16352437_Week5 (1).ipynb

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      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHmVDf33l5Jd"
      },
      "source": [
        "***Over View***\n",
        "\n",
        "i) Data Augmentation\n",
        "\n",
        "ii) Machine Learning Modeling:\n",
        "\n",
        ">Feature selection\n",
        "\n",
        ">Missing values\n",
        "\n",
        ">Filtering or normalization\n",
        "\n",
        ">Classification Models\n",
        "\n",
        "> Cross Validation\n",
        "\n",
        ">Evalutaion\n",
        "\n",
        ">Confusion Matrix\n",
        "\n",
        ">Accuracy, precision, recall, f-measure, AUC, etc.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rL7U39EHmq7a"
      },
      "source": [
        "**1) Data Augmentation on Text Data**\n",
        "\n",
        "Data Augmentation is the process that enables us to increase the size of the training data without actually collecting the data. But why do we need more data? The answer is simple — the more data we have, the better the performance of the model.\n",
        "Image data augmentation steps such as flipping, cropping, rotation, blurring, zooming, etc. helped tremendously in computer vision. Also, it is relatively easy to create augmented images but the same is not the case with Text data due to the complexities inherent in the language. We will use two methods, TextAttack (synonym based approach) library and Googletrans (transaltion based approach).\n",
        "\n",
        "\n",
        "TextAttack is a Python framework that can be used for data augmentation in Text. The textattack.Augmenter class provides six methods for data augmentation, WordNetAugmenter, EmbeddingAugmenter, CharSwapAugmenter, EasyDataAugmenter, CheckListAugmenter, and CLAREAugmenter.\n",
        "\n",
        " Googletrans is built on top of Google Translate API. This uses Google Translate Ajax API for language detection and translation.Usage\n",
        "The key parameters to translate() method are:\n",
        ">src: source language. Optional parameter as googletrans will detect it.\n",
        "\n",
        ">dest: destination language. Mandatory parameter.\n",
        "\n",
        ">text: the text to be translated from source language to the destination language. Mandatory parameter.\n",
        "\n",
        "let install the libraries to start the today's ICP\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8dUiOr3l1Hn",
        "outputId": "54f9a4fa-8269-45b5-cf44-a386a1cbca0f"
      },
      "source": [
        "!pip install textattack\n",
        "!pip uninstall googletrans\n",
        "!pip install googletrans==3.1.0a0"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hBuilding wheels for collected packages: pinyin, word2number, docopt, langdetect, pptree, sqlitedict, wikipedia-api, intervaltree\n",
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            "Successfully built pinyin word2number docopt langdetect pptree sqlitedict wikipedia-api intervaltree\n",
            "Installing collected packages: word2number, sqlitedict, pptree, pinyin, docopt, xxhash, terminaltables, semver, segtok, pyarrow, num2words, lru-dict, lemminflect, langdetect, jsonlines, jmespath, intervaltree, ftfy, dill, deprecated, conllu, anytree, wikipedia-api, OpenHowNet, multiprocess, language-tool-python, botocore, bioc, s3transfer, pytorch-revgrad, mpld3, boto3, datasets, bert-score, transformer-smaller-training-vocab, flair, textattack\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed OpenHowNet-2.0 anytree-2.12.1 bert-score-0.3.13 bioc-2.1 boto3-1.35.29 botocore-1.35.29 conllu-4.5.3 datasets-3.0.1 deprecated-1.2.14 dill-0.3.8 docopt-0.6.2 flair-0.14.0 ftfy-6.2.3 intervaltree-3.1.0 jmespath-1.0.1 jsonlines-4.0.0 langdetect-1.0.9 language-tool-python-2.8.1 lemminflect-0.2.3 lru-dict-1.3.0 mpld3-0.5.10 multiprocess-0.70.16 num2words-0.5.13 pinyin-0.4.0 pptree-3.1 pyarrow-17.0.0 pytorch-revgrad-0.2.0 s3transfer-0.10.2 segtok-1.5.11 semver-3.0.2 sqlitedict-2.1.0 terminaltables-3.1.10 textattack-0.3.10 transformer-smaller-training-vocab-0.4.0 wikipedia-api-0.7.1 word2number-1.1 xxhash-3.5.0\n",
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            "\u001b[?25hDownloading rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)\n",
            "Downloading hstspreload-2024.9.1-py3-none-any.whl (1.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m33.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading h11-0.9.0-py2.py3-none-any.whl (53 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.6/53.6 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading hpack-3.0.0-py2.py3-none-any.whl (38 kB)\n",
            "Downloading hyperframe-5.2.0-py2.py3-none-any.whl (12 kB)\n",
            "Building wheels for collected packages: googletrans\n",
            "  Building wheel for googletrans (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for googletrans: filename=googletrans-3.1.0a0-py3-none-any.whl size=16353 sha256=7305b331bf20ed6acf300b1de37a30162168cd00c3162c5aa1fe337c0748c5ff\n",
            "  Stored in directory: /root/.cache/pip/wheels/50/5d/3c/8477d0af4ca2b8b1308812c09f1930863caeebc762fe265a95\n",
            "Successfully built googletrans\n",
            "Installing collected packages: rfc3986, hyperframe, hpack, h11, chardet, idna, hstspreload, h2, httpcore, httpx, googletrans\n",
            "  Attempting uninstall: chardet\n",
            "    Found existing installation: chardet 5.2.0\n",
            "    Uninstalling chardet-5.2.0:\n",
            "      Successfully uninstalled chardet-5.2.0\n",
            "  Attempting uninstall: idna\n",
            "    Found existing installation: idna 3.10\n",
            "    Uninstalling idna-3.10:\n",
            "      Successfully uninstalled idna-3.10\n",
            "Successfully installed chardet-3.0.4 googletrans-3.1.0a0 h11-0.9.0 h2-3.2.0 hpack-3.0.0 hstspreload-2024.9.1 httpcore-0.9.1 httpx-0.13.3 hyperframe-5.2.0 idna-2.10 rfc3986-1.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbWH8e8spWZN",
        "outputId": "4e4cf133-2f61-4863-c1cd-2bec79b3a0d6"
      },
      "source": [
        "from textattack.augmentation import WordNetAugmenter, EmbeddingAugmenter, EasyDataAugmenter, CharSwapAugmenter"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "textattack: Updating TextAttack package dependencies.\n",
            "textattack: Downloading NLTK required packages.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package omw to /root/nltk_data...\n",
