Final_Project/phase1/Solution/ML_phase1_code.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML_phase1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tq3ht3CDZHDz"
      },
      "source": [
        "**Importing essential libraries and packages**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVBuFOxTRRbf",
        "outputId": "5cd2213f-5a18-4833-f72c-6d59897f534b"
      },
      "source": [
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords') \n",
        "nltk.download('wordnet')\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn as sc\n",
        "from sklearn.model_selection import train_test_split\n",
        "import re, random\n",
        "from nltk import word_tokenize, sent_tokenize, pos_tag\n",
        "from nltk.util import ngrams\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import RegexpTokenizer\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import string\n",
        "import copy\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import pickle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zSJdiPpjv2a7"
      },
      "source": [
        "**Evaluation function**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U44ObvJGuN6A"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "def analysis(labels,predictions):\n",
        "  print(\"\\nClassification Report: \\n\",classification_report(labels,predictions,target_names=['positive','negative']))\n",
        "  print(\"\\nAccuracy: \\n\",accuracy_score(labels,predictions))\n",
        "  conf_mat = confusion_matrix(labels,predictions)\n",
        "  print(\"\\nConfusion Matrix: \\n\",plot_confusion_matrix(conf_mat,colorbar=True,show_absolute=True,show_normed=True))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xExvO6_FZRHM"
      },
      "source": [
        "**Read Csv file and extract comments and sentiments(labels) seperatably into different variables**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTZ8K-J6Wnnl"
      },
      "source": [
        "raw = pd.read_csv('./drive/MyDrive/ml/dataset.csv')\n",
        "raw_comment = raw[['comment']].copy()\n",
        "raw_sentiment = raw[['sentiment']].copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCXzIN4RZhaG"
      },
      "source": [
        "**1.2) Preprocessing Phase** <br>\n",
        "1- converting text to lowercase <br>\n",
        "2- removing numbers from text <br>\n",
        "3- removing punctuations <br>\n",
        "4- removing whitespace <br>\n",
        "5- tokenization <br>\n",
        "*6- removing stop words <br>\n",
        "*7- stemming <br>\n",
        "*8- lemmatization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c279Bf__XCUY"
      },
      "source": [
        "def simple_preproc(raw_commentt):\n",
        "  cm_simple_preproc = copy.deepcopy(raw_commentt)\n",
        "  #1\n",
        "  cm_simple_preproc['comment'] = cm_simple_preproc['comment'].str.lower()\n",
        "  #2\n",
        "  cm_simple_preproc['comment'] = cm_simple_preproc['comment'].str.replace('\\d+', '')\n",
        "  #3\n",
        "  cm_simple_preproc['comment'] = cm_simple_preproc['comment'].str.replace('[^\\w\\s]','')\n",
        "  #4\n",
        "  cm_simple_preproc['comment'] = cm_simple_preproc['comment'].str.strip()\n",
        "  #5\n",
        "  cm_simple_preproc['tokenized'] = cm_simple_preproc.apply(lambda row: word_tokenize(row['comment']), axis=1)\n",
        "  #return result\n",
        "  return cm_simple_preproc\n",
        "\n",
        "def advance_preproc(raw_commentt):\n",
        "  cm_advance_preproc = copy.deepcopy(raw_commentt)\n",
        "  #1\n",
        "  cm_advance_preproc['comment'] = cm_advance_preproc['comment'].str.lower()\n",
        "  #2\n",
        "  cm_advance_preproc['comment'] = cm_advance_preproc['comment'].str.replace('\\d+', '')\n",
        "  #3\n",
        "  cm_advance_preproc['comment'] = cm_advance_preproc['comment'].str.replace('[^\\w\\s]','')\n",
        "  #4\n",
        "  cm_advance_preproc['comment'] = cm_advance_preproc['comment'].str.strip()\n",
        "  #5\n",
        "  cm_advance_preproc['tokenized'] = cm_advance_preproc.apply(lambda row: word_tokenize(row['comment']), axis=1)\n",
        "  #6\n",
        "  stop = stopwords.words('english')\n",
        "  cm_advance_preproc['tokenized'] = cm_advance_preproc['tokenized'].apply(lambda x: [item for item in x if item not in stop])\n",
        "  #7\n",
        "  stemmer= PorterStemmer()\n",
        "  cm_advance_preproc['tokenized_stem'] = cm_advance_preproc['tokenized'].apply(lambda x: [stemmer.stem(y) for y in x])\n",
        "  #8\n",
        "  lemmatizer = nltk.stem.WordNetLemmatizer()\n",
        "  cm_advance_preproc['tokenized'] = cm_advance_preproc['tokenized'].apply(lambda x: [lemmatizer.lemmatize(y) for y in x])\n",
        "  #return result\n",
        "  return cm_advance_preproc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOkqBnEYtsUK"
      },
      "source": [
        "raw_sentiment.replace(\"positive\",1,True)\n",
        "raw_sentiment.replace(\"negative\",0,True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2kN38VYu7bP"
      },
      "source": [
        "**3.1) Applying Preprocess functions to comments and make embedds of them by using BOW method(countvectorizer) and apply learning methods**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AciHWwDp4L9z"
      },
      "source": [
        "preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "di3QajqEve0j"
      },
      "source": [
        "cm_simple_preproc = simple_preproc(raw_comment)\n",
        "cm_advance_preproc = advance_preproc(raw_comment)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CM2ENUEQ4AQG"
      },
      "source": [
        "Y = np.ravel(raw_sentiment)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2dboZNO4QGc"
      },
      "source": [
        "logistic regression on data without preprocessing 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0TKZCTLZrsh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "334981db-9d35-49da-b3bd-17e8d662a077"
      },
      "source": [
        "vectorizer = CountVectorizer(\n",
        "    lowercase=False,\n",
        "    max_features = 1000,\n",
        ")\n",
        "X_none = vectorizer.fit_transform(raw_comment.comment)\n",
        "X_none = X_none.todense()\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_none, Y, test_size=0.2, random_state=1)\n",
        "LR_bow_none_1 = LogisticRegression(solver='saga').fit(X_train,Y_train,sample_weight=None)\n",
        "predict_label = LR_bow_none_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.86      0.85      0.86      4493\n",
            "    negative       0.85      0.87      0.86      4507\n",
            "\n",
            "    accuracy                           0.86      9000\n",
            "   macro avg       0.86      0.86      0.86      9000\n",
            "weighted avg       0.86      0.86      0.86      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8584444444444445\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16ee35ad0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSAtczZ2FuNb"
      },
      "source": [
        "logistic regression on data without preprocessing 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "tz0n4ipBFe_H",
        "outputId": "5c30079c-32de-4d34-c653-f5b2fe38daca"
      },
      "source": [
        "LR_bow_none_2 = LogisticRegression(solver='saga',C=1.1).fit(X_train,Y_train,sample_weight=None)\n",
        "predict_label = LR_bow_none_2.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.87      0.85      0.86      4493\n",
            "    negative       0.85      0.87      0.86      4507\n",
            "\n",
            "    accuracy                           0.86      9000\n",
