Electronics.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## **CAPSTONE PROJECT**\n",
        "## **Amazon Electronics**"
      ],
      "metadata": {
        "id": "DzSqLBNXbqnQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Library Installation"
      ],
      "metadata": {
        "id": "FQ325kRnNQPk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "poukjy7PHZKP",
        "outputId": "401943d5-b236-4824-c418-a51197943eb1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting fasttext\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pybind11>=2.2\n",
            "  Using cached pybind11-2.10.4-py3-none-any.whl (222 kB)\n",
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            "Building wheels for collected packages: fasttext\n",
            "  Building wheel for fasttext (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fasttext: filename=fasttext-0.9.2-cp39-cp39-linux_x86_64.whl size=4395568 sha256=f5f0128900d8f1775188cdd79db9479808f6fa1f685a3518e980d81d32ed7249\n",
            "  Stored in directory: /root/.cache/pip/wheels/64/57/bc/1741406019061d5664914b070bd3e71f6244648732bc96109e\n",
            "Successfully built fasttext\n",
            "Installing collected packages: pybind11, fasttext\n",
            "Successfully installed fasttext-0.9.2 pybind11-2.10.4\n"
          ]
        }
      ],
      "source": [
        "!pip install fasttext"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r32wfN1tf-KX",
        "outputId": "1919b10a-37c7-45ca-dca9-a1a1a6cb63c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting python-Levenshtein\n",
            "  Downloading python_Levenshtein-0.20.9-py3-none-any.whl (9.4 kB)\n",
            "Collecting Levenshtein==0.20.9\n",
            "  Downloading Levenshtein-0.20.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (175 kB)\n",
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            "\u001b[?25hCollecting rapidfuzz<3.0.0,>=2.3.0\n",
            "  Downloading rapidfuzz-2.13.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB)\n",
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            "\u001b[?25hInstalling collected packages: rapidfuzz, Levenshtein, python-Levenshtein\n",
            "Successfully installed Levenshtein-0.20.9 python-Levenshtein-0.20.9 rapidfuzz-2.13.7\n"
          ]
        }
      ],
      "source": [
        "!pip install gensim\n",
        "!pip install python-Levenshtein"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0Cw3S32j0Xl",
        "outputId": "62fc0bcf-cc22-4a5e-e957-534cbcc41a04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Building wheels for collected packages: pyod, plotly-resampler, fugue-sql-antlr\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-1.0.9-py3-none-any.whl size=184112 sha256=600d57b0912a5601cc873e9ee06bd3127399f5439bf0867195cfcc9d7366f324\n",
            "  Stored in directory: /root/.cache/pip/wheels/1b/9c/b8/9759d7cc64a1e01bb9872ade80cb7db445ccf506e083325106\n",
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            "  Created wheel for plotly-resampler: filename=plotly_resampler-0.8.3.2-cp39-cp39-manylinux_2_31_x86_64.whl size=75072 sha256=dc758edcfd6df75dffc9b1046c2a46744b3f49449127dfbb11df9d5795f953b9\n",
            "  Stored in directory: /root/.cache/pip/wheels/9e/36/06/4c11e300918011376af149098621ec7ebe06d8256566d43d51\n",
            "  Building wheel for fugue-sql-antlr (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fugue-sql-antlr: filename=fugue_sql_antlr-0.1.5-py3-none-any.whl size=157611 sha256=35f5f8c7983a0715f6981eef5dd9cd34049956ed8a3babc8b4f115b857dd740d\n",
            "  Stored in directory: /root/.cache/pip/wheels/61/d4/e5/7c90c375fba26867c6ec01bccb003b4cb983fe8386f2e0ffe0\n",
            "Successfully built pyod plotly-resampler fugue-sql-antlr\n",
            "Installing collected packages: trace-updater, sqlglot, dash-table, dash-html-components, dash-core-components, appdirs, antlr4-python3-runtime, slicer, retrying, orjson, nest-asyncio, multidict, lightning-utilities, jedi, fs, frozenlist, async-timeout, ansi2html, yarl, torchmetrics, aiosignal, triad, shap, pyod, nfoursid, dash, catboost, aiohttp, pmdarima, jupyter-dash, fugue-sql-antlr, adagio, tbats, qpd, pytorch-lightning, plotly-resampler, fugue, statsforecast, darts\n",
            "Successfully installed adagio-0.2.4 aiohttp-3.8.4 aiosignal-1.3.1 ansi2html-1.8.0 antlr4-python3-runtime-4.11.1 appdirs-1.4.4 async-timeout-4.0.2 catboost-1.1.1 darts-0.23.1 dash-2.9.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 frozenlist-1.3.3 fs-2.4.16 fugue-0.8.1 fugue-sql-antlr-0.1.5 jedi-0.18.2 jupyter-dash-0.4.2 lightning-utilities-0.8.0 multidict-6.0.4 nest-asyncio-1.5.6 nfoursid-1.0.1 orjson-3.8.7 plotly-resampler-0.8.3.2 pmdarima-2.0.3 pyod-1.0.9 pytorch-lightning-2.0.0 qpd-0.4.0 retrying-1.3.4 shap-0.41.0 slicer-0.0.7 sqlglot-11.4.1 statsforecast-1.5.0 tbats-1.1.2 torchmetrics-0.11.4 trace-updater-0.0.9 triad-0.8.4 yarl-1.8.2\n"
          ]
        }
      ],
      "source": [
        "!pip install darts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "goaIQKx3p3YR",
        "outputId": "2e287eda-3c90-4772-9dba-b63624d59182"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting afinn\n",
            "  Downloading afinn-0.1.tar.gz (52 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: afinn\n",
            "  Building wheel for afinn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for afinn: filename=afinn-0.1-py3-none-any.whl size=53448 sha256=8410ecff98878b169602c049e43b3a885a589a2c42fe71e9325b7578efd65c48\n",
            "  Stored in directory: /root/.cache/pip/wheels/79/91/ee/8374d9bc8c6c0896a2db75afdfd63d43653902407a0e76cd94\n",
            "Successfully built afinn\n",
            "Installing collected packages: afinn\n",
            "Successfully installed afinn-0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install afinn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GhWckt7dkiNb",
        "outputId": "c18201a2-6852-4ff0-dd1f-c070ac16c405"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyLDAvis==2.1.2\n",
            "  Downloading pyLDAvis-2.1.2.tar.gz (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m37.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: wheel>=0.23.0 in /usr/local/lib/python3.9/dist-packages (from pyLDAvis==2.1.2) (0.40.0)\n",
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            "Requirement already satisfied: numexpr in /usr/local/lib/python3.9/dist-packages (from pyLDAvis==2.1.2) (2.8.4)\n",
            "Requirement already satisfied: pytest in /usr/local/lib/python3.9/dist-packages (from pyLDAvis==2.1.2) (3.6.4)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.9/dist-packages (from pyLDAvis==2.1.2) (0.18.3)\n",
            "Collecting funcy\n",
            "  Downloading funcy-1.18-py2.py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2>=2.7.2->pyLDAvis==2.1.2) (2.1.2)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.17.0->pyLDAvis==2.1.2) (2022.7.1)\n",