            "[nltk_data] Downloading package universal_tagset to /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/universal_tagset.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ol0_qwjopgXD"
      },
      "source": [
        "text = \"Leadership requires two things: a vision of the world that does not yet exist and the ability to communicate it.\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wRhXPFR4prcY",
        "outputId": "b94667f1-69ba-4822-f181-4581469f6821"
      },
      "source": [
        "aug = WordNetAugmenter()\n",
        "aug.augment(text)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Leadership requires two things: a sight of the world that does not yet survive and the ability to communicate it.']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6b_UPtFprke",
        "outputId": "2a57fe99-8db7-43e9-d8f5-4c09a81fdacc"
      },
      "source": [
        "aug = EmbeddingAugmenter()\n",
        "aug.augment(text)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "textattack: Downloading https://textattack.s3.amazonaws.com/word_embeddings/paragramcf.\n",
            "100%|██████████| 481M/481M [00:15<00:00, 30.4MB/s]\n",
            "textattack: Unzipping file /root/.cache/textattack/tmpj2_9a8bn.zip to /root/.cache/textattack/word_embeddings/paragramcf.\n",
            "textattack: Successfully saved word_embeddings/paragramcf to cache.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Leadership requires two things: a vision of the globe that does not yet exist and the abilities to communicate it.']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KxE-LS-YprnV",
        "outputId": "e84109d2-8e20-491d-a170-522a65283176"
      },
      "source": [
        "aug = EasyDataAugmenter()\n",
        "aug.augment(text)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Leadership requires two things: a vision the world that does not yet exist and ability to communicate it.',\n",
              " 'Leadership requires two things: a vision of the populace that does not even exist and the ability to communicate it.',\n",
              " 'Leadership requires two things: a vision of the world that does not yet exist adenine and the ability to communicate it.',\n",
              " 'Leadership requires that things: a vision of the world two does not yet exist and the ability to communicate it.']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kX602BwmprrX",
        "outputId": "931155ef-0231-4667-b528-e0baadfa67f8"
      },
      "source": [
        "aug = CharSwapAugmenter()\n",
        "aug.augment(text)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Leadershi requires wo things: a vision of the world that does not yet exist and the ability to communicate it.']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1-CNSWdqqT-"
      },
      "source": [
        "import googletrans\n",
        "from googletrans import Translator\n",
        "translator = Translator()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyk3ZQ8wruBo",
        "outputId": "8725cc45-4ebd-4435-a0d9-d8b47109385e"
      },
      "source": [
        "print(googletrans.LANGUAGES)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'af': 'afrikaans', 'sq': 'albanian', 'am': 'amharic', 'ar': 'arabic', 'hy': 'armenian', 'az': 'azerbaijani', 'eu': 'basque', 'be': 'belarusian', 'bn': 'bengali', 'bs': 'bosnian', 'bg': 'bulgarian', 'ca': 'catalan', 'ceb': 'cebuano', 'ny': 'chichewa', 'zh-cn': 'chinese (simplified)', 'zh-tw': 'chinese (traditional)', 'co': 'corsican', 'hr': 'croatian', 'cs': 'czech', 'da': 'danish', 'nl': 'dutch', 'en': 'english', 'eo': 'esperanto', 'et': 'estonian', 'tl': 'filipino', 'fi': 'finnish', 'fr': 'french', 'fy': 'frisian', 'gl': 'galician', 'ka': 'georgian', 'de': 'german', 'el': 'greek', 'gu': 'gujarati', 'ht': 'haitian creole', 'ha': 'hausa', 'haw': 'hawaiian', 'iw': 'hebrew', 'he': 'hebrew', 'hi': 'hindi', 'hmn': 'hmong', 'hu': 'hungarian', 'is': 'icelandic', 'ig': 'igbo', 'id': 'indonesian', 'ga': 'irish', 'it': 'italian', 'ja': 'japanese', 'jw': 'javanese', 'kn': 'kannada', 'kk': 'kazakh', 'km': 'khmer', 'ko': 'korean', 'ku': 'kurdish (kurmanji)', 'ky': 'kyrgyz', 'lo': 'lao', 'la': 'latin', 'lv': 'latvian', 'lt': 'lithuanian', 'lb': 'luxembourgish', 'mk': 'macedonian', 'mg': 'malagasy', 'ms': 'malay', 'ml': 'malayalam', 'mt': 'maltese', 'mi': 'maori', 'mr': 'marathi', 'mn': 'mongolian', 'my': 'myanmar (burmese)', 'ne': 'nepali', 'no': 'norwegian', 'or': 'odia', 'ps': 'pashto', 'fa': 'persian', 'pl': 'polish', 'pt': 'portuguese', 'pa': 'punjabi', 'ro': 'romanian', 'ru': 'russian', 'sm': 'samoan', 'gd': 'scots gaelic', 'sr': 'serbian', 'st': 'sesotho', 'sn': 'shona', 'sd': 'sindhi', 'si': 'sinhala', 'sk': 'slovak', 'sl': 'slovenian', 'so': 'somali', 'es': 'spanish', 'su': 'sundanese', 'sw': 'swahili', 'sv': 'swedish', 'tg': 'tajik', 'ta': 'tamil', 'te': 'telugu', 'th': 'thai', 'tr': 'turkish', 'uk': 'ukrainian', 'ur': 'urdu', 'ug': 'uyghur', 'uz': 'uzbek', 'vi': 'vietnamese', 'cy': 'welsh', 'xh': 'xhosa', 'yi': 'yiddish', 'yo': 'yoruba', 'zu': 'zulu'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONIQ6-KRqqaf",
        "outputId": "8289cf77-d734-4039-c9a6-901cf617a5b6"
      },
      "source": [
        "origin_text = \"The role of a leader is not to come up with all the great ideas. The role of a leader is to create an environment in which great ideas can happen\"\n",
        "print(origin_text)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The role of a leader is not to come up with all the great ideas. The role of a leader is to create an environment in which great ideas can happen\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D71Ms-KTqqgW",
        "outputId": "c303d906-253b-4610-f7a0-5768fcf019d7"
      },
      "source": [
        "# translate from English to Italian\n",
        "text_trans = translator.translate(text=origin_text, dest='it').text\n",
        "print(text_trans)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Il ruolo di un leader non è quello di proporre tutte le grandi idee. Il ruolo di un leader è quello di creare un ambiente in cui possano nascere grandi idee\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "NNs8FQFYtFQJ",
        "outputId": "913565ff-33ee-4d37-c0e2-c34c877f7e41"
      },
      "source": [
        "# translate back to English from Italian\n",
        "translator.translate(text=text_trans, dest='en').text"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The role of a leader is not to come up with all the great ideas. The role of a leader is to create an environment where great ideas can arise'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0nDIQG1atXCM",