            "   macro avg       0.86      0.86      0.86      9000\n",
            "weighted avg       0.86      0.86      0.86      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8584444444444445\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16ef34a90>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vuy0N-br4a9j"
      },
      "source": [
        "logistic regression on simple preprocessing data 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3I9ciyyY7Y-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "32402f3f-4b33-4f58-f684-6eabc6d29ffd"
      },
      "source": [
        "def dummy(doc):\n",
        "    return doc\n",
        "\n",
        "cv = CountVectorizer(\n",
        "        tokenizer=dummy,\n",
        "        preprocessor=dummy,\n",
        "        max_features = 1000,\n",
        "    )\n",
        "\n",
        "\n",
        "X_simple = cv.fit_transform(cm_simple_preproc.tokenized)\n",
        "X_simple = X_simple.todense()\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_simple, Y, test_size=0.2, random_state=1)\n",
        "LR_bow_simple_1 = LogisticRegression(solver='saga').fit(X_train,Y_train,sample_weight=None)\n",
        "predict_label = LR_bow_simple_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.86      0.85      0.86      4493\n",
            "    negative       0.85      0.87      0.86      4507\n",
            "\n",
            "    accuracy                           0.86      9000\n",
            "   macro avg       0.86      0.86      0.86      9000\n",
            "weighted avg       0.86      0.86      0.86      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8584444444444445\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb1669a17d0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvrb_M9wGEM3"
      },
      "source": [
        "logistic regression on simple preprocessing data 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "So3BCnGSGFIx",
        "outputId": "6eb34fae-f560-44a3-b3df-fb0c650d8ae9"
      },
      "source": [
        "LR_bow_simple_2 = LogisticRegression(solver='saga',C=0.08).fit(X_train,Y_train,sample_weight=None)\n",
        "predict_label = LR_bow_simple_2.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.86      0.85      0.86      4493\n",
            "    negative       0.85      0.87      0.86      4507\n",
            "\n",
            "    accuracy                           0.86      9000\n",
            "   macro avg       0.86      0.86      0.86      9000\n",
            "weighted avg       0.86      0.86      0.86      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8584444444444445\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16ed8dad0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGjFCqFO4hSK"
      },
      "source": [
        "logistic regression on advance preprocessing data 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "gCu2Z7NCM5Px",
        "outputId": "cfa39beb-0655-4a7b-ab77-608c3e54eda9"
      },
      "source": [
        "X_advance = cv.fit_transform(cm_advance_preproc.tokenized)\n",
        "X_advance = X_advance.todense()\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_advance, Y, test_size=0.2, random_state=1)\n",
        "LR_bow_advance_1 = LogisticRegression(solver='saga').fit(X_train,Y_train,sample_weight=None)\n",
        "predict_label = LR_bow_advance_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.87      0.85      0.86      4493\n",
            "    negative       0.85      0.87      0.86      4507\n",
            "\n",
            "    accuracy                           0.86      9000\n",
            "   macro avg       0.86      0.86      0.86      9000\n",
            "weighted avg       0.86      0.86      0.86      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8592222222222222\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16f4200d0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8OTREVmGMpF"
      },
      "source": [
        "logistic regression on advance preprocessing data 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "f2roXf-0GNh1",
        "outputId": "4370204f-9fc2-4ec1-d093-1f566c4e99af"
      },
      "source": [
        "LR_bow_advance_2 = LogisticRegression(solver='saga',C=0.7).fit(X_train,Y_train,sample_weight=None)\n",
        "predict_label = LR_bow_advance_2.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.87      0.85      0.86      4493\n",
            "    negative       0.85      0.87      0.86      4507\n",
            "\n",
            "    accuracy                           0.86      9000\n",
            "   macro avg       0.86      0.86      0.86      9000\n",
            "weighted avg       0.86      0.86      0.86      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8593333333333333\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16f3d10d0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ykxfKI84mhl"
      },
      "source": [
        "KNN on data without preprocessing 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "0QnFu3j0NRzN",
        "outputId": "23e58bcb-dff9-476d-e58f-c78b282d38a5"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "KNN_bow_none_1 = KNeighborsClassifier()\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_none, Y, test_size=0.2, random_state=1)\n",
        "KNN_bow_none_1.fit(X_train, Y_train)\n",
        "predict_label = KNN_bow_none_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.65      0.51      0.57      4493\n",
            "    negative       0.60      0.73      0.66      4507\n",
            "\n",
            "    accuracy                           0.62      9000\n",
            "   macro avg       0.62      0.62      0.61      9000\n",
            "weighted avg       0.62      0.62      0.61      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.6182222222222222\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16f7f23d0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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28J/rz2TOf5/jwE6HonCAf1p6OmkZmezcvi1uZU9WIm41v2OBi4ATI15fOTncN4w9OzqOA+aGr768DFxlZuvDfb8AngByCGqEFfb0gs/qEpOComIy04Mf6Ia8Aro2q1eyr3FWBl9+t+df/C35RSWfP/56A784tl1U18pI1x5D5FzsMmrXpqggmEii/gHNS4alAWxZt5q2h/UpdfzGVcvZtCaX/1wfdBZuWbua5359Fhf85UXqZTdjyHX3AsFwuSeHD6RRi+/79IoKdpJeq1aibykpxONNFzP7mHJGAJvZpWWkvULQRC7r+JnAYbFc32t+MdheUIwkMtLEwm+30a15fepmplE3M41uzeuz8Ns9g1/DiNpbj9YNy5zRpSyN6mSwbvvOuJU9VdWp34jiomIKd+bTvuexLP10Cju2bmLH1k0s/XQK7XseW+r4Zh26cvWzU7j88clc/vhkGjRtzoUPvkK97Gbs2LqZooLgZzJv4ku07ta7ZGaYvM0byGqYTXpG2Y8+ahof25uCFq3eykFN6/L5mm2MX/QdtwwIxvb+d+F3bC8IankX9mrFh0vWs2zDDn5yeHPaNq6DGazbvpNRs1eV5HXPSV2ok5lGepo4slVDHv5oKau25NMuuw5L1uWVTIzq9k37nv1ZsXAW7Xv0p995VzPqxnMB6HfeL8hq0BiAd/5+B0cMHUaLLuVXHtbnLuath25FiAPaHcTgX91dsm/5vOl06n18Ym8kWUTxPK86SNjY3r1RHcb2tm1chwFdDuDpGYl7ZfHcI1vw2aotfLEm+Z8fVYdOmdWLFzB73DOc9OvEzbs37r5f8cOLbyC7dceEXSMe4jG2t26rg63LFY9EdezcuwYm7dher/nFaPnGHXz53TYEJOrXxsrN+dUi8FUXzTt3p+3hfSkuKiItPf7BuqhgJwf1HZD0gS+eakLNz4PfXvjkm40Jzf/jrzckNP9UdNjAsxKWd3pmLbqdWOloqhol2Z/nRcODn3MuNjXkmZ8HP+dcTIKxvdU/+nnwc87FrAbEPg9+zrnYRTNuN9l58HPOxUbe7HXOpaBd8/lVdx78nHMxSv6ha9Hw4Oeci1kNiH0e/JxzMZJ3eDjnUpC/5+ecS1ke/JxzKakGxD6fzNQ5F7t4TGYqqa2k9yQtlLRA0nVh+h8krShjansk3SopR9IXkoZEpA8N03Ik3RLNPXjNzzkXm/hNbFAI3GhmsyU1AGZJmhjue9DM/lLqslI3grU9ugOtgEmSuoa7/wkMIlizd4akcWa2sKKLe/BzzsVERLcsZWXCVddWhZ+3SFpExYuNnw6MNrN84GtJOcCuRVhyzGwJgKTR4bEVBj9v9jrnYpYmRbUBTSXNjNiGl5WfpA5AT2BamHSNpLmSnpKUHaa1BpZHnJYbppWXXvE9xHC/zjkHxLRu71oz6x2xjdwzL9UnWJXtejPbDDwKdAZ6ENQM/5qIe/Bmr3MuJorjxAaSMgkC3ygzexXAzFZH7H8ceDP8ugJoG3F6mzCNCtLL5TU/51zM0hTdVhEFEfRJYJGZ/S0ivWXEYWcC88PP44BhkmpL6gh0AaYDM4AukjpKqkXQKTKusnvwmp9zLmZxGt52LHARME/SnDDtNuB8ST0I1gj7BrgSwMwWSBpD0JFRCPzSzIoAJF0DvA2kA0+Z2YLKLl5u8JO0he8XKNt1pxZ+NjNrGMNNOudqCBH0+O4rM/sYysxofAXn3APcU0b6+IrOK0u5wc/MGsSSkXMuddSAeQ2ie+Yn6QeSfhZ+bhq2t51zqSjK0R3JPv630md+ku4EegMHA