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            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.9/dist-packages (from pytest->pyLDAvis==2.1.2) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from pytest->pyLDAvis==2.1.2) (63.4.3)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.9/dist-packages (from pytest->pyLDAvis==2.1.2) (9.1.0)\n",
            "Building wheels for collected packages: pyLDAvis\n",
            "  Building wheel for pyLDAvis (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyLDAvis: filename=pyLDAvis-2.1.2-py2.py3-none-any.whl size=97736 sha256=895b97a34d30489741cd08584eba00c5d78d4fc17d0b20463e3deb8c75178d1b\n",
            "  Stored in directory: /root/.cache/pip/wheels/59/70/ee/99a0df99d8b4a7b87c79640ebee0927c0f6ccff046e9cc2471\n",
            "Successfully built pyLDAvis\n",
            "Installing collected packages: funcy, pyLDAvis\n",
            "Successfully installed funcy-1.18 pyLDAvis-2.1.2\n"
          ]
        }
      ],
      "source": [
        "!pip install pyLDAvis==2.1.2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BvVTShjqf-tS"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jBijV5AF3H4e",
        "outputId": "bef04358-a1c1-4d77-91ad-c96b732cb295"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "from collections import Counter\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.feature_extraction.text import CountVectorizer , TfidfVectorizer\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "from prophet import Prophet\n",
        "\n",
        "import nltk\n",
        "nltk.download('vader_lexicon')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "stop=set(stopwords.words('english'))\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "sent = SentimentIntensityAnalyzer()\n",
        "\n",
        "import fasttext\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import f1_score , precision_score , recall_score , accuracy_score , classification_report , ConfusionMatrixDisplay , confusion_matrix\n",
        "\n",
        "import gensim\n",
        "from nltk.stem import WordNetLemmatizer,PorterStemmer\n",
        "from nltk.tokenize import word_tokenize\n",
        "import pyLDAvis.gensim\n",
        "\n",
        "from textblob import TextBlob\n",
        "from afinn import Afinn\n",
        "\n",
        "from darts.metrics import mape,mse,rmse\n",
        "from darts.models import Theta,Prophet,ARIMA\n",
        "from darts.utils.statistics import plot_acf , plot_pacf\n",
        "from darts.models import (\n",
        "    NaiveSeasonal,\n",
        "    NaiveDrift,\n",
        "    Prophet,\n",
        "    ExponentialSmoothing,\n",
        "    ARIMA,\n",
        "    AutoARIMA,\n",
        "    Theta,\n",
        "    FFT\n",
        ")\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mount Drive"
      ],
      "metadata": {
        "id": "rW7Q7be2NcwS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "UT3N5kCJ3KSV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f9526b2-b89e-4fb8-f53f-05f560b8ad51"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Data"
      ],
      "metadata": {
        "id": "8vMw95WtNg0N"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "id": "IQ88hhDf3UUj"
      },
      "outputs": [],
      "source": [
        "df1=pd.read_csv('/content/drive/MyDrive/NIIT/CapeStone/electronics_final.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XPw1cGyu3y1Y",
        "outputId": "4b894b75-0d39-40d0-f9e9-6e381d3f2972"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(209836, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ],
      "source": [
        "# Check Shape\n",
        "df1.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display first 5 rows\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "txqV8zdtOfMh",
        "outputId": "f4862dd5-b910-4518-b711-adb054ab9f34"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   overall  verified   reviewTime      reviewerID        asin  \\\n",
              "0      4.0      True   11 1, 2013   AJIC7H0QB0HQ8  B007136B7U   \n",
              "1      5.0      True   12 8, 2015  A24UCU88FE1FPN  B00005141S   \n",
              "2      1.0      True  08 18, 2016  A3C5MTRFKQYHMW  B016P6ETUE   \n",
              "3      4.0      True  08 17, 2010  A253WC2BIJB7ET  B00000K4KH   \n",
              "4      5.0      True  06 12, 2018  A3SMSPKX6LL5U5  B018I846KA   \n",
              "\n",
              "                                           style       reviewerName  \\\n",
              "0  {'Product Packaging:': ' Standard Packaging'}      K RAM SANTOSH   \n",
              "1                   {'Style:': ' Live Gamer HD'}       Fabian Chang   \n",
              "2                       {'Color:': ' Black/Red'}         Leon Blair   \n",
              "3                        {'Style:': ' Standard'}  Mike in San Diego   \n",
              "4      {'Size:': ' 58-62mm', 'Color:': ' black'}   Michael R. Smith   \n",
              "\n",
              "                                          reviewText  \\\n",
              "0  they are very good in technology sound is supe...   \n",
              "1  This is a pretty awesome product. It's good en...   \n",
              "2  Sent it back, worked for 10 days and then went...   \n",
              "3  Not much to say about this product. It is what...   \n",
              "4  If you encounter a lens filter that won't come...   \n",
              "\n",
              "                                             summary  unixReviewTime  \\\n",
              "0                                          liking it      1383264000   \n",
              "1  Great Product, but still needs some reconsidering      1449532800   \n",
              "2                                           One Star      1471478400   \n",
              "3          Received what I ordered on a timely basis      1282003200   \n",
              "4  If you encounter a lens filter that won't come...      1528761600   \n",
              "\n",
              "                                               title       brand  \\\n",
              "0  Skullcandy Skullcrusher (Discontinued by Manuf...  Skullcandy   \n",
              "1  AVerMedia Live Gamer Portable 2, Full HD 1080p...   AVerMedia   \n",
              "2  ShhhMouse Wireless Silent Mouse | Portable and...   Shhhmouse   \n",
              "3  Kensington 64068F MicroSaver Keyed Laptop Lock...  Kensington   \n",
              "4  Neewer Rubber-coated Metal Camera Lens Filter ...      Neewer   \n",
              "\n",
              "               main_cat               date    price  \n",
              "0  Home Audio & Theater   February 1, 2012   $18.89  \n",
              "1       All Electronics  September 4, 1999  $102.01  \n",
              "2             Computers   October 15, 2015   $10.99  \n",
              "3             Computers      April 2, 2004   $10.86  \n",
              "4        Camera & Photo  November 25, 2015   $12.99  "
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-19b5e177-5e8f-4cf6-9e95-60eb5323da3a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# feature selection\n",
        "df = df1[['asin','reviewerID','reviewerName','reviewText','overall','reviewTime','summary','main_cat']]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "jnv5W3HGPW9B",
