IMDB_Sentiment_Analysis_Logistic_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "IMDB_Sentiment_Analysis_Logistic_Analysis.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w7NZqJdaXP9L"
      },
      "source": [
        "# <font color='greyblue'>Logistic Regression: A Sentimental Analysis Case Study</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wURkyFEGoYXh"
      },
      "source": [
        "## Task 1: Loading the dataset\n",
        "_______"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abwNTsXI3Vku",
        "outputId": "921835b6-7abd-43eb-dc0d-29300c93ce4f"
      },
      "source": [
        "!wget \"https://raw.githubusercontent.com/TheClub4/IMDB_Sentiment_Analysis/master/movie_data.csv\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-11-14 19:18:09--  https://raw.githubusercontent.com/TheClub4/IMDB_Sentiment_Analysis/master/movie_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 65862309 (63M) [text/plain]\n",
            "Saving to: ‘movie_data.csv’\n",
            "\n",
            "movie_data.csv      100%[===================>]  62.81M   171MB/s    in 0.4s    \n",
            "\n",
            "2021-11-14 19:18:10 (171 MB/s) - ‘movie_data.csv’ saved [65862309/65862309]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSTcQZxvW23C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "5e95e14c-4c74-4105-aaca-f3452f71e144"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('movie_data.csv')\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>In 1974, the teenager Martha Moxley (Maggie Gr...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OK... so... I really like Kris Kristofferson a...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>***SPOILER*** Do not read this, if you think a...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hi for all the people who have seen this wonde...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I recently bought the DVD, forgetting just how...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Leave it to Braik to put on a good show. Final...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Nathan Detroit (Frank Sinatra) is the manager ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>To understand \"Crash Course\" in the right cont...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>I've been impressed with Chavez's stance again...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>This movie is directed by Renny Harlin the fin...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              review  sentiment\n",
              "0  In 1974, the teenager Martha Moxley (Maggie Gr...          1\n",
              "1  OK... so... I really like Kris Kristofferson a...          0\n",
              "2  ***SPOILER*** Do not read this, if you think a...          0\n",
              "3  hi for all the people who have seen this wonde...          1\n",
              "4  I recently bought the DVD, forgetting just how...          0\n",
              "5  Leave it to Braik to put on a good show. Final...          1\n",
              "6  Nathan Detroit (Frank Sinatra) is the manager ...          1\n",
              "7  To understand \"Crash Course\" in the right cont...          1\n",
              "8  I've been impressed with Chavez's stance again...          1\n",
              "9  This movie is directed by Renny Harlin the fin...          1"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCAVBJTrJevB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28ecdc1f-0494-4445-c5c3-a512fca82e58"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "count = CountVectorizer()\n",
        "docs = (['The sun is shining',\n",
        "         'The weather is sweet',\n",
        "         'The sun is shining, the weather is sweet, and one and one is two'])\n",
        "bag = count.fit_transform(docs)\n",
        "\n",
        "print(count.vocabulary_)\n",
        "print(bag.toarray())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'the': 6, 'sun': 4, 'is': 1, 'shining': 3, 'weather': 8, 'sweet': 5, 'and': 0, 'one': 2, 'two': 7}\n",
            "[[0 1 0 1 1 0 1 0 0]\n",
            " [0 1 0 0 0 1 1 0 1]\n",
            " [2 3 2 1 1 1 2 1 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7cV4D71OvaG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acdd0f3a-7a6d-4aa2-ff10-45379afc9750"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "tfidf = TfidfTransformer(use_idf=True, norm='l2', smooth_idf=True)\n",
        "print(tfidf.fit_transform(bag).toarray())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.   0.43 0.   0.56 0.56 0.   0.43 0.   0.  ]\n",
            " [0.   0.43 0.   0.   0.   0.56 0.43 0.   0.56]\n",
            " [0.5  0.45 0.5  0.19 0.19 0.19 0.3  0.25 0.19]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKN8_z6evUhq"
      },
      "source": [
        "# Task 2: Data Cleaning\n",
        "___"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h91jdeUYvbPi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bc4c699d-7191-4d29-b419-6617b26a1cae"
      },
      "source": [
        "import re\n",
        "def preprocessor(text):\n",
        "  text =re.sub('<[^>]*>', '', text)\n",
        "  emoticons = re.findall('(?::|;|=)(?:-)?(?:\\)|\\(|D|P)', text)\n",
        "  text = re.sub('[\\W]+', ' ', text.lower()) + ' '.join(emoticons).replace('-', '')\n",
        "  \n",
        "  return text\n",
        "\n",
        "preprocessor(\"This is a :) test :-( !\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'this is a test :) :('"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-LvnQBvxKbo"
      },
      "source": [
        "# preprocess data\n",
        "df['review'] = df['review'].apply(preprocessor)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YqTI3Q1pKpg"
      },
      "source": [
        "# Task 3: Tokenization\n",
        "___"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDcP3IOVreyV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6121c037-5299-4672-f778-83ce9027199c"
      },
      "source": [
        "from nltk.stem.porter import PorterStemmer\n",
        "\n",
        "porter = PorterStemmer()\n",
        "\n",
        "def tokenizer(text):\n",
        "  return text.split()\n",
        "\n",
        "def tokenizer_stemmer(text):\n",
        "  return[porter.stem(word) for word in text.split()]\n",
        "\n",
        "tokenizer('runners like running thus they run')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['runners', 'like', 'running', 'thus', 'they', 'run']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGilfxPTtjkp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5efd4454-cfef-4582-9908-46cf4df15d71"
