11: AutoNLP_Sentiment_Analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/gulabpatel/AutoML/blob/main/AutoNLP_Sentiment_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Automatically Build Variant Interpretable ML models fast! Auto_ViML is pronounced \"auto vimal\" (autovimal logo created by Sanket Ghanmare)\n",
        "\n",
        "NEW FEATURES in this version are:\n",
        "1. SMOTE -> now we use SMOTE for imbalanced data. Just set Imbalanced_Flag = True in input below\n",
        "2. Auto_NLP: It automatically detects Text variables and does NLP processing on those columns\n",
        "3. Date Time Variables: It automatically detects date time variables and adds extra features\n",
        "4. Feature Engineering: Now you can perform feature engineering with the available featuretools library."
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        "https://github.com/AutoViML/Auto_ViML#background"
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        "colab": {
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      "source": [
        "!pip install autoviml"
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      "execution_count": 1,
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4e/39/665a3b952ac889839eead70813dbc638fe49be4c6217ed249fc06b6485fa/distributed-2.30.0-py3-none-any.whl (656kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a5/8b/1df260f860f17cb08698170153ef7db672c497c1840dcc8613ce26a8a005/fsspec-0.8.4-py3-none-any.whl (91kB)\n",
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            "\u001b[?25hCollecting partd>=0.3.10; extra == \"dataframe\"\n",
            "  Downloading https://files.pythonhosted.org/packages/44/e1/68dbe731c9c067655bff1eca5b7d40c20ca4b23fd5ec9f3d17e201a6f36b/partd-1.1.0-py3-none-any.whl\n",
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            "  Downloading https://files.pythonhosted.org/packages/83/96/55b82d9f13763be9d672622e1b8106c85acb83edd7cc2fa5bc67cd9877e9/contextvars-2.4.tar.gz\n",
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            "Collecting locket\n",
            "  Downloading https://files.pythonhosted.org/packages/d0/22/3c0f97614e0be8386542facb3a7dcfc2584f7b83608c02333bced641281c/locket-0.2.0.tar.gz\n",
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            "Collecting immutables>=0.9\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/99/e0/ea6fd4697120327d26773b5a84853f897a68e33d3f9376b00a8ff96e4f63/immutables-0.14-cp36-cp36m-manylinux1_x86_64.whl (98kB)\n",
            "\u001b[K     |████████████████████████████████| 102kB 10.3MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: emoji, contextvars, locket\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/a5/7d/68/1ebae2668bda2228686e3c1cf16f2c2384cea6e9334ad5f6de\n",
            "  Building wheel for locket (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Successfully built emoji contextvars locket\n",
            "\u001b[31mERROR: distributed 2.30.0 has requirement cloudpickle>=1.5.0, but you'll have cloudpickle 1.3.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: catboost, xgboost, threadpoolctl, scikit-learn, imbalanced-learn, vaderSentiment, emoji, immutables, contextvars, distributed, featuretools, autoviml, fsspec, locket, partd\n",
            "  Found existing installation: xgboost 0.90\n",
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            "    Uninstalling distributed-1.25.3:\n",
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            "Successfully installed autoviml-0.1.665 catboost-0.24.2 contextvars-2.4 distributed-2.30.0 emoji-0.6.0 featuretools-0.20.0 fsspec-0.8.4 imbalanced-learn-0.7.0 immutables-0.14 locket-0.2.0 partd-1.1.0 scikit-learn-0.23.2 threadpoolctl-2.1.0 vaderSentiment-3.3.2 xgboost-1.2.1\n"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUbFvR7LDB3U"
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDH43tw-DN4y",
        "outputId": "a6d8a64b-ab8e-446e-b4b9-ae0f40938b8a",
        "colab": {
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          "height": 418,
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      },
      "source": [
        "dataset, info = tfds.load('amazon_us_reviews/Personal_Care_Appliances_v1_00', with_info=True, batch_size=-1)\n",
        "train_dataset = dataset['train']"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset amazon_us_reviews/Personal_Care_Appliances_v1_00/0.1.0 (download: 16.82 MiB, generated: Unknown size, total: 16.82 MiB) to /root/tensorflow_datasets/amazon_us_reviews/Personal_Care_Appliances_v1_00/0.1.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
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              "model_id": "583de5d0789949c4b3738713c2a353a1",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
            "  InsecureRequestWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/amazon_us_reviews/Personal_Care_Appliances_v1_00/0.1.0.incomplete3N1690/amazon_us_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "971a3b31f96642d1b6afe777940c831f",