        "outputId": "1007ae19-04ab-49d6-adad-646bdd8bbd99"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         asin      reviewerID       reviewerName  \\\n",
              "0  B007136B7U   AJIC7H0QB0HQ8      K RAM SANTOSH   \n",
              "1  B00005141S  A24UCU88FE1FPN       Fabian Chang   \n",
              "2  B016P6ETUE  A3C5MTRFKQYHMW         Leon Blair   \n",
              "3  B00000K4KH  A253WC2BIJB7ET  Mike in San Diego   \n",
              "4  B018I846KA  A3SMSPKX6LL5U5   Michael R. Smith   \n",
              "\n",
              "                                          reviewText  overall   reviewTime  \\\n",
              "0  they are very good in technology sound is supe...      4.0   11 1, 2013   \n",
              "1  This is a pretty awesome product. It's good en...      5.0   12 8, 2015   \n",
              "2  Sent it back, worked for 10 days and then went...      1.0  08 18, 2016   \n",
              "3  Not much to say about this product. It is what...      4.0  08 17, 2010   \n",
              "4  If you encounter a lens filter that won't come...      5.0  06 12, 2018   \n",
              "\n",
              "                                             summary              main_cat  \n",
              "0                                          liking it  Home Audio & Theater  \n",
              "1  Great Product, but still needs some reconsidering       All Electronics  \n",
              "2                                           One Star             Computers  \n",
              "3          Received what I ordered on a timely basis             Computers  \n",
              "4  If you encounter a lens filter that won't come...        Camera & Photo  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc35ad64-7dc0-4d0f-9ca6-b0f99f7dc06e\">\n",
              "    <div 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>asin</th>\n",
              "      <th>reviewerID</th>\n",
              "      <th>reviewerName</th>\n",
              "      <th>reviewText</th>\n",
              "      <th>overall</th>\n",
              "      <th>reviewTime</th>\n",
              "      <th>summary</th>\n",
              "      <th>main_cat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>B007136B7U</td>\n",
              "      <td>AJIC7H0QB0HQ8</td>\n",
              "      <td>K RAM SANTOSH</td>\n",
              "      <td>they are very good in technology sound is supe...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11 1, 2013</td>\n",
              "      <td>liking it</td>\n",
              "      <td>Home Audio &amp; Theater</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B00005141S</td>\n",
              "      <td>A24UCU88FE1FPN</td>\n",
              "      <td>Fabian Chang</td>\n",
              "      <td>This is a pretty awesome product. It's good en...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12 8, 2015</td>\n",
              "      <td>Great Product, but still needs some reconsidering</td>\n",
              "      <td>All Electronics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>B016P6ETUE</td>\n",
              "      <td>A3C5MTRFKQYHMW</td>\n",
              "      <td>Leon Blair</td>\n",
              "      <td>Sent it back, worked for 10 days and then went...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>08 18, 2016</td>\n",
              "      <td>One Star</td>\n",
              "      <td>Computers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>B00000K4KH</td>\n",
              "      <td>A253WC2BIJB7ET</td>\n",
              "      <td>Mike in San Diego</td>\n",
              "      <td>Not much to say about this product. It is what...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>08 17, 2010</td>\n",
              "      <td>Received what I ordered on a timely basis</td>\n",
              "      <td>Computers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>B018I846KA</td>\n",
              "      <td>A3SMSPKX6LL5U5</td>\n",
              "      <td>Michael R. Smith</td>\n",
              "      <td>If you encounter a lens filter that won't come...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>06 12, 2018</td>\n",
              "      <td>If you encounter a lens filter that won't come...</td>\n",
              "      <td>Camera &amp; Photo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc35ad64-7dc0-4d0f-9ca6-b0f99f7dc06e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-bc35ad64-7dc0-4d0f-9ca6-b0f99f7dc06e 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-bc35ad64-7dc0-4d0f-9ca6-b0f99f7dc06e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check datatypes\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FyybVg4aPW1y",
        "outputId": "e36707f8-5614-4860-c92e-751d29d4979e"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "asin             object\n",
              "reviewerID       object\n",
              "reviewerName     object\n",
              "reviewText       object\n",
              "overall         float64\n",
              "reviewTime       object\n",
              "summary          object\n",
              "main_cat         object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"reviewTime\"] = pd.to_datetime(df[\"reviewTime\"])"
      ],
      "metadata": {
        "id": "0ql2koS7PWwO"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRbYHgPJPWog",
        "outputId": "e8a970a3-88c3-4476-a592-8ed7b891bb90"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 209836 entries, 0 to 209835\n",
            "Data columns (total 8 columns):\n",
            " #   Column        Non-Null Count   Dtype         \n",
            "---  ------        --------------   -----         \n",
            " 0   asin          209836 non-null  object        \n",
            " 1   reviewerID    209836 non-null  object        \n",
            " 2   reviewerName  209822 non-null  object        \n",
            " 3   reviewText    209836 non-null  object        \n",
            " 4   overall       209836 non-null  float64       \n",
            " 5   reviewTime    209836 non-null  datetime64[ns]\n",
            " 6   summary       209834 non-null  object        \n",
            " 7   main_cat      209559 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(1), object(6)\n",
            "memory usage: 12.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Exploration"
      ],
      "metadata": {
        "id": "5LaYmmM7RsDp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking description\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "WwaLVeXLPWhh",
        "outputId": "2fffd14a-96a1-416a-c457-eb738c7792fe"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             overall\n",
              "count  209836.000000\n",
              "mean        4.229851\n",
              "std         1.288201\n",
              "min         1.000000\n",
              "25%         4.000000\n",
              "50%         5.000000\n",
              "75%         5.000000\n",
              "max         5.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-831da6a9-f59a-4e30-b9ba-ac94978ab6d4\">\n",