/8GagHPEbTXnXMpKMnjWlSi6fA4k+Dlw9kAZrYyHIrinEtBgl0vMFdr0QS/nWZmkgxAUr0El8k5l+RqwmSm0TzzGyPpX0BjSVcAk4DHE1ss51yyinZ0R7JXDiut+ZnZXyQNAjYDXYHfm9nESk5zztVgqdLsBZgHZBG85zcvccVxzlUH1T/0RdHslXQ5wRCSnwBnA1MlXZbogjnnkldKvOoC/AboaWbrACQdAHwCPJXIgjnnklPQ21vVpdh30QS/dcCWiO9bwjTnXCpSfCYzrWoVje29IfyYA0yTNJbgmd/pwNz9UDbnXJJK9iZtNCqq+e16kXlxuO0yNnHFcc4luxrf7DWzP+7Pgjjnqo+aXvMDQFIz4LcEKybV2ZVuZicmsFzOuSRW/UNfdCM8RgGfAx2BPxJMLjgjgWVyziUxCdLTFNWWzKIJfgeY2ZNAgZl9YGaXAV7rcy6F1YT3/KIJfgXhn6sk/VhST6BJAsvknEty8RjbK6mtpPckLZS0QNJ1YfoDkj4Pl658TVLjML2DpDxJc8LtsYi8ekmaJylH0sOKIvJGE/zultQIuBG4CXgC+HUU5znnaiAR3Zq9UYz/LQRuNLNuQD/gl5K6AROBw8zsCOBL4NaIcxabWY9wuyoi/VHgCoJFjboAQyu7eDQTG+xaNm4T8KPKjnfO1XBxmrHFzFYRrMuLmW2RtAhobWbvRBw2lWBYbfnFCVZ7a2hmU8PvzwJnABMqOq+il5z/zvcLGJVV8GsrynhvtGucxf+d0T3e2boEyj76mqougotB/so1ccknhud5TSXNjPg+spyFyzsQTJo8bbddlwEvRnzvKOlTglmm7jCzj4DWQG7EMblhWoUqqvnNrGCfcy5FCUiPPvitNbPeFeYn1SdYuPx6M9sckX47QdN4VJi0CmhnZusk9QJel7TXtaWKXnJ+Zm8zdc7VbPF6i0VSJkHgG2Vmr0akXwqcAgwwMwMws3wgP/w8S9JigjlGVwBtIrJtE6ZVfA/xuQXnXCpJU3RbRcIe2SeBRWb2t4j0oQQDK04zs+0R6c0kpYefOxF0bCwJnx1ultQvzPNiohiGG+1kps45B+x6jSUuVb9jgYuAeZLmhGm3AQ8DtYGJ4XWmhj27xwF3SSoAioGrzGx9eN4vgKcJJl2eQCWdHeDBzzm3F+LR7DWzjyl7pNz4co5/haCJXNa+mcBhsVw/mpmcu0qaLGl++P0ISXfEchHnXM1SExYwiuaZ3+MELxkWAJjZXGBYIgvlnEteAjKkqLZkFk2zt66ZTd+tjV+YoPI456qBJI9rUYkm+K2V1JnwhWdJZxO+le2cSz2Kbuha0osm+P0SGAkcImkF8DVwYUJL5ZxLajUg9kU1tncJMFBSPSDNzLZUdo5zrmZL8qn6ohLNTM6/3+07AGZ2V4LK5JxLYoKkn6g0GtE0e7dFfK5DMORkUWKK45xLelGM3qgOomn2/jXyu6S/AG8nrETOuaSnGrCKx96M8KhL6UHEzrkUUuOXrtxF0jy+n9cvHWgG+PM+51JYSgQ/gmd8uxQCq83MX3J2LoUl++JE0agw+IXTx7xtZofsp/I455JcsHRlVZdi31V4C2ZWBHwhqd1+Ko9zrhqI0wJGVSqaZm82sEDSdCJeezGz0xJWKudc0kqZDg/gdwkvhXOuWknySl1Uogl+J5vZzZEJku4HPkhMkZxzyU2k1YD3/KJ5bDmojLST4l0Q51z1IGr4ZKaSrg7f8TtY0tyI7Wtg7v4ronMuqQgy0hTVVmE2UltJ70laKGmBpOvC9CaSJkr6KvwzO0yXpIcl5YSx6KiIvC4Jj/9K0iXR3EZFzd7nCRYBuQ+4JSJ9S8SiIc65FLOr5hcHhcCNZjZbUgNglqSJwKXAZDMbIekWgvhzM0GLs0u49QUeBfpKagLcCfQmGJAxS9I4M9tQ0cUrWrd3E7AJOH8fb9A5V8PE4zWWcMnJVeHnLZIWAa2B04ETwsOeAd4nCH6nA8+G6/hOldRYUsvw2Im7KmVhAB0KvFDR9X31NudczGKIfU0lzYz4PtLMRu6ZnzoAPYFpQPMwMAJ8CzQPP7cGlkeclhumlZdeIQ9+zrmYiOh6SkNrzax3hflJ9QmWpLzezDZHDp0zM5Nk5Z68D2rAIBXn3H6l+I3wkJRJEPhGmdmrYfLqsDlL+OeaMH0F0Dbi9DZhWnnpFfLg55yLSTDCY9+Dn4Iq3pPAIjP7W8SuccCuHttLgLER6ReHvb79gE1h8/htYLCk7LBneDBRzDnqzV7nXMzi9ArfscBFwDxJc8K024ARwBhJPweWAueG+8YDJwM5wHbgZwBmtl7Sn4AZ4XF3RfNGigc/51zM4vGqi5l9TPlxdEAZxxvBapJl5fUU8FQs1/fg55yLkWr+fH7OObe7GHt7k5YHP+dczJJ9rr5oePBzzsVGKTCNvXPO7c6bvc65lOU1P+dcSqr+oc+Dn3MuRgLSvebnnEtFNSD2efBzzsVKqAY0fD34Oedi5jU/51zKCV51qf7Rz4PfXrjy8suYMP5Nmh14ILPmzAfg1pt/w/j/vkGtzFp07NyZkU/8m8aNGzN50kR+d9st7Ny5k1q1anHv/Q9wwo9OBGD2rFkM//ml5O3IY8jQk/nrgw/ViFcIklHtWhlMevJ6atXKICM9ndcmfcrdj43n3/dcwlHd2lFQWMTM+Uu55p4XKCwspmH9Ojx19yW0bZlNRno6//fsZP4zbirH9e7Cn286qyTfgzs05+Jb/s0b76fQml7VYGW2aNSEdxX3u4suuZSxb75VKm3AwEHMmjOfGZ/OpUuXrjxw/30AHHBAU15+/Q1mzpnH4089w2WXXlRyzrXXXM0/H3uc+Yu+YnHOV7zzduk8Xfzk7yxk6PCH6XveCPoOu4/B/bvR5/AOjJ4wgyPP/BO9z7mXrDqZ/OzM/gBcee5xfL7kW/qeN4IhVzzEiBvOJDMjnQ9nfkW/YSPoN2wEJw1/mO07djJp6qIqvrv9L16TmVYlD3574Qc/PI4mTZqUShs4aDAZGUFFuk/ffqzIzQWgR8+etGrVCoBu3buzIy+P/Px8Vq1axZYtm+nbrx+SuODCi3lj7Ov790ZSzLa8nQBkZqSTkZGOmfH2xwtL9s+cv5TWB2YDwRJg9evVBqBeVm02bNpOYVFxqfzOHNiTd6YsJG9Hwf65gSQRTGYa3ZbMPPglwLNPP8WQoXuu6/7aq6/Qo+dR1K5dm5UrVtC6dZuSfa3btGHlykpn3nb7IC1NTB19C8smj+DdqZ8zY/7Skn0ZGWmc/+M+TPwkCIaPjf6AQzq2YMk79zDzpdu46YGXCaaT+945Q45izFuz9us9JAtF+V8y82d+cXb/ffeQnpHBsAt+Wip94YIF3HHbzbw5/p0qKpkrLjb6DRtBo/pZvPi3K+jWuSULFweLhD1063lMmZ3DlE8XAzCo/6HM/SKXocMfplPbpvz30WuYct5itmzbAUCLpg3p3qUVE/+3sNzr1WRJ3qKNitf84ug/zzzN+P++ydPPjirVcZGbm8t555zJE089S6fOnQFo1bo1K1bklhyzIjeXVq0qXW3PxcGmrXl8MPNLBvfvBsBtw0+iWXZ9fvvXV0uOuei0fox99zMAlixfyzcr1nFwh+Yl+88adBTj3p1LYWHppnCqiFfNT9JTktZImh+R9qKkOeH2za4p7iV1kJQXse+xiHN6SZonKUfSw4qi5zBhwa+sm6rJ3nn7Lf721z/z8mvjqFu3bkn6xo0b+clpP+ZP94yg/7HHlqS3bNmSBg0aMm3qVMyM5597llNOO70qip4SmmbXp1H9LADq1M5kQN9D+OKb1Vx65jEM6n8oF9/6dKlm7fJvN3BCn4MBOLBJA7p2aM7XK9aW7D93aC/GvDWTVBTnZ35PEywwXsLMzjOzHmbWg2Blt1cjdi/etc/MropIfxS4AugSbqXyLEsim71PA/8Ank3gNarExReez0cfvM/atWvp3KENv/v9H3ngz/eRn5/PKUMHAUGnx98feYzHHvkHixfncN/dd3Hf3XcB8MaEdzjwwAN56O+PMPzyS8nLy2PwkJPKfE7o4qNF04Y8ftdFpKelkZYmXpk4mwkfzWfLjIdYtmo97z9zIwBj353DfSPfYsTjbzHyjxcyY8xtSHD7Q2NZt3EbAO1aNqFNi2w+mpVTlbdUdeLYk2tmH4YLlpdxGYlg8aITKy6OWgINzWxq+P1Z4AxgQoXn7f4QN57Cm3rTzA6L5vhevXrblGmp+du0uso++pqqLoKLQf4XYyjevmafItchh/e0J199N6pjf9C1yVJgbUTSSDMbGXlMeXFC0nHA34c4FFoAAA4+SURBVHYteh4etwD4EtgM3GFmH0nqDYwws4HhcT8EbjazUyoqW5V3eEgaDgwHaNuuXRWXxjlXmV3r9kZp7a7gtRfOB16I+L4KaGdm6yT1Al6X1H0v8676Dg8zG2lmvc2sd7Omzaq6OM65KCjKba/zlzKAnwAv7kozs3wzWxd+ngUsBroCK4A2Eae3CdMqVOXBzzlXDSU6+sFA4HMzK3klQlIzSenh504EHRtLzGwVsFlSv/A54cXA2Mou4MEvRnl5eQw68XiKiop47tlnOOzQLhx2aBeee/aZMo+/9ebfcORhh3B0zyM49+wz2bhxIwAvPD+Kvr16lGx1a6Xx2Zxg0fqThwxkw4YN++2earo6tTN554nrSEsTPz21L/PG/p55Y3/PT0/tW+bx/xnxM6aOvoWpo2/h8//+kamjbwGgd/f2JenTXryF0350BBCMGJn45PWkp6fOP6d4DW+T9ALwP+BgSbmSfh7uGkbpJi/AccDc8NWXl4GrzGx9uO8XwBNADkGNsMLODkhgh0d4UycATYHVwJ1m9mRF51SHDo/HHvknhYWFXHDhRRzbrzdTps5EEv379uKTabPIzs4udfykie9wwo9OJCMjg9tvvRmAe+67v9Qx8+fN49yzz2DhF8ELts89+wwrVuRy862375+b2gfVocPjynOPIyM9jef/O50po37LsT/9M2bGJ8/fTP8L7mfjlrxyzx1xw5ls2prHfSPfIqtOJjsLiigqKqZF04ZMe/FWOg2+naKiYm4bfhJLln/H6AnJ/fc3Hh0ehx7e054d+35Ux/bp3HjWPjzzS6iE/aoys/PNrKWZZZpZm8oCX3Ux+oVRnHra6Ux8520GDBhEkyZNyM7OZsCAQWVOTFDemN9IY158gXPOHVby/cennsaYF3f/pef21rCTe/PG+3MZ1P9QJk/9nA2bt7NxSx6Tp37O4GO7VXjuWYO+H8KWt6OAonB8b+1amaXeC3zj/bmcd/LRibuJZJP4Zm/CpU49PQ527tzJN18voX2HDqxcuYI2bduW7ItmbG55Y35ffulFzj3v/JLv2dnZ5Ofns27duvgVPkVlZqTToXVTlq1aT6tmjcld/f3jhBVrNtKqWeNyzz32qM6sXr+Fxcu+K0k7+rD2zHr5dma+dBvX3jO6JBguyFlJr+6p8bZCENeq/9heD34xWLt2LY0al/+PpSLljfmdPm0adbPq0v2w0q9CNmt2IKtWrtzrsrpA0+z6bNqyfa/OPXdob17abRTHjPlL6XX2Pfzgwj/zm8sGU7tWUKsvLjYKCoqoX7f2Ppc56YXz+UWzJTMPfjHIyspix45gYHurVq3JXb68ZF9FY3PLG/ML8NKY0Zw77Pw9zsnfsYOsrKw4lj415e3YSZ3amQCs/G4jbZp//0y29YGNWfndxjLPS09P4/QTj+Tlt2eXuf+Lr1ezdXs+3Q9qVZJWKzODHTtTY3qrGtDq9eAXi+zsbIqKitixYweDBg9h0qR32LBhAxs2bGDSpHcYNHjIHueUN+YXoLi4mFdeHlPqeR+AmfHt6m9p36FDIm8nJWzckkd6Whq1a2Uw8ZNFDDzmEBo3yKJxgywGHnMIEz8peyLSE/sezJffrGbFmu+DY/tWB5T06LZrmc3BHVuwdGXwaKJJo3qs27g1RSY6EFJ0WzKr8hEe1c3AgYP5ZMrHnDhgILfe9jt+cEzwkPu2239fMsHp1cMv5/LhV9Grd29+fd01ZY75Bfj4ow9p06YtHTt1KnWN2bNm0advv5KOErdvJk1dRP+enXlv2hfc9/hbfPzcbwG4d+RbbNgcNIkf+f0FPPHyx8xeuAyAc4b02mOuvv49O3HTzwZTUFhEcbFx3b0vloz3Pf7oLrz18YL9eFdVK8njWlQSOrY3VtXhVZdPZ8/m7w89yFPP/Cdh17jx19dxyqmn8aMTByTsGvFSHV516XFIG3710xP5+e8SN8fG6L9czh0PjyNn2ZqEXSMe4vGqS/cjjrLn3/wgqmN7tG+Yeq+61FQ9jzqK40/4EUVFRQm7Rvfuh1WLwFddzPk8lw9mfklaguZVz8xIZ9z7c5M+8MVVDXjo5+2qvXDJzy5LaP6XXX5FQvNPRc+OnZqwvAsKi3j+zekJyz8ZJftrLNHw4Oeci1lNeObnwc85F5tq8A5fNDz4Oedi5s1e51zKEV7zc86lqBoQ+zz4Oef2Qg2Ifh78nHMxi9fqbVXJg59zLmbVP/T5CA/n3N6I0wgPSU9JWiNpfkTaHyStkDQn3E6O2HerpBxJX0gaEpE+NEzLkXRLNLfgwc85F5M4T2b6NDC0jPQHzaxHuI0HkNSNYG2P7uE5j0hKDxc1+idwEtANOD88tkLe7HXOxSaOLzmb2YfhYuTROB0YbWb5wNeScoA+4b4cM1sCIGl0eOzCijLzmp9zLmYxtHqbSpoZsQ2P8hLXSJobNot3zUDbGlgecUxumFZeeoW85ueci1FME5Wu3YsprR4F/gRY+OdfgbjPJuLBzzkXs0S+6WJmq7+/jh4H3gy/rgDaRhzaJkyjgvRyebPXOReTaJu8exsfJbWM+HomsKsneBwwTFJtSR2BLsB0YAbQRVJHSbUIOkXGVXYdr/k552IXp5qfpBeAEwieDeYCdwInSOpB0Oz9BrgSwMwWSBpD0JFRCPzSzIrCfK4B3gbSgafMrNI1BTz4OediFq9ZXcxsz6UL4ckKjr8HuKeM9PHA+Fiu7cHPORezGjC6zYOfcy5GggQth7JfefBzzu2F6h/9PPg552Lik5k651JWDYh9Hvycc7Hzmp9zLiXFMLwtaXnwc87FrPqHPg9+zrkYydftdc6lKl+31zmXmqp/7PPg55yLXQ2IfR78nHOxki9d6ZxLPTVlhIdPZuqcS0le83POxawm1Pw8+DnnYuavujjnUk8NecnZn/k552Kyq8Mjmq3SvIJ1eddImh+R9oCkz8N1e1+T1DhM7yApT9KccHss4pxekuZJypH0sKIYfOzBzzkXM0X5XxSeBobuljYROMzMjgC+BG6N2LfYzHqE21UR6Y8CVxCs6NaljDz34MHPORezeNX8zOxDYP1uae+YWWH4dSrBOrwVlEUtgYZmNtXMDHgWOKOya3vwc87FLIZ1e5tKmhmxDY/xUpcBEyK+d5T0qaQPJP0wTGsN5EYckxumVcg7PJxzsYu+w2OtmfXeq0tItxOszzsqTFoFtDOzdZJ6Aa9L6r43eYMHP+dcjAQJH94m6VLgFGBA2JTFzPKB/PDzLEmLga7ACko3jduEaRVKquA3e/astVmZWlrV5UiApsDaqi6Ei0lN/Zm139cMZs+e9XZWpppGeXjM/w8lDQV+CxxvZtsj0psB682sSFIngo6NJWa2XtJmSf2AacDFwN8rvU4YVF0CSZq5t1V/VzX8Z7Z/SHoBOIHgl81q4E6C3t3awLrwsKlmdpWks4C7gAKgGLjTzN4I8+lN0HOcRfCM8FdWSXDz4Lcf+D+k6sd/ZjWf9/Y651KSB7/9Y2RVF8DFzH9mNZw3e51zKclrfs65lOTBzzmXkjz4JZCkoZK+CGeauKWqy+MqV9YsI65m8uCXIJLSgX8CJwHdgPMldavaUrkoPE0UM4K46s+DX+L0AXLMbImZ7QRGA6dXcZlcJcqaZcTVTB78Eqc1sDzie1QzTTjn9g8Pfs65lOTBL3FWAG0jvkc104Rzbv/w4Jc4M4AukjpKqgUMA8ZVcZmccyEPfgkSTsN9DfA2sAgYY2YLqrZUrjLhLCP/Aw6WlCvp51VdJpcYPrzNOZeSvObnnEtJHvyccynJg59zLiV58HPOpSQPfs65lOTBL0VJOkHSm+Hn0yqadUZSY0m/2Itr/EHSTdGm73bM05LOjuFaHXwmFhcLD341TDibTEzMbJyZjajgkMZAzMHPuWTmwa+aCGs2n0saJWmRpJcl1Q33fSPpfkmzgXMkDZb0P0mzJb0kqX543NAwj9nATyLyvlTSP8LPzSW9JumzcOsPjAA6S5oj6YHwuN9ImiFprqQ/RuR1u6QvJX0MHBzFfV0R5vOZpFd23VNooKSZYX6nhMenS3og4tpX7uv/W5eaPPhVLwcDj5jZocBmStfG1pnZUcAk4A5gYPh9JnCDpDrA48CpQC+gRTnXeBj4wMyOBI4CFgC3AIvNrIeZ/UbSYIIFo/sAPYBeko6T1ItgGF8P4GTg6Cju6VUzOzq83iIgckRFh/AaPwYeC+/h58AmMzs6zP8KSR2juI5zpWRUdQFcTJab2ZTw83PAtcBfwu8vhn/2I5g8dYokgFoEw7UOAb42s68AJD0HDC/jGicSrHiPmRUBmyRl73bM4HD7NPxenyAYNgBeM7Pt4TWiGct8mKS7CZrW9QmGA+4yxsyKga8kLQnvYTBwRMTzwEbhtb+M4lrOlfDgV73sPhYx8vu28E8BE83s/MgDJfWIYzkE3Gdm/9rtGtfvRV5PA2eY2WeSLgVOiNhX1v0K+JWZRQZJJHXYi2u7FObN3uqlnaRjws8XAB+XccxU4FhJBwFIqiepK/A50EFS5/C488s4F2AycHV4brqkRsAWglrdLm8Dl0U8S2wt6UDgQ+AMSVmSGhA0sSvTAFglKRP46W77zpGUFpa5E/BFeO2rw+OR1FVSvSiu41wpHvyqly+AX0paBGQDj+5+gJl9B1wKvCBpLmGT18x2EDRz/xt2eKwp5xrXAT+SNA+YBXQzs3UEzej5kh4ws3eA54H/hce9DDQws9kEze/PgAkE03pV5nfANGAKQYCOtAyYHuZ1VXgPTwALgdnhqy3/wlswbi/4rC7VRNise9PMDqviojhXI3jNzzmXkrzm55xLSV7zc86lJA9+zrmU5MHPOZeSPPg551KSBz/nXEr6fw8WVInh/ew6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J4PRir1SGbQH"
      },
      "source": [
        "KNN on data without preprocessing 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "dbgK1ZiBGcbW",
        "outputId": "b1368a39-9690-48ce-c1ec-23f2c0684908"
      },
      "source": [
        "KNN_bow_none_2 = KNeighborsClassifier(n_neighbors=100,weights='uniform')\n",
        "KNN_bow_none_2.fit(X_train, Y_train)\n",
        "predict_label = KNN_bow_none_2.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.73      0.49      0.59      4493\n",
            "    negative       0.62      0.82      0.70      4507\n",
            "\n",
            "    accuracy                           0.66      9000\n",
            "   macro avg       0.67      0.66      0.65      9000\n",
            "weighted avg       0.67      0.66      0.65      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.6565555555555556\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16f8fefd0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UoNtQdYK4rM_"
      },
      "source": [
        "KNN on simple preprocessing data 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "rt039n2pT66T",
        "outputId": "f64fa07d-a105-4d20-8912-6c34d57c723b"
      },
      "source": [
        "KNN_bow_simple_1 = KNeighborsClassifier()\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_simple, Y, test_size=0.2, random_state=1)\n",
        "KNN_bow_simple_1.fit(X_train, Y_train)\n",
        "predict_label = KNN_bow_simple_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.65      0.52      0.58      4493\n",
            "    negative       0.60      0.72      0.66      4507\n",
            "\n",
            "    accuracy                           0.62      9000\n",
            "   macro avg       0.62      0.62      0.62      9000\n",
            "weighted avg       0.62      0.62      0.62      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.62\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16fb04490>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OwoB2jcuGr9WPjeLPJ3ZkxtIs1m0rOPS4hFiydxcfkm68SUhsjO4rpaS4iCMHDmPD4nkU5udSmJ/LhsXzOHLgsAPyt+zYlZvfmM9N077gpmlf0Ci5FX/89zs0bNqcRi3asHHJdwDsyslme+YGklo5LzRs3/QTLY7oUuPXV1tsbG8EWpGVT5cWDVj52y5mLt/KXWmpALy7fAu73S7xZcek8MW67WzcUciE/q1pn5SAKmTvLual753u7ciuybRMjGNcr5aM6+W0Ph78fAP5RSV0bJrA+uzd7LO3HgOiU/9h/LIinU79hzL8vGuc8brA8POvJSHRebA067HbGHjKxAOmsT/Y8eddw7uPTObpq05FVRl52c3Ub9wUgJ9++J7UoyNkjG8daNX5I2hje6ujLoztPSIpgbTuyTz77a9VZ66m8we0YUlmHqu27ApaGYFSF8b2Zq1byfx3XmL8LQ8HrYyX/nIeE+58utzXZkJJIMb21m/TVVOvfMqvvMvuHlnh2N7aZi0/j37OKWT1b7sRgWD9u7Fp5546EfjqitapPelw1GD2lZYSFR34l8d379zBkDMvDfnAF0jh0PKz4FcNX2/YUXWmw/Dl+uCePxL1G3N20M7doElTuh07KmjnD0Whfj/PHxb8jDHehMk9Pwt+xhhPnLG9dT/6WfAzxngWBrHPgp8xxrtQH73hDwt+xhhvvM3nF7Is+BljPNk/n19dZ8HPGONR6A9d84cFP2OMZ2EQ+yz4GWM8EnvgYYyJQPaenzEmYoVD8LP5/IwxngViMlMRaSciX4jIKhFZKSI3uul/F5FN5azrgYjcKiIZIvKjiIzxSU9z0zJEZLI/12AtP2OMZwFq+ZUAf1bVxSKSCCwSkTnuvsdU9YA5yESkB87CRj2BNsCnIrJ/Btn/AKNwFixfKCKzVHVVZYVb8DPGeBO4BYyycBYiR1XzRWQ10LaSQ8YB01W1CNgoIhnA0e6+DFXdACAi0928lQY/6/YaYzwR/Fu8yH0inCwi6T7bpHLPKdIB6Iez7i7AdSKyTEReFJEkN60t4DuLcKabVlF6pazlZ4zxLMr/pl92VTM5i0hDnCUpb1LVPBF5GrgHZ92ve4BHgMsOo7rlsuBnjPEsUA97RSQWJ/C9qqpvA6jqFp/9zwHvu183Ae18Dk9x06gkvULW7TXGeCISmNXbxMnwArBaVR/1SW/tk208sML9PAuYICLxItIRSAUWAAuBVBHpKCJxOA9FZlV1HdbyM8Z4FqABHkOBC4HlIrLUTbsNmCgifXG6vT8BfwRQ1ZUiMgPnQUYJcK2qlgKIyHXAbCAaeFFVV1ZVuAU/Y4xngRjepqpzcQaMHOzDSo65D7ivnPQPKzuuPBUGPxHJx4m8+FRQ3c+qqo28FGSMCQ+C88S3rqsw+KlqYk1WxBhTd4TBvAb+PfAQkWEicqn7Odm92WiMiUR+PuwI9fG/Vd7zE5E7gYFAV+AlIA54BedmpTEmAoV4XPOLPw88xuO8eb0YQFU3u+PwjDERSPD0knPI8if4FauqiogCiEiDINfJGBPiwmEyU3/u+c0QkWeBJiJyJfAp8Fxwq2WMCVX+TmcV6o3DKlt+qvqwiIwC8oAuwB2qOqeKw4wxYSxSur0Ay4EEnPf8lgevOsaYuqDuhz4/ur0icgXO+LkzgbOB+SIS8BkWjDF1R0S86gLcDPRT1e0AItIM+BZ4MZgVM8aEJudpb23X4vD5E/y2A/k+3/PdNGNMJBIJi6e9lY3t/ZP7MQP4XkRm4tzzGwcsq4G6GWNCVKh3af1RWctv/4vM691tv5nBq44xJtSFfbdXVe+qyYoYY+qOcG/5ASAizYFbcJaLq7c/XVVPCmK9jDEhrO6HPv9GeLwKrAE6AnfhzKy6MIh1MsaEMBGIjhK/tlDmT/BrpqovAHtV9StVvQywVp8xESwc3vPzJ/jtdX9micgpItIPaBrEOhljQlwgxvaKSDsR+UJEVonIShG50U1/SETWuOv2viMiTdz0DiJSKCJL3e0Zn3MNEJHlIpIhIk+IH5HXn+B3r4g0Bv4M/AV4Hvh/fhxnjAlDghAl/m1VKAH+rKo9gMHAtSLSA5gD9FLVPsBa4FafY9aral93u8on/WngSpwV3VKBtKoK92dig/1rZuYCJ1aV3xgT5gI0Y4uqZgFZ7ud8EVkNtFXVT3yyzccZVltxdZylLhup6nz3+zTgDOCjyo6r7CXnJ/l9AaPyKn5DZSeujg5JCUz5w1GBPq0JoqRB19V2FYwHRZlbqs7kBw/385JFJN3n+xRVnVLO+TrgTJr8/UG7LgPe8PneUUSW4Mwy9TdV/QZoC2T65Ml00ypVWcsvvZJ9xpgIJUC0/8EvW1UHVno+kYbAW8BNqprnk/5XnK7xq25SFtBeVbeLyADgXRHp6bX++1X2kvPL1T2pMSa8BeotFhGJxQl8r6rq2z7plwCnAiNUVQFUtQgocj8vEpH1OHOMbgJSfE6b4qZVfg2BuQRjTCSJEv+2yrhPZF8AVqvqoz7paTgDK05X1QKf9OYiEu1+7oTzYGODe+8wT0QGu+e8CD+G4fo7makxxgD7X2MJSNNvKHAhsFxElrpptwFPAPHAHLec+e6T3eHA3SKyF9gHXKWqO9zjrgGm4ky6/BFVPOwAC37GmGoIRLdXVedS/ki5DyvI/xZOF7m8felALy/l+zOTcxcR+UxEVrjf+4jI37wUYowJL+GwgJE/9/yew3nJcC+Aqi4DJgSzUsaY0CVAjIhfWyjzp9tbX1UXHNTHLwlSfYwxdUCIxzW/+BP8skWkM+4LzyJyNu5b2caYyCP+DV0Lef4Ev2uBKUA3EdkEbAQuCGqtjDEhLQxin19jezcAI0WkARClqvlVHWOMCW8hPlWfX/yZyfmOg74DoKp3B6lOxpgQJhDyE5X6w59u726fz/VwhpysDk51jDEhz4/RG3WBP93eR3y/i8jDwOyg1cgYE/IkDFbxqM4Ij/ocOIjYGBNBwn7pyv1EZDm/z+sXDTQH7H6fMREsIoIfzj2+/UqALapqLzkbE8FCfXEif1Qa/NzpY2ararcaqo8xJsQ5S1fWdi0OX6WXoKqlwI8i0r6G6mOMqQMCtIBRrfKn25sErBSRBfi89qKqpwetVsaYkBUxDzyA24NeC2NMnRLijTq/+BP8TlbV//NNEJF/Al8Fp0rGmNAmRIXBe37+3LYcVU7a2EBXxBhTNwjhMZlpZev2Xo0zL34nEVnmsysRmBfsihljQpRATBjc9Kus5fcacBowy/25fxugqjallTERKlAtPxFpJyJfiMgqEVkpIje66U1FZI6IrHN/JrnpIiJPiEiGiCwTkf4+57rYzb9ORC725zoqW7c3F8gFJvpzImNM5AjQaywlwJ9VdbGIJAKLRGQOcAnwmao+ICKTgcnA/+Hcbkt1t2OAp4FjRKQpcCcwEGc02iIRmaWqOZVeQyCuwBgTWQLR8lPVLFVd7H7Ox5ktqi0wDnjZzfYycIb7eRwwTR3zgSYi0hoYA8xR1R1uwJsDpFV1DbZ0pTHGE8FTqylZRNJ9vk9R1SmHnFOkA9AP+B5o6S5EDvAb0NL93Bb41eewTDetovRKWfAzxngjnrq92ao6sNLTiTTEWY/3JlXN8x03rKoqIlrhwYfBur3GGE+cER6BGd4mIrE4ge9VVX3bTd7idmdxf2510zcB7XwOT3HTKkqvlAU/Y4xn4udW6TmcJt4LwGpVfdRn1yxg/xPbi4GZPukXuU99BwO5bvd4NjBaRJLcJ8Oj8WPCZev2GmM8C9ALzEOBC4HlIrLUTbsNeACYISKXAz8D57r7PgROBjKAAuBSAFXdISL3AAvdfHer6o6qCrfgZ4zxSAIyn5+qzqXiBuKIcvIrzlK65Z3rReBFL+Vb8DPGeOLxaW/IsuBnjPEs1Ofq84cFP2OMNxIB09gbY8zBrNtrjIlY1vIzxkSkuh/6LPgZYzwSINpafsaYSBQGsc+CnzHGK0HCoONrwc8Y41k4tPzC4Yl1jfvjFZfRvk0LBvTtVZZ26//dzFG9ujGoXx/OPXs8O3fuLNu3fNkyjh82hP5H9WRg397s2bMHgMWLFjGwb296djuSP910A87oHRMM8XExfPPfv/D9G5NZ9OZf+dtVJwPw0n0X88M7t5P+v9t45s7ziYk58FdiQI/25C98nPEj+wIwfGAq86dPLtty5j/GaSf0qfHrqU3Oqy7i1xbKLPhVw4UXX8LM9z8+IG3EyFEsWrqChUuWkZrahYf+eT8AJSUlXHbxBTz5n2dY/MNKZn/2JbGxsQDccN3V/OeZ51ixeh3rM9bxyeyPDynLBEZRcQlpk57gmD88wDET7mf0sT04uncHpn+0kKPG38PAc