Comment Toxicity Classification.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-07-21T14:57:26.128270Z","iopub.execute_input":"2023-07-21T14:57:26.128731Z","iopub.status.idle":"2023-07-21T14:57:26.184987Z","shell.execute_reply.started":"2023-07-21T14:57:26.128692Z","shell.execute_reply":"2023-07-21T14:57:26.183653Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/jigsaw-toxic-comment-classification-challenge/sample_submission.csv\n/kaggle/input/jigsaw-toxic-comment-classification-challenge/test_labels.csv\n/kaggle/input/jigsaw-toxic-comment-classification-challenge/train.csv\n/kaggle/input/jigsaw-toxic-comment-classification-challenge/test.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport scipy\nfrom bs4 import BeautifulSoup\nimport re\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom keras import layers\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport spacy\n!spacy download en_core_web_sm\nfrom spacy.lang.en.stop_words import STOP_WORDS","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:53:35.965491Z","iopub.execute_input":"2023-07-22T10:53:35.965828Z","iopub.status.idle":"2023-07-22T10:54:05.441684Z","shell.execute_reply.started":"2023-07-22T10:53:35.965802Z","shell.execute_reply":"2023-07-22T10:54:05.440623Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n  warnings.warn(f\"file system plugins are not loaded: {e}\")\n","output_type":"stream"},{"name":"stdout","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n  warnings.warn(f\"file system plugins are not loaded: {e}\")\nCollecting en-core-web-sm==3.6.0\n  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.6.0/en_core_web_sm-3.6.0-py3-none-any.whl (12.8 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m34.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: spacy<3.7.0,>=3.6.0 in /opt/conda/lib/python3.10/site-packages (from en-core-web-sm==3.6.0) (3.6.0)\nRequirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.0.12)\nRequirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.0.4)\nRequirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.0.9)\nRequirement already satisfied: cymem<2.1.0,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.0.7)\nRequirement already satisfied: preshed<3.1.0,>=3.0.2 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.0.8)\nRequirement already satisfied: thinc<8.2.0,>=8.1.8 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (8.1.10)\nRequirement already satisfied: wasabi<1.2.0,>=0.9.1 in /opt/conda/lib/python3.10/site-packages (from 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satisfied: numpy>=1.15.0 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.23.5)\nRequirement already satisfied: requests<3.0.0,>=2.13.0 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.31.0)\nRequirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.10.9)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.1.2)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (59.8.0)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (21.3)\nRequirement already satisfied: langcodes<4.0.0,>=3.2.0 in /opt/conda/lib/python3.10/site-packages (from 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certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2023.5.7)\nRequirement already satisfied: blis<0.8.0,>=0.7.8 in /opt/conda/lib/python3.10/site-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.7.9)\nRequirement already satisfied: confection<1.0.0,>=0.0.1 in /opt/conda/lib/python3.10/site-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.1.0)\nRequirement already satisfied: click<9.0.0,>=7.1.1 in /opt/conda/lib/python3.10/site-packages (from typer<0.10.0,>=0.3.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (8.1.3)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.1.3)\n\u001b[38;5;2m✔ Download and installation successful\u001b[0m\nYou can now load the package via spacy.load('en_core_web_sm')\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:05.443819Z","iopub.execute_input":"2023-07-22T10:54:05.444950Z","iopub.status.idle":"2023-07-22T10:54:05.448779Z","shell.execute_reply.started":"2023-07-22T10:54:05.444912Z","shell.execute_reply":"2023-07-22T10:54:05.448213Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/jigsaw-toxic-comment-classification-challenge/train.csv')\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:05.449660Z","iopub.execute_input":"2023-07-22T10:54:05.450751Z","iopub.status.idle":"2023-07-22T10:54:06.921433Z","shell.execute_reply.started":"2023-07-22T10:54:05.450697Z","shell.execute_reply":"2023-07-22T10:54:06.920555Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"                 id                                       comment_text  toxic  \\\n0  0000997932d777bf  Explanation\\nWhy the edits made under my usern...      