              "    <div 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>overall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>209836.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.229851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.288201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-831da6a9-f59a-4e30-b9ba-ac94978ab6d4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
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              "      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",
              "    [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-831da6a9-f59a-4e30-b9ba-ac94978ab6d4 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-831da6a9-f59a-4e30-b9ba-ac94978ab6d4');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Dataset Details**\n",
        "### **This file has reviewer ID , Reviewer Name, Reviewer text, Summary(obtained from Reviewer text),Overall Rating on a scale 5, Review time**\n",
        "### Description of columns in the file:\n",
        "- reviewerID - ID of the reviewer, e.g. A236WRQL1MB9HM\n",
        "\n",
        "- asin - ID of the product, e.g. 7391002801\n",
        "\n",
        "- reviewerName - name of the reviewer\n",
        "\n",
        "- reviewText - text of the review\n",
        "\n",
        "- overall - rating of the product\n",
        "\n",
        "- summary - summary of the review\n",
        "\n",
        "- reviewTime - time of the review (raw)"
      ],
      "metadata": {
        "id": "vGGNuQBcSDcp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing"
      ],
      "metadata": {
        "id": "CfzxQ3l9T_92"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing the duplicates\n",
        "df.drop_duplicates([\"reviewText\",\"asin\",\"reviewerName\"], keep = \"first\", inplace = True)"
      ],
      "metadata": {
        "id": "J6ya726wSCyO"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking null values\n",
        "(df.isnull().sum()*100)/df.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytC6NY78UDbg",
        "outputId": "c65bb665-8820-4992-c183-d23b6c200efd"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "asin            0.000000\n",
              "reviewerID      0.000000\n",
              "reviewerName    0.006693\n",
              "reviewText      0.000000\n",
              "overall         0.000000\n",
              "reviewTime      0.000000\n",
              "summary         0.000956\n",
              "main_cat        0.132426\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping null values since it is very less\n",
        "df.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "8jvY9FgHUDrF"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XTvGdVe3U1-r",
        "outputId": "f6e3bac3-f827-4f1e-80cb-4fa507033ba0"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "asin            0\n",
              "reviewerID      0\n",
              "reviewerName    0\n",
              "reviewText      0\n",
              "overall         0\n",
              "reviewTime      0\n",
              "summary         0\n",
              "main_cat        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "XAdSPhjPVQwv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "_sQIfLuAbPRg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0b8dd3a4-c5e3-4ec2-f348-174f88b4cd19"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "def word_eda():\n",
        "\n",
        "  plt.figure(figsize=(20,7))\n",
        "  n,bins,patches=plt.hist(df['reviewText'].str.len(),range=(0,2000),edgecolor = 'white');\n",
        "  plt.xticks(bins)\n",
        "  plt.title('Distribution of No of sentences in each review')\n",
        "  plt.show()\n",
        "\n",
        "  plt.figure(figsize=(20,7))\n",
        "  n,bins,patches = plt.hist(df['reviewText'].str.split().apply(lambda x : [len(i) for i in x]).map(lambda x: np.mean(x)),edgecolor='white',range=(0,10))\n",
        "  plt.xticks(bins)\n",
        "  plt.title('Distribution of Average word length of Each Review')\n",
        "  plt.show()\n",
        "\n",
        "  plt.figure(figsize=(20,7))\n",
        "  n,bins,patches=plt.hist(df['reviewText'].str.split().map(lambda x: len(x)),range=(0,600),edgecolor = 'white');\n",
        "  plt.xticks(bins)\n",
        "  plt.title('Distribution of No of Words in each review')\n",
        "  plt.show()\n",
        "\n",
        "  new= df['reviewText'].str.split()\n",
        "  new=new.values.tolist()\n",
        "  corpus=[word for i in new for word in i]\n",
        "  from collections import defaultdict\n",
        "  dic=defaultdict(int)\n",
        "  for word in corpus:\n",
        "    if word in stop:\n",
        "      dic[word]+=1\n",
        "            \n",
        "  top=sorted(dic.items(), key=lambda x:x[1],reverse=True)[:10] \n",
        "  x,y=zip(*top)\n",
        "  plt.figure(figsize=(20,7))\n",
        "  plt.bar(x,y)\n",
        "  plt.title('Top 10 Stopwords from the review text')\n",
        "\n",
        "  new= df['reviewText'].str.split()\n",
        "  new=new.values.tolist()\n",
        "  corpus=[word for i in new for word in i]\n",
        "\n",
        "  counter=Counter(corpus)\n",
        "  most=counter.most_common()\n",
        "  x, y=[], []\n",
        "  for word,count in most[:40]:\n",
        "    if (word not in stop):\n",
        "      x.append(word)\n",
        "      y.append(count)\n",
        "    \n",
        "  plt.figure(figsize=(20,7))\n",
        "  sns.barplot(x=y,y=x)\n",
        "  plt.title('Most Common Words used in Review')\n",
        "  plt.show()\n",
        "\n",
        "  new= df['reviewText'].str.split()\n",
        "  new=new.values.tolist()\n",
        "  corpus=[word for i in new for word in i]\n",
        "\n",
        "  def get_top_ngram(corpus, n=None):\n",
        "    vec = CountVectorizer(ngram_range=(n, n)).fit(corpus)\n",
        "    bag_of_words = vec.transform(corpus)\n",
        "    sum_words = bag_of_words.sum(axis=0) \n",
        "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
        "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
        "    return words_freq[:10]\n",
        "\n",
        "  top_n_bigrams= get_top_ngram(df['reviewText'],2)[:10]\n",
        "  x,y=map(list,zip(*top_n_bigrams))\n",
        "    \n",
        "  plt.figure(figsize=(20,7))\n",
        "  sns.barplot(x=y,y=x)\n",
        "  plt.title('Top 10 N-Grams')\n",
        "  plt.show()\n",
        "\n",
        "  \n",
        "word_eda()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "yqA-IKrQlmHE"
      },
      "outputs": [],
      "source": [
        "def get_lda_objects(text):\n",
        "    def _preprocess_text(text):\n",
        "        corpus=[]\n",
        "        stem=PorterStemmer()\n",
        "        lem=WordNetLemmatizer()\n",
        "        for news in text:\n",
        "            words=[w for w in word_tokenize(news) if (w not in stop)]\n",
        "\n",
        "            words=[lem.lemmatize(w) for w in words if len(w)>2]\n",
        "\n",
        "            corpus.append(words)\n",
        "        return corpus\n",
        "    \n",
        "    corpus=_preprocess_text(text)\n",
        "    \n",
        "    dic=gensim.corpora.Dictionary(corpus)\n",
        "    bow_corpus = [dic.doc2bow(doc) for doc in corpus]\n",