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        {
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              "model_id": "9b64b784d80d49a6810bed0993c3cf9e",
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              "HBox(children=(FloatProgress(value=0.0, description='Computing statistics...', max=1.0, style=ProgressStyle(de…"
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        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "ERROR:absl:Statistics generation doesn't work for nested structures yet\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "\u001b[1mDataset amazon_us_reviews downloaded and prepared to /root/tensorflow_datasets/amazon_us_reviews/Personal_Care_Appliances_v1_00/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1diITDtD9xD",
        "outputId": "763d8d40-2da7-4f9c-fdff-83dae77bdb23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        }
      },
      "source": [
        "info"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='amazon_us_reviews',\n",
              "    version=0.1.0,\n",
              "    description='Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.\n",
              "\n",
              "Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).\n",
              "\n",
              "Each Dataset contains the following columns : \n",
              "  marketplace       - 2 letter country code of the marketplace where the review was written.\n",
              "  customer_id       - Random identifier that can be used to aggregate reviews written by a single author.\n",
              "  review_id         - The unique ID of the review.\n",
              "  product_id        - The unique Product ID the review pertains to. In the multilingual dataset the reviews\n",
              "                      for the same product in different countries can be grouped by the same product_id.\n",
              "  product_parent    - Random identifier that can be used to aggregate reviews for the same product.\n",
              "  product_title     - Title of the product.\n",
              "  product_category  - Broad product category that can be used to group reviews \n",
              "                      (also used to group the dataset into coherent parts).\n",
              "  star_rating       - The 1-5 star rating of the review.\n",
              "  helpful_votes     - Number of helpful votes.\n",
              "  total_votes       - Number of total votes the review received.\n",
              "  vine              - Review was written as part of the Vine program.\n",
              "  verified_purchase - The review is on a verified purchase.\n",
              "  review_headline   - The title of the review.\n",
              "  review_body       - The review text.\n",
              "  review_date       - The date the review was written.\n",
              "',\n",
              "    homepage='https://s3.amazonaws.com/amazon-reviews-pds/readme.html',\n",
              "    features=FeaturesDict({\n",
              "        'data': FeaturesDict({\n",
              "            'customer_id': tf.string,\n",
              "            'helpful_votes': tf.int32,\n",
              "            'marketplace': tf.string,\n",
              "            'product_category': tf.string,\n",
              "            'product_id': tf.string,\n",
              "            'product_parent': tf.string,\n",
              "            'product_title': tf.string,\n",
              "            'review_body': tf.string,\n",
              "            'review_date': tf.string,\n",
              "            'review_headline': tf.string,\n",
              "            'review_id': tf.string,\n",
              "            'star_rating': tf.int32,\n",
              "            'total_votes': tf.int32,\n",
              "            'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
              "            'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
              "        }),\n",
              "    }),\n",
              "    total_num_examples=85981,\n",
              "    splits={\n",
              "        'train': 85981,\n",
              "    },\n",
              "    supervised_keys=None,\n",
              "    citation=\"\"\"\"\"\",\n",
              "    redistribution_info=,\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeuwcjT2oLWd"
      },
      "source": [
        "#Convert tf data format to numpy format\n",
        "dataset = tfds.as_numpy(train_dataset)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCy_iKoLwZWH",
        "outputId": "6a9373d1-a68d-4c95-ee71-27f93eee6407",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        }
      },
      "source": [
        "dataset"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'data': {'customer_id': array([b'13986323', b'50574716', b'50593972', ..., b'40719682',\n",
              "         b'35596948', b'29430209'], dtype=object),\n",
              "  'helpful_votes': array([0, 3, 0, ..., 0, 0, 0], dtype=int32),\n",