0   \n1  000103f0d9cfb60f  D'aww! He matches this background colour I'm s...      0   \n2  000113f07ec002fd  Hey man, I'm really not trying to edit war. It...      0   \n3  0001b41b1c6bb37e  \"\\nMore\\nI can't make any real suggestions on ...      0   \n4  0001d958c54c6e35  You, sir, are my hero. Any chance you remember...      0   \n\n   severe_toxic  obscene  threat  insult  identity_hate  \n0             0        0       0       0              0  \n1             0        0       0       0              0  \n2             0        0       0       0              0  \n3             0        0       0       0              0  \n4             0        0       0       0              0  ","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>id</th>\n      <th>comment_text</th>\n      <th>toxic</th>\n      <th>severe_toxic</th>\n      <th>obscene</th>\n      <th>threat</th>\n      <th>insult</th>\n      <th>identity_hate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0000997932d777bf</td>\n      <td>Explanation\\nWhy the edits made under my usern...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000103f0d9cfb60f</td>\n      <td>D'aww! He matches this background colour I'm s...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>000113f07ec002fd</td>\n      <td>Hey man, I'm really not trying to edit war. It...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0001b41b1c6bb37e</td>\n      <td>\"\\nMore\\nI can't make any real suggestions on ...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0001d958c54c6e35</td>\n      <td>You, sir, are my hero. Any chance you remember...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"DATA CLEANING","metadata":{}},{"cell_type":"code","source":"train_df.duplicated().sum()","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:06.923798Z","iopub.execute_input":"2023-07-22T10:54:06.924053Z","iopub.status.idle":"2023-07-22T10:54:07.155403Z","shell.execute_reply.started":"2023-07-22T10:54:06.924031Z","shell.execute_reply":"2023-07-22T10:54:07.154426Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"# A list of the columns in the training dataframe\ntrain_columns = train_df.columns","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:07.156901Z","iopub.execute_input":"2023-07-22T10:54:07.157802Z","iopub.status.idle":"2023-07-22T10:54:07.161904Z","shell.execute_reply.started":"2023-07-22T10:54:07.157770Z","shell.execute_reply":"2023-07-22T10:54:07.161036Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"for i in train_columns:\n  print(i,' contains: %f missing values' %train_df[i].isna().sum())","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:07.163609Z","iopub.execute_input":"2023-07-22T10:54:07.163960Z","iopub.status.idle":"2023-07-22T10:54:07.203883Z","shell.execute_reply.started":"2023-07-22T10:54:07.163934Z","shell.execute_reply":"2023-07-22T10:54:07.202965Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"id  contains: 0.000000 missing values\ncomment_text  contains: 0.000000 missing values\ntoxic  contains: 0.000000 missing values\nsevere_toxic  contains: 0.000000 missing values\nobscene  contains: 0.000000 missing values\nthreat  contains: 0.000000 missing values\ninsult  contains: 0.000000 missing values\nidentity_hate  contains: 0.000000 missing values\n","output_type":"stream"}]},{"cell_type":"code","source":"columns = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\ny_train = pd.DataFrame(columns = columns)\n\nfor i in columns:\n  y_train[i] = train_df[i]\n\ntrain_df = train_df.drop(['id','toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'], axis=1)\ny_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:07.206759Z","iopub.execute_input":"2023-07-22T10:54:07.207044Z","iopub.status.idle":"2023-07-22T10:54:07.269720Z","shell.execute_reply.started":"2023-07-22T10:54:07.207022Z","shell.execute_reply":"2023-07-22T10:54:07.268919Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"   toxic  severe_toxic  obscene  threat  insult  identity_hate\n0      0             0        0       0       0              0\n1      0             0        0       0       0              0\n2      0             0        0       0       0              0\n3      0             0        0       0       0              0\n4      0             0        0       0       0              0","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>toxic</th>\n      <th>severe_toxic</th>\n      <th>obscene</th>\n      <th>threat</th>\n      <th>insult</th>\n      <th>identity_hate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:07.270741Z","iopub.execute_input":"2023-07-22T10:54:07.271298Z","iopub.status.idle":"2023-07-22T10:54:07.280741Z","shell.execute_reply.started":"2023-07-22T10:54:07.271269Z","shell.execute_reply":"2023-07-22T10:54:07.279838Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"                                        comment_text\n0  Explanation\\nWhy the edits made under my usern...