/5BQr1YLh1/bNkxUVHCvTeO49P5a8rSvk5fx+AJDzB4wgOMnfQEBXuK+XT+6tq4pNrj5yzOod46tOBXDcOOG07Tpk0PSBs5ajQxMc5dhKOPGcymzEwAPp3zCb1696HPUUcB0KxZM6Kjo8nKyiI/P49jBg9GRDjvgot4b+a7NXshEWZ3YTEAsTHRxMREo6rMnruqbH/6ip9p2yKp7Ps1E47n3c9+YNuO/HLPN35kPz6Zt4rCPXuDW/EQFKj5/GqTBb8gmDb1RcakOUsbr1u7FhHhtJPHMGRQfx55+EEANm/aRNu2KWXHtE1JYfPmKudfNIchKkqYP30yv3z2AJ/PX8PCFT+X7YuJiWLiKUcz51snGLZp3pjTTzqKKf/7psLznTOmPzM+XhT0eocaZzJT/7ZQZg88Auyf999HdEwME847H4CS0hK+/XYuc79bSP369Rk7egT9+w+gUaPGtVzTyLNvnzJ4wgM0bpjAG49eSY/OrVm13lkq4vFb/8C8xRnMW7IegIduPou/PT6zwvuwrZIb0TO1DXO+W1Xu/nBnT3vNAf778lQ+/OB9Pvrks7LhP23bpjBs2HCSk5MBSBt7MkuWLGbieRewaVNm2bGbMjNp06bKNVdMAOTuKuSr9LWMPrYHq9ZncduksTRPasgf7n2+LE//Hu2Z9sClADRr0pAxw3pSUrKP975cBsBZo/oz6/NllJTsq5VrqG0h3qP1i3V7A+ST2R/z6CMP8uY7s6hfv35Z+qjRY1i5YjkFBQWUlJTwzddf0b17D1q3bk1iYiO+nz8fVeW1V6Zx6unjavEKwltyUkMaN0wAoF58LCOO6caPP23hkvFDGHVsdy66deoBrbzup/6dbqfcSbdT7uSdT5dw0/1vlAU+gHPTBjDj4/RDyokU4ud/oSxoLT8ReRE4Fdiqqr2qyl+XXHTBRL756kuys7Pp3CGF2++4i4cevJ+ioiJOTRsFOA89nnzqGZKSkrjhpj8xbMggRIQxaScz9uRTAHj8yaeYdMUlFBYWMnrM2LL7hCbwWiU34rm7LyQ6KoqoKOGtOYv56JsV5C98nF+ydvDly38GYObnS7l/SuVP3du3bkpKqyS+WZRRE1UPOfvv+dV1Eqx3y0RkOLALZ5Fhv4LfgAEDdd73kfuvaV2UNOi62q6C8aDoxxnsK9h6WKGrW+9++vzbn/uV97guTRdVtnRleY0kEXkD6OpmaQLsVNW+7tq+q4Ef3X3zVfUq95gBwFQgAWetjxu1iuAWtJafqn7tVtYYE2YC2PCbCvwbmLY/QVX/UFaOyCNArk/+9arat5zzPA1cibPo+YdAGvBRZQXX+j0/EZkkIukikr4te1ttV8cYU4VArturql8D5a605i5teS7weqX1cdb2baSq893W3jTgjKrKrvXgp6pTVHWgqg5snty8tqtjjPGDh3V7k/c3btxtkodijgO2qOo6n7SOIrJERL4SkePctLZApk+eTDetUvaqizHGO//7vdmV3fOrwkQObPVlAe1Vdbt7j+9dEelZzXPXfsuvriksLGTUScdTWlrKK9Neplf3VHp1T+WVaS+Xm7+iCQ/27t3LFZdezMC+venbu3vZWODi4mJGnjickpKSGrumcFcvPpZPnr+RqCjh/NOOYfnMO1g+8w7OP+2YcvP/94FLyyYuWPPBXcyfPhmAk47pxrxXb2HhjNuY9+otHD+oS9kxHzxzHU0SE2rkekJBsIe3iUgMcCbwxv40VS1S1e3u50XAeqALsJn9GnoAAAwhSURBVAlI8Tk8xU2r/BqqXbsqiMjrwHdAVxHJdFdfr/NefulFxp1xJrm5udx37118Pe97vvl2Affdexc5OTmH5K9owoO33vwfRcVFpC9dzrffL+L5557l559+Ii4ujhNPGsH/ZrxxyLlM9Vw8bggzP/uBxg0T+OuksQy/8GGOu+Ah/jppbLkB68LJL5VNXvDuZ0uZ+flSALbv3MXZNz3LoHP/wZV3/JcX772o7JjXPljIpHOH19g11TYP3d7qGgmsUdWy7qyINBeRaPdzJyAV2KCqWUCeiAx27xNeBMysqoCgBT9VnaiqrVU1VlVTVPWFYJVVk6a//iqnnT6OOZ/MZsSIUTRt2pSkpCRGjBhV7qwsFU14ICIU7N5NSUkJhYWFxMXFkdioEQCnnX4Gb7z+as1dVJibcPJA3vtyGaOO7c5n89eQk1fAzvxCPpu/htFDe1R67Fmjfh+/+8OPmWRtcx48rlqfRb34WOJinb/bD75cxrlpA4J7IaEkQNGvkkbSBA590DEcWCYiS4E3gatUdf/DkmuA54EMnBZhpU96we75eVJcXMxPGzdwRIcOvP32m6S0a1e2z5+JCaZNfZGzz3Ge4p951tm8/95MOrZrTUFBAQ8+/FjZTDE9e/ViUfrC4F1IBImNiaZD22R+ydrBmSP7kbnl99b5pq07adO8SYXHDu3fmS078ln/y6FvIYwf2Zela36leK9ze2JnfiHxcTE0bdyAHbm7A38hIcSJa4F52UVVJ1aQfkk5aW8Bb1WQPx3wNJjC7vl5kJ2dTeMmFf+yVObgCQ8WLlhAdFQ0G37ZzOp1G3n8X4+wccMGAKKjo4mNiyM/v/yplIz/kpMakptfUK1jz00byP/KGcLWvVMr7r1hHNfdO/2A9G078mndPAImrLD5/CJPQkJC2SzMbdq0JfPXX8v2VTYxwf4JD6ZOe7VswoMZ019j9Jg0YmNjadGiBUOGDGXRot9/0YqLiqhXr14QryYyFO4ppl68M3ns5m07SWn5+3x9bVs0YfO2neUeFx0dxbiTjuLN2YsPSG/boglvPDqJK27/Lxszsw/YFx8XS2FRcYCvIDTVwD2/oLPg50FSUhKlpaXs2bOHUaPH8Omnn5CTk0NOTg6ffvoJo0aPOeSYiiY8SGnfni+/cIYI7d69mwUL5tO1azcAtm/fTrPk5LIZn0317cwvJDoqivi4GOZ8u5qRQ7rRJDGBJokJjBzSjTnflj8L80nHdGXtT1vYtPX34Ni4YQJvP3kVtz8xk+9+2HDIMa2SG/Hz5nLf1w0zgoh/Wyiz4OfRyJGj+XbeXJo2bcqtt93OsCGDGDZkELf99Y6ye3ZXT7qCRelOK+7/3Xgd+fn5nJo2imMG9OX6a64C4Kqrr2XX7l30P6onw4YM4sKLL6V3H2ctiK++/IK0safUzgWGoU/nr+bYfp3JySvg/uc+Zu4rtzD3lVv4x5SPyclzusRP3XEe/Xu0LzvmnDEDDpmo9KoJw+ncrjm3Thpb9ipM86SGgDMF1oLlP1FaGhlTXIVDtzdoExtUR12Y2GDJ4sU8+fhjvPjyf4NWxh/OOZN773uA1C5dqs5cy+rCxAZ9u6Vw/fkncfnt06rOXE0P33wW73+1nC8XrA1aGYEQiIkNevbpr6+9/5Vfefse0ajSiQ1qk7X8POrXvz/Hn3AipaWlQTl/cXExp59+Rp0IfHXF0jWZfJW+lqggzsO0MiMr5ANfQIXBTT971aUaLr70sqCdOy4ujvMvvKjqjMaTaTPnB/X8L73zbVDPH2pCfaJSf1jwM8Z4Fur38/xhwc8Y400deJjhDwt+xhjPrNtrjIk4grX8jDERKgxinwU/Y0w1hEH0s+BnjPHscCYqDRUW/IwxntX90GfBzxhTHWEQ/Sz4GWM8CeRkprXJgp8xxpswecnZJjYwxngWqHkNRORFEdkqIit80v4uIptEZKm7neyz71YRyRCRH0VkjE96mpuWISKT/bkGC37GGI8COpnpVCCtnPTHVLWvu30IICI9cBY26uke85SIRLsruv0HGAv0ACa6eStl3V5jjGeB6vaq6tci0sHP7OOA6apaBGwUkQzgaHdfhqpucOom0928qyo7mbX8jDGe+NvlPcz4eJ2ILHO7xfsXXmkL/OqTJ9NNqyi9Uhb8jDHe+R/9kkUk3Web5MfZnwY6A32BLOCRgNcf6/YaY6rBw6su2V6nsVfVLWXliDwHvO9+3QS088ma4qZRSXqFrOVnjPEsmAsYiUhrn6/jgf1PgmcBE0QkXkQ6AqnAAmAhkCoiHUUkDuehyKyqyrGWnzHGG4FALYciIq8DJ+B0jzOBO4ETRKQvoMBPwB8BVHWliMzAeZBRAlyrqqXuea4DZgPRwIuqurKqsi34GWOqITDRT1UnlpP8QiX57wPuKyf9Q+BDL2Vb8DPGeGKTmRpjIlYYxD4LfsYY76zlZ4yJSH4OXQtpFvyMMZ7V/dBnwc8Y49HhvMMXSiz4GWM8s8lMjTGRqe7HPgt+xhjvwiD2WfAzxngltnSlMSbyhMsID5vVxRgTkazlZ4zxLBxafhb8jDGe2asuxpjIYy85G2MiUbg88LDgZ4zxzLq9xpiIZC0/Y0xECoPYZ8HPGFMNYRD9LPgZYzwRCIvhbaKqtV2HMiKyDfi5tusRBMlAdm1XwngSrn9nR6hq88M5gYh8jPPn449sVU07nPKCJaSCX7gSkXSvq9ab2mV/Z+HPxvYaYyKSBT9jTESy4FczptR2BYxn9ncW5uyenzEmIlnLzxgTkSz4GWMikgW/IBKRNBH5UUQyRGRybdfHVE1EXhSRrSKyorbrYoLLgl+QiEg08B9gLNADmCgiPWq3VsYPU4GQfCnXBJYFv+A5GshQ1Q2qWgxMB8bVcp1MFVT1a2BHbdfDBJ8Fv+BpC/zq8z3TTTPGhAALfsaYiGTBL3g2Ae18vqe4acaYEGDBL3gWAqki0lFE4oAJwKxarpMxxmXBL0hUtQS4DpgNrAZmqOrK2q2VqYqIvA58B3QVkUwRuby262SCw4a3GWMikrX8jDERyYKfMSYiWfAzxkQkC37GmIhkwc8YE5Es+EUoETlBRN53P59e2awzItJERK6pRhl/F5G/+Jt+UJ6pInK2h7I62EwsxgsLfmHGnU3GE1WdpaoPVJKlCeA5+BkTyiz41RFuy2aNiLwqIqtF5E0Rqe/u+0lE/ikii4FzRGS0iHwnIotF5H8i0tDNl+aeYzFwps+5LxGRf7ufW4rIOyLyg7sdCzwAdBaRpSLykJvvZhFZKCLLROQun3P9VUTWishcoKsf13Wle54fROSt/dfkGiki6e75TnXzR4vIQz5l//Fw/2xNZLLgV7d0BZ5S1e5AHge2xraran/gU+BvwEj3ezrwJxGpBzwHnAYMAFpVUMYTwFeqehTQH1gJTAbWq2pfVb1ZREYDqTjTdvUFBojIcBEZgDOMry9wMjDIj2t6W1UHueWtBnxHVHRwyzgFeMa9hsuBXFUd5J7/ShHp6Ec5xhwgprYrYDz5VVXnuZ9fAW4AHna/v+H+HIwzeeo8EQGIwxmu1Q3YqKrrAETkFWBSOWWcBFwEoKqlQK6IJB2UZ7S7LXG/N8QJhonAO6pa4Jbhz1jmXiJyL07XuiHOcMD9ZqjqPmCdiGxwr2E00MfnfmBjt+y1fpRlTBkLfnXLwWMRfb/vdn8KMEdVJ/pmFJG+AayHAPer6rMHlXFTNc41FThDVX8QkUuAE3z2lXe9Alyvqr5BEhHpUI2yTQSzbm/d0l5EhrifzwPmlpNnPjBURI4EEJEGItIFWAN0EJHObr6J5RwL8BlwtXtstIg0BvJxWnX7zQYu87mX2FZEWgBfA2eISIKIJOJ0sauSCGSJSCxw/kH7zhGRKLfOnYAf3bKvdvMjIl1EpIEf5RhzAAt+dcuPwLUishpIAp4+OIOqbgMuAV4XkWW4XV5V3YPTzf3AfeCxtYIybgROFJHlwCKgh6pux+lGrxCRh1T1E+A14Ds335tAoqouxul+/wB8hDOtV1VuB74H5uEEaF+/AAvcc13lXsPzwCpgsftqy7NYD8ZUg83qUke43br3VbVXLVfFmLBgLT9jTESylp8xJiJZy88YE5Es+BljIpIFP2NMRLLgZ4yJSBb8jDER6f8DUBhnM3jOgfAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9LGSK0jG6z7"
      },
      "source": [
        "KNN on simple preprocessing data 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "X2ZKFytrG76y",
        "outputId": "42d9ed65-fec6-4618-9ed9-903f5dce1ba1"
      },
      "source": [
        "KNN_bow_simple_2 = KNeighborsClassifier(n_neighbors=100,weights='uniform')\n",
        "KNN_bow_simple_2.fit(X_train, Y_train)\n",
        "predict_label = KNN_bow_simple_2.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.75      0.50      0.60      4493\n",
            "    negative       0.62      0.83      0.71      4507\n",
            "\n",
            "    accuracy                           0.67      9000\n",
            "   macro avg       0.69      0.67      0.66      9000\n",
            "weighted avg       0.69      0.67      0.66      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.6658888888888889\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16fd05bd0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AO77xzbC4xIx"
      },
      "source": [
        "KNN on advance preprocessing data 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "WUCJzpLEUK-y",
        "outputId": "50aea75b-3db1-494c-c98f-e803b8149ded"
      },
      "source": [
        "KNN_bow_advance_1 = KNeighborsClassifier()\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_advance, Y, test_size=0.2, random_state=1)\n",
        "KNN_bow_advance_1.fit(X_train, Y_train)\n",
        "predict_label = KNN_bow_advance_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.63      0.64      0.64      4493\n",
            "    negative       0.64      0.63      0.63      4507\n",
            "\n",
            "    accuracy                           0.64      9000\n",
            "   macro avg       0.64      0.64      0.63      9000\n",
