30.4, 33.0, 38.3, 14.8604, 5.2854],\n",
              "       [700.0, 30.4, 33.0, 38.5, 14.938, 5.1975],\n",
              "       [610.0, 30.9, 33.5, 38.6, 15.633, 5.1338],\n",
              "       [650.0, 31.0, 33.5, 38.7, 14.4738, 5.7276],\n",
              "       [575.0, 31.3, 34.0, 39.5, 15.1285, 5.5695],\n",
              "       [685.0, 31.4, 34.0, 39.2, 15.9936, 5.3704],\n",
              "       [620.0, 31.5, 34.5, 39.7, 15.5227, 5.2801],\n",
              "       [680.0, 31.8, 35.0, 40.6, 15.4686, 6.1306],\n",
              "       [700.0, 31.9, 35.0, 40.5, 16.2405, 5.589],\n",
              "       [725.0, 31.8, 35.0, 40.9, 16.36, 6.0532],\n",
              "       [720.0, 32.0, 35.0, 40.6, 16.3618, 6.09],\n",
              "       [714.0, 32.7, 36.0, 41.5, 16.517, 5.8515],\n",
              "       [850.0, 32.8, 36.0, 41.6, 16.8896, 6.1984],\n",
              "       [1000.0, 33.5, 37.0, 42.6, 18.957, 6.603],\n",
              "       [920.0, 35.0, 38.5, 44.1, 18.0369, 6.3063],\n",
              "       [955.0, 35.0, 38.5, 44.0, 18.084, 6.292],\n",
              "       [925.0, 36.2, 39.5, 45.3, 18.7542, 6.7497],\n",
              "       [975.0, 37.4, 41.0, 45.9, 18.6354, 6.7473],\n",
              "       [950.0, 38.0, 41.0, 46.5, 17.6235, 6.3705],\n",
              "       [40.0, 12.9, 14.1, 16.2, 4.1472, 2.268],\n",
              "       [69.0, 16.5, 18.2, 20.3, 5.2983, 2.8217],\n",
              "       [78.0, 17.5, 18.8, 21.2, 5.5756, 2.9044],\n",
              "       [87.0, 18.2, 19.8, 22.2, 5.6166, 3.1746],\n",
              "       [120.0, 18.6, 20.0, 22.2, 6.216, 3.5742],\n",
              "       [0.0, 19.0, 20.5, 22.8, 6.4752, 3.3516],\n",
              "       [110.0, 19.1, 20.8, 23.1, 6.1677, 3.3957],\n",
              "       [120.0, 19.4, 21.0, 23.7, 6.1146, 3.2943],\n",
              "       [150.0, 20.4, 22.0, 24.7, 5.8045, 3.7544],\n",
              "       [145.0, 20.5, 22.0, 24.3, 6.6339, 3.5478],\n",
              "       [160.0, 20.5, 22.5, 25.3, 7.0334, 3.8203],\n",
              "       [140.0, 21.0, 22.5, 25.0, 6.55, 3.325],\n",
              "       [160.0, 21.1, 22.5, 25.0, 6.4, 3.8],\n",
              "       [169.0, 22.0, 24.0, 27.2, 7.5344, 3.8352],\n",
              "       [161.0, 22.0, 23.4, 26.7, 6.9153, 3.6312],\n",
              "       [200.0, 22.1, 23.5, 26.8, 7.3968, 4.1272],\n",
              "       [180.0, 23.6, 25.2, 27.9, 7.0866, 3.906],\n",
              "       [290.0, 24.0, 26.0, 29.2, 8.8768, 4.4968],\n",
              "       [272.0, 25.0, 27.0, 30.6, 8.568, 4.7736],\n",
              "       [390.0, 29.5, 31.7, 35.0, 9.485, 5.355],\n",
              "       [270.0, 23.6, 26.0, 28.7, 8.3804, 4.2476],\n",
              "       [270.0, 24.1, 26.5, 29.3, 8.1454, 4.2485],\n",
              "       [306.0, 25.6, 28.0, 30.8, 8.778, 4.6816],\n",
              "       [540.0, 28.5, 31.0, 34.0, 10.744, 6.562],\n",
              "       [800.0, 33.7, 36.4, 39.6, 11.7612, 6.5736],\n",
              "       [1000.0, 37.3, 40.0, 43.5, 12.354, 6.525],\n",
              "       [55.0, 13.5, 14.7, 16.5, 6.8475, 2.3265],\n",
              "       [60.0, 14.3, 15.5, 17.4, 6.5772, 2.3142],\n",
              "       [90.0, 16.3, 17.7, 19.8, 7.4052, 2.673],\n",
              "       [120.0, 17.5, 19.0, 21.3, 8.3922, 2.9181],\n",
              "       [150.0, 18.4, 20.0, 22.4, 8.8928, 3.2928],\n",
              "       [140.0, 19.0, 20.7, 23.2, 8.5376, 3.2944],\n",
              "       [170.0, 19.0, 20.7, 23.2, 9.396, 3.4104],\n",
              "       [145.0, 19.8, 21.5, 24.1, 9.7364, 3.1571],\n",
              "       [200.0, 21.2, 23.0, 25.8, 10.3458, 3.6636],\n",
              "       [273.0, 23.0, 25.0, 28.0, 11.088, 4.144],\n",
              "       [300.0, 24.0, 26.0, 29.0, 11.368, 4.234],\n",
              "       [5.9, 7.5, 8.4, 8.8, 2.112, 1.408],\n",
              "       [32.0, 12.5, 13.7, 14.7, 3.528, 1.9992],\n",
              "       [40.0, 13.8, 15.0, 16.0, 3.824, 2.432],\n",
              "       [51.5, 15.0, 16.2, 17.2, 4.5924, 2.6316],\n",
              "       [70.0, 15.7, 17.4, 18.5, 4.588, 2.9415],\n",
              "       [100.0, 16.2, 18.0, 19.2, 5.2224, 3.3216],\n",
              "       [78.0, 16.8, 18.7, 19.4, 5.1992, 3.1234],\n",
              "       [80.0, 17.2, 19.0, 20.2, 5.6358, 3.0502],\n",
              "       [85.0, 17.8, 19.6, 20.8, 5.1376, 3.0368],\n",
              "       [85.0, 18.2, 20.0, 21.0, 5.082, 2.772],\n",
              "       [110.0, 19.0, 21.0, 22.5, 5.6925, 3.555],\n",
              "       [115.0, 19.0, 21.0, 22.5, 5.9175, 3.3075],\n",
              "       [125.0, 19.0, 21.0, 22.5, 5.6925, 3.6675],\n",
              "       [130.0, 19.3, 21.3, 22.8, 6.384, 3.534],\n",
              "       [120.0, 20.0, 22.0, 23.5, 6.11, 3.4075],\n",
              "       [120.0, 20.0, 22.0, 23.5, 5.64, 3.525],\n",
              "       [130.0, 20.0, 22.0, 23.5, 6.11, 3.525],\n",
              "       [135.0, 20.0, 22.0, 23.5, 5.875, 3.525],\n",
              "       [110.0, 20.0, 22.0, 23.5, 5.5225, 3.995],\n",
              "       [130.0, 20.5, 22.5, 24.0, 5.856, 3.624],\n",
              "       [150.0, 20.5, 22.5, 24.0, 6.792, 3.624],\n",
              "       [145.0, 20.7, 22.7, 24.2, 5.9532, 3.63],\n",
              "       [150.0, 21.0, 23.0, 24.5, 5.2185, 3.626],\n",
              "       [170.0, 21.5, 23.5, 25.0, 6.275, 3.725],\n",
              "       [225.0, 22.0, 24.0, 25.5, 7.293, 3.723],\n",
              "       [145.0, 22.0, 24.0, 25.5, 6.375, 3.825],\n",
              "       [188.0, 22.6, 24.6, 26.2, 6.7334, 4.1658],\n",
              "       [180.0, 23.0, 25.0, 26.5, 6.4395, 3.6835],\n",
              "       [197.0, 23.5, 25.6, 27.0, 6.561, 4.239],\n",
              "       [218.0, 25.0, 26.5, 28.0, 7.168, 4.144],\n",
              "       [300.0, 25.2, 27.3, 28.7, 8.323, 5.1373],\n",
              "       [260.0, 25.4, 27.5, 28.9, 7.1672, 4.335],\n",
              "       [265.0, 25.4, 27.5, 28.9, 7.0516, 4.335],\n",
              "       [250.0, 25.4, 27.5, 28.9, 7.2828, 4.5662],\n",
              "       [250.0, 25.9, 28.0, 29.4, 7.8204, 4.2042],\n",
              "       [300.0, 26.9, 28.7, 30.1, 7.5852, 4.6354],\n",
              "       [320.0, 27.8, 30.0, 31.6, 7.6156, 4.7716],\n",
              "       [514.0, 30.5, 32.8, 34.0, 10.03, 6.018],\n",
              "       [556.0, 32.0, 34.5, 36.5, 10.2565, 6.3875],\n",
              "       [840.0, 32.5, 35.0, 37.3, 11.4884, 7.7957],\n",
              "       [685.0, 34.0, 36.5, 39.0, 10.881, 6.864],\n",
              "       [700.0, 34.0, 36.0, 38.3, 10.6091, 6.7408],\n",
              "       [700.0, 34.5, 37.0, 39.4, 10.835, 6.2646],\n",
              "       [690.0, 34.6, 37.0, 39.3, 10.5717, 6.3666],\n",
              "       [900.0, 36.5, 39.0, 41.4, 11.1366, 7.4934],\n",
              "       [650.0, 36.5, 39.0, 41.4, 11.1366, 6.003],\n",
              "       [820.0, 36.6, 39.0, 41.3, 12.4313, 7.3514],\n",
              "       [850.0, 36.9, 40.0, 42.3, 11.9286, 7.1064],\n",
              "       [900.0, 37.0, 40.0, 42.5, 11.73, 7.225],\n",
              "       [1015.0, 37.0, 40.0, 42.4, 12.3808, 7.4624],\n",
              "       [820.0, 37.1, 40.0, 42.5, 11.135, 6.63],\n",
              "       [1100.0, 39.0, 42.0, 44.6, 12.8002, 6.8684],\n",
              "       [1000.0, 39.8, 43.0, 45.2, 11.9328, 7.2772],\n",
              "       [1100.0, 40.1, 43.0, 45.5, 12.5125, 7.4165],\n",
              "       [1000.0, 40.2, 43.5, 46.0, 12.604, 8.142],\n",
              "       [1000.0, 41.1, 44.0, 46.6, 12.4888, 7.5958],\n",
              "       [200.0, 30.0, 32.3, 34.8, 5.568, 3.3756],\n",
              "       [300.0, 31.7, 34.0, 37.8, 5.7078, 4.158],\n",
              "       [300.0, 32.7, 35.0, 38.8, 5.9364, 4.3844],\n",
              "       [300.0, 34.8, 37.3, 39.8, 6.2884, 4.0198],\n",
              "       [430.0, 35.5, 38.0, 40.5, 7.29, 4.5765],\n",
              "       [345.0, 36.0, 38.5, 41.0, 6.396, 3.977],\n",
              "       [456.0, 40.0, 42.5, 45.5, 7.28, 4.3225],\n",
              "       [510.0, 40.0, 42.5, 45.5, 6.825, 4.459],\n",
              "       [540.0, 40.1, 43.0, 45.8, 7.786, 5.1296],\n",
              "       [500.0, 42.0, 45.0, 48.0, 6.96, 4.896],\n",
              "       [567.0, 43.2, 46.0, 48.7, 7.792, 4.87],\n",
              "       [770.0, 44.8, 48.0, 51.2, 7.68, 5.376],\n",
              "       [950.0, 48.3, 51.7, 55.1, 8.9262, 6.1712],\n",
              "       [1250.0, 52.0, 56.0, 59.7, 10.6863, 6.9849],\n",
              "       [1600.0, 56.0, 60.0, 64.0, 9.6, 6.144],\n",
              "       [1550.0, 56.0, 60.0, 64.0, 9.6, 6.144],\n",
              "       [1650.0, 59.0, 63.4, 68.0, 10.812, 7.48],\n",
              "       [6.7, 9.3, 9.8, 10.8, 1.7388, 1.0476],\n",
              "       [7.5, 10.0, 10.5, 11.6, 1.972, 1.16],\n",
              "       [7.0, 10.1, 10.6, 11.6, 1.7284, 1.1484],\n",
              "       [9.7, 10.4, 11.0, 12.0, 2.196, 1.38],\n",
              "       [9.8, 10.7, 11.2, 12.4, 2.0832, 1.2772],\n",
              "       [8.7, 10.8, 11.3, 12.6, 1.9782, 1.2852],\n",
              "       [10.0, 11.3, 11.8, 13.1, 2.2139, 1.2838],\n",
              "       [9.9, 11.3, 11.8, 13.1, 2.2139, 1.1659],\n",
              "       [9.8, 11.4, 12.0, 13.2, 2.2044, 1.1484],\n",
              "       [12.2, 11.5, 12.2, 13.4, 2.0904, 1.3936],\n",
              "       [13.4, 11.7, 12.4, 13.5, 2.43, 1.269],\n",
              "       [12.2, 12.1, 13.0, 13.8, 2.277, 1.2558],\n",
              "       [19.7, 13.2, 14.3, 15.2, 2.8728, 2.0672],\n",
              "       [19.9, 13.8, 15.0, 16.2, 2.9322, 1.8792]], dtype=object)"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dfc18048",
      "metadata": {
        "id": "dfc18048",
        "outputId": "e7f15522-cde9-4223-e665-8affa8750487"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream',\n",
              "       'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream',\n",
              "       'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream',\n",
              "       'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream',\n",
              "       'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream',\n",
              "       'Roach', 'Roach', 'Roach', 'Roach', 'Roach', 'Roach', 'Roach',\n",
              "       'Roach', 'Roach', 'Roach', 'Roach', 'Roach', 'Roach', 'Roach',\n",
              "       'Roach', 'Roach', 'Roach', 'Roach', 'Roach', 'Roach', 'Whitefish',\n",
              "       'Whitefish', 'Whitefish', 'Whitefish', 'Whitefish', 'Whitefish',\n",
              "       'Parkki', 'Parkki', 'Parkki', 'Parkki', 'Parkki', 'Parkki',\n",
              "       'Parkki', 'Parkki', 'Parkki', 'Parkki', 'Parkki', 'Perch', 'Perch',\n",
              "       'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch',\n",
              "       'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch',\n",
              "       'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch',\n",
              "       'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch',\n",
              "       'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch',\n",
              "       'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch',\n",
              "       'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Perch',\n",
              "       'Perch', 'Perch', 'Perch', 'Perch', 'Perch', 'Pike', 'Pike',\n",
              "       'Pike', 'Pike', 'Pike', 'Pike', 'Pike', 'Pike', 'Pike', 'Pike',\n",
              "       'Pike', 'Pike', 'Pike', 'Pike', 'Pike', 'Pike', 'Pike', 'Smelt',\n",
              "       'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt',\n",