        "    \n",
        "    lda_model =  gensim.models.LdaMulticore(bow_corpus, num_topics = 4, id2word = dic, passes = 10, workers = 2)\n",
        "    \n",
        "    return lda_model, bow_corpus, dic"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "CKTjpB_Gm5Kw"
      },
      "outputs": [],
      "source": [
        "lda_model, bow_corpus, dic = get_lda_objects(df['reviewText'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 862
        },
        "id": "b66pTOpBm5DZ",
        "outputId": "04841e4e-3f07-4ba5-f70d-ad81be92dec6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "0      0.037800  0.064643       1        1  36.276130\n",
              "2      0.136637 -0.156800       2        1  24.902079\n",
              "3      0.105743  0.116090       3        1  24.677294\n",
              "1     -0.280180 -0.023933       4        1  14.144496, topic_info=           Term          Freq         Total Category  logprob  loglift\n",
              "343       great  43264.000000  43264.000000  Default  30.0000  30.0000\n",
              "405      camera  19802.000000  19802.000000  Default  29.0000  29.0000\n",
              "382       Great  15918.000000  15918.000000  Default  28.0000  28.0000\n",
              "64        cable  21265.000000  21265.000000  Default  27.0000  27.0000\n",
              "325       price  22058.000000  22058.000000  Default  26.0000  26.0000\n",
              "...         ...           ...           ...      ...      ...      ...\n",
              "459   perfectly   3616.841849   7857.217457   Topic4  -5.2006   1.1800\n",
              "38         This   4441.644625  28429.662138   Topic4  -4.9951   0.0994\n",
              "394        nice   3376.982429  11889.703835   Topic4  -5.2692   0.6972\n",
              "1081     needed   3027.487713   7367.780086   Topic4  -5.3784   1.0665\n",
              "239         buy   2982.114031  11440.523880   Topic4  -5.3935   0.6113\n",
              "\n",
              "[296 rows x 6 columns], token_table=      Topic      Freq   Term\n",
              "term                        \n",
              "10        1  0.537690    've\n",
              "10        2  0.360058    've\n",
              "10        3  0.098766    've\n",
              "10        4  0.003493    've\n",
              "0         1  0.359144    ...\n",
              "...     ...       ...    ...\n",
              "284       4  0.076622  would\n",
              "202       1  0.442181   year\n",
              "202       2  0.553840   year\n",
              "202       3  0.003971   year\n",
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              "\n",
              "function LDAvis_load_lib(url, callback){\n",
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              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "}\n",
              "\n",
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              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el30761405721278119524622853747\", ldavis_el30761405721278119524622853747_data);\n",
              "   }(LDAvis);\n",
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              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "def plot_lda_vis(lda_model, bow_corpus, dic):\n",
        "    pyLDAvis.enable_notebook()\n",
        "    vis = pyLDAvis.gensim.prepare(lda_model, bow_corpus, dic)\n",
        "    return vis\n",
        "\n",
        "plot_lda_vis(lda_model, bow_corpus, dic)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sentiment Analysis\n",
        "\n",
        "# What is sentiment analysis?\n",
        "Sentiment analysis is a text analysis method that detects polarity (e.g. a positive , negative , neutral) within the text, whether a whole document, paragraph, sentence, or clause.\n",
        "Sentiment analysis aims to measure the attitude, sentiments, evaluations, attitudes, and emotions of a speaker/writer based on the computational treatment of subjectivity in a text.\n",
        "# Creating 'Actual_sentiment' column\n",
        "This is an important preprocessing phase, we are deciding the outcome column (sentiment of review) based on the overall score. If the score is greater than 3, we take that as positive and if the value is less than 3 it is negative If it is equal to 3, we take that as neutral sentiment"
      ],
      "metadata": {
        "id": "66UcBh-qVu4B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def column(data):\n",
        "    if data['overall']==1:\n",
        "        return 'Negative'\n",
        "    elif data['overall']==2:\n",
        "        return 'Negative'\n",
        "    elif data['overall']==3:\n",
        "        return 'Neutral'\n",
        "    elif data['overall']==4:\n",
        "        return 'Positive'\n",
        "    elif data['overall']==5:\n",
        "        return 'Positive'\n",
        "\n",
        "df['Actual_Sentiment'] = df.apply(lambda data: column(data), axis=1)"
      ],
      "metadata": {
        "id": "GZ9__EJ9V5cA"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TirCbjDonWhR",
        "outputId": "396c4ae6-2783-4433-a5b4-38737f865bce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              asin      reviewerID        reviewerName  \\\n",
              "0       B007136B7U   AJIC7H0QB0HQ8       K RAM SANTOSH   \n",
              "1       B00005141S  A24UCU88FE1FPN        Fabian Chang   \n",
              "2       B016P6ETUE  A3C5MTRFKQYHMW          Leon Blair   \n",
              "3       B00000K4KH  A253WC2BIJB7ET   Mike in San Diego   \n",
              "4       B018I846KA  A3SMSPKX6LL5U5    Michael R. Smith   \n",
              "...            ...             ...                 ...   \n",
              "209831  B00WUIKWGM  A1BE576P3XA07N               Eddie   \n",
              "209832  B004QK7HI8  A1EV0LVQDW54X7            Russells   \n",
              "209833  B000067SCH  A3B1YNOMQBHV1R             SAFTech   \n",
              "209834  B00PFKOU56  A159343D3SPJCZ  Psychological_rape   \n",
              "209835  B004I2GFNC  A3P93T4J28EDVB            R. Beach   \n",
              "\n",
              "                                               reviewText  overall reviewTime  \\\n",
              "0       they are very good in technology sound is supe...      4.0 2013-11-01   \n",
              "1       This is a pretty awesome product. It's good en...      5.0 2015-12-08   \n",
              "2       Sent it back, worked for 10 days and then went...      1.0 2016-08-18   \n",
              "3       Not much to say about this product. It is what...      4.0 2010-08-17   \n",
              "4       If you encounter a lens filter that won't come...      5.0 2018-06-12   \n",
              "...                                                   ...      ...        ...   \n",
              "209831  they don't last as long as the canon brand, ho...      4.0 2014-03-25   \n",
              "209832  Works well.  There are five channels that are ...      4.0 2014-05-21   \n",
              "209833                                        Works well.      4.0 2015-09-28   \n",
              "209834  Yes its very much work like the original don't...      5.0 2015-05-08   \n",