              "  'marketplace': array([b'US', b'US', b'US', ..., b'US', b'US', b'US'], dtype=object),\n",
              "  'product_category': array([b'Personal_Care_Appliances', b'Personal_Care_Appliances',\n",
              "         b'Personal_Care_Appliances', ..., b'Personal_Care_Appliances',\n",
              "         b'Personal_Care_Appliances', b'Personal_Care_Appliances'],\n",
              "        dtype=object),\n",
              "  'product_id': array([b'B00847JQZ6', b'B00N5HD340', b'B0077L1X24', ..., b'B000UZ8X2W',\n",
              "         b'B000NURPPK', b'B001EY5GNW'], dtype=object),\n",
              "  'product_parent': array([b'997683625', b'955577225', b'120764066', ..., b'96066145',\n",
              "         b'58591097', b'986877728'], dtype=object),\n",
              "  'product_title': array([b'SE - Reading Glass - Spring Loaded Hinges, 4.0x - RTS62400',\n",
              "         b'Straight Razor',\n",
              "         b'Philips Sonicare Flexcare & Healthy White Plastic Travel Handle Case New Bulk Package',\n",
              "         ...,\n",
              "         b'Remington R-9200 Microflex Ultra TCT Shaver [Health and Beauty]',\n",
              "         b'SUNBEAM Cool Mist HUMIDIFIER with PermaFilter # 1120 \\xe2\\x80\\x93 1 Ea',\n",
              "         b'Andis Blade Set for T-Outliner Trimmer'], dtype=object),\n",
              "  'review_body': array([b\"These glasses are an excellent value.  The fit is good and they are very comfortable.  Because of my legal blindness, there aren't a lot of options to try to see better, but I believe these help with my other visual aids, and because they are reasonably priced I can have more than one pair available.\",\n",
              "         b\"Always wanted to try straight razor shaving (as a DE safety razor user), and this was a cheap way for me to determine I was not into it.<br /><br />Because the blades are disposable and always sharp, I could put a new one in and reasonably rely upon that fact that cuts were probably due to my technique and not the blade.<br /><br />It's very hard to do straight razor shaving on yourself because the ANGLE is difficult to control without switching hands. Being very right-handed, I really couldn't do that. I bet I could shave someone else's face with it though.<br /><br />An immediate upside? Using a DE safety razor (slant edged even) seems SUPER safe now! I'm increased my speed with the DE due to that confidence, and I'd been using it for years now.\",\n",
              "         b'I usually either throw my toothbrush in a plastic bag with spare head so this product is very convenient for keeping all the parts apart, dry and undamaged, and i now keep it in my travel bag all the time ready to go.',\n",
              "         ...,\n",
              "         b\"I have had a Remington before but needed a new one when the batteries died and the cutters were all but gone.  It was cheaper to buy a new one.  The new one has a nice charge level but the trimmer didn't work when I got it.\",\n",
              "         b\"I was surprised that it really didn't do much compared to the 1950s version that I'd inherited. Keeping a wet wash cloth next to my bed for when I start coughing in the middle of the night works better.\",\n",
              "         b'The blades were an excellent fit for my T-line trimmers.  Within five minutes I had my trimmers cleaned, the blades installed, and was putting them to use.  I saw the blades in several locations for almost twice the price I paid, so this worked out to be an awesome deal.'],\n",
              "        dtype=object),\n",
              "  'review_date': array([b'2015-01-04', b'2015-08-05', b'2012-11-17', ..., b'2008-02-08',\n",
              "         b'2007-09-07', b'2012-07-26'], dtype=object),\n",
              "  'review_headline': array([b'These glasses are an excellent value. The fit is good and they are ...',\n",
              "         b'A fantastic way to cheaply try straight razor shaving.',\n",
              "         b'Great for travel', ..., b'Trimmer Not Working',\n",
              "         b'Loud and ineffectual', b'Excellent product, awesomoe price'],\n",
              "        dtype=object),\n",
              "  'review_id': array([b'R3VEUFVA9QJY55', b'R2DTQV5SMJ0CK7', b'R3OJ06NK99WLNJ', ...,\n",
              "         b'R1ZQ0XZXOD9N18', b'R1FJ9OU429X00Y', b'RI28R1W94N1R6'],\n",
              "        dtype=object),\n",
              "  'star_rating': array([4, 5, 4, ..., 3, 2, 5], dtype=int32),\n",
              "  'total_votes': array([0, 3, 0, ..., 0, 0, 0], dtype=int32),\n",
              "  'verified_purchase': array([0, 0, 0, ..., 1, 0, 0]),\n",
              "  'vine': array([1, 1, 1, ..., 1, 1, 1])}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1pGPYGcoLNz"
      },
      "source": [
        "#Pich up some importat features from the original dataset\n",
        "helpful_votes=dataset['data']['helpful_votes']\n",
        "review_headline=dataset['data']['review_headline']\n",
        "review_body=dataset['data']['review_body']\n",