              "       'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt'], dtype=object)"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ee3cfa1d",
      "metadata": {
        "id": "ee3cfa1d"
      },
      "source": [
        "## <font color = red>**6. Scaling the data**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0d7d06f0",
      "metadata": {
        "id": "0d7d06f0"
      },
      "source": [
        "Once the data is split into X and Y, we have to  scale the data first.\n",
        "It is important to scale the data into the range of -3 to 3 to avoid unnecessary bias of the algorithm.\n",
        "To scale the data, we use StandardScaler function from preprocessing library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e1946d02",
      "metadata": {
        "id": "e1946d02"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "scaler.fit(X)\n",
        "X = scaler.transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "93b8d520",
      "metadata": {
        "id": "93b8d520",
        "outputId": "6e90abce-8d6c-4532-96ed-17a5f0b6eeb9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[-4.38072172e-01, -3.05788578e-01, -2.82303007e-01,\n",
              "        -1.06020232e-01,  5.96578670e-01, -2.36528948e-01],\n",
              "       [-3.03562184e-01, -2.25507242e-01, -1.98053663e-01,\n",
              "        -2.33668373e-03,  8.21260549e-01, -6.65789457e-02],\n",
              "       [-1.63447613e-01, -2.35542409e-01, -1.79331587e-01,\n",
              "        -1.09769794e-02,  7.97341291e-01,  1.65793169e-01],\n",
              "       [-9.89949100e-02,  5.30159764e-03,  5.46943678e-02,\n",
              "         1.96390116e-01,  8.79771455e-01,  2.26210031e-02],\n",
              "       [ 8.87586153e-02,  2.53719316e-02,  5.46943678e-02,\n",
              "         2.39591594e-01,  8.12834979e-01,  4.26371272e-01],\n",
              "       [ 1.44804444e-01,  5.54774324e-02,  1.20221635e-01,\n",
              "         3.00073664e-01,  1.08395111e+00,  3.03431249e-01],\n",
              "       [ 2.84919015e-01,  5.54774324e-02,  1.20221635e-01,\n",
              "         2.82793073e-01,  1.21901769e+00,  5.12357880e-01],\n",
              "       [-2.33330416e-02,  1.35758768e-01,  1.48304750e-01,\n",
              "         3.25994551e-01,  8.65728838e-01,  1.62163285e-01],\n",
              "       [ 1.44804444e-01,  1.35758768e-01,  1.48304750e-01,\n",
              "         3.34634847e-01,  1.17815367e+00,  2.53683979e-01],\n",
              "       [ 2.84919015e-01,  2.26075271e-01,  2.13832017e-01,\n",
              "         4.29678099e-01,  1.23004114e+00,  3.22473266e-01],\n",
              "       [ 2.14861729e-01,  2.16040104e-01,  2.41915132e-01,\n",
              "         4.29678099e-01,  1.23851352e+00,  4.08638393e-01],\n",
              "       [ 2.84919015e-01,  2.46145605e-01,  2.41915132e-01,\n",
              "         4.29678099e-01,  1.26393066e+00,  2.36308139e-01],\n",
              "       [ 2.84919015e-01,  2.86286273e-01,  2.88720322e-01,\n",
              "         4.46958690e-01,  1.12064915e+00, -2.94470124e-02],\n",
              "       [-1.63447613e-01,  3.26426940e-01,  3.35525513e-01,\n",
              "         5.24721351e-01,  1.15662166e+00,  3.89953414e-01],\n",
              "       [ 5.65148157e-01,  3.16391773e-01,  3.35525513e-01,\n",
              "         5.16081055e-01,  1.40037809e+00,  4.48269591e-01],\n",
              "       [ 5.65148157e-01,  3.16391773e-01,  3.35525513e-01,\n",
              "         5.16081055e-01,  1.51356159e+00,  6.91769385e-01],\n",
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              "         6.37045194e-01,  1.55920010e+00,  4.26252259e-01],\n",
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              "         6.45685490e-01,  1.28789673e+00,  7.79600689e-01],\n",
              "       [ 4.95090871e-01,  5.07059946e-01,  5.22746277e-01,\n",
              "         7.14807855e-01,  1.44112509e+00,  6.85521223e-01],\n",
              "       [ 8.03342928e-01,  5.17095113e-01,  5.22746277e-01,\n",
              "         6.88886968e-01,  1.64359623e+00,  5.67044173e-01],\n",
              "       [ 6.21193985e-01,  5.27130280e-01,  5.69551468e-01,\n",
              "         7.32088446e-01,  1.53338508e+00,  5.13309981e-01],\n",
              "       [ 7.89331471e-01,  5.57235780e-01,  6.16356659e-01,\n",
              "         8.09851107e-01,  1.52072332e+00,  1.01941109e+00],\n",
              "       [ 8.45377299e-01,  5.67270947e-01,  6.16356659e-01,\n",
              "         8.01210812e-01,  1.70138160e+00,  6.97124952e-01],\n",
              "       [ 9.15434585e-01,  5.57235780e-01,  6.16356659e-01,\n",
              "         8.35771994e-01,  1.72934981e+00,  9.73353212e-01],\n",
              "       [ 9.01423127e-01,  5.77306114e-01,  6.16356659e-01,\n",
              "         8.09851107e-01,  1.72977109e+00,  9.95251532e-01],\n",
              "       [ 8.84609379e-01,  6.47552283e-01,  7.09967041e-01,\n",
              "         8.87613768e-01,  1.76609466e+00,  8.53328999e-01],\n",
              "       [ 1.26572101e+00,  6.57587450e-01,  7.09967041e-01,\n",
              "         8.96254064e-01,  1.85329931e+00,  1.05975637e+00],\n",
              "       [ 1.68606473e+00,  7.27833619e-01,  8.03577423e-01,\n",
              "         9.82657020e-01,  2.33716110e+00,  1.30051887e+00],\n",
              "       [ 1.46188141e+00,  8.78361123e-01,  9.43992995e-01,\n",
              "         1.11226146e+00,  2.12181756e+00,  1.12396367e+00],\n",
              "       [ 1.55996161e+00,  8.78361123e-01,  9.43992995e-01,\n",
              "         1.10362116e+00,  2.13284102e+00,  1.11545426e+00],\n",
              "       [ 1.47589287e+00,  9.98783127e-01,  1.03760338e+00,\n",
              "         1.21594500e+00,  2.28969706e+00,  1.38781462e+00],\n",
              "       [ 1.61600744e+00,  1.11920513e+00,  1.17801895e+00,\n",
              "         1.26778678e+00,  2.26189267e+00,  1.38638646e+00],\n",
              "       [ 1.54595015e+00,  1.17941613e+00,  1.17801895e+00,\n",
              "         1.31962855e+00,  2.02506393e+00,  1.16216671e+00],\n",
              "       [-1.00413504e+00, -1.33941077e+00, -1.34010032e+00,\n",
              "        -1.29838103e+00, -1.12897816e+00, -1.27907938e+00],\n",
              "       [-9.22868588e-01, -9.78144764e-01, -9.56297756e-01,\n",
              "        -9.44128909e-01, -8.59570547e-01, -9.49592982e-01],\n",
              "       [-8.97647965e-01, -8.77793095e-01, -9.00131527e-01,\n",
              "        -8.66366249e-01, -7.94670250e-01, -9.00381269e-01],\n",
              "       [-8.72427342e-01, -8.07546926e-01, -8.06521145e-01,\n",
              "        -7.79963292e-01, -7.85074461e-01, -7.39595237e-01],\n",
              "       [-7.79951725e-01, -7.67406258e-01, -7.87799069e-01,\n",
              "        -7.79963292e-01, -6.44788713e-01, -5.01808049e-01],\n",
              "       [-1.11622670e+00, -7.27265590e-01, -7.40993878e-01,\n",
              "        -7.28121518e-01, -5.84124605e-01, -6.34269080e-01],\n",
              "       [-8.07974640e-01, -7.17230423e-01, -7.12910764e-01,\n",
              "        -7.02200631e-01, -6.56093020e-01, -6.08026800e-01],\n",
              "       [-7.79951725e-01, -6.87124923e-01, -6.94188687e-01,\n",
              "        -6.50358857e-01, -6.68520736e-01, -6.68366192e-01],\n",
              "       [-6.95882983e-01, -5.86773253e-01, -6.00578305e-01,\n",
              "        -5.63955901e-01, -7.41097664e-01, -3.94577690e-01],\n",
              "       [-7.09894440e-01, -5.76738086e-01, -6.00578305e-01,\n",
              "        -5.98517084e-01, -5.46981882e-01, -5.17517713e-01],\n",
              "       [-6.67860069e-01, -5.76738086e-01, -5.53773114e-01,\n",
              "        -5.12114127e-01, -4.53481454e-01, -3.55363036e-01],\n",
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              "       [-5.55768412e-01, -4.16175415e-01, -4.60162732e-01,\n",
              "        -3.82509692e-01, -3.68430001e-01, -1.72738191e-01],\n",
              "       [-6.11814240e-01, -2.65647910e-01, -3.01025083e-01,\n",
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              "       [-3.03562184e-01, -2.25507242e-01, -2.26136778e-01,\n",
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              "       [-3.54003429e-01, -1.25155573e-01, -1.32526396e-01,\n",
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              "       [-2.33330416e-02,  3.26426940e-01,  3.07442399e-01,\n",
              "         3.25994551e-01,  1.20299895e-01,  5.57880202e-01],\n",
              "       [-3.59608012e-01, -2.65647910e-01, -2.26136778e-01,\n",
              "        -2.18344075e-01, -1.38224692e-01, -1.01092602e-01],\n",
              "       [-3.59608012e-01, -2.15472075e-01, -1.79331587e-01,\n",
              "        -1.66502301e-01, -1.93224944e-01, -1.00557045e-01],\n",
              "       [-2.58725521e-01, -6.49445711e-02, -3.89160141e-02,\n",
              "        -3.68978663e-02, -4.51689475e-02,  1.57164755e-01],\n",
              "       [ 3.97010672e-01,  2.26075271e-01,  2.41915132e-01,\n",
              "         2.39591594e-01,  4.14960818e-01,  1.27612128e+00],\n",
              "       [ 1.12560644e+00,  7.47903953e-01,  7.47411194e-01,\n",
              "         7.23448151e-01,  6.53029992e-01,  1.28302402e+00],\n",
              "       [ 1.68606473e+00,  1.10916996e+00,  1.08440857e+00,\n",
              "         1.06041968e+00,  7.91771053e-01,  1.25410395e+00],\n",
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              "       [-8.64020468e-01, -9.98215098e-01, -1.00310295e+00,\n",
              "        -9.87330388e-01, -3.66464035e-01, -1.03807885e+00],\n",
              "       [-7.79951725e-01, -8.77793095e-01, -8.81409451e-01,\n",
              "        -8.57725953e-01, -1.35462978e-01, -8.92228905e-01],\n",
              "       [-6.95882983e-01, -7.87476592e-01, -7.87799069e-01,\n",
              "        -7.62682701e-01, -1.83007394e-02, -6.69258786e-01],\n",
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              "        -6.93560336e-01, -1.01433035e-01, -6.68306686e-01],\n",
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              "       [-2.75539270e-01, -2.25507242e-01, -2.26136778e-01,\n",
              "        -1.92423188e-01,  5.61004039e-01, -1.09185459e-01],\n",
              "       [-1.09969318e+00, -1.88130979e+00, -1.87367950e+00,\n",
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              "       [-1.02655337e+00, -1.37955144e+00, -1.37754447e+00,\n",
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              "       [-8.78031925e-01, -8.07546926e-01, -7.87799069e-01,\n",
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              "        -7.54042405e-01, -7.67310550e-01, -5.13233259e-01],\n",
              "       [-7.93963182e-01, -7.27265590e-01, -6.94188687e-01,\n",
              "        -7.54042405e-01, -7.14650735e-01, -6.60511360e-01],\n",
              "       [-7.65940268e-01, -7.27265590e-01, -6.94188687e-01,\n",
              "        -7.54042405e-01, -7.67310550e-01, -4.46288668e-01],\n",
              "       [-7.51928811e-01, -6.97160090e-01, -6.66105573e-01,\n",
              "        -7.28121518e-01, -6.05469384e-01, -5.25729583e-01],\n",
              "       [-7.79951725e-01, -6.26913921e-01, -6.00578305e-01,\n",
              "        -6.67639449e-01, -6.69597337e-01, -6.01005056e-01],\n",
              "       [-7.79951725e-01, -6.26913921e-01, -6.00578305e-01,\n",
              "        -6.67639449e-01, -7.79597840e-01, -5.31085150e-01],\n",
              "       [-7.51928811e-01, -6.26913921e-01, -6.00578305e-01,\n",