              "209835  I know, silly title.  But I have been looking ...      5.0 2012-11-28   \n",
              "\n",
              "                                                  summary  \\\n",
              "0                                               liking it   \n",
              "1       Great Product, but still needs some reconsidering   \n",
              "2                                                One Star   \n",
              "3               Received what I ordered on a timely basis   \n",
              "4       If you encounter a lens filter that won't come...   \n",
              "...                                                   ...   \n",
              "209831                         Half the life of the canon   \n",
              "209832                                         Works well   \n",
              "209833                                         Four Stars   \n",
              "209834                                          go for it   \n",
              "209835                                 Finally Found Red!   \n",
              "\n",
              "                    main_cat Actual_Sentiment  TextBlob_Subjectivity  \\\n",
              "0       Home Audio & Theater         Positive               0.575286   \n",
              "1            All Electronics         Positive               0.564274   \n",
              "2                  Computers         Negative               0.000000   \n",
              "3                  Computers         Positive               0.200000   \n",
              "4             Camera & Photo         Positive               0.000000   \n",
              "...                      ...              ...                    ...   \n",
              "209831        Camera & Photo         Positive               0.314286   \n",
              "209832  Home Audio & Theater         Positive               0.379167   \n",
              "209833       All Electronics         Positive               0.000000   \n",
              "209834             Computers         Positive               0.524388   \n",
              "209835       Office Products         Positive               0.334722   \n",
              "\n",
              "        TextBlob_Polarity TextBlob_Analysis  \n",
              "0                0.533429          Positive  \n",
              "1                0.082436          Positive  \n",
              "2                0.000000           Neutral  \n",
              "3               -0.100000          Negative  \n",
              "4                0.000000           Neutral  \n",
              "...                   ...               ...  \n",
              "209831           0.061905          Positive  \n",
              "209832          -0.012500          Negative  \n",
              "209833           0.000000           Neutral  \n",
              "209834           0.355340          Positive  \n",
              "209835          -0.072222          Negative  \n",
              "\n",
              "[208881 rows x 12 columns]"
            ],
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              "\n",
              "  <div id=\"df-4442356a-caa0-47fb-8a5d-5815a84f92af\">\n",
              "    <div 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>asin</th>\n",
              "      <th>reviewerID</th>\n",
              "      <th>reviewerName</th>\n",
              "      <th>reviewText</th>\n",
              "      <th>overall</th>\n",
              "      <th>reviewTime</th>\n",
              "      <th>summary</th>\n",
              "      <th>main_cat</th>\n",
              "      <th>Actual_Sentiment</th>\n",
              "      <th>TextBlob_Subjectivity</th>\n",
              "      <th>TextBlob_Polarity</th>\n",
              "      <th>TextBlob_Analysis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>B007136B7U</td>\n",
              "      <td>AJIC7H0QB0HQ8</td>\n",
              "      <td>K RAM SANTOSH</td>\n",
              "      <td>they are very good in technology sound is supe...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2013-11-01</td>\n",
              "      <td>liking it</td>\n",
              "      <td>Home Audio &amp; Theater</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.575286</td>\n",
              "      <td>0.533429</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B00005141S</td>\n",
              "      <td>A24UCU88FE1FPN</td>\n",
              "      <td>Fabian Chang</td>\n",
              "      <td>This is a pretty awesome product. It's good en...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2015-12-08</td>\n",
              "      <td>Great Product, but still needs some reconsidering</td>\n",
              "      <td>All Electronics</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.564274</td>\n",
              "      <td>0.082436</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>B016P6ETUE</td>\n",
              "      <td>A3C5MTRFKQYHMW</td>\n",
              "      <td>Leon Blair</td>\n",
              "      <td>Sent it back, worked for 10 days and then went...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2016-08-18</td>\n",
              "      <td>One Star</td>\n",
              "      <td>Computers</td>\n",
              "      <td>Negative</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>B00000K4KH</td>\n",
              "      <td>A253WC2BIJB7ET</td>\n",
              "      <td>Mike in San Diego</td>\n",
              "      <td>Not much to say about this product. It is what...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2010-08-17</td>\n",
              "      <td>Received what I ordered on a timely basis</td>\n",
              "      <td>Computers</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>-0.100000</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>B018I846KA</td>\n",
              "      <td>A3SMSPKX6LL5U5</td>\n",
              "      <td>Michael R. Smith</td>\n",
              "      <td>If you encounter a lens filter that won't come...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2018-06-12</td>\n",
              "      <td>If you encounter a lens filter that won't come...</td>\n",
              "      <td>Camera &amp; Photo</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209831</th>\n",
              "      <td>B00WUIKWGM</td>\n",
              "      <td>A1BE576P3XA07N</td>\n",
              "      <td>Eddie</td>\n",
              "      <td>they don't last as long as the canon brand, ho...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2014-03-25</td>\n",
              "      <td>Half the life of the canon</td>\n",
              "      <td>Camera &amp; Photo</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.314286</td>\n",
              "      <td>0.061905</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209832</th>\n",
              "      <td>B004QK7HI8</td>\n",
              "      <td>A1EV0LVQDW54X7</td>\n",
              "      <td>Russells</td>\n",
              "      <td>Works well.  There are five channels that are ...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2014-05-21</td>\n",
              "      <td>Works well</td>\n",