        "rating=dataset['data']['star_rating']"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmS7qQuCoLGF"
      },
      "source": [
        "reviews_df=pd.DataFrame(np.hstack((helpful_votes[:,None],review_headline[:,None],review_body[:,None],rating[:,None])),columns=['votes','headline','reviews','rating'])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0DWbO1y1Vzc"
      },
      "source": [
        "convert_dict = {'votes': int, \n",
        "                'headline': str,\n",
        "                'reviews': str,\n",
        "                'rating': int\n",
        "               } "
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWiWrKmS1lwR"
      },
      "source": [
        "reviews_df = reviews_df.astype(convert_dict) "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AsmO113-vaVY",
        "outputId": "445fb2dc-7f64-4931-928e-ac6762aef2d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        }
      },
      "source": [
        "reviews_df"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>votes</th>\n",
              "      <th>headline</th>\n",
              "      <th>reviews</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>b'These glasses are an excellent value. The fi...</td>\n",
              "      <td>b\"These glasses are an excellent value.  The f...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>b'A fantastic way to cheaply try straight razo...</td>\n",
              "      <td>b\"Always wanted to try straight razor shaving ...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Great for travel'</td>\n",
              "      <td>b'I usually either throw my toothbrush in a pl...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Five Stars'</td>\n",
              "      <td>b'Top quality.'</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>b'*Product sent not as shown'</td>\n",
              "      <td>b'Today I received  1 Fl. Oz, Natures Balance ...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85976</th>\n",
              "      <td>2</td>\n",
              "      <td>b'YES!'</td>\n",
              "      <td>b\"This is the real deal. Don't bother with the...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85977</th>\n",
              "      <td>1</td>\n",
              "      <td>b'Bryton Picks'</td>\n",
              "      <td>b'I like the Bryton Picks very much. Have orde...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85978</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Trimmer Not Working'</td>\n",
              "      <td>b\"I have had a Remington before but needed a n...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85979</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Loud and ineffectual'</td>\n",
              "      <td>b\"I was surprised that it really didn't do muc...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85980</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Excellent product, awesomoe price'</td>\n",
              "      <td>b'The blades were an excellent fit for my T-li...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85981 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       votes  ... rating\n",
              "0          0  ...      4\n",
              "1          3  ...      5\n",
              "2          0  ...      4\n",
              "3          0  ...      5\n",
              "4          1  ...      3\n",
              "...      ...  ...    ...\n",
              "85976      2  ...      5\n",
              "85977      1  ...      5\n",
              "85978      0  ...      3\n",
              "85979      0  ...      2\n",
              "85980      0  ...      5\n",
              "\n",
              "[85981 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydJ-TzNZvaHR"
      },
      "source": [
        "#For binary classification\n",
        "reviews_df[\"target\"] = reviews_df[\"rating\"].apply(lambda x: 1 if x>= 4 else 0) "
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M57qg7nw4oF-"
      },
      "source": [
        "##For multiclass classification\n",
        "#def convert_rating(rating):\n",
        "#  if rating <= 2:\n",
        "#    out = 0\n",
        "#  elif rating == 3:\n",
        "#    out = 1\n",
        "#  else:\n",
        "#    out = 2\n",
        "#  return out\n",
        "\n",
        "#reviews_df[\"target\"] = reviews_df[\"rating\"].apply(lambda x: convert_rating(x))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZOZarx4GGX3",
        "outputId": "e894cca2-3677-412a-8d05-3fa6b156ee4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        }
      },
      "source": [
        "reviews_df"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>votes</th>\n",
              "      <th>headline</th>\n",
              "      <th>reviews</th>\n",
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              "      <th>target</th>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>b'These glasses are an excellent value. The fi...</td>\n",