              "        -6.67639449e-01, -6.69597337e-01, -5.31085150e-01],\n",
              "       [-7.37917354e-01, -6.26913921e-01, -6.00578305e-01,\n",
              "        -6.67639449e-01, -7.24597589e-01, -5.31085150e-01],\n",
              "       [-8.07974640e-01, -6.26913921e-01, -6.00578305e-01,\n",
              "        -6.67639449e-01, -8.07097966e-01, -2.51405524e-01],\n",
              "       [-7.51928811e-01, -5.76738086e-01, -5.53773114e-01,\n",
              "        -6.24437970e-01, -7.29044417e-01, -4.72173910e-01],\n",
              "       [-6.95882983e-01, -5.76738086e-01, -5.53773114e-01,\n",
              "        -6.24437970e-01, -5.09979585e-01, -4.72173910e-01],\n",
              "       [-7.09894440e-01, -5.56667752e-01, -5.35051038e-01,\n",
              "        -6.07157379e-01, -7.06295377e-01, -4.68603531e-01],\n",
              "       [-6.95882983e-01, -5.26562251e-01, -5.06967923e-01,\n",
              "        -5.81236492e-01, -8.78247228e-01, -4.70983784e-01],\n",
              "       [-6.39837154e-01, -4.76386416e-01, -4.60162732e-01,\n",
              "        -5.38035014e-01, -6.30980139e-01, -4.12072543e-01],\n",
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              "        -2.18344075e-01, -1.51658797e-01,  4.28334980e-01],\n",
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              "        -2.01063484e-01, -4.22166417e-01, -4.90840925e-02],\n",
              "       [-3.73619469e-01, -8.50149050e-02, -8.57212050e-02,\n",
              "        -2.01063484e-01, -4.49221860e-01, -4.90840925e-02],\n",
              "       [-4.15653841e-01, -8.50149050e-02, -8.57212050e-02,\n",
              "        -2.01063484e-01, -3.95110974e-01,  8.84944810e-02],\n",
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              "       [-2.75539270e-01,  6.55125994e-02,  2.66112532e-02,\n",
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              "       [-2.19493441e-01,  1.55829102e-01,  1.48304750e-01,\n",
              "         3.22244989e-02, -3.17221256e-01,  2.10720428e-01],\n",
              "       [ 3.24151095e-01,  4.26778610e-01,  4.10413819e-01,\n",
              "         2.39591594e-01,  2.47853670e-01,  9.52406994e-01],\n",
              "       [ 4.41847334e-01,  5.77306114e-01,  5.69551468e-01,\n",
              "         4.55598986e-01,  3.00864551e-01,  1.17228278e+00],\n",
              "       [ 1.23769810e+00,  6.27481949e-01,  6.16356659e-01,\n",
              "         5.24721351e-01,  5.89182892e-01,  2.01025055e+00],\n",
              "       [ 8.03342928e-01,  7.78009454e-01,  7.56772232e-01,\n",
              "         6.71606377e-01,  4.47024794e-01,  1.45583032e+00],\n",
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              "         6.11124307e-01,  3.83388333e-01,  1.38251855e+00],\n",
              "       [ 8.45377299e-01,  8.28185288e-01,  8.03577423e-01,\n",
              "         7.06167559e-01,  4.36258788e-01,  1.09914954e+00],\n",
              "       [ 8.17354385e-01,  8.38220455e-01,  8.03577423e-01,\n",
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              "         8.78973472e-01,  5.06846345e-01,  1.83036299e+00],\n",
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              "         1.15546293e+00,  8.96201318e-01,  1.45844860e+00],\n",
              "       [ 1.68606473e+00,  1.36004914e+00,  1.36523971e+00,\n",
              "         1.20730471e+00,  6.93191878e-01,  1.70171037e+00],\n",
              "       [ 1.96629387e+00,  1.39015464e+00,  1.36523971e+00,\n",
              "         1.23322559e+00,  8.28866967e-01,  1.78460265e+00],\n",
              "       [ 1.68606473e+00,  1.40018981e+00,  1.41204490e+00,\n",
              "         1.27642707e+00,  8.50281959e-01,  2.21632088e+00],\n",
              "       [ 1.68606473e+00,  1.49050631e+00,  1.45885010e+00,\n",
              "         1.32826885e+00,  8.23320133e-01,  1.89129745e+00],\n",
              "       [-5.55768412e-01,  3.76602775e-01,  3.63608628e-01,\n",
              "         3.08713960e-01, -7.96448981e-01, -6.19987567e-01],\n",
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              "         6.54325786e-01, -7.10227310e-01, -1.96879786e-02],\n",
              "       [-2.75539270e-01,  8.58290789e-01,  8.31660537e-01,\n",
              "         7.40728742e-01, -6.27843954e-01, -2.36647961e-01],\n",
              "       [ 8.87586153e-02,  9.28536958e-01,  8.97187805e-01,\n",
              "         8.01210812e-01, -3.93425860e-01,  9.46236302e-02],\n",
              "       [-1.49436156e-01,  9.78712793e-01,  9.43992995e-01,\n",
              "         8.44412290e-01, -6.02660860e-01, -2.62116659e-01],\n",
              "       [ 1.61618192e-01,  1.38011947e+00,  1.31843452e+00,\n",
              "         1.23322559e+00, -3.95766297e-01, -5.65223804e-02],\n",
              "       [ 3.12941929e-01,  1.38011947e+00,  1.31843452e+00,\n",
              "         1.23322559e+00, -5.02256146e-01,  2.47037237e-02],\n",
              "       [ 3.97010672e-01,  1.39015464e+00,  1.36523971e+00,\n",
              "         1.25914648e+00, -2.77340223e-01,  4.23752994e-01],\n",
              "       [ 2.84919015e-01,  1.58082281e+00,  1.55246048e+00,\n",
              "         1.44923299e+00, -4.70660256e-01,  2.84746270e-01],\n",
              "       [ 4.72672540e-01,  1.70124481e+00,  1.64607086e+00,\n",
              "         1.50971505e+00, -2.75935961e-01,  2.69274631e-01],\n",
              "       [ 1.04153770e+00,  1.86180749e+00,  1.83329162e+00,\n",
              "         1.72572245e+00, -3.02148847e-01,  5.70376526e-01],\n",
              "       [ 1.54595015e+00,  2.21303833e+00,  2.17965004e+00,\n",
              "         2.06269398e+00, -1.04836824e-02,  1.04357065e+00],\n",
              "       [ 2.38663758e+00,  2.58433951e+00,  2.58217468e+00,\n",
              "         2.46014758e+00,  4.01456501e-01,  1.52777344e+00],\n",
              "       [ 3.36743958e+00,  2.98574618e+00,  2.95661621e+00,\n",
              "         2.83168029e+00,  1.47214912e-01,  1.02738494e+00],\n",
              "       [ 3.22732501e+00,  2.98574618e+00,  2.95661621e+00,\n",
              "         2.83168029e+00,  1.47214912e-01,  1.02738494e+00],\n",
              "       [ 3.50755415e+00,  3.28680119e+00,  3.27489150e+00,\n",
              "         3.17729212e+00,  4.30875784e-01,  1.82238915e+00],\n",
              "       [-1.09745134e+00, -1.70067679e+00, -1.74262496e+00,\n",
              "        -1.76495700e+00, -1.69264883e+00, -2.00529431e+00],\n",
              "       [-1.09520951e+00, -1.63043062e+00, -1.67709770e+00,\n",
              "        -1.69583463e+00, -1.63806985e+00, -1.93840923e+00],\n",
              "       [-1.09661066e+00, -1.62039545e+00, -1.66773666e+00,\n",
              "        -1.69583463e+00, -1.69508288e+00, -1.94531196e+00],\n",
              "       [-1.08904447e+00, -1.59028995e+00, -1.63029251e+00,\n",
              "        -1.66127345e+00, -1.58564408e+00, -1.80749536e+00],\n",
              "       [-1.08876424e+00, -1.56018445e+00, -1.61157043e+00,\n",
              "        -1.62671227e+00, -1.61204420e+00, -1.86866784e+00],\n",
              "       [-1.09184676e+00, -1.55014928e+00, -1.60220939e+00,\n",
              "        -1.60943167e+00, -1.63661878e+00, -1.86390733e+00],\n",
              "       [-1.08820378e+00, -1.49997345e+00, -1.55540420e+00,\n",
              "        -1.56623020e+00, -1.58145470e+00, -1.86474042e+00],\n",
              "       [-1.08848401e+00, -1.49997345e+00, -1.55540420e+00,\n",
              "        -1.56623020e+00, -1.58145470e+00, -1.93489835e+00],\n",
              "       [-1.08876424e+00, -1.48993828e+00, -1.53668212e+00,\n",
              "        -1.55758990e+00, -1.58367812e+00, -1.94531196e+00],\n",
              "       [-1.08203874e+00, -1.47990311e+00, -1.51796005e+00,\n",
              "        -1.54030931e+00, -1.61035909e+00, -1.79940250e+00],\n",
              "       [-1.07867599e+00, -1.45983278e+00, -1.49923797e+00,\n",
              "        -1.53166901e+00, -1.53087787e+00, -1.87354735e+00],\n",
              "       [-1.08203874e+00, -1.41969211e+00, -1.44307174e+00,\n",
              "        -1.50574813e+00, -1.56668655e+00, -1.88140219e+00],\n",
              "       [-1.06102155e+00, -1.30930527e+00, -1.32137825e+00,\n",
              "        -1.38478399e+00, -1.42724336e+00, -1.39856804e+00],\n",
              "       [-1.06046110e+00, -1.24909427e+00, -1.25585098e+00,\n",
              "        -1.29838103e+00, -1.41334117e+00, -1.51043989e+00]])"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "489dd4b3",
      "metadata": {
        "id": "489dd4b3"
      },
      "source": [
        "## <font color = red>**7.  Performing train_test split**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0efbc964",
      "metadata": {
        "id": "0efbc964"
      },
      "source": [
        "Once done with scaling we have to split it further into train and test data.\n",
        "Here test data is 20 % of the total data, whereas train data is the remaining 80%.\n",
        "Splitting the data into train & test data is done by importing 'train_test_split' function from\n",
        "sklearn library and model selection sub-library as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b63b5a21",
      "metadata": {
        "id": "b63b5a21"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c48136ca",
      "metadata": {
        "id": "c48136ca",
        "outputId": "edd948c6-bf63-46d9-961c-8f11843bc391"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(127, 6)"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "881c1e24",
      "metadata": {
        "id": "881c1e24",
        "outputId": "7cfdb89f-4319-4756-959f-1a4f406b3af5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(127,)"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "104c99d2",
      "metadata": {
        "id": "104c99d2"
      },
      "source": [
        "## <font color = red>**8. Applying KNeighbors Classifier**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d8c7885c",
      "metadata": {
        "id": "d8c7885c"
      },
      "source": [
        "It is time to apply K-NN logic to find out the nearest neighbours. This we execute using KNeighborsClassifier function from neighbors sublibrary of the sklearn library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cff6d696",
      "metadata": {
        "id": "cff6d696"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model_KNN = KNeighborsClassifier(n_neighbors=int(np.sqrt(len(X_train))),metric='euclidean')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7f59571c",
      "metadata": {
        "id": "7f59571c"
      },
      "source": [
        "here \"n_neighbors=int(np.sqrt(len(X_train))),metric='euclidean'\" can be simply replaced by\n",
        "\"n_neighbors=5,metric='euclidean'\" or any number for n_neighbors so to say.\n",
        "\n",
        "n_neighbors is simply the hyperparameter 'k', which can be manually adjusted.\n",
        "The metric of finding the shortest distance here is Eucledian.\n",
        "\n",
        "Now let us train the data using fit function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cd7b4364",
      "metadata": {
        "id": "cd7b4364",
        "outputId": "61398b6a-7458-4555-c11f-8921f437c4a9"
      },
      "outputs": [
        {
          "data": {
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            ],
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean', n_neighbors=11)"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_KNN.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "def7d426",
      "metadata": {
        "id": "def7d426"
      },
      "source": [