        "outputId": "97e0cd5a-2916-44ea-e947-233c3cf5700c"
      },
      "source": [
        "# translate from English to Urdu\n",
        "text_trans = translator.translate(text=origin_text, dest='ur').text\n",
        "print(text_trans)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ایک لیڈر کا کردار یہ نہیں ہے کہ تمام عظیم خیالات سامنے آئے۔ لیڈر کا کردار ایک ایسا ماحول پیدا کرنا ہے جس میں عظیم خیالات رونما ہو سکیں\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "4GtVia_0tbC3",
        "outputId": "9a644de8-a7c2-46a4-e732-635a881c9da8"
      },
      "source": [
        "# translate back to English from Urdu\n",
        "translator.translate(text=text_trans, dest='en').text"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"A leader's role is not to come up with all the great ideas. A leader's role is to create an environment in which great ideas can happen\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S595JEkvtjMd"
      },
      "source": [
        "**Lets Start the Machine Learning Part Now**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JlWPov9to2v"
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRvyPUOst8E5"
      },
      "source": [
        "**Data Set**\n",
        "\n",
        "The Pima Indians Diabetes Dataset involves predicting the onset of diabetes within 5 years in Pima Indians given medical details. It is a binary (2-class) classification problem. The Pima Indian Diabetes Dataset, originally from the National Institute of Diabetes and Digestive and Kidney Diseases, contains information of 768 women from a population near Phoenix, Arizona, USA. The outcome tested was Diabetes, 258 tested positive and 500 tested negative. Therefore, there is one target (dependent) variable and the 8 attributes.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "39W6IabBt67v",
        "outputId": "0fa99066-0296-4c66-97a9-7e88136be285"
      },
      "source": [
        "df_pima=pd.read_csv('https://raw.githubusercontent.com/npradaschnor/Pima-Indians-Diabetes-Dataset/master/diabetes.csv')\n",
        "df_pima.head(8)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "5            5      116             74              0        0  25.6   \n",
              "6            3       78             50             32       88  31.0   \n",
              "7           10      115              0              0        0  35.3   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  \n",
              "5                     0.201   30        0  \n",
              "6                     0.248   26        1  \n",
              "7                     0.134   29        0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44052028-5c25-44a5-91ea-7a14b8de426e\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <td>0</td>\n",
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              "      <td>64</td>\n",
              "      <td>0</td>\n",
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              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>78</td>\n",
              "      <td>50</td>\n",
              "      <td>32</td>\n",
              "      <td>88</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.248</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
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              "      const quickchartButtonEl =\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_pima",
              "summary": "{\n  \"name\": \"df_pima\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rbVFz8Ju-99"
      },
      "source": [
        "So you can see 8 different features labeled into the outcomes of 1 and 0 where 1 stands for the observation has diabetes, and 0 denotes the observation does not have diabetes\n",
        "\n",
        "lets explore the data more"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-ost9n72u_vj",
        "outputId": "3d9e394f-f01b-47c5-a5aa-fab8361742f9"
      },
      "source": [
        "df_pima.describe()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
              "std      7.884160                  0.331329   11.760232    0.476951  \n",
              "min      0.000000                  0.078000   21.000000    0.000000  \n",
              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ],
            "text/html": [
              "\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
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              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
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              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_pima\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 269.85223453356366,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8450520833333335,\n          3.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 243.73802348295857,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          120.89453125,\n          117.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 252.8525053581062,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          69.10546875,\n          72.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 263.7684730531098,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          20.536458333333332,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 350.26059167945886,\n        \"min\": 0.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          79.79947916666667,\n          127.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.05117817552093,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.992578124999998,\n          32.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3005221658502,\n        \"min\": 0.078,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.47187630208333325,\n          0.3725,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.1941178528413,\n        \"min\": 11.760231540678685,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.240885416666664,\n          29.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3865920388932,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3489583333333333,\n          1.0,\n          0.47695137724279896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vWKmno6wvJNY"
      },
      "source": [
        "Check Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "blvL01B-vFvw",
        "outputId": "c6cabd47-fd32-47aa-eac2-cdeac52ddb3c"
      },
      "source": [
