Intent_NLP.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-yXXPzgufYg",
        "outputId": "ef95d7ab-3ead-4e14-d18d-69ea03e114bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Package                          Version\n",
            "-------------------------------- ---------------------\n",
            "absl-py                          1.4.0\n",
            "aiohttp                          3.9.5\n",
            "aiosignal                        1.3.1\n",
            "alabaster                        0.7.16\n",
            "albumentations                   1.3.1\n",
            "altair                           4.2.2\n",
            "annotated-types                  0.7.0\n",
            "anyio                            3.7.1\n",
            "argon2-cffi                      23.1.0\n",
            "argon2-cffi-bindings             21.2.0\n",
            "array_record                     0.5.1\n",
            "arviz                            0.15.1\n",
            "astropy                          5.3.4\n",
            "astunparse                       1.6.3\n",
            "async-timeout                    4.0.3\n",
            "atpublic                         4.1.0\n",
            "attrs                            23.2.0\n",
            "audioread                        3.0.1\n",
            "autograd                         1.6.2\n",
            "Babel                            2.15.0\n",
            "backcall                         0.2.0\n",
            "beautifulsoup4                   4.12.3\n",
            "bidict                           0.23.1\n",
            "bigframes                        1.8.0\n",
            "bleach                           6.1.0\n",
            "blinker                          1.4\n",
            "blis                             0.7.11\n",
            "blosc2                           2.0.0\n",
            "bokeh                            3.3.4\n",
            "bqplot                           0.12.43\n",
            "branca                           0.7.2\n",
            "build                            1.2.1\n",
            "CacheControl                     0.14.0\n",
            "cachetools                       5.3.3\n",
            "catalogue                        2.0.10\n",
            "certifi                          2024.6.2\n",
            "cffi                             1.16.0\n",
            "chardet                          5.2.0\n",
            "charset-normalizer               3.3.2\n",
            "chex                             0.1.86\n",
            "click                            8.1.7\n",
            "click-plugins                    1.1.1\n",
            "cligj                            0.7.2\n",
            "cloudpathlib                     0.18.1\n",
            "cloudpickle                      2.2.1\n",
            "cmake                            3.27.9\n",
            "cmdstanpy                        1.2.3\n",
            "colorcet                         3.1.0\n",
            "colorlover                       0.3.0\n",
            "colour                           0.1.5\n",
            "community                        1.0.0b1\n",
            "confection                       0.1.5\n",
            "cons                             0.4.6\n",
            "contextlib2                      21.6.0\n",
            "contourpy                        1.2.1\n",
            "cryptography                     42.0.8\n",
            "cuda-python                      12.2.1\n",
            "cudf-cu12                        24.4.1\n",
            "cufflinks                        0.17.3\n",
            "cupy-cuda12x                     12.2.0\n",
            "cvxopt                           1.3.2\n",
            "cvxpy                            1.3.4\n",
            "cycler                           0.12.1\n",
            "cymem                            2.0.8\n",
            "Cython                           3.0.10\n",
            "dask                             2023.8.1\n",
            "datascience                      0.17.6\n",
            "db-dtypes                        1.2.0\n",
            "dbus-python                      1.2.18\n",
            "debugpy                          1.6.6\n",
            "decorator                        4.4.2\n",
            "deep-translator                  1.11.4\n",
            "defusedxml                       0.7.1\n",
            "distributed                      2023.8.1\n",
            "distro                           1.7.0\n",
            "dlib                             19.24.4\n",
            "dm-tree                          0.1.8\n",
            "docstring_parser                 0.16\n",
            "docutils                         0.18.1\n",
            "dopamine_rl                      4.0.9\n",
            "duckdb                           0.10.3\n",
            "earthengine-api                  0.1.406\n",
            "easydict                         1.13\n",
            "ecos                             2.0.13\n",
            "editdistance                     0.6.2\n",
            "eerepr                           0.0.4\n",
            "en-core-web-lg                   3.7.1\n",
            "en-core-web-md                   3.7.1\n",
            "en-core-web-sm                   3.7.1\n",
            "entrypoints                      0.4\n",
            "et-xmlfile                       1.1.0\n",
            "etils                            1.7.0\n",
            "etuples                          0.3.9\n",
            "exceptiongroup                   1.2.1\n",
            "fastai                           2.7.15\n",
            "fastcore                         1.5.45\n",
            "fastdownload                     0.0.7\n",
            "fastjsonschema                   2.19.1\n",
            "fastprogress                     1.0.3\n",
            "fastrlock                        0.8.2\n",
            "filelock                         3.14.0\n",
            "fiona                            1.9.6\n",
            "firebase-admin                   5.3.0\n",
            "Flask                            2.2.5\n",
            "flatbuffers                      24.3.25\n",
            "flax                             0.8.4\n",
            "folium                           0.14.0\n",
            "fonttools                        4.53.0\n",
            "frozendict                       2.4.4\n",
            "frozenlist                       1.4.1\n",
            "fsspec                           2023.6.0\n",
            "future                           0.18.3\n",
            "gast                             0.5.4\n",
            "gcsfs                            2023.6.0\n",
            "GDAL                             3.6.4\n",
            "gdown                            5.1.0\n",
            "geemap                           0.32.1\n",
            "gensim                           4.3.2\n",
            "geocoder                         1.38.1\n",
            "geographiclib                    2.0\n",
            "geopandas                        0.13.2\n",
            "geopy                            2.3.0\n",
            "gin-config                       0.5.0\n",
            "glob2                            0.7\n",
            "google                           2.0.3\n",
            "google-ai-generativelanguage     0.6.4\n",
            "google-api-core                  2.11.1\n",
            "google-api-python-client         2.84.0\n",
            "google-auth                      2.27.0\n",
            "google-auth-httplib2             0.1.1\n",
            "google-auth-oauthlib             1.2.0\n",
            "google-cloud-aiplatform          1.54.1\n",
            "google-cloud-bigquery            3.21.0\n",
            "google-cloud-bigquery-connection 1.12.1\n",
            "google-cloud-bigquery-storage    2.25.0\n",
            "google-cloud-core                2.3.3\n",
            "google-cloud-datastore           2.15.2\n",
            "google-cloud-firestore           2.11.1\n",
            "google-cloud-functions           1.13.3\n",
            "google-cloud-iam                 2.15.0\n",
            "google-cloud-language            2.13.3\n",
            "google-cloud-resource-manager    1.12.3\n",
            "google-cloud-storage             2.8.0\n",
            "google-cloud-translate           3.11.3\n",
            "google-colab                     1.0.0\n",
            "google-crc32c                    1.5.0\n",
            "google-generativeai              0.5.4\n",
            "google-pasta                     0.2.0\n",
            "google-resumable-media           2.7.0\n",
            "google-trans-new                 1.1.9\n",
            "googleapis-common-protos         1.63.1\n",
            "googledrivedownloader            0.4\n",
            "graphviz                         0.20.3\n",
            "greenlet                         3.0.3\n",
            "grpc-google-iam-v1               0.13.0\n",
            "grpcio                           1.64.1\n",
            "grpcio-status                    1.48.2\n",
            "gspread                          6.0.2\n",
            "gspread-dataframe                3.3.1\n",
            "gym                              0.25.2\n",
            "gym-notices                      0.0.8\n",
            "h5netcdf                         1.3.0\n",
            "h5py                             3.9.0\n",
            "holidays                         0.50\n",
            "holoviews                        1.17.1\n",
            "html5lib                         1.1\n",
            "httpimport                       1.3.1\n",
            "httplib2                         0.22.0\n",
            "huggingface-hub                  0.23.3\n",
            "humanize                         4.7.0\n",
            "hyperopt                         0.2.7\n",
            "ibis-framework                   8.0.0\n",
            "idna                             3.7\n",
            "imageio                          2.31.6\n",
            "imageio-ffmpeg                   0.5.1\n",
            "imagesize                        1.4.1\n",
            "imbalanced-learn                 0.10.1\n",
            "imgaug                           0.4.0\n",
            "immutabledict                    4.2.0\n",
            "importlib_metadata               7.1.0\n",
            "importlib_resources              6.4.0\n",
            "imutils                          0.5.4\n",
            "inflect                          7.0.0\n",
            "iniconfig                        2.0.0\n",
            "intel-openmp                     2023.2.4\n",
            "ipyevents                        2.0.2\n",
            "ipyfilechooser                   0.6.0\n",
            "ipykernel                        5.5.6\n",