              "      <td>Home Audio &amp; Theater</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.379167</td>\n",
              "      <td>-0.012500</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209833</th>\n",
              "      <td>B000067SCH</td>\n",
              "      <td>A3B1YNOMQBHV1R</td>\n",
              "      <td>SAFTech</td>\n",
              "      <td>Works well.</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2015-09-28</td>\n",
              "      <td>Four Stars</td>\n",
              "      <td>All Electronics</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209834</th>\n",
              "      <td>B00PFKOU56</td>\n",
              "      <td>A159343D3SPJCZ</td>\n",
              "      <td>Psychological_rape</td>\n",
              "      <td>Yes its very much work like the original don't...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2015-05-08</td>\n",
              "      <td>go for it</td>\n",
              "      <td>Computers</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.524388</td>\n",
              "      <td>0.355340</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209835</th>\n",
              "      <td>B004I2GFNC</td>\n",
              "      <td>A3P93T4J28EDVB</td>\n",
              "      <td>R. Beach</td>\n",
              "      <td>I know, silly title.  But I have been looking ...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2012-11-28</td>\n",
              "      <td>Finally Found Red!</td>\n",
              "      <td>Office Products</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.334722</td>\n",
              "      <td>-0.072222</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>208881 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4442356a-caa0-47fb-8a5d-5815a84f92af')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ],
      "source": [
        "def sentiment_analysis(df):\n",
        "    def getSubjectivity(text):\n",
        "        return TextBlob(text).sentiment.subjectivity\n",
        "  \n",
        " #Create a function to get the polarity\n",
        "    def getPolarity(text):\n",
        "        return TextBlob(text).sentiment.polarity\n",
        "  \n",
        " #Create two new columns ‘Subjectivity’ & ‘Polarity’\n",
        "    df['TextBlob_Subjectivity'] =  df['reviewText'].apply(getSubjectivity)\n",
        "    df['TextBlob_Polarity'] = df['reviewText'].apply(getPolarity)\n",
        "    def getAnalysis(score):\n",
        "        if score < 0:\n",
        "            return 'Negative'\n",
        "        elif score == 0:\n",
        "            return 'Neutral'\n",
        "        elif score > 0:\n",
        "            return 'Positive'\n",
        "    df['TextBlob_Analysis'] = df['TextBlob_Polarity'].apply(getAnalysis)\n",
        "    return df\n",
        "\n",
        "sentiment_analysis(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "id": "8DqXndsEm46f"
      },
      "outputs": [],
      "source": [
        "def vader(x):\n",
        "    polarity=sent.polarity_scores(x)\n",
        "    return polarity\n",
        "\n",
        "df['Vader_Polarity']=df['reviewText'].apply(vader)\n",
        "\n",
        "neg=[]\n",
        "pos=[]\n",
        "neu=[]\n",
        "comp = []\n",
        "for i in df['Vader_Polarity']:\n",
        "    neg.append(i['neg'])\n",
        "    pos.append(i['pos'])\n",
        "    neu.append(i['neu'])\n",
        "    comp.append(i['compound'])\n",
        "\n",
        "\n",
        "pol = ['Positive' if score > 0.05\n",
        "                          else 'Negative' if score < -0.05\n",
        "                              else 'Neutral'\n",
        "                                  for score in comp]\n",
        "\n",
        "df['Vader_score'] = comp\n",
        "df['Vader_sentiment'] = pol"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "neg_2=df[df[\"Vader_sentiment\"] == \"Negative\"][\"reviewText\"]\n",
        "pos_2=df[df[\"Vader_sentiment\"] == \"Positive\"][\"reviewText\"]\n",
        "neu_2=df[df[\"Vader_sentiment\"] == \"Neutral\"][\"reviewText\"]"
      ],
      "metadata": {
        "id": "trQ33bgPp3Vv"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting BIGRAM\n",
        "def get_top_n_bigram(corpus, n=None):\n",
        "    vec = CountVectorizer(ngram_range=(2, 2),stop_words='english').fit(corpus)   # converting a text documents to a matrix of token counts.      \n",
        "    bag_of_words = vec.transform(corpus)                                         # Transforming the corpus into numbers\n",
        "    sum_words = bag_of_words.sum(axis=0) \n",
        "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]     # it provides a dictionary with the mapping of the word item index \n",
        "    words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
        "    return words_freq[:n]"
      ],
      "metadata": {
        "id": "AOybSUznqPkp"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a function for bigram plots\n",
        "def n_gram_plot(data,title,colour):                            # Creating n_gram_plot function\n",
        "    x=[x[0] for x in data]\n",
        "    y=[x[1] for x in data]\n",
        "    sns.barplot(x=y,y=x,color=colour)\n",
        "    plt.title('{} Reviews Bigrams'.format(title),fontsize=15)\n",
        "    plt.yticks(rotation=0,fontsize=15)\n",
        "\n",
        "common_words_good = get_top_n_bigram(pos_2, 10)                  # Calling get_top_n_bigram for pos columns\n",
        "common_words_neutral = get_top_n_bigram(neu_2, 10)               # Calling get_top_n_bigram for neu columns\n",
        "common_words_bad = get_top_n_bigram(neg_2, 10)                   # Calling get_top_n_bigram for neg columns"
      ],
      "metadata": {
        "id": "Jn2AAC0wqPXK"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# bigram plot using function above\n",
        "plt.figure(figsize=(15,10))\n",
        "# good reviews bigrams\n",
        "plt.subplot(151)\n",
        "n_gram_plot(common_words_good,'Good','green')                  # Calling n_gram_plot for pos \n",
        "#============================================= \n",
        "#neutral reviews bigrams\n",
        "plt.subplot(153)\n",
        "n_gram_plot(common_words_neutral,'Neutral','yellow')           # Calling n_gram_plot for pos\n",
        "#============================================= \n",
        "#bad reviews bigrams\n",
        "plt.subplot(155)\n",
        "n_gram_plot(common_words_bad,'Bad','red')                      # Calling n_gram_plot for pos\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "BTkBVJy5h1Rj",
        "outputId": "3e281942-0f94-4b22-b340-5d7ab2607593"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "id": "b-5apaprpPhn"
      },
      "outputs": [],
      "source": [
        "def count_plot(col1,col2,col3):\n",
        "    plt.figure(figsize=(10,7))\n",
        "    plt.subplot(2,2,1)\n",
        "    ax = sns.countplot(x=df['Actual_Sentiment']);\n",
        "    for bar in ax.patches:\n",
        "        count = bar.get_height()\n",
        "        x = bar.get_x() + bar.get_width()/2\n",