        "df_pima.isnull().sum()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ],
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              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UMR45tIXvMkI"
      },
      "source": [
        "No missing values? Oh wait, but I just saw somebody's Blood Pressure as 0! LOL.\n",
        "\n",
        "The dataset is known to have missing values. Specifically, there are missing observations for some columns that are marked as a zero value. You can deduce this by the definition of those columns, and it is impractical to have a zero value is invalid for those measures, e.g., zero for body mass index or blood pressure is invalid.\n",
        "\n",
        "so lets fix this issue. Have to fill these 0's with NaN. Not changing Pregnancies and Outcome variable because 0 is a valid answer for both of them\n",
        "\n",
        "**Replacing 0's with 'NaN'**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "T9ff4FCJv2ZM",
        "outputId": "fae9fa07-e2dd-422c-a92a-b9067a427161"
      },
      "source": [
        "df_pima['Glucose'] = df_pima['Glucose'].replace(0, np.nan)\n",
        "df_pima['BloodPressure'] = df_pima['BloodPressure'].replace(0, np.nan)\n",
        "df_pima['SkinThickness'] = df_pima['SkinThickness'].replace(0, np.nan)\n",
        "df_pima['Insulin'] = df_pima['Insulin'].replace(0, np.nan)\n",
        "df_pima['BMI'] = df_pima['BMI'].replace(0, np.nan)\n",
        "df_pima['DiabetesPedigreeFunction'] = df_pima['DiabetesPedigreeFunction'].replace(0, np.nan)\n",
        "df_pima['Age'] = df_pima['Age'].replace(0, np.nan)\n",
        "\n",
        "df_pima.head(8)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6    148.0           72.0           35.0      NaN  33.6   \n",
              "1            1     85.0           66.0           29.0      NaN  26.6   \n",
              "2            8    183.0           64.0            NaN      NaN  23.3   \n",
              "3            1     89.0           66.0           23.0     94.0  28.1   \n",
              "4            0    137.0           40.0           35.0    168.0  43.1   \n",
              "5            5    116.0           74.0            NaN      NaN  25.6   \n",
              "6            3     78.0           50.0           32.0     88.0  31.0   \n",
              "7           10    115.0            NaN            NaN      NaN  35.3   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  \n",
              "5                     0.201   30        0  \n",
              "6                     0.248   26        1  \n",
              "7                     0.134   29        0  "
            ],
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              "      <td>74.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>78.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.248</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>115.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-de022b84-0ae6-40a0-b444-dad5dc47863c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_pima",
              "summary": "{\n  \"name\": \"df_pima\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.53564107280403,\n        \"min\": 44.0,\n        \"max\": 199.0,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          135.0,\n          134.0,\n          86.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.38215821010526,\n        \"min\": 24.0,\n        \"max\": 122.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          102.0,\n          85.0,\n          86.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.476982369987208,\n        \"min\": 7.0,\n        \"max\": 99.0,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          36.0,\n          12.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 118.77585518724518,\n        \"min\": 14.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 185,\n        \"samples\": [\n          240.0,\n          18.0,\n          274.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.924988332105903,\n        \"min\": 18.2,\n        \"max\": 67.1,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          27.6,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "VlyeD8cPw6IR",
        "outputId": "bfe330f0-c67f-45c1-efb0-1c43600f95ea"
      },
      "source": [
        "df_pima.isnull().sum()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                   0\n",
              "Glucose                       5\n",
              "BloodPressure                35\n",
              "SkinThickness               227\n",
              "Insulin                     374\n",
              "BMI                          11\n",
              "DiabetesPedigreeFunction      0\n",
              "Age                           0\n",
              "Outcome                       0\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pregnancies</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Glucose</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BloodPressure</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SkinThickness</th>\n",
              "      <td>227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Insulin</th>\n",
              "      <td>374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outcome</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHPAamlZxstT"
      },
      "source": [
        "There you go. We have to now deal with these missing values in order to proceed.\n",
        "\n",
        "**Filling Missing Values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "WDP4eOeaxpPB",
        "outputId": "1364baaf-44fe-4c2b-a73d-a70de309e205"
      },
      "source": [
        "df_pima['BMI'].fillna(df_pima['BMI'].median(), inplace=True)\n",
        "df_pima['Glucose'].fillna(df_pima['Glucose'].median(), inplace=True)\n",
        "df_pima['BloodPressure'].fillna(df_pima['BloodPressure'].median(), inplace=True)\n",
        "df_pima['SkinThickness'].fillna(df_pima['SkinThickness'].median(), inplace=True)\n",
        "df_pima['Insulin'].fillna(df_pima['Insulin'].median(), inplace=True)\n",
        "\n",
        "df_pima.describe()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  121.656250      72.386719      29.108073  140.671875   \n",
              "std       3.369578   30.438286      12.096642       8.791221   86.383060   \n",
              "min       0.000000   44.000000      24.000000       7.000000   14.000000   \n",
              "25%       1.000000   99.750000      64.000000      25.000000  121.500000   \n",