            "weighted avg       0.64      0.64      0.63      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.635\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16fda2250>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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igtiYKOZ/tZl2f61OyWKFKVmsMO3+Wp35X529EOkni9fTspFnybEq5csQEx1FatoftOv7MtWvG0b164bx6sTFvPDOPN6YshSAUiWKsHf/H7bQQR6Ii4sjMzOTo0eP0j7pGhYsmEdaWhppaWksWDCP9knXnHVMyrZtWfufzppJtcurA3D99V34avkyMjIyOHz4MMnJK6levQYAe/fu5S/x8URHR+dPxYLMVnJ2oQUrNtOsXmUWrdzCs2/NYdn7nqXP/zV2DmkHPV3i15+4hbenLWPNpl8Y//HXvPlkL1Z9OIT045n0e+K/uV6jZaOqzFlW4MeGQka7dkl8tXwZbdq249Ehj3PVXz2DSUOGPkGpUqUAuKd/P/r1/zsNGjbksaGD2bZ1CxESQfkKFRj12hsAVK9Rg/bXdKBR/TpERERwR59+1KrtWRZ/yeJFdOh4XXAqmN8KQKvOFwGb23s+CsLc3rrVE7m/Vxv6Pj4h98znafLIfjw2ahYpv/yee+YgKwhze79ds4bRr/yHceNz/8Nzvm7udiPPjHiOqtWq5Z45iPJibu9FZS/Xqne97lPe9U+1y3Zub7BZy89Pa7/fwZJVW4mIEE6cyPs/HNFRkcxavL5ABL6Col79+rRs1ZrMzMys1VnyUnp6Op07dw35wJeXwqHlZ8HvPEyYuSJg5z6ekckHn34TsPO7Ve8+dwbs3DExMfS67fbcM4aRUL+f5wsLfsYY/4TJPT8LfsYYv3jm9hb86GfBzxjjtzCIfRb8jDH+C/XZG76w4GeM8U+YrOdnwc8Y45eT6/kVdBb8jDF+Cv2pa76w4GeM8VsYxD4LfsYYP4kNeBhjXMie8zPGuJYFP2OMK4VB7LPFTI0x/suLxUxFpJyILBKRTSKyUUQGnvH9gyKiIhLvfBYRGSUiKSKyXkTqe+XtLSLbnK23L3Wwlp8xxj95t7BBBvCgqq4RkWLAahGZr6qbRKQckAT84pW/I1DV2ZoAY4AmIlIKGAY0BNQ5zyxVTSMH1vIzxvhF8O3lRbmNCKvqblVd4+wfwvOK25MvVfkP8AieYHZSF2CCeqwASorIpcA1wHxV3ecEvPlAh9zqYS0/Y4zfInxv+sWLyCqvz2NVdeyZmUSkIlAPWCkiXYCdqrrujK5zAvCr1+cdTlp26Tmy4GeM8Zsf3d7U3JaxF5GiwHRgEJ6u8BA8Xd6Asm6vMcYvInn39jYRicYT+Caq6gygMlAJWCci24FEYI2IXALsBMp5HZ7opGWXniMLfsYYv0WIb1tOxBMd3wE2q+pLAKq6QVXLqGpFVa2IpwtbX1V/A2YBtzujvk2BA6q6G5gLJIlInIjE4Wk1zs2tDtbtNcb4LY+mtzUHbgM2iMhaJ22Iqs7OJv9s4FogBTgM9AFQ1X0i8jSQ7OR7SlX35XbxbIOfiBzi1EjLyZqqs6+qWjy3kxtjwo/gGfG9UKq6DHI+kdP6O7mvwIBs8o0Dxvlz/WyDn6oW8+dExhj3CIN1DXy75yciV4lIH2c/XkQqBbZYxpiQ5eNgR6jP/831np+InHxy+nLgXSAGeB9Pf90Y40IhHtd84suAxw14Hj48+ST2LmcqijHGhQS/HnIOWb4Ev3RVVRFRABEpEuAyGWNCXDgsZurLPb+pIvImnnl0dwELgLcCWyxjTKgS8X0LZbm2/FR1pIi0Bw4C1YAnVHV+wEtmjAlZbun2AmwACuN5zm9D4IpjjCkICn7o86HbKyL9gG+AG4GbgBUicmegC2aMCV2ueNQFeBiop6p7AUTkL8BX+Pk0tTEmPHhGe4NdigvnS/DbCxzy+nzISTPGuJHkvlBpQZDT3N7/c3ZT8CwwOBPPPb8uwPp8KJsxJkSFepfWFzm1/E4+yPyDs500M3DFMcaEurDv9qrq8PwsiDGm4Aj3lh8AIlIaz4tEagGFTqarapsAlssYE8IKfujzbYbHROB7PEtLDwe2c2rRQGOMy4hAZIT4tIUyX4LfX1T1HeC4qi5R1TsBa/UZ42Juec7vuPPvbhG5DtgFlApckYwxoS7E45pPfAl+z4hICeBBYDRQHPhHQEtljAlZgrhjbq+qfursHgBaB7Y4xpiQVwBWbPFFTg85j+bUC4zOoqoP5HVh6lQvx8KlL+f1aU0AxbV6LNhFMH44tiXX19n6JNTv5/kip5bfqnwrhTGmwBAgMpyDn6qOz8+CGGMKjhB/isUn9tJyY4zfLPgZY1zHs0R9wY9+Pr231xhjvEWIb1tORKSciCwSkU0islFEBjrpL4jI9yKyXkQ+EpGSXsc8KiIpIrJFRK7xSu/gpKWIyGCf6pBbBhGpJiILReQ753MdEbEhPmNcLI9eYJQBPKiqNYGmwAARqQnMB2qrah1gK/Co55pSE+iBZ52BDsDrIhIpIpHAa0BHoCbQ08mbI19afm85Fz8OoKrrnQIYY1xIgCgRn7acqOpuVT35PvBDwGYgQVXnqWqGk20FkOjsdwEmq+oxVf0Jz1qjjZ0tRVV/VNV0YLKTN0e+BL+LVPWbM9IyzpnTGOMKfrT84kVkldfW/9znk4pAPWDlGV/dCXzu7CcAv3p9t8NJyy49R74MeKSKSGWcB55F5CZgtw/HGWPCkIhf09tSVbVhLucrCkwHBqnqQa/0oXgaWhPPt6w58SX4DQDGAtVFZCfwE3BrIApjjCkY8mqwV0Si8QS+iao6wyv9DqAT0FZVT8402wmU8zo80Ukjh/Rs+TK390egnYgUASKcvrkxxsXy4jk/8Twv8w6wWVVf8krvgGcB5ZaqetjrkFnAByLyElAWqIrntboCVBWRSniCXg/gltyu78tKzk+c8RkAVX0qt2ONMeFHIK8WKm0O3AZsEJG1TtoQYBQQC8x34s0KVf27qm4UkanAJjzd4QGqmgkgIvcBc4FIYJyqbszt4r50e//02i+Epym62ZeaGWPCkA/P8PlCVZdx7hXxZ+dwzAhgxDnSZ+d03Ln40u190fuziIzEE2GNMS4lYfAWj/OZ3nYRp567Mca4TNi/uvIkEdnAqXX9IoHSgN3vM8bFXBH88NzjOykD+J/X09fGGBcKh4UNcgx+zpy5uapaPZ/KY4wJcZ5XVwa7FBcuxyo4w8hbRKR8PpXHGFMARDizPHLbQpkv3d44YKOIfIPXYy+q2jlgpTLGhCzXDHgAjwe8FMaYAiXEG3U+8SX4Xauq//ROEJF/A0sCUyRjTGgTIsLgOT9fblu2P0dax7wuiDGmYBDybDHToMrpvb33APcCl4nIeq+vigHLA10wY0yIEogKg5t+OXV7P8CziOCzgPea+IdUdV9AS2WMCVknW34FXU7v7T0AHAB65l9xjDEFQag/xuILe3WlMcZvYRD7LPgZY/wjhMc7by34GWP8I9btNca4kGeGhwU/Y4wLFfzQZ8HPGHMewqDhZ8HPGOMvCf/1/Iwx5kw22muMcS0b8DDGuI+4YBl7Y4w5k3V7jTGuFQ4tv3AI4MaYfCY+bjmeQ6SciCwSkU0islFEBjrppURkvohsc/6Nc9JFREaJSIqIrBeR+l7n6u3k3yYivX2pgwU/Y4xfBIgU8WnLRQbwoKrWBJoCA0SkJp4l9BaqalVgIaeW1OsIVHW2/sAY8ARLYBjQBGgMDDsZMHNiwc8Y47e8WMlZVXer6hpn/xCwGUgAugDjnWzjga7OfhdggnqsAEqKyKXANcB8Vd2nqmnAfKBDbnWwe37GGD8J4vsEt3gRWeX1eayqjj3rjCIVgXrASuBiVd3tfPUbcLGznwD86nXYDictu/QcWfAzxvjNj/GOVFVtmPO5pCgwHRikqge9B1NUVUVEz7ecObFu73l44J5+VK9Ulqsa1z0t/a03XqVp/do0b3QlTz7muU3x4ZQPaNWsQdZWungMG9avBeCj6VNp0bQezRtdyfDHH833erhJYpkSzBl1J2v++wCr/3s/A7r9FYA6VS5hyZt3s+LdASx7+x4a1vA0GHq0v5Jv3ruP5PH3sWhMf66ocknWue7v3ozV/72fVRPuZ/yT3YmNcVcbwvOoi/i05XoukWg8gW+iqs5wkv/ndGdx/v3dSd8JlPM6PNFJyy49Rxb8zkOPXr2Z8tGnp6V9uXQxn3/2CUu+Xs3y5HUMGPh/AHS7+RYWf7WaxV+t5vW33qNCxUpcUacu+/bu5cnHBjPjk3ksT17H7//7jaWLvwhGdVwhIzOTwa9+Tv3bRtGy/5vcfWMTqlcszYh7OzDi3S9o2uc1nn57ISPu9dwq2r57H0n3v02j3q/y7PhFvPZIFwDKxhfj3pv+SvO+Y2h4+2giI4Ruba8IZtXyn4/3+3JrHYqnifcOsFlVX/L6ahZwcsS2NzDTK/12Z9S3KXDA6R7PBZJEJM4Z6Ehy0nLkrj9ZeaTZVVfzy8/bT0t77+03Gfh/jxAbGwtA6dJlzjpuxodTuOFv3QHYvv1HLqtchfjSpQFo2botn8ycQYtWbQJbeJf6be8f/Lb3DwD+OJLO99v3UDa+OKpK8Ys8P7MSRQuxO/UgACu+O3UL6ZuNv5JQukTW56jICArHRnM88wSFY6PZnXooH2sSGvJoeltz4DZgg4isddKGAM8BU0WkL/Az0N35bjZwLZACHAb6AKjqPhF5Gkh28j3ly0vWLPjlkR9StvL1V8sY8dTjxMYWYviIf1O/QaPT8nw840P+O3k6AJddVoWUbVv55eftlE1IZPans0hPTw9G0V2n/CUlqVvtUpI37eDhUbP55KXePDugIxERQuu/n3Uvnjs6NWDuiq0A7Eo9xMuTl7F1+kMcOZbBwuQUFian5HcVgsqzmOmFn0dVl5H944Btz5FfgQHZnGscMM6f61u3N49kZGSyP20fc79YzvBnnqNf71vw/Kw8VievpHDhwtSoWRuAknFxvPCfV+l3xy10SmpFufIViIyMDFbxXaNI4RgmjejJw6/M5tDhY/Tv2phHRs2m6t9e4JHRsxnz6A2n5W9RrxK9r2vAY2M8vaiSxQrR6aoa1Oj+Ipd1/TdFCkXTI+nKYFQlqMTH/0KZBb88UjYhges634CIUL9hYyIiItibmpr1/YzpU7nxph6nHdPh2k7MW/QVc75YRpWq1ahcpWp+F9tVoiIjmPRMT6bMW8fMpZsA6NWxHh8v8exP/+K7rAEPgNqVL2bM4Bvo9uhE9h08AkCbhpXZvjuN1P2Hycg8wcdLN9H0ivL5X5kgy4t7fsFmwS+PdOzUmWVLFwOQsm0r6enp/CU+HoATJ04wc8Y0brip+2nH7NnjGcTan5bGu2+/wa2978zXMrvNG4/ewJaf9zBqyldZabtTD3J1vUoAtGpwGSk79gJQ7uISTB5xC32f/pCUX/dm5f/1fwdoXCuRwrHRALRuUJkt2/fkYy1CQzi0/AJ2z09ExgGdgN9VtXagrhMMd/W5leVfLmHf3lSuuLwi/xzyBL1u68MD9/bjqsZ1iY6J5tU3x2VN/v5q+ZckJCRSsdJlp51nyCP/x8YN6wF4aPBQqlStlu91cYtmdSrQq0M9NqT8xop3PbeNhr05nwHPz+SFgdcSFRnBsfQM7nveM7D46B2tKVXiIl5+sDMAGZknuKrfGJI37eCjRRv5ety9ZGSeYN3W3bwzKznb64ajvLrnF2zifV8qT08s0gL4A890FJ+CX936DXTh0pUBKY8JjMQOTwa7CMYPx9a+w4k/dl9Q6Kp+RT19e4Zvj2VdXa3U6twecg6WgHV7VXUpkOtwszGm4MmLVV2CLeiPuohIfzwrNJBYzn03jo0paMLlvb1BH/BQ1bGq2lBVG54cIDDGhDZr+Rlj3CnUI5sPgt7yK2iOHDnC9R3akJmZyeSJE2hUtwaN6tZg8sQJ58z/7NPDaNG0Hq2aNeCmLh3ZvXsXAKNffjFrsYOrGtelTIlY0vbtIz09nU7XtCYjIyM/qxXWCsVEMW90XyIixDPiO2kQGyYNoleHetkec8/fmrJ24kBW//d+RtxzDQANaySw4t0BrHh3ACvfG0DnFjUAiI6KZP6r/YiMdM+vU4SIT1soC+Ro7ySgFRAP/A8Ypqrv5HRMQRjtfWfs62RkZNC9x620a9mUBUtWICK0bdGEhUtXUjLu9AVkDx08SLHixQEYO2Y0W77fzIuvvH5anjmzP+WN117h48/mA5Hq/yQAAAvOSURBVPD8s09T6bLKdLv5lvyp1AUoCKO9d9/YhKjICD6Ys5bl79xD875jUJSv3rmXZn1fZ/+ho6flb1GvEv/s3YobHp5A+vFMSpcswp79f1I4Npr0jEwyM09wyV+KsvK9+7is6/NkZp5gSJ/W/LhjH5PnrwtSLX2TF6O9Na6opxNmLvYpb+PKJV052ttTVS9V1WhVTcwt8BUU06ZMouN1nfli4Txatm5LXKlSlIyLo2XrtixccPZCEicDH8DhPw+f88UvM6ZN4cabbs76fG2nzkyfOikwFXChHu2v5JMvN9O+SVUWJv9A2qEj7D90lIXJP5DU5OxnK/vf0JiR7y8l/XgmAHv2/wnAkWPHycw8AUBsTDTe7YZPvtzMzUl1Al+ZUBEGN/3c007PA+np6fy8/SfKV6jI7l27SEg8tYRY2YREdu/adc7jRgx/nDrVKzFt6iQGD33ytO8OHz7MFwvmcn2XG7PSatSszberV2EuXHRUJBXLxvHLb/spW7oYO34/kPXdzt8PULZ0sbOOqVIunuZ1KrB07N3MG92XBtVPTXlrVDPRs5bf+Pt4YOTMrGC48cf/0aB6YuArFAI8ca3gz/Cw4OeHvXtTKV6ipN/HDR32NOu//4mbuvfk7bGnd3nnfv4pjZs0I65Uqay0yMhIomNiOHTIfUsl5bX4Ehdx4I+juWf0EhUZQanihWnR/02GvD6H9586NSc7edMOGtw2mqvueoOHb22ZtZDpiRPK8YxMihaOydPyh6Q8Ws8v2Cz4+aFwocIcO+b5Rbq0bFl27ji15tuunTu4tGzZHI+/6eaefDrzo9PSPpo2lRu73XxW3vRjxyhUqFAelNrdjqQfp5AToHbtOURimVPr8iWUKcGuPWf/gdm550DWYgerNu/khCrxJS86Lc+Wn/fwx5F0alU6tW5jTHQkR9PdMVAVBr1eC37+KBkXR2ZmJkePHqVN2yQWf7GA/Wlp7E9LY/EXC2jTNumsY35I2Za1//lns6ha7fKszwcPHOCr5UvpeF3n047Zt3cvpf4ST3R0dOAq4xL7Dx0lMiKC2Jgo5q/cRrtGVShZrBAlixWiXaMqzF+57axjPlm6mZb1PfOwq5T7CzFRkaTuP0yFS+OyRnTLX1ySyyvE8/Nv+wEoVbwwew94VnoJf4KIb1sos+f8/NS6TTtWfr2clq3b8uAjQ2jfyvMuiIf+OTSr6zpwQH/u6NufevUb8vSwoaRs20pEhJBYrgIvvvJa1rk+++RjWrVpT5EiRU67xrIvF9P+mo75V6kwtyA5hWZ1KrBo1Q88O34Ry966B4B/vbeItEOepape/2dX3v74G9Zs2cX4z9bw5qM3sGrC/aQfz6TfCM8CtM3qVOChW6/meMYJTpxQBr74CXsPHAagZf3LmPP1luBUMAhCPK75JGCPupyPgvCoy7q1a3jjtVcY89b43DOfp963dOPx4SMKxCovBeFRl7rVLuX+7s3p+8y0gF1j8oiePPbGvNOWvwpFefGoS6069fWDT5f4lLduheLue9QlXF1Ztz5XXd2KzMzMgJw/PT2dazt1LhCBr6BYu3U3S779kYgArcMUHRXJrC83h3zgy1NhcNPPur3nodftfQJ27piYGG6+5baAnd+tJny2JmDnPp6RyQdz1uaeMYyE+mMsvrDgZ4zxWzjc87PgZ4zxTwF4hs8XFvyMMX6zbq8xxnUEa/kZY1wqDGKfPepijDkPefSoi4iME5HfReQ7r7S6IrJCRNaKyCoRaeyki4iMEpEUEVkvIvW9juktItucrbcvVbDgZ4zxWx4uZvoe0OGMtOeB4apaF3jC+QzQEajqbP2BMQAiUgoYBjQBGgPDRCSOXFjwM8b4La+ecc7mLY8KnFwIswRwcq24LnhehauqugIoKSKXAtcA81V1n6qmAfM5O6Cexe75GWP85/tNv3gR8V6ccqyqjs3lmEHAXBEZiaeB1sxJTwB+9cq3w0nLLj1HFvyMMX45uZipj1LPY27vPcA/VHW6iHQH3gHa+XmOXFm31xjjn8AvZtobmOHsf4jnPh7ATqCcV75EJy279BxZ8DPG+C3A6xrsAlo6+22Ak4suzgJud0Z9mwIHVHU3MBdIEpE4Z6AjyUnLkXV7jTF+yruFSr3f8igiO/CM2t4FvCIiUcBRPCO7ALOBa4EU4DDQB0BV94nI00Cyk+8pVT1zEOUsFvyMMX7Lqxkeqtozm68anCOvAgOyOc84YJw/17bgZ4zxSwFYqs8nFvyMMf4Lg+hnwc8Y4zdb1cUY40q2qosxxn0EAvQ6lHxlwc8Ycx4KfvSz4GeM8YstZmqMca0wiH0W/Iwx/rOWnzHGlfJqelswWfAzxvit4Ic+C37GGD9d4HJVIcOCnzHGbzbDwxjjTgU/9lnwM8b4LwxinwU/Y4y/fH4tZUiz4GeM8Uu4zPCwd3gYY1zJWn7GGL+FQ8vPgp8xxm/2qIsxxn3sIWdjjBuFy4CHBT9jjN+s22uMcSVr+RljXCkMYp8FP2PMeQiD6GfBzxjjF4GwmN4mqhrsMmQRkT3Az8EuRwDEA6nBLoTxS7j+zCqoaukLOYGIzMHz/8cXqara4UKuFyghFfzClYisUtWGwS6H8Z39zMKfze01xriSBT9jjCtZ8MsfY4NdAOM3+5mFObvnZ4xxJWv5GWNcyYKfMcaVLPgFkIh0EJEtIpIiIoODXR6TOxEZJyK/i8h3wS6LCSwLfgEiIpHAa0BHoCbQU0RqBrdUxgfvASH5UK7JWxb8AqcxkKKqP6pqOjAZ6BLkMplcqOpSYF+wy2ECz4Jf4CQAv3p93uGkGWNCgAU/Y4wrWfALnJ1AOa/PiU6aMSYEWPALnGSgqohUEpEYoAcwK8hlMsY4LPgFiKpmAPcBc4HNwFRV3RjcUpnciMgk4GvgchHZISJ9g10mExg2vc0Y40rW8jPGuJIFP2OMK1nwM8a4kgU/Y4wrWfAzxriSBT+XEpFWIvKps985p1VnRKSkiNx7Htd4UkQe8jX9jDzvichNflyroq3EYvxhwS/MOKvJ+EVVZ6nqczlkKQn4HfyMCWUW/AoIp2XzvYhMFJHNIjJNRC5yvtsuIv8WkTVANxFJEpGvRWSNiHwoIkWdfB2cc6wBbvQ69x0i8qqzf7GIfCQi65ytGfAcUFlE1orIC06+h0UkWUTWi8hwr3MNFZGtIrIMuNyHet3lnGediEw/WSdHOxFZ5Zyvk5M/UkRe8Lr23Rf6/9a4kwW/guVy4HVVrQEc5PTW2F5VrQ8sAB4D2jmfVwH/JyKFgLeA64EGwCXZXGMUsERVrwTqAxuBwcAPqlpXVR8WkSSgKp5lu+oCDUSkhYg0wDONry5wLdDIhzrNUNVGzvU2A94zKio617gOeMOpQ1/ggKo2cs5/l4hU8uE6xpwmKtgFMH75VVWXO/vvAw8AI53PU5x/m+JZPHW5iADE4JmuVR34SVW3AYjI+0D/c1yjDXA7gKpmAgdEJO6MPEnO9q3zuSieYFgM+EhVDzvX8GUuc20ReQZP17oonumAJ01V1RPANhH50alDElDH635gCefaW324ljFZLPgVLGfORfT+/KfzrwDzVbWnd0YRqZuH5RDgWVV984xrDDqPc70HdFXVdSJyB9DK67tz1VeA+1XVO0giIhXP49rGxazbW7CUF5G/Ovu3AMvOkWcF0FxEqgCISBERqQZ8D1QUkcpOvp7nOBZgIXCPc2ykiJQADuFp1Z00F7jT615igoiUAZYCXUWksIgUw9PFzk0xYLeIRAO9zvium4hEOGW+DNjiXPseJz8iUk1EivhwHWNOY8GvYNkCDBCRzUAcMObMDKq6B7gDmCQi63G6vKp6FE839zNnwOP3bK4xEGgtIhuA1UBNVd2Lpxv9nYi8oKrzgA+Ar51804BiqroGT/d7HfA5nmW9cvM4sBJYjidAe/sF+MY519+dOrwNbALWOI+2vIn1YMx5sFVdCginW/epqtYOclGMCQvW8jPGuJK1/IwxrmQtP2OMK1nwM8a4kgU/Y4wrWfAzxriSBT9jjCv9P2bdRMKyjz4wAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_TmhFpBfHecZ"
      },
      "source": [
        "KNN on advance preprocessing data 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "sEU34J04Hf97",
        "outputId": "e3754aed-55a6-4a89-eb56-3e4600a699e2"
      },
      "source": [
        "KNN_bow_advance_2 = KNeighborsClassifier(n_neighbors=100,weights='uniform')\n",
        "KNN_bow_advance_2.fit(X_train, Y_train)\n",
        "predict_label = KNN_bow_advance_2.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.72      0.64      0.68      4493\n",
            "    negative       0.68      0.76      0.72      4507\n",
            "\n",
            "    accuracy                           0.70      9000\n",
            "   macro avg       0.70      0.70      0.70      9000\n",
            "weighted avg       0.70      0.70      0.70      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.698\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb16ff4e790>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvduDTGC40MK"
      },
      "source": [
        "SVM on data without preprocessing 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "IyYpOhZQaLN4",
        "outputId": "21170cdd-6acf-4319-d861-b2d743f6e7d7"
      },
      "source": [
        "from sklearn import svm\n",
        "SVM_bow_none_1 = svm.SVC()\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_none, Y, test_size=0.2, random_state=1)\n",
        "SVM_bow_none_1.fit(X_train,Y_train)\n",
        "predict_label = SVM_bow_none_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.86      0.83      0.85      4493\n",
            "    negative       0.84      0.87      0.85      4507\n",
            "\n",
            "    accuracy                           0.85      9000\n",
            "   macro avg       0.85      0.85      0.85      9000\n",
            "weighted avg       0.85      0.85      0.85      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8517777777777777\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb17001d190>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z02tt73pHvJM"
      },
      "source": [
        "SVM on data without preprocessing 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "NfWn1Hc_Hy9o",
        "outputId": "f3ccb08e-82be-4c51-9037-064ef75faff5"
      },
      "source": [
        "SVM_bow_none_2 = svm.SVC(C=0.01)\n",
        "SVM_bow_none_2.fit(X_train,Y_train)\n",
        "predict_label = SVM_bow_none_2.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.69      0.64      0.66      4493\n",
            "    negative       0.67      0.71      0.69      4507\n",
            "\n",
            "    accuracy                           0.68      9000\n",
            "   macro avg       0.68      0.68      0.68      9000\n",
            "weighted avg       0.68      0.68      0.68      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.6764444444444444\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb1700142d0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FXKJSkIt45QA"
      },
      "source": [
        "SVM on simple preprocessing data 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "RyARxkvkbmt-",
        "outputId": "f39db2b5-b438-4b0f-86a0-3f2dea26465d"
      },
      "source": [
        "SVM_bow_simple_1 = svm.SVC()\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_simple, Y, test_size=0.2, random_state=1)\n",
        "SVM_bow_simple_1.fit(X_train,Y_train)\n",
        "predict_label = SVM_bow_simple_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.86      0.84      0.85      4493\n",
            "    negative       0.84      0.87      0.86      4507\n",
            "\n",
            "    accuracy                           0.85      9000\n",
            "   macro avg       0.85      0.85      0.85      9000\n",
            "weighted avg       0.85      0.85      0.85      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8531111111111112\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a36787b10>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngV7_fid48Wj"
      },
      "source": [
        "SVM on advance preprocessing data 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "iK647amkbnbM",
        "outputId": "ffb2f7b3-af33-4267-ac56-bb99cf13c453"
      },
      "source": [
        "SVM_bow_advance_1 = svm.SVC()\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_advance, Y, test_size=0.2, random_state=1)\n",
        "SVM_bow_advance_1.fit(X_train,Y_train)\n",
        "predict_label = SVM_bow_advance_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.87      0.85      0.86      4493\n",
            "    negative       0.85      0.87      0.86      4507\n",
            "\n",
            "    accuracy                           0.86      9000\n",
            "   macro avg       0.86      0.86      0.86      9000\n",
            "weighted avg       0.86      0.86      0.86      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8594444444444445\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7f6a9cc795d0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AK1u_dhAIvDa"
      },
      "source": [
        "SVM on advance preprocessing data 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "1gegwI9jIvzM",