        "## <font color = red>**9. Predicting Y_pred based on X_test**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let us predict Y_pred based on our test data. We further print a zip list to compare Y_test and Y_pred. This will let us know the degree of\n",
        "misclassification done by our model."
      ],
      "metadata": {
        "id": "0v3PMBtQg6hy"
      },
      "id": "0v3PMBtQg6hy"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a0a1f66c",
      "metadata": {
        "id": "a0a1f66c",
        "outputId": "c2cd582b-5d3c-4ba8-e656-96c4692b1f74"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[('Perch', 'Perch'), ('Bream', 'Bream'), ('Roach', 'Perch'), ('Roach', 'Perch'), ('Perch', 'Perch'), ('Smelt', 'Smelt'), ('Whitefish', 'Perch'), ('Perch', 'Perch'), ('Smelt', 'Smelt'), ('Bream', 'Bream'), ('Perch', 'Perch'), ('Perch', 'Perch'), ('Bream', 'Bream'), ('Perch', 'Perch'), ('Roach', 'Perch'), ('Bream', 'Bream'), ('Bream', 'Bream'), ('Perch', 'Perch'), ('Smelt', 'Smelt'), ('Perch', 'Smelt'), ('Roach', 'Perch'), ('Parkki', 'Parkki'), ('Perch', 'Perch'), ('Roach', 'Perch'), ('Smelt', 'Smelt'), ('Perch', 'Perch'), ('Bream', 'Bream'), ('Pike', 'Pike'), ('Perch', 'Perch'), ('Bream', 'Bream'), ('Bream', 'Bream'), ('Perch', 'Perch')]\n"
          ]
        }
      ],
      "source": [
        "Y_pred = model_KNN.predict(X_test)\n",
        "print(list(zip(Y_test,Y_pred)))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b11e1613",
      "metadata": {
        "id": "b11e1613"
      },
      "source": [
        "After looking at the dictionary above we get to know not all the categories of Species are correctly classified.\n",
        "To give an example\n",
        "('Bream', 'Bream') = Correctly classified.\n",
        "('Whitefish', 'Perch') = Incorrect classification.\n",
        "\n",
        "Let us get the overall statistics about misclassification and accuracy of the model using confusion matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ffe1cf69",
      "metadata": {
        "id": "ffe1cf69"
      },
      "source": [
        "## <font color = red>**10. Evaluation using Confusion Matrix**</font>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aff14912",
      "metadata": {
        "id": "aff14912",
        "outputId": "8e4ea102-85d3-40e2-955c-10e0045abb49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 8  0  0  0  0  0  0]\n",
            " [ 0  1  0  0  0  0  0]\n",
            " [ 0  0 11  0  0  1  0]\n",
            " [ 0  0  0  1  0  0  0]\n",
            " [ 0  0  5  0  0  0  0]\n",
            " [ 0  0  0  0  0  4  0]\n",
            " [ 0  0  1  0  0  0  0]]\n",
            "Classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Bream       1.00      1.00      1.00         8\n",
            "      Parkki       1.00      1.00      1.00         1\n",
            "       Perch       0.65      0.92      0.76        12\n",
            "        Pike       1.00      1.00      1.00         1\n",
            "       Roach       0.00      0.00      0.00         5\n",
            "       Smelt       0.80      1.00      0.89         4\n",
            "   Whitefish       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.78        32\n",
            "   macro avg       0.64      0.70      0.66        32\n",
            "weighted avg       0.66      0.78      0.71        32\n",
            "\n",
            "Accuracy of the model: 0.78125\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\ninad\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "C:\\Users\\ninad\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "C:\\Users\\ninad\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
        "\n",
        "cfm = confusion_matrix(Y_test,Y_pred)\n",
        "print(cfm)\n",
        "\n",
        "print(\"Classification report:\")\n",
        "print(classification_report(Y_test,Y_pred))\n",
        "\n",
        "acc = accuracy_score(Y_test,Y_pred)\n",
        "print(\"Accuracy of the model:\",acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ca448238",
      "metadata": {
        "id": "ca448238"
      },
      "source": [
        "Here, if you look at the accuracy obtained. It is around 0.78 (i.e. 78 %), which is quite low. This is called as\n",
        "Underfitting.\n",
        "\n",
        "Underfitting occurs when say if we have too many categories and the relevant data for the categories is not increased\n",
        "with the increase in categories.\n",
        "To define Underfitting one can simply say, model has not learned patterns on training data well maybe due to\n",
        "insufficient features and low variance.\n",
        "In simpler terms, Underfitting indicates either the data is \"too simple\" or \"lack of data\". This reduces model performance\n",
        "both on training and testing data. Hence, the predictions are not relaible.\n",
        "\n",
        "On the contrary, Overfitting is another concept where the machine learns a lot on training data, but performs poorly on\n",
        "testing data and hence cannot predict the future data well. This is so because, while doing training it learns on details\n",
        "(high variance) and noise so much so that it affects the performance on test data.\n",
        "\n",
        "\n",
        "So, quote on quote one can say:\n",
        "\n",
        "\"Underfitting happens due to high bias and low variance.\"\n",
        "\n",
        "\"Overfitting happens due to low bias and high variance.\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e657b7e",
      "metadata": {
        "id": "8e657b7e"
      },
      "source": [
        "Underfitting can be resolved by increasing the model complexity or by increasing the data.\n",
        "\n",
        "Overfitting can be resolved by increasing the variance in the data. Say, in the above case try increasing the categories\n",
        "to avoid Overfitting.\n",
        "\n",
        "However, when you increase the number of categories, make sure also to increase the complexity of data to avoid\n",
        "Underfitting."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "df7eb49b",
      "metadata": {
        "id": "df7eb49b"
      },
      "source": [
        "# <font color = green>**Code for Overfitting**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ea46a77",
      "metadata": {
        "id": "4ea46a77"
      },
      "source": [
        "To extract fewer categories (only Bream & Smelt) from the Fish_dataset I queried via SQL and updated the dataset."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "6HWUnwKFp87t",
        "outputId": "06ff805e-86c7-44e3-ab7c-2aeb85e97dbb"
      },
      "id": "6HWUnwKFp87t",
      "execution_count": 2,
      "outputs": [
        {
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              "<IPython.core.display.HTML object>"
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              "     <output id=\"result-5041a506-c9dc-45f6-af7a-6b16f3e8ade0\">\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",
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              "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",
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              "\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",
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              "    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",
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              "  const cancel = document.createElement('button');\n",
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              "  cancel.textContent = 'Cancel upload';\n",
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              "    li.appendChild(percent);\n",
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              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
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              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
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              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving KNN_db_SQL_upd_1.jpg to KNN_db_SQL_upd_1.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Image('KNN_db_SQL_upd_1.jpg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "Ehqsmk4nqSFr",
        "outputId": "6eb763a8-52f8-4d6a-a176-d3e56bde5511"
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      "id": "Ehqsmk4nqSFr",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = red>**1. Reading the dataset**</font>"
      ],
      "metadata": {
        "id": "0Lht-JQXqwNT"
      },
      "id": "0Lht-JQXqwNT"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ba504233",
      "metadata": {
        "id": "ba504233"
      },
      "outputs": [],
      "source": [
        "data_new=pd.read_csv(\"fish_dataset_updated.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ae609f08",
      "metadata": {
        "id": "ae609f08",
        "outputId": "27968327-4c5a-406c-ff1e-328c0911e605"
      },
      "outputs": [
        {
          "data": {
            "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>ï»¿Species</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Length1</th>\n",
              "      <th>Length2</th>\n",
              "      <th>Length3</th>\n",
              "      <th>Height</th>\n",
              "      <th>Width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bream</td>\n",
              "      <td>242</td>\n",
              "      <td>23.2</td>\n",
              "      <td>25.4</td>\n",
              "      <td>30.0</td>\n",
              "      <td>11.5200</td>\n",
              "      <td>4.0200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bream</td>\n",
              "      <td>290</td>\n",
              "      <td>24.0</td>\n",
              "      <td>26.3</td>\n",
              "      <td>31.2</td>\n",
              "      <td>12.4800</td>\n",
              "      <td>4.3056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bream</td>\n",
              "      <td>340</td>\n",
              "      <td>23.9</td>\n",
              "      <td>26.5</td>\n",
              "      <td>31.1</td>\n",
              "      <td>12.3778</td>\n",
              "      <td>4.6961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bream</td>\n",
              "      <td>363</td>\n",
              "      <td>26.3</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.5</td>\n",
              "      <td>12.7300</td>\n",
              "      <td>4.4555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bream</td>\n",
              "      <td>430</td>\n",
              "      <td>26.5</td>\n",
              "      <td>29.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>12.4440</td>\n",
              "      <td>5.1340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Bream</td>\n",
              "      <td>450</td>\n",
              "      <td>26.8</td>\n",
              "      <td>29.7</td>\n",
              "      <td>34.7</td>\n",
              "      <td>13.6024</td>\n",
              "      <td>4.9274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bream</td>\n",
              "      <td>500</td>\n",
              "      <td>26.8</td>\n",
              "      <td>29.7</td>\n",
              "      <td>34.5</td>\n",
              "      <td>14.1795</td>\n",
              "      <td>5.2785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Bream</td>\n",
              "      <td>390</td>\n",
              "      <td>27.6</td>\n",
              "      <td>30.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>12.6700</td>\n",
              "      <td>4.6900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Bream</td>\n",
              "      <td>450</td>\n",
              "      <td>27.6</td>\n",
              "      <td>30.0</td>\n",