\n1  D'aww! He matches this background colour I'm s...\n2  Hey man, I'm really not trying to edit war. It...\n3  \"\\nMore\\nI can't make any real suggestions on ...\n4  You, sir, are my hero. Any chance you remember...","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>comment_text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Explanation\\nWhy the edits made under my usern...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>D'aww! He matches this background colour I'm s...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Hey man, I'm really not trying to edit war. It...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>\"\\nMore\\nI can't make any real suggestions on ...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>You, sir, are my hero. Any chance you remember...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y_train.sum(axis = 0).plot.bar()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:07.282926Z","iopub.execute_input":"2023-07-22T10:54:07.283901Z","iopub.status.idle":"2023-07-22T10:54:07.496283Z","shell.execute_reply.started":"2023-07-22T10:54:07.283865Z","shell.execute_reply":"2023-07-22T10:54:07.495236Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"test_df = pd.read_csv('/kaggle/input/jigsaw-toxic-comment-classification-challenge/test.csv')\ntest_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:07.498721Z","iopub.execute_input":"2023-07-22T10:54:07.499013Z","iopub.status.idle":"2023-07-22T10:54:09.008002Z","shell.execute_reply.started":"2023-07-22T10:54:07.498990Z","shell.execute_reply":"2023-07-22T10:54:09.006811Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"                 id                                       comment_text\n0  00001cee341fdb12  Yo bitch Ja Rule is more succesful then you'll...\n1  0000247867823ef7  == From RfC == \\n\\n The title is fine as it is...\n2  00013b17ad220c46  \" \\n\\n == Sources == \\n\\n * Zawe Ashton on Lap...\n3  00017563c3f7919a  :If you have a look back at the source, the in...\n4  00017695ad8997eb          I don't anonymously edit articles at all.","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>id</th>\n      <th>comment_text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00001cee341fdb12</td>\n      <td>Yo bitch Ja Rule is more succesful then you'll...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0000247867823ef7</td>\n      <td>== From RfC == \\n\\n The title is fine as it is...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00013b17ad220c46</td>\n      <td>\" \\n\\n == Sources == \\n\\n * Zawe Ashton on Lap...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00017563c3f7919a</td>\n      <td>:If you have a look back at the source, the in...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>00017695ad8997eb</td>\n      <td>I don't anonymously edit articles at all.</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y_test = pd.read_csv('/kaggle/input/jigsaw-toxic-comment-classification-challenge/test_labels.csv')\ny_test = y_test.drop('id', axis=1)\ny_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:11.704584Z","iopub.execute_input":"2023-07-22T10:54:11.705081Z","iopub.status.idle":"2023-07-22T10:54:11.890832Z","shell.execute_reply.started":"2023-07-22T10:54:11.705053Z","shell.execute_reply":"2023-07-22T10:54:11.890239Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"   toxic  severe_toxic  obscene  threat  insult  identity_hate\n0     -1            -1       -1      -1      -1             -1\n1     -1            -1       -1      -1      -1             -1\n2     -1            -1       -1      -1      -1             -1\n3     -1            -1       -1      -1      -1             -1\n4     -1            -1       -1      -1      -1             -1","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>toxic</th>\n      <th>severe_toxic</th>\n      <th>obscene</th>\n      <th>threat</th>\n      <th>insult</th>\n      <th>identity_hate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y_test = y_test.replace(-1, 0)\ny_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:12.007185Z","iopub.execute_input":"2023-07-22T10:54:12.007761Z","iopub.status.idle":"2023-07-22T10:54:12.026021Z","shell.execute_reply.started":"2023-07-22T10:54:12.007731Z","shell.execute_reply":"2023-07-22T10:54:12.025092Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"   