            "ipyleaflet                       0.18.2\n",
            "ipython                          7.34.0\n",
            "ipython-genutils                 0.2.0\n",
            "ipython-sql                      0.5.0\n",
            "ipytree                          0.2.2\n",
            "ipywidgets                       7.7.1\n",
            "itsdangerous                     2.2.0\n",
            "jax                              0.4.26\n",
            "jaxlib                           0.4.26+cuda12.cudnn89\n",
            "jeepney                          0.7.1\n",
            "jellyfish                        1.0.4\n",
            "jieba                            0.42.1\n",
            "Jinja2                           3.1.4\n",
            "joblib                           1.4.2\n",
            "jsonpickle                       3.2.1\n",
            "jsonschema                       4.19.2\n",
            "jsonschema-specifications        2023.12.1\n",
            "jupyter-client                   6.1.12\n",
            "jupyter-console                  6.1.0\n",
            "jupyter_core                     5.7.2\n",
            "jupyter-server                   1.24.0\n",
            "jupyterlab_pygments              0.3.0\n",
            "jupyterlab_widgets               3.0.11\n",
            "kaggle                           1.6.14\n",
            "kagglehub                        0.2.6\n",
            "keras                            2.15.0\n",
            "keyring                          23.5.0\n",
            "kiwisolver                       1.4.5\n",
            "langcodes                        3.4.0\n",
            "langdetect                       1.0.9\n",
            "language_data                    1.2.0\n",
            "launchpadlib                     1.10.16\n",
            "lazr.restfulclient               0.14.4\n",
            "lazr.uri                         1.0.6\n",
            "lazy_loader                      0.4\n",
            "libclang                         18.1.1\n",
            "librosa                          0.10.2.post1\n",
            "lightgbm                         4.1.0\n",
            "linkify-it-py                    2.0.3\n",
            "llvmlite                         0.41.1\n",
            "locket                           1.0.0\n",
            "logical-unification              0.4.6\n",
            "lxml                             4.9.4\n",
            "malloy                           2023.1067\n",
            "marisa-trie                      1.2.0\n",
            "Markdown                         3.6\n",
            "markdown-it-py                   3.0.0\n",
            "MarkupSafe                       2.1.5\n",
            "matplotlib                       3.7.1\n",
            "matplotlib-inline                0.1.7\n",
            "matplotlib-venn                  0.11.10\n",
            "mdit-py-plugins                  0.4.1\n",
            "mdurl                            0.1.2\n",
            "miniKanren                       1.0.3\n",
            "missingno                        0.5.2\n",
            "mistune                          0.8.4\n",
            "mizani                           0.9.3\n",
            "mkl                              2023.2.0\n",
            "ml-dtypes                        0.2.0\n",
            "mlxtend                          0.22.0\n",
            "more-itertools                   10.1.0\n",
            "moviepy                          1.0.3\n",
            "mpmath                           1.3.0\n",
            "msgpack                          1.0.8\n",
            "multidict                        6.0.5\n",
            "multipledispatch                 1.0.0\n",
            "multitasking                     0.0.11\n",
            "murmurhash                       1.0.10\n",
            "music21                          9.1.0\n",
            "natsort                          8.4.0\n",
            "nbclassic                        1.1.0\n",
            "nbclient                         0.10.0\n",
            "nbconvert                        6.5.4\n",
            "nbformat                         5.10.4\n",
            "nest-asyncio                     1.6.0\n",
            "networkx                         3.3\n",
            "nibabel                          4.0.2\n",
            "nltk                             3.8.1\n",
            "notebook                         6.5.5\n",
            "notebook_shim                    0.2.4\n",
            "numba                            0.58.1\n",
            "numexpr                          2.10.0\n",
            "numpy                            1.25.2\n",
            "nvtx                             0.2.10\n",
            "oauth2client                     4.1.3\n",
            "oauthlib                         3.2.2\n",
            "opencv-contrib-python            4.8.0.76\n",
            "opencv-python                    4.8.0.76\n",
            "opencv-python-headless           4.10.0.82\n",
            "openpyxl                         3.1.3\n",
            "opt-einsum                       3.3.0\n",
            "optax                            0.2.2\n",
            "orbax-checkpoint                 0.4.4\n",
            "osqp                             0.6.2.post8\n",
            "packaging                        24.1\n",
            "pandas                           2.0.3\n",
            "pandas-datareader                0.10.0\n",
            "pandas-gbq                       0.19.2\n",
            "pandas-stubs                     2.0.3.230814\n",
            "pandocfilters                    1.5.1\n",
            "panel                            1.3.8\n",
            "param                            2.1.0\n",
            "parso                            0.8.4\n",
            "parsy                            2.1\n",
            "partd                            1.4.2\n",
            "pathlib                          1.0.1\n",
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            "peewee                           3.17.5\n",
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            "Pillow                           9.4.0\n",
            "pip                              23.1.2\n",
            "pip-tools                        6.13.0\n",
            "platformdirs                     4.2.2\n",
            "plotly                           5.15.0\n",
            "plotnine                         0.12.4\n",
            "pluggy                           1.5.0\n",
            "polars                           0.20.2\n",
            "pooch                            1.8.2\n",
            "portpicker                       1.5.2\n",
            "prefetch-generator               1.0.3\n",
            "preshed                          3.0.9\n",
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            "progressbar2                     4.2.0\n",
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            "prophet                          1.1.5\n",
            "proto-plus                       1.23.0\n",
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            "psutil                           5.9.5\n",
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            "py-cpuinfo                       9.0.0\n",
            "py4j                             0.10.9.7\n",
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            "pyasn1_modules                   0.4.0\n",
            "pycocotools                      2.0.7\n",
            "pycparser                        2.22\n",
            "pydantic                         2.7.3\n",
            "pydantic_core                    2.18.4\n",
            "pydata-google-auth               1.8.2\n",
            "pydot                            1.4.2\n",
            "pydot-ng                         2.0.0\n",
            "pydotplus                        2.0.2\n",
            "PyDrive                          1.3.1\n",
            "PyDrive2                         1.6.3\n",
            "pyerfa                           2.0.1.4\n",
            "pygame                           2.5.2\n",
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            "PyJWT                            2.3.0\n",
            "pymc                             5.10.4\n",
            "pymystem3                        0.2.0\n",
            "pynvjitlink-cu12                 0.2.3\n",
            "PyOpenGL                         3.1.7\n",
            "pyOpenSSL                        24.1.0\n",
            "pyparsing                        3.1.2\n",
            "pyperclip                        1.8.2\n",
            "pyproj                           3.6.1\n",
            "pyproject_hooks                  1.1.0\n",
            "pyshp                            2.3.1\n",
            "PySocks                          1.7.1\n",
            "pytensor                         2.18.6\n",
            "pytest                           7.4.4\n",
            "python-apt                       0.0.0\n",
            "python-box                       7.1.1\n",
            "python-dateutil                  2.8.2\n",
            "python-louvain                   0.16\n",
            "python-slugify                   8.0.4\n",
            "python-utils                     3.8.2\n",
            "pytz                             2023.4\n",
            "pyviz_comms                      3.0.2\n",
            "PyWavelets                       1.6.0\n",
            "PyYAML                           6.0.1\n",
            "pyzmq                            24.0.1\n",
            "qdldl                            0.1.7.post2\n",
            "qudida                           0.0.4\n",
            "ratelim                          0.1.6\n",
            "referencing                      0.35.1\n",
            "regex                            2024.5.15\n",
            "requests                         2.31.0\n",
            "requests-oauthlib                1.3.1\n",
            "requirements-parser              0.9.0\n",
            "rich                             13.7.1\n",
            "rmm-cu12                         24.4.0\n",
            "rpds-py                          0.18.1\n",
            "rpy2                             3.4.2\n",
            "rsa                              4.9\n",
            "safetensors                      0.4.3\n",
            "scikit-image                     0.19.3\n",
            "scikit-learn                     1.2.2\n",
            "scipy                            1.11.4\n",
            "scooby                           0.10.0\n",
            "scs                              3.2.4.post2\n",
            "seaborn                          0.13.1\n",
            "SecretStorage                    3.3.1\n",
            "Send2Trash                       1.8.3\n",
            "sentencepiece                    0.1.99\n",