              "      <td>35.1</td>\n",
              "      <td>14.0049</td>\n",
              "      <td>4.8438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bream</td>\n",
              "      <td>500</td>\n",
              "      <td>28.5</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.2</td>\n",
              "      <td>14.2266</td>\n",
              "      <td>4.9594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Bream</td>\n",
              "      <td>475</td>\n",
              "      <td>28.4</td>\n",
              "      <td>31.0</td>\n",
              "      <td>36.2</td>\n",
              "      <td>14.2628</td>\n",
              "      <td>5.1042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Bream</td>\n",
              "      <td>500</td>\n",
              "      <td>28.7</td>\n",
              "      <td>31.0</td>\n",
              "      <td>36.2</td>\n",
              "      <td>14.3714</td>\n",
              "      <td>4.8146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Bream</td>\n",
              "      <td>500</td>\n",
              "      <td>29.1</td>\n",
              "      <td>31.5</td>\n",
              "      <td>36.4</td>\n",
              "      <td>13.7592</td>\n",
              "      <td>4.3680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Bream</td>\n",
              "      <td>340</td>\n",
              "      <td>29.5</td>\n",
              "      <td>32.0</td>\n",
              "      <td>37.3</td>\n",
              "      <td>13.9129</td>\n",
              "      <td>5.0728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Bream</td>\n",
              "      <td>600</td>\n",
              "      <td>29.4</td>\n",
              "      <td>32.0</td>\n",
              "      <td>37.2</td>\n",
              "      <td>14.9544</td>\n",
              "      <td>5.1708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Bream</td>\n",
              "      <td>600</td>\n",
              "      <td>29.4</td>\n",
              "      <td>32.0</td>\n",
              "      <td>37.2</td>\n",
              "      <td>15.4380</td>\n",
              "      <td>5.5800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Bream</td>\n",
              "      <td>700</td>\n",
              "      <td>30.4</td>\n",
              "      <td>33.0</td>\n",
              "      <td>38.3</td>\n",
              "      <td>14.8604</td>\n",
              "      <td>5.2854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Bream</td>\n",
              "      <td>700</td>\n",
              "      <td>30.4</td>\n",
              "      <td>33.0</td>\n",
              "      <td>38.5</td>\n",
              "      <td>14.9380</td>\n",
              "      <td>5.1975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Bream</td>\n",
              "      <td>610</td>\n",
              "      <td>30.9</td>\n",
              "      <td>33.5</td>\n",
              "      <td>38.6</td>\n",
              "      <td>15.6330</td>\n",
              "      <td>5.1338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bream</td>\n",
              "      <td>650</td>\n",
              "      <td>31.0</td>\n",
              "      <td>33.5</td>\n",
              "      <td>38.7</td>\n",
              "      <td>14.4738</td>\n",
              "      <td>5.7276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Bream</td>\n",
              "      <td>575</td>\n",
              "      <td>31.3</td>\n",
              "      <td>34.0</td>\n",
              "      <td>39.5</td>\n",
              "      <td>15.1285</td>\n",
              "      <td>5.5695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Bream</td>\n",
              "      <td>685</td>\n",
              "      <td>31.4</td>\n",
              "      <td>34.0</td>\n",
              "      <td>39.2</td>\n",
              "      <td>15.9936</td>\n",
              "      <td>5.3704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Bream</td>\n",
              "      <td>620</td>\n",
              "      <td>31.5</td>\n",
              "      <td>34.5</td>\n",
              "      <td>39.7</td>\n",
              "      <td>15.5227</td>\n",
              "      <td>5.2801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Bream</td>\n",
              "      <td>680</td>\n",
              "      <td>31.8</td>\n",
              "      <td>35.0</td>\n",
              "      <td>40.6</td>\n",
              "      <td>15.4686</td>\n",
              "      <td>6.1306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Bream</td>\n",
              "      <td>700</td>\n",
              "      <td>31.9</td>\n",
              "      <td>35.0</td>\n",
              "      <td>40.5</td>\n",
              "      <td>16.2405</td>\n",
              "      <td>5.5890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Bream</td>\n",
              "      <td>725</td>\n",
              "      <td>31.8</td>\n",
              "      <td>35.0</td>\n",
              "      <td>40.9</td>\n",
              "      <td>16.3600</td>\n",
              "      <td>6.0532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Bream</td>\n",
              "      <td>720</td>\n",
              "      <td>32.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>40.6</td>\n",
              "      <td>16.3618</td>\n",
              "      <td>6.0900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Bream</td>\n",
              "      <td>714</td>\n",
              "      <td>32.7</td>\n",
              "      <td>36.0</td>\n",
              "      <td>41.5</td>\n",
              "      <td>16.5170</td>\n",
              "      <td>5.8515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Bream</td>\n",
              "      <td>850</td>\n",
              "      <td>32.8</td>\n",
              "      <td>36.0</td>\n",
              "      <td>41.6</td>\n",
              "      <td>16.8896</td>\n",
              "      <td>6.1984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Bream</td>\n",
              "      <td>1000</td>\n",
              "      <td>33.5</td>\n",
              "      <td>37.0</td>\n",
              "      <td>42.6</td>\n",
              "      <td>18.9570</td>\n",
              "      <td>6.6030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Bream</td>\n",
              "      <td>920</td>\n",
              "      <td>35.0</td>\n",
              "      <td>38.5</td>\n",
              "      <td>44.1</td>\n",
              "      <td>18.0369</td>\n",
              "      <td>6.3063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Bream</td>\n",
              "      <td>955</td>\n",
              "      <td>35.0</td>\n",
              "      <td>38.5</td>\n",
              "      <td>44.0</td>\n",
              "      <td>18.0840</td>\n",
              "      <td>6.2920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Bream</td>\n",
              "      <td>925</td>\n",
              "      <td>36.2</td>\n",
              "      <td>39.5</td>\n",
              "      <td>45.3</td>\n",
              "      <td>18.7542</td>\n",
              "      <td>6.7497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Bream</td>\n",
              "      <td>975</td>\n",
              "      <td>37.4</td>\n",
              "      <td>41.0</td>\n",
              "      <td>45.9</td>\n",
              "      <td>18.6354</td>\n",
              "      <td>6.7473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Bream</td>\n",
              "      <td>950</td>\n",
              "      <td>38.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>46.5</td>\n",
              "      <td>17.6235</td>\n",
              "      <td>6.3705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>9.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>1.7388</td>\n",
              "      <td>1.0476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>8</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.5</td>\n",
              "      <td>11.6</td>\n",
              "      <td>1.9720</td>\n",
              "      <td>1.1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>7</td>\n",
              "      <td>10.1</td>\n",
              "      <td>10.6</td>\n",
              "      <td>11.6</td>\n",
              "      <td>1.7284</td>\n",
              "      <td>1.1484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>10</td>\n",
              "      <td>10.4</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2.1960</td>\n",
              "      <td>1.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>10</td>\n",
              "      <td>10.7</td>\n",
              "      <td>11.2</td>\n",
              "      <td>12.4</td>\n",
              "      <td>2.0832</td>\n",
              "      <td>1.2772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>9</td>\n",
              "      <td>10.8</td>\n",
              "      <td>11.3</td>\n",
              "      <td>12.6</td>\n",
              "      <td>1.9782</td>\n",
              "      <td>1.2852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>10</td>\n",
              "      <td>11.3</td>\n",
              "      <td>11.8</td>\n",
              "      <td>13.1</td>\n",
              "      <td>2.2139</td>\n",
              "      <td>1.2838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>10</td>\n",
              "      <td>11.3</td>\n",
              "      <td>11.8</td>\n",
              "      <td>13.1</td>\n",
              "      <td>2.2139</td>\n",
              "      <td>1.1659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>10</td>\n",
              "      <td>11.4</td>\n",
              "      <td>12.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>2.2044</td>\n",
              "      <td>1.1484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>12</td>\n",
              "      <td>11.5</td>\n",
              "      <td>12.2</td>\n",
              "      <td>13.4</td>\n",
              "      <td>2.0904</td>\n",
              "      <td>1.3936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>13</td>\n",
              "      <td>11.7</td>\n",
              "      <td>12.4</td>\n",
              "      <td>13.5</td>\n",
              "      <td>2.4300</td>\n",
              "      <td>1.2690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>12</td>\n",
              "      <td>12.1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.8</td>\n",
              "      <td>2.2770</td>\n",
              "      <td>1.2558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>20</td>\n",
              "      <td>13.2</td>\n",
              "      <td>14.3</td>\n",
              "      <td>15.2</td>\n",
              "      <td>2.8728</td>\n",
              "      <td>2.0672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>20</td>\n",
              "      <td>13.8</td>\n",
              "      <td>15.0</td>\n",
              "      <td>16.2</td>\n",
              "      <td>2.9322</td>\n",
              "      <td>1.8792</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ï»¿Species  Weight  Length1  Length2  Length3   Height   Width\n",
              "0       Bream     242     23.2     25.4     30.0  11.5200  4.0200\n",
              "1       Bream     290     24.0     26.3     31.2  12.4800  4.3056\n",
              "2       Bream     340     23.9     26.5     31.1  12.3778  4.6961\n",
              "3       Bream     363     26.3     29.0     33.5  12.7300  4.4555\n",
              "4       Bream     430     26.5     29.0     34.0  12.4440  5.1340\n",
              "5       Bream     450     26.8     29.7     34.7  13.6024  4.9274\n",
              "6       Bream     500     26.8     29.7     34.5  14.1795  5.2785\n",