toxic  severe_toxic  obscene  threat  insult  identity_hate\n0      0             0        0       0       0              0\n1      0             0        0       0       0              0\n2      0             0        0       0       0              0\n3      0             0        0       0       0              0\n4      0             0        0       0       0              0","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>toxic</th>\n      <th>severe_toxic</th>\n      <th>obscene</th>\n      <th>threat</th>\n      <th>insult</th>\n      <th>identity_hate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y_test.sum(axis = 0).plot.bar()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:46.124787Z","iopub.execute_input":"2023-07-22T10:54:46.125121Z","iopub.status.idle":"2023-07-22T10:54:46.278321Z","shell.execute_reply.started":"2023-07-22T10:54:46.125095Z","shell.execute_reply":"2023-07-22T10:54:46.277669Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"def clean_data(text):\n  # Remove hyperlinks\n  res = re.sub('https\\S+|www\\S+|https\\S+', '', text)\n\n  # Remove special character\n  res = re.sub('[^\\w\\s]', '', res)\n\n  # Remove numbers\n  res = re.sub('\\d+', '', res)\n\n  return res","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:52.291518Z","iopub.execute_input":"2023-07-22T10:54:52.291847Z","iopub.status.idle":"2023-07-22T10:54:52.297086Z","shell.execute_reply.started":"2023-07-22T10:54:52.291821Z","shell.execute_reply":"2023-07-22T10:54:52.296080Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"train_df = train_df['comment_text'].apply(clean_data)\nprint(train_df.head())\n\ntest_df = test_df['comment_text'].apply(clean_data)\nprint(test_df.head())","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:54:53.132937Z","iopub.execute_input":"2023-07-22T10:54:53.133248Z","iopub.status.idle":"2023-07-22T10:54:59.509881Z","shell.execute_reply.started":"2023-07-22T10:54:53.133223Z","shell.execute_reply":"2023-07-22T10:54:59.508981Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"0    Yo bitch Ja Rule is more succesful then youll ...\n1        From RfC  \\n\\n The title is fine as it is IMO\n2     \\n\\n  Sources  \\n\\n  Zawe Ashton on Lapland     \n3    If you have a look back at the source the info...\n4              I dont anonymously edit articles at all\nName: comment_text, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"vectorizer = layers.TextVectorization(max_tokens = 6000, output_mode=\"tf-idf\", ngrams=1)\n\n\nwith tf.device('CPU'):\n  vectorizer.adapt(train_df)","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:58:55.968955Z","iopub.execute_input":"2023-07-22T10:58:55.969296Z","iopub.status.idle":"2023-07-22T10:59:10.766399Z","shell.execute_reply.started":"2023-07-22T10:58:55.969269Z","shell.execute_reply":"2023-07-22T10:59:10.765457Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"train_df = vectorizer(train_df)\ntest_df = vectorizer(test_df)\n\nprint(\"train_df:\", train_df.shape)\nprint(\"test_df:\", test_df.shape)","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:59:12.808807Z","iopub.execute_input":"2023-07-22T10:59:12.809204Z","iopub.status.idle":"2023-07-22T10:59:19.337267Z","shell.execute_reply.started":"2023-07-22T10:59:12.809149Z","shell.execute_reply":"2023-07-22T10:59:19.335771Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"train_df: (159571, 6000)\ntest_df: (153164, 6000)\n","output_type":"stream"}]},{"cell_type":"code","source":"test_df[:10]","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:59:25.662085Z","iopub.execute_input":"2023-07-22T10:59:25.662456Z","iopub.status.idle":"2023-07-22T10:59:25.671786Z","shell.execute_reply.started":"2023-07-22T10:59:25.662424Z","shell.execute_reply":"2023-07-22T10:59:25.670508Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(10, 6000), dtype=float32, numpy=\narray([[82.613434  ,  0.        ,  0.98910034, ...,  0.        ,\n         0.        ,  0.        ],\n       [ 0.        ,  0.91389847,  0.        , ...,  0.        ,\n         0.        ,  0.        ],\n       [19.064638  ,  0.        ,  0.        , ...,  0.        ,\n         0.        ,  0.        ],\n       ...,\n       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n         0.        ,  0.        ],\n       [63.548794  ,  3.6555939 ,  2.967301  , ...,  0.        ,\n         0.        ,  0.        ],\n       [12.709759  ,  1.8277969 ,  2.967301  , ...,  0.        ,\n         0.        ,  0.        ]], dtype=float32)>"},"metadata":{}}]},{"cell_type":"markdown","source":"Build the Model","metadata":{}},{"cell_type":"code","source":"model = keras.Sequential([\n    layers.Dense(100, input_shape=(6000,), activation = \"relu\"),\n\n    layers.Dense(50, activation = \"relu\"),\n\n    layers.Dense(6, activation = \"sigmoid\")\n])\nmodel.compile(\noptimizer = \"adam\",\n    loss = \"binary_crossentropy\",\n    metrics = [\"binary_accuracy\"]\n)","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:59:30.676616Z","iopub.execute_input":"2023-07-22T10:59:30.676940Z","iopub.status.idle":"2023-07-22T10:59:30.730526Z","shell.execute_reply.started":"2023-07-22T10:59:30.676914Z","shell.execute_reply":"2023-07-22T10:59:30.729489Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"nn = model.fit(train_df, y_train, epochs= 50, validation_data = (test_df, y_test))","metadata":{"execution":{"iopub.status.busy":"2023-07-22T10:59:31.991566Z","iopub.execute_input":"2023-07-22T10:59:31.991882Z","iopub.status.idle":"2023-07-22T11:28:54.644558Z","shell.execute_reply.started":"2023-07-22T10:59:31.991858Z","shell.execute_reply":"2023-07-22T11:28:54.643248Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"Epoch 1/50\n4987/4987 [==============================] - 36s 7ms/step - loss: 0.0886 - binary_accuracy: 0.9781 - val_loss: 0.2108 - val_binary_accuracy: 0.9473\nEpoch 2/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0521 - binary_accuracy: 0.9834 - val_loss: 0.2522 - val_binary_accuracy: 0.9351\nEpoch 3/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0397 - binary_accuracy: 0.9866 - val_loss: 0.2792 - val_binary_accuracy: 0.9353\nEpoch 4/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0298 - binary_accuracy: 0.9894 - val_loss: 0.2809 - val_binary_accuracy: 0.9362\nEpoch 5/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0237 - binary_accuracy: 0.9919 - val_loss: 0.4074 - val_binary_accuracy: 0.9279\nEpoch 6/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0218 - binary_accuracy: 0.9932 - val_loss: 0.3748 - val_binary_accuracy: 0.9314\nEpoch 7/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0165 - binary_accuracy: 0.9946 - val_loss: 0.3711 - val_binary_accuracy: 0.9377\nEpoch 8/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0140 - binary_accuracy: 0.9955 - val_loss: 0.4642 - val_binary_accuracy: 0.9336\nEpoch 9/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0122 - binary_accuracy: 0.9962 - val_loss: 0.5125 - val_binary_accuracy: 0.9363\nEpoch 10/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0111 - binary_accuracy: 0.9966 - val_loss: 0.5746 - val_binary_accuracy: 0.9320\nEpoch 11/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0107 - binary_accuracy: 0.9970 - val_loss: 0.6285 - val_binary_accuracy: 0.9327\nEpoch 12/50\n4987/4987 [==============================] - 36s 7ms/step - loss: 0.0101 - binary_accuracy: 0.9972 - val_loss: 0.6151 - val_binary_accuracy: 0.9326\nEpoch 13/50\n4987/4987 [==============================] - 36s 7ms/step - loss: 0.0083 - binary_accuracy: 0.9976 - val_loss: 0.6544 - val_binary_accuracy: 0.9308\nEpoch 14/50\n4987/4987 [==============================] - 36s 7ms/step - loss: 0.0082 - binary_accuracy: 0.9976 - val_loss: 0.7377 - val_binary_accuracy: 0.9302\nEpoch 15/50\n4987/4987 [==============================] - 36s 7ms/step - loss: 0.0072 - binary_accuracy: 0.9978 - val_loss: 0.7118 - val_binary_accuracy: 0.9331\nEpoch 16/50\n4987/4987 [==============================] - 38s 8ms/step - loss: 0.0079 - binary_accuracy: 0.9979 - val_loss: 0.7439 - val_binary_accuracy: 0.9325\nEpoch 17/50\n4987/4987 [==============================] - 37s 7ms/step - loss: 0.0070 - binary_accuracy: 0.9981 - val_loss: 0.7415 - val_binary_accuracy: 0.9332\nEpoch 18/50\n4987/4987 [==============================] - 38s 8ms/step - loss: 0.0068 - binary_accuracy: 0.9982 - val_loss: 0.7791 - val_binary_accuracy: 0.9328\nEpoch 19/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0068 - binary_accuracy: 0.9982 - val_loss: 0.8197 - val_binary_accuracy: 0.9325\nEpoch 20/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0059 - binary_accuracy: 0.9983 - val_loss: 0.8889 - val_binary_accuracy: 0.9295\nEpoch 21/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0062 - binary_accuracy: 0.9983 - val_loss: 0.9449 - val_binary_accuracy: 0.9305\nEpoch 22/50\n4987/4987 [==============================] - 37s 7ms/step - loss: 0.0060 - binary_accuracy: 0.9984 - val_loss: 0.9598 - val_binary_accuracy: 0.9292\nEpoch 23/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0069 - binary_accuracy: 0.9985 - val_loss: 0.8853 - val_binary_accuracy: 0.9357\nEpoch 24/50\n4987/4987 [==============================] - 37s 7ms/step - loss: 0.0059 - binary_accuracy: 0.9985 - val_loss: 1.0456 - val_binary_accuracy: 0.9292\nEpoch 