            "setuptools                       67.7.2\n",
            "shapely                          2.0.4\n",
            "shellingham                      1.5.4\n",
            "simple_parsing                   0.1.5\n",
            "six                              1.16.0\n",
            "sklearn-pandas                   2.2.0\n",
            "smart-open                       7.0.4\n",
            "sniffio                          1.3.1\n",
            "snowballstemmer                  2.2.0\n",
            "sortedcontainers                 2.4.0\n",
            "soundfile                        0.12.1\n",
            "soupsieve                        2.5\n",
            "soxr                             0.3.7\n",
            "spacy                            3.7.5\n",
            "spacy-legacy                     3.0.12\n",
            "spacy-loggers                    1.0.5\n",
            "Sphinx                           5.0.2\n",
            "sphinxcontrib-applehelp          1.0.8\n",
            "sphinxcontrib-devhelp            1.0.6\n",
            "sphinxcontrib-htmlhelp           2.0.5\n",
            "sphinxcontrib-jsmath             1.0.1\n",
            "sphinxcontrib-qthelp             1.0.7\n",
            "sphinxcontrib-serializinghtml    1.1.10\n",
            "SQLAlchemy                       2.0.30\n",
            "sqlglot                          20.11.0\n",
            "sqlparse                         0.5.0\n",
            "srsly                            2.4.8\n",
            "stanio                           0.5.0\n",
            "statsmodels                      0.14.2\n",
            "StrEnum                          0.4.15\n",
            "sympy                            1.12.1\n",
            "tables                           3.8.0\n",
            "tabulate                         0.9.0\n",
            "tbb                              2021.12.0\n",
            "tblib                            3.0.0\n",
            "tenacity                         8.3.0\n",
            "tensorboard                      2.15.2\n",
            "tensorboard-data-server          0.7.2\n",
            "tensorflow                       2.15.0\n",
            "tensorflow-datasets              4.9.6\n",
            "tensorflow-estimator             2.15.0\n",
            "tensorflow-gcs-config            2.15.0\n",
            "tensorflow-hub                   0.16.1\n",
            "tensorflow-io-gcs-filesystem     0.37.0\n",
            "tensorflow-metadata              1.15.0\n",
            "tensorflow-probability           0.23.0\n",
            "tensorstore                      0.1.45\n",
            "termcolor                        2.4.0\n",
            "terminado                        0.18.1\n",
            "text-unidecode                   1.3\n",
            "textblob                         0.17.1\n",
            "tf_keras                         2.15.1\n",
            "tf-slim                          1.1.0\n",
            "thinc                            8.2.4\n",
            "threadpoolctl                    3.5.0\n",
            "tifffile                         2024.5.22\n",
            "tinycss2                         1.3.0\n",
            "tokenizers                       0.19.1\n",
            "toml                             0.10.2\n",
            "tomli                            2.0.1\n",
            "toolz                            0.12.1\n",
            "torch                            2.3.0+cu121\n",
            "torchaudio                       2.3.0+cu121\n",
            "torchsummary                     1.5.1\n",
            "torchtext                        0.18.0\n",
            "torchvision                      0.18.0+cu121\n",
            "tornado                          6.3.3\n",
            "tqdm                             4.66.4\n",
            "traitlets                        5.7.1\n",
            "traittypes                       0.2.1\n",
            "transformers                     4.41.2\n",
            "triton                           2.3.0\n",
            "tweepy                           4.14.0\n",
            "typer                            0.12.3\n",
            "types-pytz                       2024.1.0.20240417\n",
            "types-setuptools                 70.0.0.20240524\n",
            "typing_extensions                4.12.2\n",
            "tzdata                           2024.1\n",
            "tzlocal                          5.2\n",
            "uc-micro-py                      1.0.3\n",
            "uritemplate                      4.1.1\n",
            "urllib3                          2.0.7\n",
            "vega-datasets                    0.9.0\n",
            "wadllib                          1.3.6\n",
            "wasabi                           1.1.3\n",
            "wcwidth                          0.2.13\n",
            "weasel                           0.4.1\n",
            "webcolors                        24.6.0\n",
            "webencodings                     0.5.1\n",
            "websocket-client                 1.8.0\n",
            "Werkzeug                         3.0.3\n",
            "wheel                            0.43.0\n",
            "widgetsnbextension               3.6.6\n",
            "wordcloud                        1.9.3\n",
            "wrapt                            1.14.1\n",
            "xarray                           2023.7.0\n",
            "xarray-einstats                  0.7.0\n",
            "xgboost                          2.0.3\n",
            "xlrd                             2.0.1\n",
            "xyzservices                      2024.6.0\n",
            "yarl                             1.9.4\n",
            "yellowbrick                      1.5\n",
            "yfinance                         0.2.40\n",
            "zict                             3.0.0\n",
            "zipp                             3.19.2\n"
          ]
        }
      ],
      "source": [
        "# Requirements: Packages being used:\n",
        "%pip list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "mhu3lNkdu_YJ"
      },
      "outputs": [],
      "source": [
        "# Loading Packages\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "pKVAz0ggwzHn"
      },
      "outputs": [],
      "source": [
        "# Exploratory Data Analysis\n",
        "df = pd.read_csv(\"search_queries.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "S3SYaKazxnZH",
        "outputId": "91dd4141-9a70-46e3-94b2-6043d30e05e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DF shape: (599, 8)\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 599,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2024-01-06\",\n          \"2024-01-05\",\n          \"2023-12-20\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"search_query\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 370,\n        \"samples\": [\n          \"outlook login\",\n          \"free\",\n          \"db streik\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"market\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"fr-fr\",\n          \"zh-cn\",\n          \"en-gb\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geo_country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"GB\",\n          \"FR\",\n          \"GH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"device_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Desktop\",\n          \"Mobile\",\n          \"Tablet\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"browser_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Chrome\",\n          \"Ecosia ios\",\n          \"Firefox\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"intent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"DICTIONARY\",\n          \"QA\",\n          \"TRANSLATION\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"daily_query_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2,\n        \"max\": 42,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          2,\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-5756005d-06f0-411a-adc4-73b01ba1d3b5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>search_query</th>\n",
              "      <th>market</th>\n",
              "      <th>geo_country</th>\n",
              "      <th>device_type</th>\n",
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              "      <th>daily_query_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-12-08</td>\n",
              "      <td>google translate</td>\n",
              "      <td>en-gb</td>\n",
              "      <td>GB</td>\n",
              "      <td>Desktop</td>\n",
              "      <td>Chrome</td>\n",
              "      <td>TRANSLATION</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-12-08</td>\n",
              "      <td>rephrase</td>\n",
              "      <td>en-gb</td>\n",
              "      <td>GB</td>\n",
              "      <td>Desktop</td>\n",
              "      <td>Chrome</td>\n",
              "      <td>DICTIONARY</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-12-31</td>\n",
              "      <td>amazon</td>\n",
              "      <td>en-gb</td>\n",
              "      <td>GB</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>Chrome</td>\n",
              "      <td>NAVIGATION</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-12-29</td>\n",
              "      <td>facebook</td>\n",
              "      <td>fr-fr</td>\n",
              "      <td>FR</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>Ecosia ios</td>\n",
              "      <td>NAVIGATION</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-12-23</td>\n",
              "      <td>tv programm</td>\n",
              "      <td>de-de</td>\n",
              "      <td>DE</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>Safari</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5756005d-06f0-411a-adc4-73b01ba1d3b5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5756005d-06f0-411a-adc4-73b01ba1d3b5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5756005d-06f0-411a-adc4-73b01ba1d3b5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-767131e7-1548-4ad1-81f6-5868767978dc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-767131e7-1548-4ad1-81f6-5868767978dc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         date      search_query market geo_country device_type browser_name  \\\n",
              "0  2023-12-08  google translate  en-gb          GB     Desktop       Chrome   \n",
              "1  2023-12-08          rephrase  en-gb          GB     Desktop       Chrome   \n",
              "2  2023-12-31            amazon  en-gb          GB      Mobile       Chrome   \n",
              "3  2023-12-29          facebook  fr-fr          FR      Mobile   Ecosia ios   \n",
              "4  2023-12-23       tv programm  de-de          DE      Mobile       Safari   \n",