              "7       Bream     390     27.6     30.0     35.0  12.6700  4.6900\n",
              "8       Bream     450     27.6     30.0     35.1  14.0049  4.8438\n",
              "9       Bream     500     28.5     30.7     36.2  14.2266  4.9594\n",
              "10      Bream     475     28.4     31.0     36.2  14.2628  5.1042\n",
              "11      Bream     500     28.7     31.0     36.2  14.3714  4.8146\n",
              "12      Bream     500     29.1     31.5     36.4  13.7592  4.3680\n",
              "13      Bream     340     29.5     32.0     37.3  13.9129  5.0728\n",
              "14      Bream     600     29.4     32.0     37.2  14.9544  5.1708\n",
              "15      Bream     600     29.4     32.0     37.2  15.4380  5.5800\n",
              "16      Bream     700     30.4     33.0     38.3  14.8604  5.2854\n",
              "17      Bream     700     30.4     33.0     38.5  14.9380  5.1975\n",
              "18      Bream     610     30.9     33.5     38.6  15.6330  5.1338\n",
              "19      Bream     650     31.0     33.5     38.7  14.4738  5.7276\n",
              "20      Bream     575     31.3     34.0     39.5  15.1285  5.5695\n",
              "21      Bream     685     31.4     34.0     39.2  15.9936  5.3704\n",
              "22      Bream     620     31.5     34.5     39.7  15.5227  5.2801\n",
              "23      Bream     680     31.8     35.0     40.6  15.4686  6.1306\n",
              "24      Bream     700     31.9     35.0     40.5  16.2405  5.5890\n",
              "25      Bream     725     31.8     35.0     40.9  16.3600  6.0532\n",
              "26      Bream     720     32.0     35.0     40.6  16.3618  6.0900\n",
              "27      Bream     714     32.7     36.0     41.5  16.5170  5.8515\n",
              "28      Bream     850     32.8     36.0     41.6  16.8896  6.1984\n",
              "29      Bream    1000     33.5     37.0     42.6  18.9570  6.6030\n",
              "30      Bream     920     35.0     38.5     44.1  18.0369  6.3063\n",
              "31      Bream     955     35.0     38.5     44.0  18.0840  6.2920\n",
              "32      Bream     925     36.2     39.5     45.3  18.7542  6.7497\n",
              "33      Bream     975     37.4     41.0     45.9  18.6354  6.7473\n",
              "34      Bream     950     38.0     41.0     46.5  17.6235  6.3705\n",
              "35      Smelt       7      9.3      9.8     10.8   1.7388  1.0476\n",
              "36      Smelt       8     10.0     10.5     11.6   1.9720  1.1600\n",
              "37      Smelt       7     10.1     10.6     11.6   1.7284  1.1484\n",
              "38      Smelt      10     10.4     11.0     12.0   2.1960  1.3800\n",
              "39      Smelt      10     10.7     11.2     12.4   2.0832  1.2772\n",
              "40      Smelt       9     10.8     11.3     12.6   1.9782  1.2852\n",
              "41      Smelt      10     11.3     11.8     13.1   2.2139  1.2838\n",
              "42      Smelt      10     11.3     11.8     13.1   2.2139  1.1659\n",
              "43      Smelt      10     11.4     12.0     13.2   2.2044  1.1484\n",
              "44      Smelt      12     11.5     12.2     13.4   2.0904  1.3936\n",
              "45      Smelt      13     11.7     12.4     13.5   2.4300  1.2690\n",
              "46      Smelt      12     12.1     13.0     13.8   2.2770  1.2558\n",
              "47      Smelt      20     13.2     14.3     15.2   2.8728  2.0672\n",
              "48      Smelt      20     13.8     15.0     16.2   2.9322  1.8792"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_new"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5c5a0200",
      "metadata": {
        "id": "5c5a0200",
        "outputId": "1a775c6b-4357-4ad7-cf42-b98735d0ed6c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Bream', 'Smelt'], dtype=object)"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_new['ï»¿Species'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = red>**2. Null value check**</font>"
      ],
      "metadata": {
        "id": "IdDm8bmRq5xo"
      },
      "id": "IdDm8bmRq5xo"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "344a02d7",
      "metadata": {
        "id": "344a02d7",
        "outputId": "06f34732-cbfa-4b5a-f25a-0c83ba284cc1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ï»¿Species    0\n",
              "Weight        0\n",
              "Length1       0\n",
              "Length2       0\n",
              "Length3       0\n",
              "Height        0\n",
              "Width         0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_new.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = red>**3. Unique value check**</font>"
      ],
      "metadata": {
        "id": "rm3RHE5brBjN"
      },
      "id": "rm3RHE5brBjN"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "30f8e1a7",
      "metadata": {
        "id": "30f8e1a7",
        "outputId": "4f1ee945-3d55-4403-f53d-50cc05ea0809"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique values for coulmn: ï»¿Species\n",
            "\n",
            "['Bream' 'Smelt']\n",
            "\n",
            "Unique values for coulmn: Weight\n",
            "\n",
            "[ 242  290  340  363  430  450  500  390  475  600  700  610  650  575\n",
            "  685  620  680  725  720  714  850 1000  920  955  925  975  950    7\n",
            "    8   10    9   12   13   20]\n",
            "\n",
            "Unique values for coulmn: Length1\n",
            "\n",
            "[23.2 24.  23.9 26.3 26.5 26.8 27.6 28.5 28.4 28.7 29.1 29.5 29.4 30.4\n",
            " 30.9 31.  31.3 31.4 31.5 31.8 31.9 32.  32.7 32.8 33.5 35.  36.2 37.4\n",
            " 38.   9.3 10.  10.1 10.4 10.7 10.8 11.3 11.4 11.5 11.7 12.1 13.2 13.8]\n",
            "\n",
            "Unique values for coulmn: Length2\n",
            "\n",
            "[25.4 26.3 26.5 29.  29.7 30.  30.7 31.  31.5 32.  33.  33.5 34.  34.5\n",
            " 35.  36.  37.  38.5 39.5 41.   9.8 10.5 10.6 11.  11.2 11.3 11.8 12.\n",
            " 12.2 12.4 13.  14.3 15. ]\n",
            "\n",
            "Unique values for coulmn: Length3\n",
            "\n",
            "[30.  31.2 31.1 33.5 34.  34.7 34.5 35.  35.1 36.2 36.4 37.3 37.2 38.3\n",
            " 38.5 38.6 38.7 39.5 39.2 39.7 40.6 40.5 40.9 41.5 41.6 42.6 44.1 44.\n",
            " 45.3 45.9 46.5 10.8 11.6 12.  12.4 12.6 13.1 13.2 13.4 13.5 13.8 15.2\n",
            " 16.2]\n",
            "\n",
            "Unique values for coulmn: Height\n",
            "\n",
            "[11.52   12.48   12.3778 12.73   12.444  13.6024 14.1795 12.67   14.0049\n",
            " 14.2266 14.2628 14.3714 13.7592 13.9129 14.9544 15.438  14.8604 14.938\n",
            " 15.633  14.4738 15.1285 15.9936 15.5227 15.4686 16.2405 16.36   16.3618\n",
            " 16.517  16.8896 18.957  18.0369 18.084  18.7542 18.6354 17.6235  1.7388\n",
            "  1.972   1.7284  2.196   2.0832  1.9782  2.2139  2.2044  2.0904  2.43\n",
            "  2.277   2.8728  2.9322]\n",
            "\n",
            "Unique values for coulmn: Width\n",
            "\n",
            "[4.02   4.3056 4.6961 4.4555 5.134  4.9274 5.2785 4.69   4.8438 4.9594\n",
            " 5.1042 4.8146 4.368  5.0728 5.1708 5.58   5.2854 5.1975 5.1338 5.7276\n",
            " 5.5695 5.3704 5.2801 6.1306 5.589  6.0532 6.09   5.8515 6.1984 6.603\n",
            " 6.3063 6.292  6.7497 6.7473 6.3705 1.0476 1.16   1.1484 1.38   1.2772\n",
            " 1.2852 1.2838 1.1659 1.3936 1.269  1.2558 2.0672 1.8792]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for i in data_new.columns:\n",
        "    print(f\"Unique values for coulmn: {i}\\n\\n{data_new[i].unique()}\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c611301",
      "metadata": {
        "id": "9c611301",
        "outputId": "80ca6a44-d2c2-4fcb-cad0-ea4ecbe3f458"
      },
      "outputs": [
        {
          "data": {
            "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>ï»¿Species</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Length1</th>\n",
              "      <th>Length2</th>\n",
              "      <th>Length3</th>\n",
              "      <th>Height</th>\n",
              "      <th>Width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bream</td>\n",
              "      <td>242</td>\n",
              "      <td>23.2</td>\n",
              "      <td>25.4</td>\n",
              "      <td>30.0</td>\n",
              "      <td>11.5200</td>\n",
              "      <td>4.0200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bream</td>\n",
              "      <td>290</td>\n",
              "      <td>24.0</td>\n",
              "      <td>26.3</td>\n",
              "      <td>31.2</td>\n",
              "      <td>12.4800</td>\n",
              "      <td>4.3056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bream</td>\n",
              "      <td>340</td>\n",
              "      <td>23.9</td>\n",
              "      <td>26.5</td>\n",
              "      <td>31.1</td>\n",
              "      <td>12.3778</td>\n",
              "      <td>4.6961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bream</td>\n",
              "      <td>363</td>\n",
              "      <td>26.3</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.5</td>\n",
              "      <td>12.7300</td>\n",
              "      <td>4.4555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bream</td>\n",
              "      <td>430</td>\n",
              "      <td>26.5</td>\n",
              "      <td>29.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>12.4440</td>\n",
              "      <td>5.1340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Bream</td>\n",
              "      <td>450</td>\n",
              "      <td>26.8</td>\n",
              "      <td>29.7</td>\n",
              "      <td>34.7</td>\n",
              "      <td>13.6024</td>\n",
              "      <td>4.9274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bream</td>\n",
              "      <td>500</td>\n",
              "      <td>26.8</td>\n",
              "      <td>29.7</td>\n",
              "      <td>34.5</td>\n",
              "      <td>14.1795</td>\n",
              "      <td>5.2785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Bream</td>\n",
              "      <td>390</td>\n",
              "      <td>27.6</td>\n",
              "      <td>30.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>12.6700</td>\n",
              "      <td>4.6900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Bream</td>\n",
              "      <td>450</td>\n",
              "      <td>27.6</td>\n",
              "      <td>30.0</td>\n",
              "      <td>35.1</td>\n",
              "      <td>14.0049</td>\n",
              "      <td>4.8438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bream</td>\n",
              "      <td>500</td>\n",
              "      <td>28.5</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.2</td>\n",
              "      <td>14.2266</td>\n",
              "      <td>4.9594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Bream</td>\n",
              "      <td>475</td>\n",
              "      <td>28.4</td>\n",
              "      <td>31.0</td>\n",
              "      <td>36.2</td>\n",
              "      <td>14.2628</td>\n",
              "      <td>5.1042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Bream</td>\n",
              "      <td>500</td>\n",
              "      <td>28.7</td>\n",
              "      <td>31.0</td>\n",
              "      <td>36.2</td>\n",
              "      <td>14.3714</td>\n",
              "      <td>4.8146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Bream</td>\n",
              "      <td>500</td>\n",
              "      <td>29.1</td>\n",