              "50%       3.000000  117.000000      72.000000      29.000000  125.000000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    32.455208                  0.471876   33.240885    0.348958  \n",
              "std      6.875177                  0.331329   11.760232    0.476951  \n",
              "min     18.200000                  0.078000   21.000000    0.000000  \n",
              "25%     27.500000                  0.243750   24.000000    0.000000  \n",
              "50%     32.300000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a028d33b-9442-480a-a002-774092a66bff\" class=\"colab-df-container\">\n",
              "    <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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>121.656250</td>\n",
              "      <td>72.386719</td>\n",
              "      <td>29.108073</td>\n",
              "      <td>140.671875</td>\n",
              "      <td>32.455208</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>30.438286</td>\n",
              "      <td>12.096642</td>\n",
              "      <td>8.791221</td>\n",
              "      <td>86.383060</td>\n",
              "      <td>6.875177</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>18.200000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.750000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>121.500000</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>32.300000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a028d33b-9442-480a-a002-774092a66bff')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-a028d33b-9442-480a-a002-774092a66bff button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a028d33b-9442-480a-a002-774092a66bff');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8a782cdc-6977-49ef-af13-80d20421318c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8a782cdc-6977-49ef-af13-80d20421318c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_pima\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 269.85223453356366,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8450520833333335,\n          3.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 239.51168557183297,\n        \"min\": 30.43828582241517,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          121.65625,\n          117.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 251.2764376086552,\n        \"min\": 12.096641733978139,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          72.38671875,\n          72.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 261.4829113155296,\n        \"min\": 7.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.108072916666668,\n          29.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 329.20249788669736,\n        \"min\": 14.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          140.671875,\n          125.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.93916115232287,\n        \"min\": 6.875176818080995,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.45520833333333,\n          32.3,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3005221658502,\n        \"min\": 0.078,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.47187630208333325,\n          0.3725,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.1941178528413,\n        \"min\": 11.760231540678685,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.240885416666664,\n          29.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3865920388932,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3489583333333333,\n          1.0,\n          0.47695137724279896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5L0blSKwyNz3"
      },
      "source": [
        "Missing values are replaced by medians as seen above\n",
        "\n",
        "**Next lets make a corelation plot for feature selection**\n",
        "\n",
        "The importance of feature selection can best be recognized when you are dealing with a dataset that contains a vast number of features. This type of dataset is often referred to as a high dimensional dataset. Now, with this high dimensionality, comes a lot of problems such as - this high dimensionality will significantly increase the training time of your machine learning model, it can make your model very complicated which in turn may lead to Overfitting.\n",
        "Often in a high dimensional feature set, there remain several features which are redundant meaning these features are nothing but extensions of the other essential features. These redundant features do not effectively contribute to the model training as well. So, clearly, there is a need to extract the most important and the most relevant features for a dataset in order to get the most effective predictive modeling performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "Remf-v1vyLvU",
        "outputId": "d1e09b08-af4d-42ac-e168-04c44be704e6"
      },
      "source": [
        "corr = df_pima[df_pima.columns].corr()\n",
        "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "sns.heatmap(corr, cmap=cmap, annot = True)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g67oERrjy8Cr"
      },
      "source": [
        "**Feature selcetion with SelectKBest**\n",
        "\n",
        "Let's convert the DataFrame object to a NumPy array to achieve faster computation. Also, let's segregate the data into separate variables so that the features and the labels are separated, this will help in feature selection.The scikit-learn library provides the SelectKBest class that can be used with a suite of different statistical tests to select a specific number of features, in this case, it is Chi-Squared. First,we will implement a Chi-Squared statistical test for non-negative features to select 4 of the best features from the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1h4ZKVl3zuvf"
      },
      "source": [
        "array = df_pima.values\n",
        "X = array[:,0:8]\n",
        "Y = array[:,8]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pm9dzmUve_G7",
        "outputId": "cfdc98f1-a40d-4a67-ccbb-5978644ddd0e"
      },
      "source": [
        "Y"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       0., 0., 0., 0., 1., 1., 0., 0., 0., 1., 0., 1., 1., 0., 0., 1., 0.,\n",
              "       0., 1., 1., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
              "       1., 1., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 0., 0., 1., 0.,\n",
              "       1., 1., 1., 0., 0., 1., 1., 1., 0., 1., 0., 1., 0., 1., 0., 0., 0.,\n",
              "       0., 1., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LN4WZZ0YyfWr",