              "\n",
              "        intent  daily_query_count  \n",
              "0  TRANSLATION                  2  \n",
              "1   DICTIONARY                  2  \n",
              "2   NAVIGATION                  2  \n",
              "3   NAVIGATION                  3  \n",
              "4        OTHER                  3  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"DF shape:\", df.shape)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "id": "Bl5_yTUVySsM",
        "outputId": "e4b79c9c-8161-45ef-9beb-ef84d1effb04"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Intent Categories + Counts: \n",
            " intent\n",
            "OTHER          173\n",
            "QA             109\n",
            "TRANSLATION    100\n",
            "SHOPPING        66\n",
            "NAVIGATION      60\n",
            "DICTIONARY      19\n",
            "WEATHER          5\n",
            "COMPUTATION      5\n",
            "Name: count, dtype: int64 \n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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MGaMxY8ZkeUxwcLDmzJlzrx8NAAC8RK54igcAAHgXAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALCcHA8oERERstlsGbb+/ftLkho1apShrW/fvjldBgAAyMXy5PQb7tixQzdu3HC83rdvn5o1a6YXXnjBsa93794aM2aM43X+/PlzugwAAJCL5XhAKVasmNPr8ePHq1y5cmrYsKFjX/78+RUSEpLTHw0AADyES8egXLt2TV9++aV69uwpm83m2D979mwVLVpUVatW1fDhw/XHH39k+z6pqalKTk522gAAgOfK8R6U2y1ZskQXLlxQ9+7dHfs6deqk0qVLKywsTHv37tXQoUN16NAhLVq0KMv3GTdunEaPHu3KUgEAgIW4NKDExMQoMjJSYWFhjn19+vRx/LlatWoKDQ1VkyZNlJiYqHLlymX6PsOHD1d0dLTjdXJyssLDw11XOAAAMJXLAsrRo0e1evXqbHtGJKlu3bqSpISEhCwDip+fn/z8/HK8RgAAYE0uG4MSGxur4sWL6+mnn872uPj4eElSaGioq0oBAAC5jEt6UNLT0xUbG6tu3bopT57//yMSExM1Z84ctWrVSkWKFNHevXs1ePBgPfnkk6pevborSgEAALmQSwLK6tWrdezYMfXs2dNpv6+vr1avXq1JkyYpJSVF4eHhateund566y1XlAEAAHIplwSU5s2byzCMDPvDw8O1fv16V3wkAADwIKzFAwAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALIeAAgAALMcliwUCsIaIYctN++wj45827bMB5H70oAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMthLR4AHoc1iIDcjx4UAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOQQUAABgOTkeUEaNGiWbzea0Va5c2dF+9epV9e/fX0WKFFHBggXVrl07nT59OqfLAAAAuZhLelAeeughnTx50rFt2rTJ0TZ48GAtXbpUX3/9tdavX68TJ06obdu2rigDAADkUnlc8qZ58igkJCTD/osXLyomJkZz5szRU089JUmKjY1VlSpVtHXrVtWrV88V5QAAgFzGJT0ohw8fVlhYmMqWLavOnTvr2LFjkqRdu3bp+vXratq0qePYypUrq1SpUtqyZUuW75eamqrk5GSnDQAAeK4cDyh169ZVXFycVqxYoU8//VRJSUl64okndOnSJZ06dUq+vr4qVKiQ08+UKFFCp06dyvI9x40bp6CgIMcWHh6e02UDAAALyfFbPJGRkY4/V69eXXXr1lXp0qX11Vdfyd/f/y+95/DhwxUdHe14nZycTEgBAMCDufwx40KFCqlixYpKSEhQSEiIrl27pgsXLjgdc/r06UzHrNzi5+enwMBApw0AAHgulweUy5cvKzExUaGhoapVq5by5s2rNWvWONoPHTqkY8eOqX79+q4uBQAA5BI5fotnyJAheuaZZ1S6dGmdOHFCI0eOlI+Pjzp27KigoCD16tVL0dHRCg4OVmBgoAYMGKD69evzBA8AAHDI8YDy66+/qmPHjvr9999VrFgxPf7449q6dauKFSsmSfr4449lt9vVrl07paamqkWLFpo2bVpOlwEAAHKxHA8o8+bNy7Y9X758mjp1qqZOnZrTHw0AADwEa/EAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLyfGAMm7cONWpU0cBAQEqXry42rRpo0OHDjkd06hRI9lsNqetb9++OV0KAADIpXI8oKxfv179+/fX1q1btWrVKl2/fl3NmzdXSkqK03G9e/fWyZMnHduECRNyuhQAAJBL5cnpN1yxYoXT67i4OBUvXly7du3Sk08+6difP39+hYSE5PTHAwAAD+DyMSgXL16UJAUHBzvtnz17tooWLaqqVatq+PDh+uOPP7J8j9TUVCUnJzttAADAc+V4D8rt0tPTNWjQID322GOqWrWqY3+nTp1UunRphYWFae/evRo6dKgOHTqkRYsWZfo+48aN0+jRo11ZKgAAsBCXBpT+/ftr37592rRpk9P+Pn36OP5crVo1hYaGqkmTJkpMTFS5cuUyvM/w4cMVHR3teJ2cnKzw8HDXFQ4AAEzlsoDy6quvatmyZdqwYYNKliyZ7bF169aVJCUkJGQaUPz8/OTn5+eSOgEAgPXkeEAxDEMDBgzQ4sWL9cMPP6hMmTJ3/Jn4+HhJUmhoaE6XAwAAcqEcDyj9+/fXnDlz9M033yggIECnTp2SJAUFBcnf31+JiYmaM2eOWrVqpSJFimjv3r0aPHiwnnzySVWvXj2nywEAALlQjgeUTz/9VNLNydhuFxsbq+7du8vX11erV6/WpEmTlJKSovDwcLVr105vvfVWTpcCAAByKZfc4slOeHi41q9fn9MfCwAAPAhr8QAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMtx6VT3AAD3iRi23LTPPjL+adM+G56JHhQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5ecwuAACA+xExbLlpn31k/NOmfbannzc9KAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHJMDShTp05VRESE8uXLp7p162r79u1mlgMAACzCtIAyf/58RUdHa+TIkdq9e7dq1KihFi1a6MyZM2aVBAAALMK0gPLRRx+pd+/e6tGjhx588EF99tlnyp8/v2bOnGlWSQAAwCJMmajt2rVr2rVrl4YPH+7YZ7fb1bRpU23ZsiXD8ampqUpNTXW8vnjxoiQpOTn5L9eQnvrHX/7Z+3U/dd8vztv9OG/347zdj/N2v9x43rd+zjCMOx9smOB///ufIcnYvHmz0/7XXnvNePTRRzMcP3LkSEMSGxsbGxsbmwdsx48fv2NWyBVT3Q8fPlzR0dGO1+np6Tp37pyKFCkim83m1lqSk5MVHh6u48ePKzAw0K2fbSbOm/P2Bpw35+0NzDxvwzB06dIlhYWF3fFYUwJK0aJF5ePjo9OnTzvtP336tEJCQjIc7+fnJz8/P6d9hQoVcmWJdxQYGOhV/0Pfwnl7F87bu3De3sWs8w4KCrqr40wZJOvr66tatWppzZo1jn3p6elas2aN6tevb0ZJAADAQky7xRMdHa1u3bqpdu3aevTRRzVp0iSlpKSoR48eZpUEAAAswrSA0qFDB509e1Zvv/22Tp06pYcfflgrVqxQiRIlzCrprvj5+WnkyJEZbjl5Os6b8/YGnDfn7Q1yy3nbDONunvUBAABwH9biAQAAlkNAAQAAlkNAAQAAlkNAAQAAlkNAAQAAlpMrprq3qqtXr2rKlCkaMmSI2aW43blz5xQcHGx2GTnm2LFjd3VcqVKlXFwJADN48/XcqnjM+A7Onj2rbdu2ydfXV02aNJGPj4+uX7+uadOmady4cUpLS9Nvv/1mdplu8/3332vGjBlaunSprly5YnY5OcZut2e6rpNhGI79NptNaWlp7i7N5S5cuKC5c+fqlVdekSR17tzZ6e/Wx8dH//rXv0xfXgI54/XXX9e7774rX19fs0txO2+8nm/YsOGujnvyySddXMm9I6BkY9OmTWrdurWSk5Nls9lUu3ZtxcbGqk2bNsqTJ48GDhyobt26yd/f3+xSXero0aOaOXOmZs2apfPnzysyMlLt2rXTCy+8YHZpOWbPnj2Z7jcMQ/PmzdPkyZNVsGBBnTlzxs2Vud7EiRMVHx+v2bNnS5ICAgLUokULBQQESJK2bNmiqKgojRo1ysQqkVPKlSun/Pnz64svvtDDDz9sdjlu463Xc7s965Eclv/ydcf1jr1Yw4YNjY4dOxo//fSTMWTIEMNmsxkVK1Y0vv76a7NLc7nU1FRj7ty5RpMmTYx8+fIZrVu3Nnx8fIy9e/eaXZrbrFq1yqhVq5YREBBgjBw50khOTja7JJd49NFHjVWrVjleFyxY0EhMTHS8XrRokfHwww+bUZpL2Ww2w263Z7v5+PiYXWaOS0lJMfr162fky5fPeOedd4wbN26YXZJbeOv1/MKFC5luJ06cMIYOHWr4+/sbDz30kNllZooelGwUKVJEGzdu1IMPPqgrV66oYMGCWrRokZ577jmzS3OpAQMGaO7cuapQoYK6dOmiqKgoFSlSRHnz5tWePXv04IMPml2iS+3evVtDhw7Vxo0b9dJLL+ntt99W8eLFzS7LZYoVK6bdu3crPDxcklS7dm0tWbJEJUuWlCT98ssvql69ui5fvmxmmTnum2++ybJty5Ytmjx5stLT03X16lU3VuU+69atU69evVSsWDENGzZMPj4+Tu3PPvusSZW5hrdez/8sPT1dM2fO1OjRo2W32zVq1Ch169Yt254W05idkKzMZrMZp0+fdrwuWLCgkZCQYGJF7uHj42O88cYbGXoM8uTJY+zfv9+kqlwvISHBaN++veHj42N07NjRqRfBk/n7+xs//fRTlu179+41/P393ViReX7++WejTZs2ho+Pj/Hiiy8aR44cMbskl/rmm28MHx8fw2azOW12u93s0nKct17Pb7dw4UKjUqVKRnBwsDFx4kTj6tWrZpeULQtGJms5cOCA9u7dq71798owDB06dMjx+tbmab744gtt375doaGh6tChg5YtW6YbN26YXZZL9evXTw8++KAuXryonTt3as6cOSpbtqzZZblF2bJltXv37izbd+7cqTJlyrixIvc7ceKEevfurWrVqiktLU3x8fGaNWuWSpcubXZpLnHlyhUNHDhQ7du314gRI3T9+nWlp6c7Nk/99+6N13NJWr9+verVq6euXbuqbdu2+uWXXzRkyBAWC8zNbj3Zkdl/olv7bTabx/5jTkpKUlxcnOLi4vTHH3/o3Llzmj9/vp5//nmzS8txdrtd+fLlU+XKlbM9Lrtf5LnViBEjNGvWLO3YsSPDauKnTp3So48+qhdffFHvvvuuSRW6zsWLFzV27Fh98sknevjhh/X+++/riSeeMLssl9q8ebO6desmPz8/zZo1S7Vq1TK7JLfw1ut5q1attHr1avXs2VOjRo1SSEiI2SXdNQJKNo4ePXpXx3nqt6xbDMPQ999/r5iYGH377bcqWrSo2rZtq8mTJ5tdWo4ZPXr0XR03cuRIF1fifpcuXVLdunX166+/qmvXrqpYsaIk6dChQ/ryyy/1wAMPaPv27Y6nejzFhAkT9P777yskJERjx471mrEIvr6+GjhwoN577z3Lf4POSd56Pbfb7cqTJ48KFCiQ6VQKt5w7d86NVd0dAgru6LfffpOvr68CAwN17tw5ff7554qNjc3y0VzkPufPn9fw4cP11Vdf6cKFC5KkQoUKqX379ho7dqxHTcp3i91ul7+/v5o2bZphgOjtFi1a5MaqXG/Dhg3Zznmxe/duvf3221q2bJkbq4KrzJo1666O69atm4sruXcElGxMmDBBAwYMcDwX/+OPP6p27dqObx2XLl3S0KFDNW3aNDPLdIkLFy7ozTff1Pz583X+/HlJN5/26NGjh0aMGKH8+fObXKHr7N27V//9738lSRUrVlT16tVNrsh9DMPQ2bNnJd38+87uG1du171797s6v9jYWDdU414rV67UqlWr5Ovrq5deeklly5bVzz//rGHDhmnp0qVq0aKF/v3vf5tdZo7y5ut5bkVAyYaPj49OnjzpeMQ0MDBQ8fHxjsGTp0+fVlhYmMfdszx37pzq16+v//3vf+rcubOqVKki6eYAszlz5qhy5cratGmT9u7dq61bt2rgwIEmV5wztm/frl69eunAgQOO+9Q2m00PPfSQYmJiVKdOHZMrBO5fTEyMevfureDgYJ0/f15FihTRRx99pAEDBqhDhw76+9//7vg370m89Xp+y5UrV7Rq1SrHl69KlSqpadOmlp6YjrV4svHn7OYtWW7MmDHy9fVVYmJihkGTY8aMUfPmzdW1a1d9//33HjMO5cCBA2rSpImqVKmiL7/80imUffzxx2rSpIm2bt3qkXPANG7c+I49CTabTWvWrHFTRe534cIFJSQkSJLKly/v0dP6/9///Z/ef/99vfbaa1q4cKFeeOEFTZs2TT/99JNj7htP5K3Xc0n69ttv9dJLL2WYxr9o0aKKiYnRM888Y1Jl2aMHJRt2u12nTp1yJO6AgADt2bPH4xN3RESEpk+frhYtWmTavmLFCrVq1UojR470mEGj7du3V1pamhYuXJjhl7VhGGrbtq3y5s2rr776yqQKXWfw4MFZtl26dElz5sxRamqqx/1/LklHjhxR//79tXLlSqdes5YtW2rKlCmKiIgwt0AXKFCggPbv36+IiAgZhiE/Pz+tW7dOjz32mNmluZS3Xs83b96sRo0a6dlnn9U//vEPpy9fH374oZYtW+Z4DNlqCCjZ8Nb/of38/JSYmJjlt6lff/1VERER1ly74S8qVqyYvvvuO9WuXTvT9h07dqhVq1aO8RmeLi0tTVOnTtV7772noKAgvfPOO4qKijK7rBx1/Phx1alTR3nz5lW/fv2cLtyffvqp0tLStGPHDo/rVbjTdc1Teev1vFWrVgoPD9f06dMzbX/55Zd1/PhxS4454hbPHcyYMUMFCxaUdPOiHRcXp6JFi0q6+e3SExUtWlRHjhzJ8sKclJTkcVO/X7p0KcPtrNuFhIR47N/3n82ePVtvv/22rly5olGjRqlPnz7Kk8fzLhWjRo1SpUqVtHLlSuXLl8+xv02bNho8eLBatmypUaNGacaMGSZW6RrZXddu8ZSxZbfzxuv51q1b9f7772fZ3r9/fzVs2NCNFd09elCyERERcVej/JOSktxQjfv07NlTiYmJjlH+t0tNTVWLFi1UtmxZzZw506QKc16lSpU0duxYtWvXLtP2BQsW6M0339ShQ4fcXJn7rFixQsOGDVNSUpKGDBmi6OhoFShQwOyyXOaBBx7Q/Pnz9fjjj2favmHDBkVFRenEiRNursy17ua6ZrPZ9Msvv7ipIvfw1uu5v7+/fv755yzndzl69KgqV66sK1euuLmyO/O8r0U56MiRI2aXYIoxY8aodu3aqlChgvr376/KlSvLMAwdPHhQ06ZNU2pqqj7//HOzy8xRUVFRio6OVqVKlVS1alWntp9++klDhgzRiy++aFJ1rrV9+3YNHTpUW7duVd++fbV69eoM36Y90W+//ZbtGJOyZctacvKq++Wt1zVvPe8KFSpo7dq16tGjR6bta9asUYUKFdxc1d0hoCCDkiVLasuWLerXr5+GDx/uNHiwWbNmmjJlikqVKmVylTlr+PDhWr16tR5++GE1a9ZMVapUcYSy1atX69FHH9Ubb7xhdpkuUa9ePfn7+6tv374qU6aM5syZk+lxntblHxoaqgMHDmR5K3Pfvn25alrwnHLhwgV9+eWXevXVV80uBTmgR48eGjJkiEqUKKFWrVo5tS1fvlyvv/66Za9t3OLJxt0+QutpF+7bnT9/XocPH5Z08/FLT5xR9JZr167p448/1ty5c50maouKitLgwYM9dlpwb+3yHzRokNauXas1a9aoWLFiTm1nzpxRs2bN1LhxY02aNMmcAt1szZo1iomJ0eLFi5U/f379/vvvZpeUo7z1ep6enq4OHTpo4cKFqlSpktOXr8OHD6tNmzb6+uuvZbdbb+1gAko2/ryC6/HjxxUaGuo0YNATL9yANzh//rzq1q2rU6dOqUuXLk63MufMmaOQkBBt3brVo0P58ePHFRsbq9jYWB07dkxRUVHq2rWrmjRporx585pdXo7y9uv5/PnzM/3yZeWn8wgo98BbHscDvMX58+f1xhtvaP78+V6zBtH169e1ZMkSzZgxQxs3blTLli3VqVMndezYUXv27PHIyQgzw/Xc+ggo94D/oT1X4cKF72qEvycOmvTWru/bedMaRMWLF1flypXVpUsXvfDCCypcuLAkKW/evAQUD5ScnHxXxwUGBrq4knvHIFlA8ppxBpn5+OOP73iMzWbz6IBis9k8bm6frKSlpclms8lms2W7ijM8Q6FChbIN3IZhyGazWXKCOgIKoJv3pxs0aOCRE5LdiafN+3C3ataseVc9Jbt373ZDNe5z4sQJLVy4UDExMfr73/+uyMhIdenSxaN7jbzZunXrzC7hL/O+q/E9+HPXmM1m0+XLlzPst2LXGO5N48aNnVY69TaGYSghIUHXrl1TpUqVvCKotWnTxuwSTJEvXz517txZnTt3VmJiomJjYzVw4EClpaXpvffeU/fu3fXUU095XO+Kt17Pjx49qg4dOuTKpxAZg5INu93u9K3iVlfYn19bsWsM9+bP63R4k6SkJD377LM6cOCApJszrC5cuFB16tQxuTLXOnbsmEqWLGnJxyvdLT09XStXrlRMTIyWLl2qgICADCvf5nbeej338fHJtV++PP9r0n3IzV1juHfe2sX92muvKS0tTV9++aXy5cunDz74QH379tWuXbvMLs2lypQpk2sv3DnNbrcrMjJSkZGROnv2rL744guzS8px3no9z819EPSgZOPzzz/PtV1juDe3LtB3+rtetGiRmypyn5CQEC1YsMCxJs3JkydVsmRJJScne/RaPN7ca+aNvPV6brfbdfr06QyTEeYGBJRs5OauMdwbu92u9u3by9/fP9vjYmNj3VSR+9jtdp08edJpNeeCBQvqp59+yjC5lSfJzRfu+1GmTJm7mjk4MTHRTRW5h7dez+12u6pWrXrHcWVWHAzOLZ5skN28y+TJk73u4iX9/4MFbw9ndrtdly5dchpA6GmDByVpxIgRyp8/f7bHfPTRR26qxj0GDRqUZduRI0c0ffp0paamuq8gN/Hm63mLFi1UsGBBs8u4Z/SgZMNbv2F5I2/9diVlHDwoOQ8g9NTBg3a7XfXr15evr2+Wx9hsNq1du9aNVZnj3Llzeuedd/Tpp5+qbt26ev/991WvXj2zy8pR3no9z823MulBuYMmTZrkyq4x3Js75fSDBw8qJiZGH3zwgZsqch9vHTwoSYsXL86VF+6ccuXKFX300Uf64IMPVLp0aS1atCjDireexBuv57l58D8B5Q5ya9cY7s26desyrLuSkpKiefPmKSYmRlu3btWDDz7okQGlYcOGZpdgitx84b5fN27c0L/+9S+NHj1a+fLl0+TJk71isjZvvJ5n9+XLMAytWLFCMTExWrBggRuruksGsmSz2YzTp0+bXQbcbNOmTUaPHj2MAgUKGHa73fjHP/5hHDx40OyyXObGjRvG+PHjjQYNGhi1a9c2hg4davzxxx9ml+Vyd/r3ff78eeOTTz5xY0XuMX/+fKNChQpGsWLFjEmTJhmpqalml+QW3no9P3LkiHHjxg2nfb/88ovx1ltvGSVLljT8/PyMp59+2qTqsscYlGx487gEb3PmzBnFxcVp5syZunjxojp27KhOnTqpfv36Hr+A2jvvvKNRo0apadOm8vf318qVK9WxY0fNnDnT7NJcatasWYqKisrw2OmaNWsUExOjxYsXK3/+/Pr9999NqtA17Ha7/P391bFjx2wHPnva4GBvv56npqZqwYIFiomJ0aZNm3Tjxg198MEH6tWrl2UHwBNQsvHnwUW3ZlYsWrSomWXBBfz9/fX888+rS5cuatasmWN2UW9Y4bVChQoaMmSIXn75ZUnS6tWr9fTTT+vKlSteM8vq8ePHFRsbq9jYWB07dkxRUVHq2rWrmjRporx585pdXo5q1KjRXT1m7GmDg7MbLGpY/VbHfdi1a5diYmI0d+5clS9fXl27dlWHDh1UsmRJ61/bTO2/sbgjR44Y586dM/r162cUKVLEsNvtht1uN4oUKWL079/fOH/+vNklIodUqlTJiIiIMN544w2n2zl58uQx9u/fb2Jlrufr62scO3bMaZ+fn59x/Phxkypyj2vXrhlfffWV0bx5c8Pf39/429/+Znz99dde8XfujXLzrY774ePjYwwaNMj4+eefnfbnhv/PGSSbjYCAANWrV0//+9//1LlzZ1WpUkWSdODAAcXFxWnNmjXavHmzChcubHKluF8///yzfvzxR8XExKhOnTqqWLGiunTpIsnzB1OmpaUpX758Tvvy5s2r69evm1SRezzwwAOqXLmyunTponnz5jn+HXfs2NHkytzLW3qGS5cuLSl33uq4H02aNFFMTIzOnDmjrl27qkWLFrnmmkZAycaYMWPk6+urxMREp1k2b7U1b95cY8aM0ccff2xShchJjz32mB577DFNnjxZc+fOVWxsrG7cuKF+/fqpU6dOatOmjUfOoWAYhrp37+40FuPq1avq27ev01T3njbNf1pammw2m2w2m8et3HsnFy5c0Jtvvqn58+fr/PnzkqTChQsrKipK7777rgoVKmRugS6Q2a2OuXPnqmTJkmrRooVHhhNJWrlypeMW5iuvvKIrV66oQ4cOkqz/5YsxKNmIiIjQ9OnT1aJFi0zbV6xYob59++rIkSPuLQxuc2v+ky+++ELnzp3zyF6FHj163NVxnjbN/9WrV7Vw4ULHY+SRkZHq0qWLOnTooPj4eGvfm78P586dU/369TPtGZ4zZ47Cw8M9smc4T548GjBggPr27atKlSo59nvDOLPbrVq1SrGxsVq8eLHCw8P1/PPP6/nnn9cjjzxidmkZmXyLydJ8fX2zvQ9//Phxw8/Pz40VwSzXr183Fi5caHYZcJGEhATjzTffNEqWLGnYbDajU6dOxvfff2+kpaWZXVqO+/vf/25UrVrVOHXqVIa2kydPGtWqVTMGDRpkQmWu1bx5cyMgIMDo1KmT8d133xnp6emGYeSOsRj3o0ePHkZycnKG/efOnTMmT55sPPzww4bdbjehsjsjoGQjLCzM2LhxY5btGzZsMEJDQ91YEcxy8OBBo0KFCmaX4XLnz583duzYYezYscMrB4HfuHHDWL58udGuXTvD19fXKFKkiNkl5bjSpUsbK1asyLL9u+++M0qXLu2+gtzo2LFjxujRo42IiAijRIkSxsCBA408efIYBw4cMLs0l7Hb7Xec/2XXrl1uqubecIsnGz179lRiYqJWrVqVYb2O1NRUtWjRQmXLlvX4+SIg7dmzR4888ojHrUdzy5EjR9S/f3+tXLnSMfOkzWZTy5YtNWXKFEVERJhboAnOnj2rL774QtHR0WaXkqP8/PyUmJiokiVLZtr+66+/qnz58rp69aqbK3OvXHWr4z7k5rV4CCjZ+PXXX1W7dm35+fmpf//+qly5sgzD0MGDBzVt2jSlpqZq586dCg8PN7tUuJgnB5Tjx4+rTp06yps3r/r16+c0JuHTTz9VWlqaduzYkeUvNE9x6dIlp2nB7Xa7R06L/sADD2j+/Pl6/PHHM23fuHGjOnTooBMnTri5MnOcP39eX375pWbOnKm9e/d63L9xu92uw4cP33GAvxUHCRNQ7iApKUn9+vXT999/7/TNslmzZpoyZYrKly9vcoVwB08OKL169VJCQoJWrlyZ4XHjK1euqGXLlqpQoYJmzJhhUoWuER8frzfeeEP//ve/Jd2cVuCPP/5wtNtsNm3dulW1a9c2q0SXoGc4a7t37/bIHpTsntYxLLxaOQHlLp0/f16HDx+WJJUvXz7DwnLwbJ4cUO70jXrDhg2KioryuG/UvXr1Urly5fTGG29IuhlQpk+frgceeECGYWjmzJkyDENffPGFyZXmLG/tGZ4wYYIGDBggf39/SdKPP/7o+O8g3exBGzp0qKZNm2ZmmTnObrdr4cKFd/ydZcVFQwkogG7OAZHdt4y0tDSlpKR4ZEDx1jEJVapU0Zw5c1SzZk1JNwPKnj17VLZsWUnStm3b1L59ex09etTMMl3CG3uG/7wWT2BgoOLj4x1/36dPn1ZYWJjH/RvPzWNQmKgNkDRp0iSzSzBNaGioDhw4kGVA2bdvn0JCQtxclesdPXrU6b78mDFjnGZTDQ0N1enTp80ozeXKlCmj7777zqt6hv/8XZzv5tZHQAEkdevWzewSTNOmTRsNGTJEa9asyTCQ7syZMxo6dKjatGljTnEulC9fPh09etQRzAYPHuzUfvz4ceXPn9+M0tymcOHCevTRR80uAy5UunTpXDtTMgEFyMLVq1c1f/58paSkqFmzZqpQoYLZJbnEyJEj9e9//1vlypVTly5dnMYkzJkzRyEhIXr77bfNLjPH1axZU0uWLNFjjz2WafuiRYsct388Sdu2be/qOE9b2sBbJSUlmV3CX0ZAASRFR0fr+vXr+uSTTyRJ165dU/369bV//37lz59fr7/+ulatWqX69eubXGnOK1y4sLZt26Y33nhD8+bN04ULFyRJhQoVUqdOnTR27FiP7Prv16+foqKiFBERoVdeeUV2u12SdOPGDU2bNk2ffPKJ5syZY3KVOS8wMNDya7C4yowZMxyPjqelpSkuLs5xW+/SpUtmluYyWY2vCwoKUsWKFTVkyBA1a9bMhMrujEGygKSqVatq7NixevbZZyXdXHfmH//4h/7zn/+oVKlS6tmzp86cOaPly5ebXKlrGYahs2fPSpKKFSvm8b/Ihg4dqokTJyogIMAxWPKXX37R5cuXFR0drYkTJ5pcIXJKRETEXf3/nJt7HDITFxeX6XlfuHBBu3bt0vz587VgwQI988wzJlSXPQIKoJvfKnfv3u14eqFjx44KCAjQP//5T0k358xo1aqVxz1qC2nr1q2aO3euY7BohQoV1LFjR9WrV8/kylzjz0+zeIukpCSVKVPG7DIs56OPPtKCBQu0efNms0vJgFs8gG4+ind7Vt+6datGjBjheF2oUCHHsvSepmbNmnf1zXL37t1uqMb96tWr57FhJDPe+p20XLlyKl26tBo3bqynnnpKjRs31gMPPGB2WaZr3bq13n33XbPLyBQBBdDNOTGWLl2q6Oho7d+/X8eOHVPjxo0d7UePHlWJEiVMrNB1bn9CxzAMjRs3Tn379vXIcSe327t3710dV716dRdXAndYu3atfvjhB/3www+aO3eurl27prJlyzrCSuPGjT3233h2UlNTM8wobBXc4gEkLV68WFFRUXr88ce1f/9+1alTR0uXLnW0Dx06VElJSfrqq69MrNI9/jxhmae6NQV4dpdAq04Bfj/sdrvefffdO64zNHDgQDdV5H5Xr17V5s2bHYFl+/btun79uipXrqz9+/ebXZ5bDRo0SD///LNWrFhhdikZEFCA/2fNmjVatmyZQkJCNGDAAKc5MEaPHq2GDRuqUaNG5hXoJt4SUO52htjSpUu7uBL3stvtKlmyZLZzY9hsNv3yyy9urMoc165d048//qjvvvtO06dP1+XLlz0ukGa1GvfFixe1e/du/fe//9WGDRtUq1YtN1d2ZwQUAE68JaDs27dPVatWNbsMt8vNU5/fr2vXrmnr1q1at26dfvjhB23btk3h4eF68skn9eSTT6phw4YqVaqU2WXmqNtvVd8uMDBQlSpV0iuvvGLZwcMEFEDSsWPH7uo4T7t4ZcZbAordbledOnX00ksvKSoqSgEBAWaX5Bbe+hTPU089pW3btqlMmTJq2LChnnjiCTVs2FChoaFml4YsEFAAZb0k+a2lyKWb3d5paWnuLs3lJk+e7PR66NCheu2115zWpZE8b0zCxo0bFRsbqwULFig9PV3t2rXTSy+9pCeeeMLs0lzKW3tQ8ubNq9DQULVp00aNGjVSw4YNVaRIEbPLcosjR45o1apVun79uho2bKiHHnrI7JLuCgEFkLRnz55M9xuGoXnz5mny5MkqWLCgzpw54+bKXO9uunc9eUxCSkqKvvrqK8XFxWnjxo0qX768evXqpW7dunnkIomjR4/Wa6+95vHrDP1ZSkqKNm7cqB9++EHr1q1TfHy8Klas6Bhb1rBhwwxrUXmCdevWqXXr1rpy5YokKU+ePJo5c6a6dOlicmV3wQCQqVWrVhm1atUyAgICjJEjRxrJyclmlwQXO3z4sPHGG28Y4eHhRt68eY1nnnnG7JJy3H//+18jKirKuHjxYoa2CxcuGB07djQSExNNqMy9kpOTjX//+9/Ga6+9ZtSpU8fw9fU1HnroIbPLynGPPfaY8dxzzxknTpwwzp07Z/Tr188IDQ01u6y7Yjc7IAFWs3v3bjVr1kytW7dWvXr1lJCQoFGjRnnsGIUtW7Zo2bJlTvs+//xzlSlTRsWLF1efPn2UmppqUnXuVb58eb3xxht66623FBAQ4JFLG3zwwQcKDw9XYGBghragoCCFh4d7xRT/BQoUUHBwsIKDg1W4cGHlyZNHBw8eNLusHLdv3z6NHTtWoaGhKly4sCZOnKgzZ87o999/N7u0OyKgAP9PYmKiOnTooEcffVTFihXTgQMHNGXKFI+/Vz969GinuR9++ukn9erVS02bNtWwYcO0dOlSjRs3zsQK3WPDhg3q3r27QkJC9Nprr6lt27b68ccfzS4rx61bt04vvPBClu3t27fX2rVr3ViRe6Snp2v79u2aMGGCIiMjVahQITVo0EDTpk1TSEiIpk6d6pG3MZOTk53Gk+XPn1/+/v66ePGiiVXdHWaSBXRzZduYmBg1btxYO3fu1MMPP2x2SW6zZ88ep6mu582bp7p16+pf//qXJCk8PFwjR47UqFGjTKrQdU6cOKG4uDjFxcUpISFBDRo00OTJk9W+fXsVKFDA7PJc4vjx49mG7qJFi+r48eNurMg9ChUqpJSUFIWEhKhx48b6+OOP1ahRI5UrV87s0lxu5cqVCgoKcrxOT0/XmjVrtG/fPse+WwulWgkBBZD02WefKV++fDpz5ox69uyZ5XGeuB7N+fPnnab4Xr9+vSIjIx2v69Sp45G/sCIjI7V69WoVLVpUL774onr27KlKlSqZXZbLBQUFKTExMcsJ6BISEjK9/ZPbTZw4UY0bN1bFihXNLsXtunXrlmHfyy+/7PizVWdMJqAAkkaOHGl2CaYpUaKEkpKSFB4ermvXrmn37t0aPXq0o/3SpUvKmzeviRW6Rt68ebVgwQK1bt0601lVDx48qJiYGH3wwQcmVOc6Tz75pD755BM99dRTmbZPnjzZIx+1vv0XsjdJT083u4S/jMeMAS/3yiuvaM+ePXr//fe1ZMkSzZo1SydOnHAsIDZ79mxNmjRJO3bsMLlS10tJSdG8efMUExOjrVu36sEHH3TqBvcE//nPf1S/fn21bt1ar7/+uqPX6Oeff9aECRO0fPlybd68WY888ojJlcLbEVCAO0hOTtbs2bMVExOjnTt3ml1Ojvvtt9/Utm1bbdq0SQULFtSsWbP0t7/9zdHepEkT1atXT++9956JVbrWjz/+qJiYGH311Ve6cuWKBg8erJdeekmVK1c2uzSXWLZsmXr27JnhSY4iRYpoxowZlhyPgL9mw4YNd3Xck08+6eJK7h0BBcjCunXrNHPmTC1atEhBQUH629/+pqlTp5pdlstcvHhRBQsWzHC749y5cypYsKBll2T/q86cOaO4uDjNnDlTFy9eVMeOHdWpUyfVr19fe/bs0YMPPmh2iS515coVrVixQgkJCTIMQxUrVlTz5s29bgI3T3f7LNlZ/bq36hgUAgpwm//973+Ki4tTbGysLly4oPPnz2vOnDlq3759plPhI/fy9/fX888/ry5duqhZs2ay22/OupA3b16vCCjwDkWKFFFAQIC6d++url27ZljC4pbbn/KxCgbJApIWLlyomJgYbdiwQZGRkfrwww8VGRmpAgUKqFq1aoQTD1S6dGlt2rRJpUqVUunSpT32ds6f/Xntpax42tpL3urkyZNavHixZs6cqQkTJqhVq1bq1auXWrZsafnrGj0ogG6uTzF06FANGzbMacZYvk17tltjT77++mtVrFhRXbp00euvv669e/eqSpUqZpfnEt6+9pI3O3bsmOLi4jRr1iylpqaqW7duGj16tPLksWZfBQEF0M1HEOfPn6+HHnpIXbt2VYcOHVS4cGECipe4fPmy5s6dq9jYWG3dulUNGzZUp06d1KZNG49cQA7eLSkpSb169dL69et19uxZBQcHm11SppjqHpA0ffp0nTx5Un369NHcuXMVGhqq5557ToZh5Op5BHB3ChYsqN69e2vz5s3at2+fHnnkEb311lsKCwszuzSXSE9P18yZM9W6dWtVrVpV1apV03PPPafPP/88y4GUyN1SU1M1Z84cNW3aVFWrVlXRokW1fPlyy4YTiR4UIFOHDx9WbGysZs2apcuXL+vpp5/W888/r7Zt25pdGtwkLS1N3377rcf9nRuGodatW+u7775TjRo1VLlyZRmGoYMHD+qnn37Ss88+qyVLlphdJnLI9u3bFRsbq3nz5ikiIkI9evRQly5dLB1MbiGgANlIT0/X8uXLFRMTo++++85rVvX1Brc/fpkVm82mtLQ0N1XkHrGxsfr73/+ub775Ro0bN3ZqW7t2rdq0aaMpU6boxRdfNKlC5CS73a5SpUqpW7duqlWrVpbHWXHuGwIKcBeuXLmiKVOm6LXXXjO7FOSQb775Jsu2LVu2aPLkyTIMQ1euXHFjVa7XvHlzPfXUUxo2bFim7WPHjtX69eu1cuVKN1cGV7j1+Hx2mAcFsLizZ89q27Zt8vX1VZMmTeTj46Pr169r2rRpGj9+vK5fv67ffvvN7DLhQocOHdKwYcO0dOlSde7cWWPGjMlyUb3cKiQkRCtWrMhyxe7//Oc/ioyM1KlTp9xbGPAnDJIFJG3atEkVKlTQs88+q8jISDVo0EAHDhzQQw89pOnTp2vkyJEeuaIvbjpx4oR69+6tatWqKS0tTfHx8Zo1a5bHhRPp5szAt69e/WclSpTQ+fPn3VgRkDkCCiDprbfeUqtWrbR3715FR0drx44d+tvf/qaxY8fqwIED6tu3r/z9/c0uEzns4sWLGjp0qMqXL6/9+/drzZo1Wrp0qapWrWp2aS5z48aNbOe98PHx8bhxN5C+/vprtW3bVlWrVlXVqlXVtm1bLViwwOyyssUtHkA3p4PeuHGjHnzwQV25ckUFCxbUokWL9Nxzz5ldGlxkwoQJev/99xUSEqKxY8d6zd+13W5XZGSk/Pz8Mm1PTU3VihUrLDkmAfcuPT1dHTt2dExGeGvG5IMHDyohIUEvvPCC5s6da8lZZQkogG5etE+dOqXixYtLkgICAhQfH69y5cqZXBlcxW63y9/fX02bNs2wQOLtFi1a5MaqXK9Hjx53dVxsbKyLK4E7fPzxx3r33Xc1a9YstW7d2qnt22+/VY8ePTRixAgNGjTInAKzQUABdPOX1dq1ax1zAzRo0EBfffWVSpYs6XRc9erVzSgPLtC9e/e7+tbIL2rkZtWrV9egQYPUs2fPTNtjYmL0f//3f9q7d6+bK7szAgqg/39OjMz+Odzab9VH8QAgK/7+/jp06JBKlSqVafvRo0dVuXJlSz5Ob80VggA3S0pKMrsEAMhx/v7+unDhQpYBJTk5Wfny5XNzVXeHgAJImjVrloYMGaL8+fObXQoA5Jj69evr008/1aeffppp+9SpU1W/fn03V3V3uMUD6OajlSdPnnQMkgUAT7B582Y1atRIbdq00ZAhQ5zWXvrwww/1zTffaN26dXrsscfMLjUDAgqgjE/xAICnWLx4sfr06aNz58457S9cuLCmT5+udu3amVRZ9ggogG4GlNOnT6tYsWJmlwIAOe6PP/7QypUrdfjwYUlSxYoV1bx5c0vf1iagALoZUIKCgu742Omfv4EAgJWtXbtWr776qrZu3arAwECntosXL6pBgwb67LPP9MQTT5hUYdYYJAv8P6NHj1ZQUJDZZQBAjpk0aZJ69+6dIZxIUlBQkF5++WV99NFHlgwo9KAAYgwKAM9UunRprVixQlWqVMm0/eeff1bz5s117NgxN1d2ZywWCEiWXIcCAO7X6dOnlTdv3izb8+TJo7Nnz7qxortHQAGkTGeQBYDc7oEHHtC+ffuybN+7d69CQ0PdWNHdI6AAurniJ7d3AHiaVq1aacSIEbp69WqGtitXrmjkyJEZFhG0CsagAADgoU6fPq1HHnlEPj4+evXVV1WpUiVJN8eeTJ06VTdu3NDu3btVokQJkyvNiIACAIAHO3r0qF555RWtXLnScTvbZrOpRYsWmjp1qsqUKWNyhZkjoAAA4AXOnz+vhIQEGYahChUqqHDhwmaXlC0CCgAAsBwGyQIAAMshoAAAAMshoAAAAMshoAAAAMshoADIEY0aNdKgQYPMLgOAhyCgAMgRixYt0jvvvHNXxx45ckQ2m03x8fE5XkdcXJwKFSqU4+8LwL3ymF0AAM8QHBxsdgkAPAg9KAByxO23eCIiIjR27Fj17NlTAQEBKlWqlP75z386jr01c2XNmjVls9nUqFEjR9uMGTNUpUoV5cuXT5UrV9a0adMcbbd6XhYtWqTGjRsrf/78qlGjhrZs2SJJ+uGHH9SjRw9dvHhRNptNNptNo0aNcvm5A8h5BBQALvHhhx+qdu3a+s9//qN+/frplVde0aFDhyRJ27dvlyStXr1aJ0+e1KJFiyRJs2fP1ttvv6333ntPBw8e1NixYzVixAjNmjXL6b3ffPNNDRkyRPHx8apYsaI6duyotLQ0NWjQQJMmTVJgYKBOnjypkydPasiQIe49cQA5gls8AFyiVatW6tevnyRp6NCh+vjjj7Vu3TpVqlRJxYoVkyQVKVJEISEhjp8ZOXKkPvzwQ7Vt21bSzZ6WAwcOaPr06erWrZvjuCFDhujpp5+WJI0ePVoPPfSQEhISVLlyZQUFBclmszm9L4Dch4ACwCWqV6/u+POtwHDmzJksj09JSVFiYqJ69eql3r17O/anpaUpKCgoy/cODQ2VJJ05c0aVK1fOqfIBmIyAAsAl8ubN6/TaZrMpPT09y+MvX74sSfrXv/6lunXrOrX5+Phk+d42m02Ssn1vALkPAQWA2/n6+kqSbty44dhXokQJhYWF6ZdfflHnzp3v671vf18AuRMBBYDbFS9eXP7+/lqxYoVKliypfPnyKSgoSKNHj9bAgQMVFBSkli1bKjU1VTt37tT58+cVHR19V+8dERGhy5cva82aNapRo4by58+v/Pnzu/iMAOQ0nuIB4HZ58uTR5MmTNX36dIWFhem5556TJL300kuaMWOGYmNjVa1aNTVs2FBxcXGOx5LvRoMGDdS3b1916NBBxYoV04QJE1x1GgBcyGYYhmF2EQAAALejBwUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFjO/we6bQGcYhAJygAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from typing_extensions import Annotated\n",
        "# Breakdown of intent categories\n",
        "print(\"Intent Categories + Counts: \\n\", df['intent'].value_counts(), \"\\n\")\n",
        "\n",
        "ax = df.intent.value_counts().plot.bar()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9yh3fNLHMMv",
        "outputId": "6abeb060-d615-4444-a153-79030cbfcaf5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Country Categories + Counts: \n",
            " geo_country\n",
            "FR    213\n",
            "DE    204\n",
            "GB    105\n",
            "CN      6\n",
            "US      3\n",
            "AT      3\n",
            "GH      1\n",
            "Name: count, dtype: int64 \n",
            "\n",
            "English Search Queries Examples: \n",
            " ['google translate' 'rephrase' 'amazon' 'weather ' 'etsy' 'outlook'\n",
            " 'boots' 'Recruiting' 'grammarly' 'front door handle' 'google'\n",
            " 'hotel chocolat uk' 'indeed jobs' 'google scholar' 'setofilm 4 mg'\n",
            " 'bill granger' 'gmail' 'tesco groceries' 'apple tv' 'translate' 'next'\n",
            " 'vivobarefoot uk' 'minerva' 'yahoo mail' 'epiphany in welsh'\n",
            " 'steve cooper' 'esr' 'lincoln' 'zoopla' 'wordle' 'guardian' 'tfl'\n",