              "      <td>31.5</td>\n",
              "      <td>36.4</td>\n",
              "      <td>13.7592</td>\n",
              "      <td>4.3680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Bream</td>\n",
              "      <td>340</td>\n",
              "      <td>29.5</td>\n",
              "      <td>32.0</td>\n",
              "      <td>37.3</td>\n",
              "      <td>13.9129</td>\n",
              "      <td>5.0728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Bream</td>\n",
              "      <td>600</td>\n",
              "      <td>29.4</td>\n",
              "      <td>32.0</td>\n",
              "      <td>37.2</td>\n",
              "      <td>14.9544</td>\n",
              "      <td>5.1708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Bream</td>\n",
              "      <td>600</td>\n",
              "      <td>29.4</td>\n",
              "      <td>32.0</td>\n",
              "      <td>37.2</td>\n",
              "      <td>15.4380</td>\n",
              "      <td>5.5800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Bream</td>\n",
              "      <td>700</td>\n",
              "      <td>30.4</td>\n",
              "      <td>33.0</td>\n",
              "      <td>38.3</td>\n",
              "      <td>14.8604</td>\n",
              "      <td>5.2854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Bream</td>\n",
              "      <td>700</td>\n",
              "      <td>30.4</td>\n",
              "      <td>33.0</td>\n",
              "      <td>38.5</td>\n",
              "      <td>14.9380</td>\n",
              "      <td>5.1975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Bream</td>\n",
              "      <td>610</td>\n",
              "      <td>30.9</td>\n",
              "      <td>33.5</td>\n",
              "      <td>38.6</td>\n",
              "      <td>15.6330</td>\n",
              "      <td>5.1338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bream</td>\n",
              "      <td>650</td>\n",
              "      <td>31.0</td>\n",
              "      <td>33.5</td>\n",
              "      <td>38.7</td>\n",
              "      <td>14.4738</td>\n",
              "      <td>5.7276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Bream</td>\n",
              "      <td>575</td>\n",
              "      <td>31.3</td>\n",
              "      <td>34.0</td>\n",
              "      <td>39.5</td>\n",
              "      <td>15.1285</td>\n",
              "      <td>5.5695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Bream</td>\n",
              "      <td>685</td>\n",
              "      <td>31.4</td>\n",
              "      <td>34.0</td>\n",
              "      <td>39.2</td>\n",
              "      <td>15.9936</td>\n",
              "      <td>5.3704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Bream</td>\n",
              "      <td>620</td>\n",
              "      <td>31.5</td>\n",
              "      <td>34.5</td>\n",
              "      <td>39.7</td>\n",
              "      <td>15.5227</td>\n",
              "      <td>5.2801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Bream</td>\n",
              "      <td>680</td>\n",
              "      <td>31.8</td>\n",
              "      <td>35.0</td>\n",
              "      <td>40.6</td>\n",
              "      <td>15.4686</td>\n",
              "      <td>6.1306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Bream</td>\n",
              "      <td>700</td>\n",
              "      <td>31.9</td>\n",
              "      <td>35.0</td>\n",
              "      <td>40.5</td>\n",
              "      <td>16.2405</td>\n",
              "      <td>5.5890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Bream</td>\n",
              "      <td>725</td>\n",
              "      <td>31.8</td>\n",
              "      <td>35.0</td>\n",
              "      <td>40.9</td>\n",
              "      <td>16.3600</td>\n",
              "      <td>6.0532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Bream</td>\n",
              "      <td>720</td>\n",
              "      <td>32.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>40.6</td>\n",
              "      <td>16.3618</td>\n",
              "      <td>6.0900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Bream</td>\n",
              "      <td>714</td>\n",
              "      <td>32.7</td>\n",
              "      <td>36.0</td>\n",
              "      <td>41.5</td>\n",
              "      <td>16.5170</td>\n",
              "      <td>5.8515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Bream</td>\n",
              "      <td>850</td>\n",
              "      <td>32.8</td>\n",
              "      <td>36.0</td>\n",
              "      <td>41.6</td>\n",
              "      <td>16.8896</td>\n",
              "      <td>6.1984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Bream</td>\n",
              "      <td>1000</td>\n",
              "      <td>33.5</td>\n",
              "      <td>37.0</td>\n",
              "      <td>42.6</td>\n",
              "      <td>18.9570</td>\n",
              "      <td>6.6030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Bream</td>\n",
              "      <td>920</td>\n",
              "      <td>35.0</td>\n",
              "      <td>38.5</td>\n",
              "      <td>44.1</td>\n",
              "      <td>18.0369</td>\n",
              "      <td>6.3063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Bream</td>\n",
              "      <td>955</td>\n",
              "      <td>35.0</td>\n",
              "      <td>38.5</td>\n",
              "      <td>44.0</td>\n",
              "      <td>18.0840</td>\n",
              "      <td>6.2920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Bream</td>\n",
              "      <td>925</td>\n",
              "      <td>36.2</td>\n",
              "      <td>39.5</td>\n",
              "      <td>45.3</td>\n",
              "      <td>18.7542</td>\n",
              "      <td>6.7497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Bream</td>\n",
              "      <td>975</td>\n",
              "      <td>37.4</td>\n",
              "      <td>41.0</td>\n",
              "      <td>45.9</td>\n",
              "      <td>18.6354</td>\n",
              "      <td>6.7473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Bream</td>\n",
              "      <td>950</td>\n",
              "      <td>38.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>46.5</td>\n",
              "      <td>17.6235</td>\n",
              "      <td>6.3705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>7</td>\n",
              "      <td>9.3</td>\n",
              "      <td>9.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>1.7388</td>\n",
              "      <td>1.0476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>8</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.5</td>\n",
              "      <td>11.6</td>\n",
              "      <td>1.9720</td>\n",
              "      <td>1.1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>7</td>\n",
              "      <td>10.1</td>\n",
              "      <td>10.6</td>\n",
              "      <td>11.6</td>\n",
              "      <td>1.7284</td>\n",
              "      <td>1.1484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>10</td>\n",
              "      <td>10.4</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2.1960</td>\n",
              "      <td>1.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>10</td>\n",
              "      <td>10.7</td>\n",
              "      <td>11.2</td>\n",
              "      <td>12.4</td>\n",
              "      <td>2.0832</td>\n",
              "      <td>1.2772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>9</td>\n",
              "      <td>10.8</td>\n",
              "      <td>11.3</td>\n",
              "      <td>12.6</td>\n",
              "      <td>1.9782</td>\n",
              "      <td>1.2852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>10</td>\n",
              "      <td>11.3</td>\n",
              "      <td>11.8</td>\n",
              "      <td>13.1</td>\n",
              "      <td>2.2139</td>\n",
              "      <td>1.2838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>10</td>\n",
              "      <td>11.3</td>\n",
              "      <td>11.8</td>\n",
              "      <td>13.1</td>\n",
              "      <td>2.2139</td>\n",
              "      <td>1.1659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>10</td>\n",
              "      <td>11.4</td>\n",
              "      <td>12.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>2.2044</td>\n",
              "      <td>1.1484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>12</td>\n",
              "      <td>11.5</td>\n",
              "      <td>12.2</td>\n",
              "      <td>13.4</td>\n",
              "      <td>2.0904</td>\n",
              "      <td>1.3936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>13</td>\n",
              "      <td>11.7</td>\n",
              "      <td>12.4</td>\n",
              "      <td>13.5</td>\n",
              "      <td>2.4300</td>\n",
              "      <td>1.2690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>12</td>\n",
              "      <td>12.1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.8</td>\n",
              "      <td>2.2770</td>\n",
              "      <td>1.2558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>20</td>\n",
              "      <td>13.2</td>\n",
              "      <td>14.3</td>\n",
              "      <td>15.2</td>\n",
              "      <td>2.8728</td>\n",
              "      <td>2.0672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Smelt</td>\n",
              "      <td>20</td>\n",
              "      <td>13.8</td>\n",
              "      <td>15.0</td>\n",
              "      <td>16.2</td>\n",
              "      <td>2.9322</td>\n",
              "      <td>1.8792</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ï»¿Species  Weight  Length1  Length2  Length3   Height   Width\n",
              "0       Bream     242     23.2     25.4     30.0  11.5200  4.0200\n",
              "1       Bream     290     24.0     26.3     31.2  12.4800  4.3056\n",
              "2       Bream     340     23.9     26.5     31.1  12.3778  4.6961\n",
              "3       Bream     363     26.3     29.0     33.5  12.7300  4.4555\n",
              "4       Bream     430     26.5     29.0     34.0  12.4440  5.1340\n",
              "5       Bream     450     26.8     29.7     34.7  13.6024  4.9274\n",
              "6       Bream     500     26.8     29.7     34.5  14.1795  5.2785\n",
              "7       Bream     390     27.6     30.0     35.0  12.6700  4.6900\n",
              "8       Bream     450     27.6     30.0     35.1  14.0049  4.8438\n",
              "9       Bream     500     28.5     30.7     36.2  14.2266  4.9594\n",
              "10      Bream     475     28.4     31.0     36.2  14.2628  5.1042\n",
              "11      Bream     500     28.7     31.0     36.2  14.3714  4.8146\n",
              "12      Bream     500     29.1     31.5     36.4  13.7592  4.3680\n",
              "13      Bream     340     29.5     32.0     37.3  13.9129  5.0728\n",
              "14      Bream     600     29.4     32.0     37.2  14.9544  5.1708\n",
              "15      Bream     600     29.4     32.0     37.2  15.4380  5.5800\n",
              "16      Bream     700     30.4     33.0     38.3  14.8604  5.2854\n",
              "17      Bream     700     30.4     33.0     38.5  14.9380  5.1975\n",
              "18      Bream     610     30.9     33.5     38.6  15.6330  5.1338\n",