        "outputId": "dc015bfd-0b4a-4cbb-b5a0-d6615760cc99"
      },
      "source": [
        "X_train, X_val, Y_train, Y_val = train_test_split(X_advance, Y, test_size=0.2, random_state=1)\n",
        "SVM_bow_advance_2 = svm.SVC(C=0.01)\n",
        "SVM_bow_advance_2.fit(X_train,Y_train)\n",
        "predict_label = SVM_bow_advance_2.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.80      0.59      0.68      4493\n",
            "    negative       0.67      0.85      0.75      4507\n",
            "\n",
            "    accuracy                           0.72      9000\n",
            "   macro avg       0.74      0.72      0.71      9000\n",
            "weighted avg       0.74      0.72      0.71      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.7194444444444444\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb17022a610>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGSpkrcIDlDx"
      },
      "source": [
        "***3.2) defining word2vec method and function for declaring sentence's vector based on word's vectors in the sentence***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pp4l2XyG6yl4"
      },
      "source": [
        "from gensim.models import word2vec\n",
        "def make_word2vec(sentence):\n",
        "    num_features = 250\n",
        "    min_count = 40\n",
        "    num_processor = 4\n",
        "    context = 10\n",
        "    downsampling = 0.001\n",
        "    model = word2vec.Word2Vec(sentence, workers = num_processor, size = num_features, min_count = min_count,window = context, sample = downsampling)\n",
        "    model.init_sims(replace = True)\n",
        "    model_name = str(num_features)+\"features_\"+str(min_count)+\"minwords\"\n",
        "    model.save(model_name)\n",
        "    return model\n",
        "def makeFeatureVec(review, model, num_features): \n",
        "    featureVec = np.zeros((num_features,), dtype = \"float32\")\n",
        "    word_index = set(model.wv.index2word)\n",
        "    nword = 0\n",
        "    for word in review:\n",
        "        if word in word_index:\n",
        "            nword += 1\n",
        "            featureVec = np.add(featureVec, model[word])\n",
        "    featureVec = np.divide(featureVec, nword)        \n",
        "    return featureVec"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmsd0ff1D9Fx"
      },
      "source": [
        "creating word2vec vectorize method on advance preprocessed data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9VzgLpK5BHss",
        "outputId": "aca5d199-f7ab-4daa-9fe4-f04f8f82530a"
      },
      "source": [
        "model = make_word2vec(cm_advance_preproc.tokenized)\n",
        "output_w2v = []\n",
        "for comment in cm_advance_preproc.tokenized:\n",
        "    featureVec = makeFeatureVec(comment,model,250)\n",
        "    output_w2v.append(featureVec)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-MYFKUZEEat"
      },
      "source": [
        "Run logistic regression on word2vec data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0gIHIZqCDEr"
      },
      "source": [
        "X_train ,X_val,Y_train,Y_val= train_test_split(output_w2v,Y,test_size=0.2,random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Z342sP_UCmao",
        "outputId": "cd7e7b0c-7040-4698-b2a3-888cb9e49514"
      },
      "source": [
        "LR_w2v_1 = LogisticRegression(solver='saga',C=1,warm_start=True).fit(X_train,Y_train,sample_weight=None)\n",
        "predict_label = LR_w2v_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.87      0.86      0.87      4493\n",
            "    negative       0.86      0.87      0.87      4507\n",
            "\n",
            "    accuracy                           0.87      9000\n",
            "   macro avg       0.87      0.87      0.87      9000\n",
            "weighted avg       0.87      0.87      0.87      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8668888888888889\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7f056192b210>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vt1RKtwOEN_-"
      },
      "source": [
        "Run KNN on word2vec data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "-4tJTYu4CqJN",
        "outputId": "cbeacfad-5070-4710-faf7-9995267b686c"
      },
      "source": [
        "KNN_w2v_1 = KNeighborsClassifier(n_neighbors=50)\n",
        "KNN_w2v_1.fit(X_train, Y_train)\n",
        "predict_label = KNN_w2v_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.81      0.87      0.84      4493\n",
            "    negative       0.86      0.80      0.83      4507\n",
            "\n",
            "    accuracy                           0.83      9000\n",
            "   macro avg       0.83      0.83      0.83      9000\n",
            "weighted avg       0.83      0.83      0.83      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8332222222222222\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb1729f57d0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4wdjO9wES3R"
      },
      "source": [
        "Run SVM on word2vec data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "0IzCyNZ3C1IC",
        "outputId": "992d1ca4-6859-48df-c73f-a683a34ae4f3"
      },
      "source": [
        "SVM_w2v_1 = svm.SVC(C=1.28)\n",
        "SVM_w2v_1.fit(X_train,Y_train)\n",
        "predict_label = SVM_w2v_1.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.89      0.88      0.88      4493\n",
            "    negative       0.88      0.89      0.89      4507\n",
            "\n",
            "    accuracy                           0.88      9000\n",
            "   macro avg       0.88      0.88      0.88      9000\n",
            "weighted avg       0.88      0.88      0.88      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.8842222222222222\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb172bad790>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5gMp8-AHqZ3"
      },
      "source": [
        "Run MLP on word2vec and advance preprocessing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "a9JvNm31FdfK",
        "outputId": "9c277da3-779f-4d15-fff3-9aa9d281afb3"
      },
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "clf = MLPClassifier(random_state=1, max_iter=300,hidden_layer_sizes=(500,200,50,10,)).fit(X_train, Y_train)\n",
        "predict_label = clf.predict(X_val)\n",
        "analysis(Y_val,predict_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.87      0.85      0.86      4493\n",
            "    negative       0.86      0.87      0.86      4507\n",
            "\n",
            "    accuracy                           0.86      9000\n",
            "   macro avg       0.86      0.86      0.86      9000\n",
            "weighted avg       0.86      0.86      0.86      9000\n",
            "\n",
            "\n",
            "Accuracy: \n",
            " 0.862\n",
            "\n",
            "Confusion Matrix: \n",
            " (<Figure size 432x288 with 2 Axes>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fb172af8610>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JMqDSG9LkHM"
      },
      "source": [
        "Save best models in all scenarios"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "50m_ZfV5LsZl"
      },
      "source": [
        "filename = 'LR.pkl'\n",
        "pickle.dump(LR_w2v_1, open(filename, 'wb'))\n",
        "\n",
        "#filename2 = 'kNN.pkl'\n",
        "#pickle.dump(KNN_w2v_1, open(filename2, 'wb'))\n",
        "\n",
        "#filename3 = 'SVM.pkl'\n",
        "#pickle.dump(SVM_w2v_1, open(filename3, 'wb'))\n",
        "\n",
        "filename4 = 'best.pkl'\n",
        "pickle.dump(LR_w2v_1, open(filename4, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}