            " 'weather' 'lg 38 ultrawide' 'chatgpt' 'Basketball' 'john lewis'\n",
            " 'bbc iplayer' 'mens under armour graphic fleece' 'schuh' 'deliveroo'\n",
            " 'sainsburys' 'amazon uk' 'chanel coco mademoiselle set'\n",
            " 'victorian engagement ring' 'sketchpad' 'calculator' 'paypal login'\n",
            " 'google drive' 'guardian uk' 'yahoo' 'nhs jobs' \"sainsbury's groceries\"\n",
            " 'maps' 'PR' 'bbc news' 'glow' 'tony robbins' 'argos' 'parasite' 'nhsmail'\n",
            " 'spotify' 'bbc weather plymouth' 'just eat' 'nhs mail'\n",
            " 'santa tracker 2023' 'bialetti perfetto moka' 'facebook' 'outlook login'\n",
            " 'person wearing hawaiian shirt' 'Automobile' 'mizuno br-d4c cart bag'\n",
            " 'funny slippers men' 'kittens youbtube' 'yt' 'jd' 'pare' 'google maps'] \n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Breakdown of Queries by Country\n",
        "print(\"Country Categories + Counts: \\n\", df['geo_country'].value_counts(), \"\\n\")\n",
        "\n",
        "# Printing Examples of the Types of Search Queries (In English)\n",
        "print(\"English Search Queries Examples: \\n\", df[(df.geo_country) == 'GB']['search_query'].unique(), \"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6WEYYZ_4GV_"
      },
      "source": [
        "There is a noticeable class imbalance on the \"intent\" column.\n",
        "\n",
        "The \"geo_country\" looks standardized.\n",
        "\n",
        "Looking at the sample of queries in the cell above, you can see some various characters/punctuations, numeric values, and not many stop words. We will remove them just in case during the preprocessing stage.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "2I8oKtwH1T8V",
        "outputId": "d7c29bd4-86a1-4ccc-ec0b-74da399e0be5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of missing values in intent column: 62 \n",
            "\n",
            "Country queries of missing data: \n",
            " geo_country\n",
            "DE    10\n",
            "FR     9\n",
            "GB     5\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
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            ],
            "text/plain": [
              "          date                         search_query market geo_country  \\\n",
              "6   2023-12-07                             weather     NaN          GB   \n",
              "7   2023-12-28          vorwissenschaftliche arbeit  de-de         NaN   \n",
              "21  2024-01-05                                 ikea  de-de         NaN   \n",
              "24  2023-12-28           ecksofa mit schlaffunktion    NaN         NaN   \n",
              "27  2023-12-13  blumenkohl und brokkoli im backofen  de-de          DE   \n",
              "\n",
              "   device_type browser_name intent  daily_query_count  \n",
              "6       Mobile     Chromium    NaN                  2  \n",
              "7      Desktop       Chrome    NaN                  2  \n",
              "21     Desktop       Chrome    NaN                  2  \n",
              "24         NaN          NaN    NaN                  2  \n",
              "27         NaN          NaN    NaN                  2  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Data Quality Checks\n",
        "\n",
        "#missing_intent\n",
        "print(\"Number of missing values in intent column:\", df['intent'].isnull().sum(), \"\\n\")\n",
        "\n",
        "missing_intent = df.loc[df['intent'].isnull()]\n",
        "\n",
        "print(\"Country queries of missing data: \\n\", missing_intent.geo_country.value_counts())\n",
        "\n",
        "missing_intent.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96LRomJFJbIf",
        "outputId": "fd7c972e-411b-4ec4-fd19-b11fda351e61"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of missing values in search_query column: 0 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of missing values in search_query column:\", df['search_query'].isnull().sum(), \"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbMOHayd0Tlz"
      },
      "source": [
        "# Overall Analysis & Preprocessing Approach\n",
        "\n",
        "1) Convert search_query to one language, English specifically\n",
        "\n",
        "2) For the model, I will be using the search_query and intent columns for analysis. Date, market, device_type, browser_name, and daily_query_count are helpful metrics for other types of analysis, however these data values typically do not influence the intent of a query. For this first pass, I will not include them.\n",
        "\n",
        "3) Preprocessing steps:\n",
        "\n",
        "*   Remove rows where intent is missing\n",
        "*   Convert \"search_query\" column all to one language\n",
        "*   Convert \"intent\" column to numerical categories\n",
        "*   Preprocess \"search_query\" data, remove stop words, remove puntuations, and conduct lemma  \n",
        "*   Address class imbalance issue in \"intent\" column\n",
        "*   TF-IDF approach on \"search_query\" colum\n",
        "*   Test out NaiveBayes vs KNN vs Gradient Boost Classifiers\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5dD7ffzyvUk",
        "outputId": "25bb766f-afe9-46ba-ea04-727f8aa6d657"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "New dataframe shape: \n",
            " (537, 8)\n"
          ]
        }
      ],
      "source": [
        "# Remove rows of data without intent\n",
        "\n",
        "df_intent = df.dropna(subset=['intent'])\n",
        "\n",
        "print(\"New dataframe shape: \\n\", df_intent.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKE5TjZ54Y9m",
        "outputId": "4f554594-bbbc-4cc8-967a-f1f96424e4f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: deep-translator in /usr/local/lib/python3.10/dist-packages (1.11.4)\n",
            "Requirement already satisfied: beautifulsoup4<5.0.0,>=4.9.1 in /usr/local/lib/python3.10/dist-packages (from deep-translator) (4.12.3)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from deep-translator) (2.31.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4<5.0.0,>=4.9.1->deep-translator) (2.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (2024.6.2)\n",
            "Translating 'traduction anglais français' to English: English French translation \n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-10-5c61af41c1c9>:21: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_intent['translated_txt'] = df_intent.search_query.apply(lambda x: translate_to_english(x))\n"
          ]
        }
      ],
      "source": [
        "# Creating a new column for translated search queries\n",
        "!pip install -U deep-translator\n",
        "# Documentation: https://pypi.org/project/deep-translator/\n",
        "\n",
        "from deep_translator import GoogleTranslator\n",
        "\n",
        "# Creating a Function that will detect the language of the query and translate it if it's not in english\n",
        "def translate_to_english(text):\n",
        "  target_language = 'en'\n",
        "  translated_text = GoogleTranslator(source='auto',\n",
        "                                     target=target_language).translate(text)\n",
        "  return translated_text\n",
        "\n",
        "\n",
        "# Testing Function\n",
        "print(\"Translating 'traduction anglais français' to English:\",\n",
        "      translate_to_english('traduction anglais français'), \"\\n\")\n",
        "\n",
        "\n",
        "# Creating New Translated Search Query Column\n",
        "df_intent['translated_txt'] = df_intent.search_query.apply(lambda x: translate_to_english(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "Q3Xx5wv06_9B",
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            ],
            "text/plain": [
              "          date                  search_query market geo_country device_type  \\\n",
              "3   2023-12-29                      facebook  fr-fr          FR      Mobile   \n",
              "8   2023-12-17  vacances scolaires 2023 2024  fr-fr          FR      Mobile   \n",
              "9   2023-12-14                    traduction  fr-fr          FR     Desktop   \n",
              "10  2024-01-01               gens definition  fr-fr          FR      Mobile   \n",
              "11  2023-12-19                        twitch  fr-fr          FR     Desktop   \n",
              "\n",
              "   browser_name       intent  daily_query_count             translated_txt  \n",
              "3    Ecosia ios   NAVIGATION                  3                   facebook  \n",
              "8      Chromium           QA                  4  school holidays 2023 2024  \n",
              "9        Chrome  TRANSLATION                  2                translation  \n",
              "10         Edge   DICTIONARY                  2            gens definition  \n",
              "11       Chrome        OTHER                  3                     twitch  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking if Query Translation Occured:\n",
        "df_intent.loc[df_intent[\"geo_country\"] =='FR'].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
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        "id": "8RA-1EHX_JCP",
        "outputId": "a0c97cfe-53d5-4b98-a54d-f5b03c45b8e7"
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          "text": [
            "Collecting en-core-web-sm==3.7.1\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl (12.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m20.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.8.0,>=3.7.2 in /usr/local/lib/python3.10/dist-packages (from en-core-web-sm==3.7.1) (3.7.5)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2024.6.2)\n",
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            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.5)\n",
            "Requirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7)\n",
            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.5.4)\n",
            "Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (13.7.1)\n",
            "Requirement already satisfied: cloudpathlib<1.0.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.18.1)\n",
            "Requirement already satisfied: smart-open<8.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (7.0.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.5)\n",
            "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.2.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.16.1)\n",
            "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.14.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.2)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_sm')\n",
            "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