              "19      Bream     650     31.0     33.5     38.7  14.4738  5.7276\n",
              "20      Bream     575     31.3     34.0     39.5  15.1285  5.5695\n",
              "21      Bream     685     31.4     34.0     39.2  15.9936  5.3704\n",
              "22      Bream     620     31.5     34.5     39.7  15.5227  5.2801\n",
              "23      Bream     680     31.8     35.0     40.6  15.4686  6.1306\n",
              "24      Bream     700     31.9     35.0     40.5  16.2405  5.5890\n",
              "25      Bream     725     31.8     35.0     40.9  16.3600  6.0532\n",
              "26      Bream     720     32.0     35.0     40.6  16.3618  6.0900\n",
              "27      Bream     714     32.7     36.0     41.5  16.5170  5.8515\n",
              "28      Bream     850     32.8     36.0     41.6  16.8896  6.1984\n",
              "29      Bream    1000     33.5     37.0     42.6  18.9570  6.6030\n",
              "30      Bream     920     35.0     38.5     44.1  18.0369  6.3063\n",
              "31      Bream     955     35.0     38.5     44.0  18.0840  6.2920\n",
              "32      Bream     925     36.2     39.5     45.3  18.7542  6.7497\n",
              "33      Bream     975     37.4     41.0     45.9  18.6354  6.7473\n",
              "34      Bream     950     38.0     41.0     46.5  17.6235  6.3705\n",
              "35      Smelt       7      9.3      9.8     10.8   1.7388  1.0476\n",
              "36      Smelt       8     10.0     10.5     11.6   1.9720  1.1600\n",
              "37      Smelt       7     10.1     10.6     11.6   1.7284  1.1484\n",
              "38      Smelt      10     10.4     11.0     12.0   2.1960  1.3800\n",
              "39      Smelt      10     10.7     11.2     12.4   2.0832  1.2772\n",
              "40      Smelt       9     10.8     11.3     12.6   1.9782  1.2852\n",
              "41      Smelt      10     11.3     11.8     13.1   2.2139  1.2838\n",
              "42      Smelt      10     11.3     11.8     13.1   2.2139  1.1659\n",
              "43      Smelt      10     11.4     12.0     13.2   2.2044  1.1484\n",
              "44      Smelt      12     11.5     12.2     13.4   2.0904  1.3936\n",
              "45      Smelt      13     11.7     12.4     13.5   2.4300  1.2690\n",
              "46      Smelt      12     12.1     13.0     13.8   2.2770  1.2558\n",
              "47      Smelt      20     13.2     14.3     15.2   2.8728  2.0672\n",
              "48      Smelt      20     13.8     15.0     16.2   2.9322  1.8792"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_new"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = red>**4. Splitting the values**</font>"
      ],
      "metadata": {
        "id": "_5M72pFxrI90"
      },
      "id": "_5M72pFxrI90"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "52cdfb71",
      "metadata": {
        "id": "52cdfb71"
      },
      "outputs": [],
      "source": [
        "X= data_new.values[:,1:]\n",
        "Y= data_new.values[:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "243bf8ff",
      "metadata": {
        "id": "243bf8ff",
        "outputId": "6873a6ac-e99b-4a5e-fec8-440a1c7baf9d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[242, 23.2, 25.4, 30.0, 11.52, 4.02],\n",
              "       [290, 24.0, 26.3, 31.2, 12.48, 4.3056],\n",
              "       [340, 23.9, 26.5, 31.1, 12.3778, 4.6961],\n",
              "       [363, 26.3, 29.0, 33.5, 12.73, 4.4555],\n",
              "       [430, 26.5, 29.0, 34.0, 12.444, 5.134],\n",
              "       [450, 26.8, 29.7, 34.7, 13.6024, 4.9274],\n",
              "       [500, 26.8, 29.7, 34.5, 14.1795, 5.2785],\n",
              "       [390, 27.6, 30.0, 35.0, 12.67, 4.69],\n",
              "       [450, 27.6, 30.0, 35.1, 14.0049, 4.8438],\n",
              "       [500, 28.5, 30.7, 36.2, 14.2266, 4.9594],\n",
              "       [475, 28.4, 31.0, 36.2, 14.2628, 5.1042],\n",
              "       [500, 28.7, 31.0, 36.2, 14.3714, 4.8146],\n",
              "       [500, 29.1, 31.5, 36.4, 13.7592, 4.368],\n",
              "       [340, 29.5, 32.0, 37.3, 13.9129, 5.0728],\n",
              "       [600, 29.4, 32.0, 37.2, 14.9544, 5.1708],\n",
              "       [600, 29.4, 32.0, 37.2, 15.438, 5.58],\n",
              "       [700, 30.4, 33.0, 38.3, 14.8604, 5.2854],\n",
              "       [700, 30.4, 33.0, 38.5, 14.938, 5.1975],\n",
              "       [610, 30.9, 33.5, 38.6, 15.633, 5.1338],\n",
              "       [650, 31.0, 33.5, 38.7, 14.4738, 5.7276],\n",
              "       [575, 31.3, 34.0, 39.5, 15.1285, 5.5695],\n",
              "       [685, 31.4, 34.0, 39.2, 15.9936, 5.3704],\n",
              "       [620, 31.5, 34.5, 39.7, 15.5227, 5.2801],\n",
              "       [680, 31.8, 35.0, 40.6, 15.4686, 6.1306],\n",
              "       [700, 31.9, 35.0, 40.5, 16.2405, 5.589],\n",
              "       [725, 31.8, 35.0, 40.9, 16.36, 6.0532],\n",
              "       [720, 32.0, 35.0, 40.6, 16.3618, 6.09],\n",
              "       [714, 32.7, 36.0, 41.5, 16.517, 5.8515],\n",
              "       [850, 32.8, 36.0, 41.6, 16.8896, 6.1984],\n",
              "       [1000, 33.5, 37.0, 42.6, 18.957, 6.603],\n",
              "       [920, 35.0, 38.5, 44.1, 18.0369, 6.3063],\n",
              "       [955, 35.0, 38.5, 44.0, 18.084, 6.292],\n",
              "       [925, 36.2, 39.5, 45.3, 18.7542, 6.7497],\n",
              "       [975, 37.4, 41.0, 45.9, 18.6354, 6.7473],\n",
              "       [950, 38.0, 41.0, 46.5, 17.6235, 6.3705],\n",
              "       [7, 9.3, 9.8, 10.8, 1.7388, 1.0476],\n",
              "       [8, 10.0, 10.5, 11.6, 1.972, 1.16],\n",
              "       [7, 10.1, 10.6, 11.6, 1.7284, 1.1484],\n",
              "       [10, 10.4, 11.0, 12.0, 2.196, 1.38],\n",
              "       [10, 10.7, 11.2, 12.4, 2.0832, 1.2772],\n",
              "       [9, 10.8, 11.3, 12.6, 1.9782, 1.2852],\n",
              "       [10, 11.3, 11.8, 13.1, 2.2139, 1.2838],\n",
              "       [10, 11.3, 11.8, 13.1, 2.2139, 1.1659],\n",
              "       [10, 11.4, 12.0, 13.2, 2.2044, 1.1484],\n",
              "       [12, 11.5, 12.2, 13.4, 2.0904, 1.3936],\n",
              "       [13, 11.7, 12.4, 13.5, 2.43, 1.269],\n",
              "       [12, 12.1, 13.0, 13.8, 2.277, 1.2558],\n",
              "       [20, 13.2, 14.3, 15.2, 2.8728, 2.0672],\n",
              "       [20, 13.8, 15.0, 16.2, 2.9322, 1.8792]], dtype=object)"
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1b47ba64",
      "metadata": {
        "id": "1b47ba64",
        "outputId": "9645890c-3e20-4759-d1b4-da2b460767e0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream',\n",
              "       'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream',\n",
              "       'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream',\n",
              "       'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream',\n",
              "       'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream', 'Bream',\n",
              "       'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt',\n",
              "       'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt', 'Smelt'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = red>**5. Scaling the data**</font>"
      ],
      "metadata": {
        "id": "JU1ZzaSArXto"
      },
      "id": "JU1ZzaSArXto"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eaffd707",
      "metadata": {
        "id": "eaffd707"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "scaler.fit(X)\n",
        "X = scaler.transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "20ea6647",
      "metadata": {
        "id": "20ea6647"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "32edb333",
      "metadata": {
        "id": "32edb333",
        "outputId": "8d318e59-cc05-4ed6-b710-8fdf6a69e021"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(39, 6)"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "52774436",
      "metadata": {
        "id": "52774436",
        "outputId": "4902bb37-89d7-4baa-dff5-2cf30681a0cf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(39,)"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = red>**6. Applying KNeighbors classifier**</font>"
      ],
      "metadata": {
        "id": "iFsdCdxmrfxR"
      },
      "id": "iFsdCdxmrfxR"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a90d669a",
      "metadata": {
        "id": "a90d669a"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model_KNN = KNeighborsClassifier(n_neighbors=int(np.sqrt(len(X_train))),metric='euclidean')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67f0f1bd",
      "metadata": {
        "id": "67f0f1bd",
        "outputId": "be16181f-7dbe-4e12-97f3-c936046b8c4d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(metric=&#x27;euclidean&#x27;, n_neighbors=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(metric=&#x27;euclidean&#x27;, n_neighbors=6)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean', n_neighbors=6)"
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_KNN.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = red>**7. Predicting Y_pred on X_test**</font>"
      ],
      "metadata": {
        "id": "LUQizKmXrqHr"
      },
      "id": "LUQizKmXrqHr"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8a7a8e98",
      "metadata": {
        "id": "8a7a8e98",
        "outputId": "f684077f-4c0f-4127-d431-c6136674d31a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[('Smelt', 'Smelt'), ('Bream', 'Bream'), ('Bream', 'Bream'), ('Bream', 'Bream'), ('Bream', 'Bream'), ('Bream', 'Bream'), ('Bream', 'Bream'), ('Bream', 'Bream'), ('Bream', 'Bream'), ('Bream', 'Bream')]\n"
          ]
        }
      ],
      "source": [
        "Y_pred = model_KNN.predict(X_test)\n",
        "print(list(zip(Y_test,Y_pred)))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = red>**8. Evaluation using confusion matrix**</font>"
      ],
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        "id": "lXp-LABjsgPF"
      },
      "id": "lXp-LABjsgPF"
    },
    {
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      "id": "71fae622",
      "metadata": {
        "id": "71fae622",
        "outputId": "805c9c1b-d5ad-49e9-a442-33ce3a14fa66"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[9 0]\n",
            " [0 1]]\n",
            "Classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Bream       1.00      1.00      1.00         9\n",
            "       Smelt       1.00      1.00      1.00         1\n",
            "\n",
            "    accuracy                           1.00        10\n",
            "   macro avg       1.00      1.00      1.00        10\n",
            "weighted avg       1.00      1.00      1.00        10\n",
            "\n",
            "Accuracy of the model: 1.0\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
        "\n",
        "cfm = confusion_matrix(Y_test,Y_pred)\n",
        "print(cfm)\n",
        "\n",
        "print(\"Classification report:\")\n",
        "print(classification_report(Y_test,Y_pred))\n",
        "\n",
        "acc = accuracy_score(Y_test,Y_pred)\n",
        "print(\"Accuracy of the model:\",acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5eb5f2a2",
      "metadata": {
        "id": "5eb5f2a2"
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      "source": [
        "So as one can clearly see here, on reducing the number of categories the accuracy has increased to 100 %.\n",
        "Even the confusion matrix shows 0 misclassified values.\n",
        "This is what we call as \"Overfitting\". Although it does look like it has been trained well for high variance,\n",
        "this data will perform poor on test data."
      ]
    }
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