        "outputId": "6d3584f7-4bb7-45c0-f12c-f4023ec09d52"
      },
      "source": [
        "# Import the necessary libraries first\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2\n",
        "\n",
        "# Feature extraction\n",
        "test = SelectKBest(score_func=chi2, k=5)\n",
        "fit = test.fit(X, Y)\n",
        "\n",
        "# Summarize scores\n",
        "np.set_printoptions(precision=3)\n",
        "print(fit.scores_)\n",
        "\n",
        "\n",
        "features = fit.transform(X)\n",
        "# Summarize selected features\n",
        "print(features[0:5,:])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 111.52  1418.442   42.583   94.025 1689.711  108.766    5.393  181.304]\n",
            "[[  6.  148.  125.   33.6  50. ]\n",
            " [  1.   85.  125.   26.6  31. ]\n",
            " [  8.  183.  125.   23.3  32. ]\n",
            " [  1.   89.   94.   28.1  21. ]\n",
            " [  0.  137.  168.   43.1  33. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WK9HMx6sz4y9"
      },
      "source": [
        "Interpretation:\n",
        "You can see the scores for each attribute and the 4 attributes chosen (those with the highest scores): Pregnancies, Glucose, Insulin, and age. This scores will help you further in determining the best features for training your model. This scores will help you further in determining the best features for training your model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "j2EVRsn6z6q1",
        "outputId": "de15217e-e80c-4c18-dec2-7a28c76ab240"
      },
      "source": [
        "df_pima"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6    148.0           72.0           35.0    125.0  33.6   \n",
              "1              1     85.0           66.0           29.0    125.0  26.6   \n",
              "2              8    183.0           64.0           29.0    125.0  23.3   \n",
              "3              1     89.0           66.0           23.0     94.0  28.1   \n",
              "4              0    137.0           40.0           35.0    168.0  43.1   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
              "763           10    101.0           76.0           48.0    180.0  32.9   \n",
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              "767            1     93.0           70.0           31.0    125.0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                       0.627   50        1  \n",
              "1                       0.351   31        0  \n",
              "2                       0.672   32        1  \n",
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              "4                       2.288   33        1  \n",
              "..                        ...  ...      ...  \n",
              "763                     0.171   63        0  \n",
              "764                     0.340   27        0  \n",
              "765                     0.245   30        0  \n",
              "766                     0.349   47        1  \n",
              "767                     0.315   23        0  \n",
              "\n",
              "[768 rows x 9 columns]"
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            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epGSMp-h1vc_"
      },
      "source": [
        "Based on this analysis lets reduce our features"
      ]
    },
    {
      "cell_type": "code",
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        "id": "UAkb8c9N1v2h",
        "outputId": "e555f1a9-48d9-4ba3-9b0a-50772cd742b2"
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      "source": [
        "X_features = pd.DataFrame(data = df_pima, columns = [\"Pregnancies\",\"Glucose\",\"Insulin\",\"BMI\",\"Age\"])\n",
        "X_graph=X_features.copy()\n",
        "X_features.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  Insulin   BMI  Age\n",
              "0            6    148.0    125.0  33.6   50\n",
              "1            1     85.0    125.0  26.6   31\n",
              "2            8    183.0    125.0  23.3   32\n",
              "3            1     89.0     94.0  28.1   21\n",
              "4            0    137.0    168.0  43.1   33"
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            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 178
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        "id": "bHPn-P2m2Lfa",
        "outputId": "745a2b5c-0efc-4227-dfb4-fb2336615e23"
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      "source": [
        "Y = df_pima.iloc[:,8]\n",
        "Y.head(3)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "0    1\n",
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      "source": [
        "**Data Normalization/Scaling**\n",
        "\n",
        "What is Feature Scaling?\n",
        "\n",
        "It refers to putting the values in the same range or same scale so that no variable is dominated by the other.\n",
        "\n",
        "Why Scaling?\n",
        "\n",
        "Most of the times, your dataset will contain features highly varying in magnitudes, units and range. But since, most of the machine learning algorithms use Euclidean distance between two data points in their computations, this is a problem.If left alone, these algorithms only take in the magnitude of features neglecting the units. The results would vary greatly between different units, 5kg and 5000gms. The features with high magnitudes will weigh in a lot more in the distance calculations than features with low magnitudes. To suppress this effect, we need to bring all features to the same level of magnitudes. This can be achieved by scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JA6lC8b23d0",
        "outputId": "63a9e03f-07cb-4266-e06b-0aaac2ee21cc"
      },
      "source": [
        "#Standard Scaling\n",
        "scaler = StandardScaler()\n",
        "X_features = scaler.fit_transform(X_features)\n",
        "X_features"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.64 ,  0.866, -0.182,  0.167,  1.426],\n",
              "       [-0.845, -1.205, -0.182, -0.852, -0.191],\n",
              "       [ 1.234,  2.017, -0.182, -1.333, -0.106],\n",
              "       ...,\n",
              "       [ 0.343, -0.022, -0.332, -0.91 , -0.276],\n",
              "       [-0.845,  0.143, -0.182, -0.343,  1.171],\n",
              "       [-0.845, -0.942, -0.182, -0.299, -0.871]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHr1pKNk3IPv"
      },
      "source": [