            "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
            "order to load all the package's dependencies. You can do this by selecting the\n",
            "'Restart kernel' or 'Restart runtime' option.\n"
          ]
        }
      ],
      "source": [
        "# Preprocess Search Query text - Remove punctuations, stop words, and conducting lemmatization\n",
        "\n",
        "import spacy\n",
        "!spacy download en_core_web_sm\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "def preprocess_text(text):\n",
        "    doc = nlp(text)\n",
        "\n",
        "    filtered_tokens = []\n",
        "    for token in doc:\n",
        "        # Removing stop words and punctuation\n",
        "        if token.is_stop or token.is_punct:\n",
        "            continue\n",
        "        # taking the lemma token\n",
        "        filtered_tokens.append(token.lemma_)\n",
        "\n",
        "    return \" \".join(filtered_tokens)\n",
        "\n",
        "\n",
        "# Testing the function:\n",
        "# print(preprocess_and_vectorize_text(\"I want to eat bananas\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbyctoS4CR1o",
        "outputId": "cec963ae-a531-4a61-96f9-f4774d68436f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-13-3c5f6f230f58>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_intent['clean_txt'] = df_intent['translated_txt'].apply(lambda x: preprocess_text(x))\n"
          ]
        }
      ],
      "source": [
        "# Applying function to the queries in df_intent\n",
        "df_intent['clean_txt'] = df_intent['translated_txt'].apply(lambda x: preprocess_text(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "E677UIGGEpFq",
        "outputId": "0d453895-1d4f-470c-a340-4218f3ee2e36"
      },
      "outputs": [
        {
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              "                                                    [key], {});\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         date      search_query market geo_country device_type browser_name  \\\n",
              "0  2023-12-08  google translate  en-gb          GB     Desktop       Chrome   \n",
              "1  2023-12-08          rephrase  en-gb          GB     Desktop       Chrome   \n",
              "2  2023-12-31            amazon  en-gb          GB      Mobile       Chrome   \n",
              "3  2023-12-29          facebook  fr-fr          FR      Mobile   Ecosia ios   \n",
              "4  2023-12-23       tv programm  de-de          DE      Mobile       Safari   \n",
              "\n",
              "        intent  daily_query_count    translated_txt         clean_txt  \n",
              "0  TRANSLATION                  2  google translate  google translate  \n",
              "1   DICTIONARY                  2          rephrase          rephrase  \n",
              "2   NAVIGATION                  2            amazon            amazon  \n",
              "3   NAVIGATION                  3          facebook          facebook  \n",
              "4        OTHER                  3        TV program        tv program  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_intent.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "821VS1taIm7v"
      },
      "source": [
        "### Address class imbalance issue in \"intent\" column\n",
        "\n",
        "Now we will address the class imbalance issue with the intent categories.\n",
        "\n",
        "As a reference, here are the category counts from the dataset:\n",
        "\n",
        "*   OTHER - 173\n",
        "*   QA - 109\n",
        "*   TRANSLATION - 100\n",
        "*   SHOPPING - 66\n",
        "*   NAVIGATION - 60\n",
        "*   DICTIONARY - 19\n",
        "*   WEATHER - 5\n",
        "*   COMPUTATION - 5\n",
        "\n",
        "Since \"WEATHER\", \"COMPUTATION\", and \"DICTIONARY\" are such small categories, I will remove these samples all together for this analysis.\n",
        "\n",
        "Since \"OTHER\" category is so large, I will take a 109 random sample to become more aligned with the \"QA\" and \"TRANSLATION\" categories, **however due to time, I will not be able to for this assignment**.   \n",
        "\n",
        "Regarding undersampling, I would apply a Synthetic Minority Over-sampling Technique (SMOTE) to the \"SHOPPING\", \"NAVIGATION\", and \"DICTIONARY\" columns, **however due to time, I will not be able to for this assignment**.  \n",
        "\n",
        "Ideally for real life projects, we want to make sure our training dataset is robust and has equal representation of all intent categories we're interested in."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "OHKbB0z8JKU_",
        "outputId": "6422002a-5eca-4154-ee38-f4ca67b2e1a4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Before removing columns: (537, 10) \n",
            "\n",
            "After removing columns: (508, 10) \n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-15-c1c219cb0dc1>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_intent['intent_num'] = df_intent.intent.map({\n"
          ]
        },
        {
          "data": {
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              "summary": "{\n  \"name\": \"df_intent\",\n  \"rows\": 508,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2024-01-06\",\n          \"2023-12-25\",\n          \"2023-12-24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"search_query\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 318,\n        \"samples\": [\n          \"immobilienscout24\",\n          \"booking.com\",\n          \"pull and bear\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"market\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"fr-fr\",\n          \"zh-cn\",\n          \"en-gb\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geo_country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"GB\",\n          \"FR\",\n          \"GH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"device_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Desktop\",\n          \"Mobile\",\n          \"Tablet\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"browser_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Chrome\",\n          \"Ecosia ios\",\n          \"Edge\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"intent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"NAVIGATION\",\n          \"QA\",\n          \"OTHER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"daily_query_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2,\n        \"max\": 42,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          2,\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"translated_txt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 314,\n        \"samples\": [\n          \"christmas greetings\",\n          \"aquaman 2\",\n          \"Media Markt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clean_txt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 310,\n        \"samples\": [\n          \"Saint Nick\",\n          \"google\",\n          \"hotel chocolat uk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"intent_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_intent"
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              "      <th>date</th>\n",
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              "      <td>NAVIGATION</td>\n",
              "      <td>3</td>\n",
              "      <td>facebook</td>\n",
              "      <td>facebook</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-12-23</td>\n",
              "      <td>tv programm</td>\n",
              "      <td>de-de</td>\n",
              "      <td>DE</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>Safari</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2023-12-11</td>\n",
              "      <td>fußmatten ford focus kombi</td>\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "         date                search_query market geo_country device_type  \\\n",
              "0  2023-12-08            google translate  en-gb          GB     Desktop   \n",
              "2  2023-12-31                      amazon  en-gb          GB      Mobile   \n",
              "3  2023-12-29                    facebook  fr-fr          FR      Mobile   \n",
              "4  2023-12-23                 tv programm  de-de          DE      Mobile   \n",
              "5  2023-12-11  fußmatten ford focus kombi  de-de          DE      Mobile   \n",
              "\n",
              "  browser_name       intent  daily_query_count  \\\n",
              "0       Chrome  TRANSLATION                  2   \n",
              "2       Chrome   NAVIGATION                  2   \n",
              "3   Ecosia ios   NAVIGATION                  3   \n",
              "4       Safari        OTHER                  3   \n",
              "5       Chrome     SHOPPING                  2   \n",
              "\n",
              "                        translated_txt                           clean_txt  \\\n",
              "0                     google translate                    google translate   \n",
              "2                               amazon                              amazon   \n",
              "3                             facebook                            facebook   \n",
              "4                           TV program                          tv program   \n",
              "5  floor mats ford focus station wagon  floor mat ford focus station wagon   \n",
              "\n",
              "   intent_num  \n",
              "0           2  \n",
              "2           4  \n",
              "3           4  \n",
              "4           0  \n",
              "5           3  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Removing \"WEATHER\" and \"COMPUTATION\" categories\n",
        "\n",
        "print(\"Before removing columns:\", df_intent.shape, \"\\n\")\n",
        "\n",
        "df_intent = df_intent.loc[df_intent[\"intent\"].isin([\"OTHER\", \"QA\", \"TRANSLATION\", \"SHOPPING\", \"NAVIGATION\"])]\n",
        "\n",
        "print(\"After removing columns:\", df_intent.shape, \"\\n\")\n",
        "\n",
        "\n",
        "# Convert Intent Column to Numerical Categories\n",
        "\n",
        "df_intent['intent_num'] = df_intent.intent.map({\n",
        "    \"OTHER\": 0,\n",
        "    \"QA\": 1,\n",
        "    \"TRANSLATION\": 2,\n",
        "    \"SHOPPING\": 3,\n",
        "    \"NAVIGATION\": 4\n",
        "})\n",
        "\n",
        "df_intent.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZGgbqVj9V06p"
      },
      "source": [
        "## ML Models:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "ANmf2U3YVrXe"
      },
      "outputs": [],
      "source": [