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              "      <td>4</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>b'A fantastic way to cheaply try straight razo...</td>\n",
              "      <td>b\"Always wanted to try straight razor shaving ...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Great for travel'</td>\n",
              "      <td>b'I usually either throw my toothbrush in a pl...</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Five Stars'</td>\n",
              "      <td>b'Top quality.'</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>b'*Product sent not as shown'</td>\n",
              "      <td>b'Today I received  1 Fl. Oz, Natures Balance ...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85976</th>\n",
              "      <td>2</td>\n",
              "      <td>b'YES!'</td>\n",
              "      <td>b\"This is the real deal. Don't bother with the...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85977</th>\n",
              "      <td>1</td>\n",
              "      <td>b'Bryton Picks'</td>\n",
              "      <td>b'I like the Bryton Picks very much. Have orde...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85978</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Trimmer Not Working'</td>\n",
              "      <td>b\"I have had a Remington before but needed a n...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85979</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Loud and ineffectual'</td>\n",
              "      <td>b\"I was surprised that it really didn't do muc...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85980</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Excellent product, awesomoe price'</td>\n",
              "      <td>b'The blades were an excellent fit for my T-li...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85981 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       votes                                           headline  ... rating  target\n",
              "0          0  b'These glasses are an excellent value. The fi...  ...      4       1\n",
              "1          3  b'A fantastic way to cheaply try straight razo...  ...      5       1\n",
              "2          0                                b'Great for travel'  ...      4       1\n",
              "3          0                                      b'Five Stars'  ...      5       1\n",
              "4          1                      b'*Product sent not as shown'  ...      3       0\n",
              "...      ...                                                ...  ...    ...     ...\n",
              "85976      2                                            b'YES!'  ...      5       1\n",
              "85977      1                                    b'Bryton Picks'  ...      5       1\n",
              "85978      0                             b'Trimmer Not Working'  ...      3       0\n",
              "85979      0                            b'Loud and ineffectual'  ...      2       0\n",
              "85980      0               b'Excellent product, awesomoe price'  ...      5       1\n",
              "\n",
              "[85981 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbU4KHtEyJdM",
        "outputId": "8a4f11a3-f20c-46d0-b3c5-3bc5abfa75ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "reviews_df.shape[0]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85981"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1p0u2X8yJaX",
        "outputId": "6389b73e-bdc8-4be5-c867-a9e466bfd2f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "reviews_df[\"target\"].value_counts()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    62554\n",
              "0    23427\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nkj87kcOyyhE",
        "outputId": "fdceadec-408d-4a43-a7b3-a4c9e8e29d6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "reviews_df.info()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 85981 entries, 0 to 85980\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   votes     85981 non-null  int64 \n",
            " 1   headline  85981 non-null  object\n",
            " 2   reviews   85981 non-null  object\n",
            " 3   rating    85981 non-null  int64 \n",
            " 4   target    85981 non-null  int64 \n",
            "dtypes: int64(3), object(2)\n",
            "memory usage: 3.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inj7K9BixwSl"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train, test = train_test_split(reviews_df, test_size=0.25)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_9D_SnZxZ2P",
        "outputId": "b270ba42-5305-4956-8aa4-092866887bdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        }
      },
      "source": [
        "from autoviml.Auto_NLP import Auto_NLP"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading collection 'popular'\n",
            "[nltk_data]    | \n",
            "[nltk_data]    | Downloading package cmudict to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/cmudict.zip.\n",