        "Split Data: Training & Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3qKTpVu3JRn"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X_features, Y, test_size=0.20, random_state=10)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZtOu4E-63TJ2",
        "outputId": "66f029c6-44b4-4564-ca65-41e80140dba5"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "models = []\n",
        "\n",
        "models.append((\"Logistic Regression:\",LogisticRegression()))\n",
        "models.append((\"Support Vector Machine-linear:\",SVC(kernel=\"linear\",C=0.2)))\n",
        "models.append((\"Support Vector Machine-rbf:\",SVC(kernel=\"rbf\")))\n",
        "models.append((\"Random Forest:\",RandomForestClassifier(n_estimators=5)))\n",
        "models.append((\"eXtreme Gradient Boost:\",XGBClassifier()))\n",
        "\n",
        "print('Models appended...')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Models appended...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yIO1UIIQ3ng0",
        "outputId": "a76d7e50-937a-4ef7-eb60-1134a9e5df0b"
      },
      "source": [
        "results = []\n",
        "names = []\n",
        "for name,model in models:\n",
        "    kfold = KFold(n_splits=5, random_state=3, shuffle = True)\n",
        "    cv_result = cross_val_score(model,X_train,Y_train, cv = kfold,scoring = \"accuracy\")\n",
        "    names.append(name)\n",
        "    results.append(cv_result)\n",
        "for i in range(len(names)):\n",
        "    print(names[i],results[i].mean()*100)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression: 76.21618019458882\n",
            "Support Vector Machine-linear: 76.86791949886714\n",
            "Support Vector Machine-rbf: 76.21751299480208\n",
            "Random Forest: 76.06690657070506\n",
            "eXtreme Gradient Boost: 71.98320671731308\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTUfetBB8aXf"
      },
      "source": [
        "from sklearn import metrics\n",
        "from IPython.display import Image\n",
        "from sklearn import tree\n",
        "from pydotplus import graph_from_dot_data"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4JkmoVB6U6Q"
      },
      "source": [
        "# Instantiate Decision Tree with a max depth of 3\n",
        "tree_model = DecisionTreeClassifier(max_depth=3)\n",
        "# Fit a decision tree\n",
        "tree_model = tree_model.fit(X_train, Y_train)\n",
        "# Training accuracy\n",
        "tree_model.score(X_train, Y_train)\n",
        "\n",
        "# Predictions/probs on the test dataset\n",
        "predicted = pd.DataFrame(tree_model.predict(X_test))\n",
        "probs = pd.DataFrame(tree_model.predict_proba(X_test))\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Y7j7u_wTk5Oz",
        "outputId": "baffef90-1d0c-4c58-f49b-e24d0ff3b373"
      },
      "source": [
        "probs"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "152  0.523256  0.476744\n",
              "153  0.923077  0.076923\n",
              "\n",
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              "        if (!dataTable) return;\n",
              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "</div>\n",
              "\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('probs')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "        document.querySelector('#id_ebbbac1e-d973-4ed7-89aa-82477275a980 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('probs');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "probs",
              "summary": "{\n  \"name\": \"probs\",\n  \"rows\": 154,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2593264775767782,\n        \"min\": 0.13793103448275862,\n        \"max\": 0.9705882352941176,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.5232558139534884,\n          0.9230769230769231,\n          0.45454545454545453\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2593264775767781,\n        \"min\": 0.029411764705882353,\n        \"max\": 0.8620689655172413,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.47674418604651164,\n          0.07692307692307693,\n          0.5454545454545454\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zef2eTnW7p4R"
      },
      "source": [
        "# Score metrics\n",
        "tree_accuracy = metrics.accuracy_score(Y_test, predicted)\n",
        "tree_roc_auc = metrics.roc_auc_score(Y_test, probs[1])\n",
        "tree_confus_matrix = metrics.confusion_matrix(Y_test, predicted)\n",
        "tree_classification_report = metrics.classification_report(Y_test, predicted)\n",
        "tree_precision = metrics.precision_score(Y_test, predicted, pos_label=1)\n",
        "tree_recall = metrics.recall_score(Y_test, predicted, pos_label=1)\n",
        "tree_f1 = metrics.f1_score(Y_test, predicted, pos_label=1)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jU_PqZzg858I",
        "outputId": "a0cbe276-1f03-4c06-85ad-80906d401d1b"
      },
      "source": [
        "print(\"accuracy: \", tree_accuracy)\n",
        "print(\"AUC: \", tree_roc_auc)\n",
        "print(\"Precision: \",tree_precision)\n",
        "print(\"Recall: \", tree_recall)\n",
        "print(\"F1 Score: \", tree_f1)\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy:  0.6883116883116883\n",
            "AUC:  0.78028545941124\n",
            "Precision:  0.7894736842105263\n",
            "Recall:  0.2542372881355932\n",
            "F1 Score:  0.38461538461538464\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ro24xqtR_DyS",
        "outputId": "61daa56d-f87f-4d09-e052-1b6bdd447b5d"
      },
      "source": [
        "print(tree_classification_report)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.96      0.79        95\n",
            "           1       0.79      0.25      0.38        59\n",
            "\n",
            "    accuracy                           0.69       154\n",
            "   macro avg       0.73      0.61      0.59       154\n",
            "weighted avg       0.72      0.69      0.64       154\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nU60tclP_R4h",
        "outputId": "9cca0eeb-f63a-4f79-beef-b3d3c4937fb5"
      },
      "source": [
        "print(tree_confus_matrix)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[91  4]\n",
            " [44 15]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "-BcKWtXV_HuH",
        "outputId": "11aa1f2b-824d-4e8d-f783-e0834b290475"
      },
      "source": [
        "# Creating the confusion matrix graphs\n",
        "plt.figure(figsize=(10,8))\n",