        "# Creating train and test datasets:\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    df_intent.clean_txt,\n",
        "    df_intent.intent_num,\n",
        "    test_size = 0.2,\n",
        "    random_state = 9876,\n",
        "    stratify=df_intent.intent_num\n",
        ")\n",
        "\n",
        "# Applying TF-IDF Vectorizer\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "v = TfidfVectorizer()\n",
        "X_train_cv = v.fit_transform(X_train)\n",
        "X_test_cv = v.transform(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFQCCFhjWa0D",
        "outputId": "62d34227-6022-4537-c1f5-2a3ffbdb4873"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(410,)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train_cv.toarray()[:2][0].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_baeJGsyOE9y",
        "outputId": "e8f4b32f-21af-40a1-e47d-06d9617cf7e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Examining the category distribution between test and training datasets: \n",
            "\n",
            "Training Intent Categories Count: \n",
            " intent_num\n",
            "0    138\n",
            "1     87\n",
            "2     80\n",
            "3     53\n",
            "4     48\n",
            "Name: count, dtype: int64 \n",
            "\n",
            "Testing Intent Categories Count: \n",
            " intent_num\n",
            "0    35\n",
            "1    22\n",
            "2    20\n",
            "3    13\n",
            "4    12\n",
            "Name: count, dtype: int64 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Examining the category distribution between test and training datasets: \\n\")\n",
        "\n",
        "print(\"Training Intent Categories Count: \\n\", y_train.value_counts(), \"\\n\")\n",
        "\n",
        "print(\"Testing Intent Categories Count: \\n\",y_test.value_counts(), \"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTlw4k7WNshW"
      },
      "source": [
        "### Model #1: Naive Bayes Classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JjQJratHOe7_",
        "outputId": "9b54683a-c3d4-4668-fb69-4c34a8896c67"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.54      0.77      0.64        35\n",
            "           1       0.50      0.36      0.42        22\n",
            "           2       0.87      1.00      0.93        20\n",
            "           3       1.00      0.31      0.47        13\n",
            "           4       0.89      0.67      0.76        12\n",
            "\n",
            "    accuracy                           0.66       102\n",
            "   macro avg       0.76      0.62      0.64       102\n",
            "weighted avg       0.70      0.66      0.64       102\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "clf = MultinomialNB()\n",
        "clf.fit(X_train_cv, y_train)\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "y_pred = clf.predict(X_test_cv)\n",
        "print(classification_report(y_test, y_pred))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kl_1RHEbPD70"
      },
      "source": [
        "### Model #2: KNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IXStmp2dO7Ue",
        "outputId": "0e8b2d3a-39fb-41a6-9a8e-d4fd81c0c052"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.52      0.91      0.66        35\n",
            "           1       0.00      0.00      0.00        22\n",
            "           2       0.95      0.95      0.95        20\n",
            "           3       1.00      0.46      0.63        13\n",
            "           4       0.91      0.83      0.87        12\n",
            "\n",
            "    accuracy                           0.66       102\n",
            "   macro avg       0.68      0.63      0.62       102\n",
            "weighted avg       0.60      0.66      0.60       102\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# creating model object\n",
        "clf2 = KNeighborsClassifier(n_neighbors=5, metric=\"euclidean\")\n",
        "\n",
        "# fit the training datasets\n",
        "clf2.fit(X_train_cv, y_train)\n",
        "\n",
        "# get the predictions\n",
        "y_pred = clf2.predict(X_test_cv)\n",
        "\n",
        "# output the classification report\n",
        "print(classification_report(y_test, y_pred))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLZ8oE36Rdw5"
      },
      "source": [
        "### Model #3: Gradient Boosting Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rdeWC4teRdTY",
        "outputId": "0d853ec4-53bd-4b93-cbfa-4acd68f7b96a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.55      0.89      0.68        35\n",
            "           1       0.62      0.23      0.33        22\n",
            "           2       0.95      1.00      0.98        20\n",
            "           3       0.83      0.38      0.53        13\n",
            "           4       0.91      0.83      0.87        12\n",
            "\n",
            "    accuracy                           0.70       102\n",
            "   macro avg       0.77      0.67      0.68       102\n",
            "weighted avg       0.72      0.70      0.67       102\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "clf = GradientBoostingClassifier()\n",
        "clf.fit(X_train_cv, y_train)\n",
        "y_pred = clf.predict(X_test_cv)\n",
        "\n",
        "\n",
        "# output the classification report\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "oMyTgSrmKG3o",
        "outputId": "aab7c966-2ea1-45fe-cde6-98b4fefcfd87"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(83.22222222222221, 0.5, 'Truth')"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 900x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Creating Confusion Matrix Diagram to help visualize output from the Gradient Boost Classifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sn\n",
        "plt.figure(figsize = (9,6))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel(\"Prediction\")\n",
        "plt.ylabel(\"Truth\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmcSH0OlcC6v"
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      "source": [
        "## Results & Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-UhjejKcH69"
      },
      "source": [
        "### Overview\n",
        "My approach to creating a machine learning model for the search queries was to apply the TF-IDF vectorizer to the preprocessed search query text. My initial approach was to convert the search query text to word embedding vectors, however this produced low model performance and thus I changed how I would vectorize the data. One reason for word embeddings not being successful is because word embeddings is better for semantic similarity, and for classification type problems TF-IDF is a better technique.\n",
        "\n",
        "Afterwards I ran the data through three model classifiers: MultinomialNB, KNeighborsClassifier, and GradientBoostingClassifier.\n",
        "\n",
        "**Overall the GradientBoostingClassifier performed the best.** Our average F1-score (which is the harmonic mean between Precision and Recall) and accuracy was the highest with GradientBoosting compared to the other two models.\n",
        "\n",
        "### Issues & Problems:\n",
        "Across the 3 models, each had poor performance with predicting the \"QA\" or \"1\" category. One of the issues I noticed with the training dataset is that the labels are not consistent or accurately assigned.\n",
        "\n",
        "For example, in the cell below, you'll see that the search query of \"chat gpt\" could be assigned to either \"QA\" category or \"OTHER\" category equally.\n",
        "\n",
        "To enhance the performance of the models, we need to make sure our dataset is clean, standardized, and has good quality data.\n",
        "\n",
        "### Next Steps:\n",
        "If I were to enhance this analysis, I would address the class imbalance issue. I would either use SMOTE to create new samples for the underrepresented classes, and also minimize over sampled classes. If possible, I would also increase the sample size for each category so it lead to a more robust analysis."
      ]
    },
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Example of the Chat GPT Problem:\n",
            "intent\n",
            "QA       5\n",
            "OTHER    5\n",
            "Name: count, dtype: int64\n"
          ]
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            "text/plain": [
              "           date search_query market geo_country device_type browser_name  \\\n",
              "60   2023-12-15     chat gpt  de-de          DE     Desktop       Safari   \n",
              "137  2023-12-29     chat gpt  de-de          DE     Desktop      Firefox   \n",
              "177  2023-12-15     chat gpt  fr-fr          FR     Desktop       Safari   \n",
              "203  2023-12-14     chat gpt  de-de          DE     Desktop       Chrome   \n",
              "245  2023-12-10      chatgpt  en-gb          GB     Desktop         Edge   \n",
              "289  2023-12-19      chatgpt  de-de          DE     Desktop       Chrome   \n",
              "315  2023-12-18      chatgpt  en-gb          GB     Desktop         Edge   \n",
              "467  2023-12-25      chatgpt  de-de          DE      Mobile   Ecosia ios   \n",
              "539  2023-12-13     chat gpt  de-de          DE     Desktop      Firefox   \n",
              "584  2023-12-07     chat gpt  fr-fr          FR     Desktop       Safari   \n",
              "\n",
              "    intent  daily_query_count translated_txt clean_txt  intent_num  \n",
              "60      QA                  2       chat gpt  chat gpt           1  \n",
              "137  OTHER                  2       chat gpt  chat gpt           0  \n",
              "177     QA                  2       chat gpt  chat gpt           1  \n",
              "203     QA                  4       chat gpt  chat gpt           1  \n",
              "245  OTHER                  2        chatgpt   chatgpt           0  \n",
              "289  OTHER                  2        chatgpt   chatgpt           0  \n",
              "315  OTHER                  2        chatgpt   chatgpt           0  \n",
              "467  OTHER                  3        chatgpt   chatgpt           0  \n",
              "539     QA                  2       chat gpt  chat gpt           1  \n",
              "584     QA                  5       chat gpt  chat gpt           1  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Example of the Chat GPT Problem:\")\n",
        "\n",
        "print(df_intent[df_intent['search_query'].str.contains(\"chat\")]['intent'].value_counts())\n",
        "\n",
        "df_intent[df_intent['search_query'].str.contains(\"chat\")]"
      ]
    }
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