            "[nltk_data]    | Downloading package gazetteers to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/gazetteers.zip.\n",
            "[nltk_data]    | Downloading package genesis to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/genesis.zip.\n",
            "[nltk_data]    | Downloading package gutenberg to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/gutenberg.zip.\n",
            "[nltk_data]    | Downloading package inaugural to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/inaugural.zip.\n",
            "[nltk_data]    | Downloading package movie_reviews to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/movie_reviews.zip.\n",
            "[nltk_data]    | Downloading package names to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/names.zip.\n",
            "[nltk_data]    | Downloading package shakespeare to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/shakespeare.zip.\n",
            "[nltk_data]    | Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data]    | Downloading package treebank to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/treebank.zip.\n",
            "[nltk_data]    | Downloading package twitter_samples to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/twitter_samples.zip.\n",
            "[nltk_data]    | Downloading package omw to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/omw.zip.\n",
            "[nltk_data]    | Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data]    | Downloading package wordnet_ic to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet_ic.zip.\n",
            "[nltk_data]    | Downloading package words to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/words.zip.\n",
            "[nltk_data]    | Downloading package maxent_ne_chunker to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping chunkers/maxent_ne_chunker.zip.\n",
            "[nltk_data]    | Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data]    | Downloading package snowball_data to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    | Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection popular\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>.container { width:95% !important; }</style>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Imported Auto_NLP version: 0.0.44.. Call using:\n",
            "     train_nlp, test_nlp, nlp_pipeline, predictions = Auto_NLP(\n",
            "                nlp_column, train, test, target, score_type='balanced_accuracy',\n",
            "                modeltype='Classification',top_num_features=200, verbose=0,\n",
            "                build_model=True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJDEh3pgxZtz",
        "outputId": "b12546cd-e14f-4918-92dd-fa8d70a2928e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "nlp_column = 'reviews'\n",
        "target = 'target'\n",
        "train_nlp, test_nlp, nlp_transformer, preds = Auto_NLP(\n",
        "                nlp_column, train, test, target, score_type='balanced_accuracy',\n",
        "                modeltype='Classification',top_num_features=50, verbose=2,\n",
        "                build_model=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Auto NLP processing on NLP Column: reviews\n",
            "Shape of Train Data: 64485 rows\n",
            "    Shape of Test Data: 21496 rows\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x3000 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "    Added 9 summary columns for counts of words and characters in each row\n",
            "Cleaning text in Train data for reviews column\n",
            "    Faster text processing using clean_text function, since top_num_features is below 300\n",
            "    Time Taken for Expanding emojis and abbreviations in data = 105 seconds\n",
            "        Time Taken for Processing text in data = 238 seconds\n",
            "Train data Text cleaning completed. Time taken = 363 seconds\n",
            "\n",
            "For target = target\n",
            "\n",
            "    For class = 0\n",
            "Top 200 n-grams\n",
            ": ['do', 'but', 'work', 'use', 'product', 'would', 'buy', 'good', 'they', 'like', 'if', 'will', 'time', 'me', 'after', 'when', 'well', 'no', 'make', 'try', 'money', 'what', 'your', 'these', 'back', 'them', 'think', 'go', 'off', 'great', 'really', 'could', 'because', 'look', 'return', 'item', 'give', 'purchase', 'much', 'take', 'come', 'need', 'there', 'even', 'small', 'first', 'way', 'thing', 'order', 'cheap', 'week', 'find', 'recommend', 'as', 'month', 'waste', 'day', 'two', 'which', 'bad', 'see', 'break', 'little', 'price', 'want', 'also', 'should', 'last', 'never', 'receive', 'long', 'enough', 'however', 'expect', 'quality', 'problem', 'now', 'put', 'fit', 'help', 'hard', 'year', 'know', 'seem', 'cannot', 'before', 'feel', 'ok', 'battery', 'we', 'keep', 'review', 'send', 'hair', 'while', 'how', 'different', 'big', 'hold', 'another', 'pay', 'nothing', 'nice', 'company', 'right', 'stop', 'size', 'worth', 'anything', 'stay', 'new', 'something', 