        "ax=sns.heatmap(tree_confus_matrix,cmap=\"YlGnBu\", annot=True, fmt='d')\n",
        "ax.set(ylabel=\"True Label\", xlabel=\"Predicted Label\")\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AYQS7ZXG7vt_",
        "outputId": "5fd969bc-5258-40de-a0d1-3a039f7c5ad7"
      },
      "source": [
        "# output decision plot\n",
        "dot_data = tree.export_graphviz(tree_model, out_file=None,\n",
        "                     feature_names=X_graph.columns.tolist(),\n",
        "                     class_names=['Dibetic', 'NonDibetic'],\n",
        "                     filled=True, rounded=True,\n",
        "                     special_characters=True)\n",
        "graph = graph_from_dot_data(dot_data)\n",
        "graph.write_png(\"decision_tree.png\")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **I have Fine tunined two hyper parameters of two models(LogisticRegression,SVM) and shown the results along with one additionl model(Random Forest)**"
      ],
      "metadata": {
        "id": "KkYHiZzHsFik"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Load dataset\n",
        "iris = load_iris()\n",
        "X, Y = iris.data, iris.target\n",
        "\n",
        "# Define Models\n",
        "log_reg_model = LogisticRegression(max_iter=200, random_state=40)\n",
        "rf_model = RandomForestClassifier(random_state=40)\n",
        "svm_model = SVC(kernel='linear', random_state=40)\n",
        "\n",
        "# Set Hyperparameter Grids\n",
        "log_reg_param_grid = {\n",
        "    'C': [0.01, 0.1, 1, 10],\n",
        "    'solver': ['liblinear', 'saga']\n",
        "}\n",
        "\n",
        "svm_param_grid = {\n",
        "    'C': [0.01, 0.1, 1, 10],\n",
        "    'max_iter': [100, 200, 300]\n",
        "}\n",
        "\n",
        "rf_param_grid = {\n",
        "    'n_estimators': [60, 150, 250],\n",
        "    'max_depth': [None, 2, 3, 4, 6],\n",
        "    'min_samples_split': [2, 3, 4]\n",
        "}\n",
        "\n",
        "# Perform Hyperparameter Tuning\n",
        "# For Logistic Regression\n",
        "log_reg_grid_search = GridSearchCV(log_reg_model, log_reg_param_grid, cv=5, scoring='accuracy')\n",
        "log_reg_grid_search.fit(X, Y)\n",
        "\n",
        "# For SVM\n",
        "svm_grid_search = GridSearchCV(svm_model, svm_param_grid, cv=5, scoring='accuracy')\n",
        "svm_grid_search.fit(X, Y)\n",
        "\n",
        "# For Random Forest (using a subset of hyperparameters for fine-tuning)\n",
        "rf_grid_search = GridSearchCV(rf_model, rf_param_grid, cv=5, scoring='accuracy')\n",
        "rf_grid_search.fit(X, Y)\n",
        "\n",
        "# Show Results\n",
        "print(\"Best parameters for Logistic Regression:\", log_reg_grid_search.best_params_)\n",
        "print(\"Best cross-validation score for Logistic Regression:\", log_reg_grid_search.best_score_)\n",
        "\n",
        "print(\"\\nBest parameters for SVM:\", svm_grid_search.best_params_)\n",
        "print(\"Best cross-validation score for SVM:\", svm_grid_search.best_score_)\n",
        "\n",
        "print(\"\\nBest parameters for Random Forest:\", rf_grid_search.best_params_)\n",
        "print(\"Best cross-validation score for Random Forest:\", rf_grid_search.best_score_)\n",
        "\n",
        "# Optional: Fit and evaluate best models\n",
        "log_reg_best_model = log_reg_grid_search.best_estimator_\n",
        "svm_best_model = svm_grid_search.best_estimator_\n",
        "rf_best_model = rf_grid_search.best_estimator_\n",
        "\n",
        "# Generate classification report\n",
        "log_reg_predictions = log_reg_best_model.predict(X)\n",
        "svm_predictions = svm_best_model.predict(X)\n",
        "rf_predictions = rf_best_model.predict(X)\n",
        "\n",
        "print(\"\\nClassification Report for Logistic Regression:\\n\", classification_report(Y, log_reg_predictions))\n",
        "print(\"\\nClassification Report for SVM:\\n\", classification_report(Y, svm_predictions))\n",
        "print(\"\\nClassification Report for Random Forest:\\n\", classification_report(Y, rf_predictions))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bA_6z0w_slAC",
        "outputId": "6aea57d7-13a8-47f2-d93b-07c64342427f"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=200).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=200).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=300).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters for Logistic Regression: {'C': 1, 'solver': 'saga'}\n",
            "Best cross-validation score for Logistic Regression: 0.9800000000000001\n",
            "\n",
            "Best parameters for SVM: {'C': 1, 'max_iter': 100}\n",
            "Best cross-validation score for SVM: 0.9800000000000001\n",
            "\n",
            "Best parameters for Random Forest: {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 250}\n",
            "Best cross-validation score for Random Forest: 0.96\n",
            "\n",
            "Classification Report for Logistic Regression:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        50\n",
            "           1       1.00      0.96      0.98        50\n",
            "           2       0.96      1.00      0.98        50\n",
            "\n",
            "    accuracy                           0.99       150\n",
            "   macro avg       0.99      0.99      0.99       150\n",
            "weighted avg       0.99      0.99      0.99       150\n",
            "\n",
            "\n",
            "Classification Report for SVM:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        50\n",
            "           1       1.00      0.98      0.99        50\n",
            "           2       0.98      1.00      0.99        50\n",
            "\n",
            "    accuracy                           0.99       150\n",
            "   macro avg       0.99      0.99      0.99       150\n",
            "weighted avg       0.99      0.99      0.99       150\n",
            "\n",
            "\n",
            "Classification Report for Random Forest:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        50\n",
            "           1       1.00      1.00      1.00        50\n",
            "           2       1.00      1.00      1.00        50\n",
            "\n",
            "    accuracy                           1.00       150\n",
            "   macro avg       1.00      1.00      1.00       150\n",
            "weighted avg       1.00      1.00      1.00       150\n",
            "\n"
          ]
        }
      ]
    }
  ]
}