25/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0055 - binary_accuracy: 0.9986 - val_loss: 1.0442 - val_binary_accuracy: 0.9326\nEpoch 26/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0054 - binary_accuracy: 0.9986 - val_loss: 1.0432 - val_binary_accuracy: 0.9301\nEpoch 27/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0063 - binary_accuracy: 0.9986 - val_loss: 1.0431 - val_binary_accuracy: 0.9306\nEpoch 28/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0050 - binary_accuracy: 0.9987 - val_loss: 1.0351 - val_binary_accuracy: 0.9318\nEpoch 29/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0052 - binary_accuracy: 0.9987 - val_loss: 1.1309 - val_binary_accuracy: 0.9302\nEpoch 30/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0056 - binary_accuracy: 0.9986 - val_loss: 1.1647 - val_binary_accuracy: 0.9268\nEpoch 31/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0057 - binary_accuracy: 0.9988 - val_loss: 1.1583 - val_binary_accuracy: 0.9272\nEpoch 32/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0060 - binary_accuracy: 0.9987 - val_loss: 1.2921 - val_binary_accuracy: 0.9296\nEpoch 33/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0066 - binary_accuracy: 0.9988 - val_loss: 1.2630 - val_binary_accuracy: 0.9289\nEpoch 34/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0046 - binary_accuracy: 0.9988 - val_loss: 1.2663 - val_binary_accuracy: 0.9311\nEpoch 35/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0055 - binary_accuracy: 0.9988 - val_loss: 1.1936 - val_binary_accuracy: 0.9333\nEpoch 36/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0043 - binary_accuracy: 0.9988 - val_loss: 1.4066 - val_binary_accuracy: 0.9324\nEpoch 37/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0057 - binary_accuracy: 0.9989 - val_loss: 1.3796 - val_binary_accuracy: 0.9308\nEpoch 38/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0051 - binary_accuracy: 0.9989 - val_loss: 1.5024 - val_binary_accuracy: 0.9300\nEpoch 39/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0056 - binary_accuracy: 0.9988 - val_loss: 1.4423 - val_binary_accuracy: 0.9295\nEpoch 40/50\n4987/4987 [==============================] - 36s 7ms/step - loss: 0.0050 - binary_accuracy: 0.9989 - val_loss: 1.4294 - val_binary_accuracy: 0.9276\nEpoch 41/50\n4987/4987 [==============================] - 36s 7ms/step - loss: 0.0038 - binary_accuracy: 0.9989 - val_loss: 1.4186 - val_binary_accuracy: 0.9322\nEpoch 42/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0034 - binary_accuracy: 0.9989 - val_loss: 1.4260 - val_binary_accuracy: 0.9304\nEpoch 43/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0044 - binary_accuracy: 0.9989 - val_loss: 1.6560 - val_binary_accuracy: 0.9306\nEpoch 44/50\n4987/4987 [==============================] - 36s 7ms/step - loss: 0.0037 - binary_accuracy: 0.9990 - val_loss: 1.6497 - val_binary_accuracy: 0.9301\nEpoch 45/50\n4987/4987 [==============================] - 34s 7ms/step - loss: 0.0037 - binary_accuracy: 0.9989 - val_loss: 1.5535 - val_binary_accuracy: 0.9328\nEpoch 46/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0043 - binary_accuracy: 0.9990 - val_loss: 1.6374 - val_binary_accuracy: 0.9291\nEpoch 47/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0053 - binary_accuracy: 0.9990 - val_loss: 1.6327 - val_binary_accuracy: 0.9322\nEpoch 48/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0048 - binary_accuracy: 0.9990 - val_loss: 1.5884 - val_binary_accuracy: 0.9320\nEpoch 49/50\n4987/4987 [==============================] - 33s 7ms/step - loss: 0.0035 - binary_accuracy: 0.9990 - val_loss: 1.6110 - val_binary_accuracy: 0.9285\nEpoch 50/50\n4987/4987 [==============================] - 35s 7ms/step - loss: 0.0054 - binary_accuracy: 0.9990 - val_loss: 1.5616 - val_binary_accuracy: 0.9321\n","output_type":"stream"}]},{"cell_type":"code","source":"import pickle","metadata":{"execution":{"iopub.status.busy":"2023-07-22T11:56:55.349129Z","iopub.execute_input":"2023-07-22T11:56:55.350345Z","iopub.status.idle":"2023-07-22T11:56:55.355020Z","shell.execute_reply.started":"2023-07-22T11:56:55.350307Z","shell.execute_reply":"2023-07-22T11:56:55.354086Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"# Save the model\n\nfilename = 'finalized_model.pkl'\npickle.dump(model, open(filename, 'wb'))","metadata":{"execution":{"iopub.status.busy":"2023-07-22T12:28:13.926841Z","iopub.execute_input":"2023-07-22T12:28:13.927216Z","iopub.status.idle":"2023-07-22T12:28:13.969654Z","shell.execute_reply.started":"2023-07-22T12:28:13.927188Z","shell.execute_reply":"2023-07-22T12:28:13.968632Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}