      },
      "source": [
        "tokenizer_stemmer('runners like running thus they run')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['runner', 'like', 'run', 'thu', 'they', 'run']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DncACLVbs0rs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f5f6cda-59ee-4702-f20d-9d482d819c9a"
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjaxqBMks9nb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ddc1458-b8e2-41bc-8885-71cd056f1033"
      },
      "source": [
        "from nltk.corpus import stopwords\n",
        "\n",
        "stop = stopwords.words('english')\n",
        "[w for w in tokenizer_stemmer('runners like running thus they run') if w not in stop]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['runner', 'like', 'run', 'thu', 'run']"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MrnlJZfnsmRB"
      },
      "source": [
        "# Task 4: Transform Text Data into TF-IDF Vectors\n",
        "___"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCx6sZ5bswOa"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "tfidf = TfidfVectorizer(strip_accents=None,\n",
        "                         lowercase=True,\n",
        "                         preprocessor=None,  # applied preprocessor in Data Cleaning\n",
        "                         tokenizer=tokenizer_stemmer,\n",
        "                         use_idf=True,\n",
        "                         norm='l2',\n",
        "                         smooth_idf=True)\n",
        "\n",
        "y = df.sentiment.values\n",
        "X = tfidf.fit_transform(df.review)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uz4ifZsCFd0o",
        "outputId": "a9af2952-22a7-4fc9-8ce1-a2d4040ae6c4"
      },
      "source": [
        "type(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "scipy.sparse.csr.csr_matrix"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Weuh20fNHTCS"
      },
      "source": [
        "from scipy.sparse import csr_matrix\n",
        "X=csr_matrix(X, shape=(50000,73814)).toarray()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEs6rpDTHWLu",
        "outputId": "1c28f03e-7205-44ad-d82b-49d64fd47488",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "type(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-179cf50426b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSWWr3mX7Nxk"
      },
      "source": [
        "# save train/test data\n",
        "np.save(\"movie_trainX.npy\",X)\n",
        "np.save(\"movie_trainy.npy\",y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkqYxECZ8kIy",
        "outputId": "6e86f59c-cefa-4073-9938-59377ff22f70"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21jK8XLq9kvD"
      },
      "source": [
        "#!mkdir \"/content/drive/MyDrive/movie_dataset\"\n",
        "!cp \"/content/movie_trainX.npy\" \"/content/drive/MyDrive/movie_dataset\"\n",
        "!cp \"/content/movie_trainy.npy\" \"/content/drive/MyDrive/movie_dataset\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hDqzZRSBxs3r"
      },
      "source": [
        "# Task 5: Document Classification using Logistic Regression\n",
        "___"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ebGfczNy2e9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4260c9bc-c0fe-4d7d-8f0d-d659900ec5cd"
      },
      "source": [
        "import pickle\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegressionCV\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, test_size=0.5, shuffle=False)\n",
        "\n",
        "clf = LogisticRegressionCV(cv=5,\n",
        "                           scoring='accuracy',\n",
        "                           random_state=0,\n",
        "                           n_jobs=-1,\n",
        "                           verbose=3,\n",
        "                           max_iter=300).fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:  2.5min finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvyYKbsWznIY"
      },
      "source": [
        "saved_model = open('saved_model.sav', 'wb')\n",
        "pickle.dump(clf, saved_model)\n",
        "saved_model.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNb1TJ7M-6_D"
      },
      "source": [
        "!cp \"/content/saved_model.sav\" \"/content/drive/MyDrive/movie_dataset/logistic.sav\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hRthxGO5hpF"
      },
      "source": [
        "# Task 6: Model Evaluation\n",
        "___"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUrG-Gni5gpW"
      },
      "source": [
        "filename = 'saved_model.sav'\n",
        "saved_clf = pickle.load(open(filename, 'rb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHTUMFL35yzH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae71da74-7e8a-4f89-afed-2a32397067a0"
      },
      "source": [
        "clf.score(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.89608"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VpjsQu-32az",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "760bc67e-747c-4ec1-b984-f607c8a33392"
      },
      "source": [
        "yhat = clf.predict(X_test)\n",
        "yhat"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, ..., 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DdxWLBAR4EMH"
      },
      "source": [
        "### Confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dULjrws94Csm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ed6b61d-5188-45a0-ae8a-57d0fc69bc29"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import itertools\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "print(confusion_matrix(y_test, yhat, labels=[1,0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[11209  1264]\n",
            " [ 1334 11193]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4nKywhc4U0K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "3c86828c-dea8-4372-951c-f29c1ad5bc0f"
      },
      "source": [
        "# Compute confusion matrix\n",
        "cnf_matrix = confusion_matrix(y_test, yhat, labels=[1,0])\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plt.figure(figsize=(13,5))\n",
        "plot_confusion_matrix(cnf_matrix, classes=['positive=1','negative=0'],normalize= False,  title='Confusion matrix')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[11209  1264]\n",
            " [ 1334 11193]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}