'amazon', 'plastic', 'less', 'piece', 'large', 'result', 'start', 'lot', 'disappointed', 'since', 'old', 'who', 'around', 'sure', 'high', 'read', 'part', 'though', 'easy', 'star', 'fine', 'do not', 'not work', 'b do', 'b not', 'would not', 'will not', 'not buy', 'b product', 'b buy', 'but not', 'but do', 'not recommend', 'work well', 'waste money', 'they do', 'could not', 'your money', 'b these', 'not even', 'not fit', 'b item', 'product do', 'b purchase', 'there no', 'think would', 'b ok', 'not do', 'look like', 'not like', 'b use', 'b work', 'not worth', 'work great', 'do not work', 'b do not', 'do not buy', 'but do not', 'do not fit', 'they do not', 'would not recommend', 'product do not', 'do not know', 'save your money', 'do not do', 'do not recommend', 'do not like', 'waste your money', 'do not even', 'not waste your', 'if do not', 'will not buy', 'do not waste', 'do not hold', 'do not want', 'do not think', 'do not stay', 'do not really', 'do not feel', 'not work well', 'not recommend product', 'would not buy', 'do not help', 'do not use', 'not work me', 'do not seem', 'b buy these']\n",
            "    Top n-grams that are most frequent in this class are: 199\n",
            "\n",
            "    For class = 1\n",
            "Top 200 n-grams\n",
            ": ['great', 'good', 'product', 'use', 'work', 'not', 'do', 'but', 'love', 'well', 'buy', 'like', 'me', 'would', 'price', 'they', 'time', 'easy', 'will', 'these', 'make', 'really', 'recommend', 'need', 'if', 'when', 'what', 'them', 'much', 'take', 'your', 'no', 'help', 'back', 'find', 'go', 'purchase', 'year', 'after', 'day', 'best', 'as', 'try', 'also', 'little', 'look', 'give', 'nice', 'now', 'order', 'feel', 'because', 'long', 'keep', 'quality', 'come', 'works', 'we', 'thing', 'want', 'problem', 'could', 'excellent', 'last', 'even', 'item', 'perfect', 'first', 'fit', 'who', 'which', 'there', 'think', 'how', 'pain', 'lot', 'happy', 'seem', 'every', 'expect', 'see', 'before', 'right', 'small', 'way', 'fast', 'without', 'he', 'week', 'she', 'highly', 'hair', 'know', 'while', 'put', 'hard', 'ever', 'worth', 'month', 'off', 'far', 'two', 'cannot', 'new', 'comfortable', 'old', 'since', 'size', 'enough', 'husband', 'job', 'clean', 'hand', 'big', 'battery', 'hold', 'thank', 'wear', 'her', 'definitely', 'never', 'review', 'sure', 'free', 'shave', 'many', 'around', 'always', 'exactly', 'awesome', 'light', 'fine', 'receive', 'b great', 'do not', 'b good', 'b love', 'work great', 'great product', 'b works', 'work well', 'b use', 'easy use', 'b work', 'b buy', 'b product', 'good product', 'b these', 'highly recommend', 'would recommend', 'b excellent', 'as well', 'b nice', 'works great', 'b best', 'but do', 'great price', 'b really', 'could not', 'recommend product', 'b purchase', 'will not', 'help me', 'good quality', 'b like', 'well make', 'b great product', 'b works great', 'b good product', 'but do not', 'b excellent product', 'b works well', 'do not know', 'b do not', 'they do not', 'do not want', 'highly recommend product', 'b really like', 'b work great', 'worth every penny', 'would highly recommend', 'do not work', 'work as well', 'would recommend anyone', 'b use product', 'b these great', 'b love product', 'do not like', 'do not need', 'last long time', 'do good job', 'well worth money', 'b easy use', 'would definitely recommend', 'b love these', 'work really well', 'if do not', 'b great price', 'would recommend product']\n",
            "    Top n-grams that are most frequent in this class are: 199\n",
            "Time Taken = 1 seconds\n",
            "##################    THIS IS FOR BUILD_MODEL = TRUE           #################\n",
            "Building Model and Pipeline for NLP column = reviews. This will take time...\n",
            "       Class  -> Counts -> Percent\n",
            "           0:   17501  ->   27.1%\n",
            "           1:   46984  ->   72.9%\n",
            "    Selected the maximum number of features limit = 311286\n",
            "Performing RandomizedSearchCV across 30 params. Optimizing for accuracy\n",
            "    Using train data = (51588,) and Cross Validation data = (12897,)\n",
            "Using a Calibrated Classifier in this Multi_Classification dataset to improve results...\n",
            "Since top_num_features < 300, Multinomial NB model selected. If you need different model, increase it >= 300.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZ6DDWuExZli"
      },
      "source": [
        "nlp_transformer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svsToSQr4y6B"
      },
      "source": [
        "nlp_transformer.predict(test[nlp_column])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYFdvCdi48cc"
      },
      "source": [
        "nlp_transformer."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXySATmM3I5o"
      },
      "source": [
        "### Reference youtube videos is available on  the link https://www.youtube.com/watch?v=D-taRpi4OJk\n",
        "\n",
        "For more details of AutoviML visit the link https://github.com/AutoViML"
      ]
    }
  ]
}