        "        y = bar.get_height()\n",
        "        ax.annotate(count, (x,y), va = 'bottom', ha = 'center')\n",
        "    plt.title('Actual Sentiment Count')\n",
        "\n",
        "    plt.figure(figsize=(10,7))\n",
        "    plt.subplot(2,2,2)\n",
        "    ax = sns.countplot(x=df[col1]);\n",
        "    for bar in ax.patches:\n",
        "        count = bar.get_height()\n",
        "        x = bar.get_x() + bar.get_width()/2\n",
        "        y = bar.get_height()\n",
        "        ax.annotate(count, (x,y), va = 'bottom', ha = 'center')\n",
        "    plt.title(f'Sentiment Count of {col1} Model')\n",
        "    \n",
        "\n",
        "    plt.figure(figsize=(10,7))\n",
        "    plt.subplot(2,2,3)\n",
        "    ax = sns.countplot(x=df[col2]);\n",
        "    for bar in ax.patches:\n",
        "        count = bar.get_height()\n",
        "        x = bar.get_x() + bar.get_width()/2\n",
        "        y = bar.get_height()\n",
        "        ax.annotate(count, (x,y), va = 'bottom', ha = 'center')\n",
        "    plt.title(f'Sentiment Count of {col2} Model')\n",
        "    \n",
        "\n",
        "    plt.figure(figsize=(10,7))\n",
        "    plt.subplot(2,2,4)\n",
        "    ax = sns.countplot(x=df[col3]);\n",
        "    for bar in ax.patches:\n",
        "        count = bar.get_height()\n",
        "        x = bar.get_x() + bar.get_width()/2\n",
        "        y = bar.get_height()\n",
        "        ax.annotate(count, (x,y), va = 'bottom', ha = 'center')\n",
        "    plt.title(f'Sentiment Count of {col3} Model')\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "id": "iUwwTbJQpuwD"
      },
      "outputs": [],
      "source": [
        "afn = Afinn()\n",
        "scores = [afn.score(i) for i in df.reviewText]\n",
        "sentiment = ['Positive' if score > 0.05\n",
        "                          else 'Negative' if score < -0.05\n",
        "                              else 'Neutral'\n",
        "                                  for score in scores]\n",
        "\n",
        "df['Afinn_score'] = scores\n",
        "df['Afinn_sentiment'] = sentiment"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_plot('TextBlob_Analysis','Vader_sentiment','Afinn_sentiment')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "id": "JQsqRWCmkxI7",
        "outputId": "b35fa994-ef65-4e03-b5a5-7e6a21ceb7ee"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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9qkxE9gK+V9VNqvoloSvz3eKcs1fiZUxdU5kZyJoSuiUaTOjy/SVCoNtDVXfGcb4mBLYfELpZKksZQK6INFDVHYkyROS/gP8CuPzyy+nXr1+xlXzhhRfKt3a1lN3baGq6st51UZlBcTPwWWzRISI5IpIF5IhIvRgY9yf0k7aZkHSKQmXtiyhLjQeQVTggAqjq34C/xbclnlNs1apM/VHWepk652xMdVVpt+So6lZgg4g0FpFGQMN4yLwQ6BMD5A8JHbfOA1KJtQcQUjMuJGTvyitT1S1AVpznIYRAaYwx5ZbRlqKITCMcMncUkYcIPeK+ROhp+LY42ljC1edRwIOqulVEXgYGicgc4H1VfSfOb6qIzCV0bjA0Tn8zoQtzT+hl2Rhjyq0u9rxd4grbxYWC6uD+YWqfMn2p7YkWY4xJsKBojDEJFhSNMSbBgqIxxiRYUDTGmAQLisYYk2BB0RhjEiwoGmNMggVFY4xJsKBojDEJFhSNMSbBgqIxxiRkrJeconK0xPLDgE+BH8a8LUcQ+jrMAm5R1Zmxa7EngIOAF1X17jjtWEKv3MuAEaqaIyJnA1cBW4FhqroyU+tkjKn9MtlSTOVoeaZQ+fWE/hFTRgMXAycBd8ayS4BpqnoccIKItBSR7kBLVT0eWAycFftgvBroA9wK3JKhdTHG1BGVlqMFQETaELru+jJRfLCqLlHVb4CNItKUgjlaXgOOoei8LR2AT1R1h6rOBbplan2MMXVDZaYjgJC57y7g9kRZMjCXlo9lTQnjAdQvaqGWo6X8LEeLqemqbY4WEWkHoKrLRCQ5aGfi/8K5VzbHsuWEuhaVtyWZo+X7opZtOVrKzzqZNXVNZV597g5ki8h0oB/woIjsCawRkXYisi9wgKqup2COlhOBdyk6b8sSoJOINIgpU/9ReatjjKmNMhoUY46W/sDDwH6qeryqnkQ4T/gLVd0GjATGAzPIP6x+BBgoIm8Tcj2vVNUPgLUxb0s28Kyq5gDjgFmEHC+jMrk+xpjaz3K0FGI5Wgqqg/uHqX0sR4sxxpSXBUVjjEmwoGiMMQkWFI0xJsGCojHGJFhQNMaYBAuKxhiTYEHRGGMSLCgaY0yCBUVjjEmwoGiMMQkWFI0xJsGCojHGJFRa4ipCR7EvxGXmAhep6nJLXGWMqU4qM3FVDnC+qv4EGAtcF8stcZUxptrIWEsxdgC7LpV6IHYouzoO3kF+GoKDVXUJgIgkE1elgmYqcdWBFExcdRHwATFxFTBXRO7N1PoYY+qGyk5chYg0IPSwfUksssRV1ZglrjI1XbVNXJXwN+CvqdYhlriqWrOet01dU6lXn0XkNuALVX0qUWyJq4wx1UZGc7TExFU9CC29acBtwNtx8DuqepOIdAYeIhz63qaqr4nIPsBEoCnwsqreFed3D+FK9peEq9c7RGQwcCWwjXD1eUUp1bIcLWVgLUVTC5TpS22JqwqxoFhQHdw/TO1jiauMMaa8LCgaY0yCBUVjjEmwoGiMMQkWFI0xJsGCojHGJFhQNMaYBAuKxhiTYEHRGGMSLCgaY0yCBUVjjEmwoGiMMQmVlqNFVT8qKp+K5WgxxlQnlZajpYR8KpajxRhTbWQsKKpqjqquSxR1IOZTUdW5QLdYfrCqLlHVb4BkjpZUPpZUjpZk2XTg2BLmaYwx5VKZ6QiKy6diOVqqMcvRYmq66pyjZTNF51OxHC3VmHUya+qatA6fReRCETks8b5JzIlSFsXlU7EcLcaYaiPdluJ/A0MILTaAfsDfKeZwNSWRo6UjIQ/LOGAWMZ9KHG0kMD7O67ZY9ggwUURGEHK0rARWishaEZlDyNFyb7z6XNQ8jTGmXErM0SIiA4FBwHBgNrA0DjoSOFxVG2W4fplgOVrKwA6fTS1Qpi91aS3FIwkB0QO94yvl72WqljHG1AClBcW/AVOBBYTD3FcJAXKTqi4taUJjjKmJ0k5xGm+UbkbiPKKqfpmhemWSHT6XgR0+m1qgQg+fARCRK4C7gD0TxT7d6Y0xpqZIN6jdQbi6+xaQm7nqGGNM1Uo3KH4JPKSqD2SyMsYYU9XSDYofA7eIyMHApljmVfWPmamWMcZUjXSD4pD4d2SizAMWFI0xtUq6QXEEpVy1NcaY2iDtW3JqEbslpwzq4P5hap+M3JLzRRHFXlXblWVhxhhT3aV7+HwQ+S2svQi962zJSI2MMaYKpRUUVXWf1P8i0oBwI7clvTLG1DrpHj4fVWiaBsB5wK/LsjARqQc8BrQjHOdfAjQF7iZ0Nnupqv5TRJoDjwONgAdUdaKI1AceJqQgeE9Vfx3neSVwDrABOD+mNTDGmHJJt7WnwML4ege4DPi0HMvrATSMyaduIiSd+h0hwdV5wNg43g2EQNkb+KWI7AmcBqyO0zYSkWNiPpeBwHHAU8Avy1EnY4zJk+45xcfJP6f4PSHF6MPlWN5KwImII+RX+Q74XlU3AZtE5IA4Xk/gGlXdKSIKdCEkrpoah6cSVzUGZquqF5HpwIRy1MkYY/Kke05xeDx8PTwWfaqqReZDKcV6IIeQonRP4HjgT4nhufGc5R6qmsrdUloyq8Jlu7DEVeVniatMTZeRxFUi0gl4gXAuEOAzERmkqp+UrXr0B3JVtaOICPB7CiaeylLVHSKSIyL1YmAsKklVsqx9obJdWOKq8rP7FE1dk+45xb8ALYAn46sF8OdyLM8RLohAaDXuC2SJSGMROYT8oLYQ6BP7cPwhsIiiE1ctBH5SqMwYY8ot3XOKAtykqvcDiMjlwOhyLO81YLiIzAYaEi60ZAHTCC24y+J4YwnnMUcBD6rqVhF5GRgUE1e9r6rvxLpMFZG5hI4qhpajTsYYkyetx/xEZBnwASGIQegIopuqtslYzTLHHvMrAzt8NrVAxT/mR0g5eifws8RCbi7LgowxpiZINyg+AfyLkO8ZYAbhUNgYY2qVdC+0vA4coqpnq+rZwGFYUDTG1ELpBsWWhBu2U5YDdu+KMabWSffw+QvgWhFZRTifeE0sM8aYWiXdoDiW8Ahd6jE7B1yQkRoZY0wVSrvnbRH5CaFTBoCXVfWtjNUqs+yWnDKwW3JMLVCmL7WlIyjEgmJBdXD/MLVPmb7U1lGsMcYkWFA0xpgEC4rGGJNgQdEYYxLSvSWnwohIH+AWQkD+E7AOy9FijKkmKrWlKCJ7EW78PllV+6rqFCxHizGmGqnsw+djgK3ASyIyRURaEHO0qOqX5KcT6Am8oaq5hKRZqRwtr8bhqRwtRxNztCTKjDGm3Cr78LkZIX3Ajwi9aN9Bfo4VsBwt1Y7laDE1XUZytFSgzcDcmIdlJiEofpusj+VoqV7s5m1T11T24fNCoFNMcdoD+BjL0WKMqUYqtaWoqutFZAowm9BiG0HolsxytBhjqgV79rkQe/a5oDq4f5jax559NsaY8rKgaIwxCRYUjTEmwYKiMcYkWFA0xpgEC4rGGJNgQdEYYxIsKBpjTIIFRWOMSbCgaIwxCRYUjTEmwYKiMcYkWFA0xpiESk9cBSAi5wJ/UtUDReRs4CpCmoJhqrpSRI4gdAqbBdyiqjNFpBHwBHAQ8KKq3h3nNZaQqmAZMEJVcyp/jYwxtUWltxRjVr6zgRWxE9mrgT7ArYQsfwCjgYuBk4A7Y9klwDRVPQ44QURaikh3oGVMZrUYOKvSVsQYUytVxeHzucDThJSmHYBPVHWHqs4FusVxDlbVJTFd6caYtS+ZuOo1QhKsopJZGWNMuVXq4XNsJZ4DDCKkOk0mngKoH/8mg3VpiavWFCorarmWuKqcLHGVqemqe+Kq84HJqrpTRKBgMiqA7+PfnYmywomrNsey5YT6F05mtQtLXFV+1vO2qWsq+/C5M3ChiEwnHDpfQUhk1UBEfgz8I463RkTaici+wAGqup6CiatOBN6l6GRWxhhTbpUaFFX1BlXtr6onAUtU9VJgHDCLkKRqVBx1JDAemAHcHsseAQaKyNvAbFVdqaofAGtjMqts4NnKWRNjap4VK1bQt29fOnfuTHZ2Nvfddx8AgwcPpkePHvTo0YPWrVvTo0cPABYsWJBX3r17d6ZMmZI3r+nTp9OxY0fat2/PXXfdlVc+fPhw2rRpkzfdBx98UGRdJkyYQIcOHejQoQMTJkzI2DqXi/e+rr1KRDi8tld8mdpj9erV/r333vPee//NN9/4Dh06+EWLFhUY5+qrr/Z33HGH99777777zufk5ORNe+CBB/qcnByfm5vr27Zt6z///HO/fft2361bt7z5DBs2zD/99NMl1mPDhg2+TZs2fsOGDX7jxo2+TZs2fuPGjRW9ukllihF287YxdUSLFi046qijANh3333p1KlTgQtp3nsmT57MueeeC8Dee+9NVla47LBt27a8TJcLFiygffv2tG3blgYNGjBkyJAyXaCcMWMG/fr144ADDuAHP/gB/fr1Y/r06RW1mrvNgqIxddCyZct4//336dWrV17ZnDlzaNasGR06dMgrmz9/PtnZ2XTt2pUHH3yQrKwsVq1axSGHHJI3TqtWrQoE15EjR9KtWzeuuuoqtm/fvsuyS5u+qllQNKaO+fe//82ZZ57JuHHj2G+//Js/nnzyybxWYkqvXr1YtGgRCxcuZMyYMWzbtq3EeY8ZM4bFixezcOFCNm7cyNixYzOyDplkQdGYOiQnJ4czzzyToUOHcsYZZ+SV5+bm8txzzzF48OAip+vUqRP77LMPH330ES1btmTFihV5w1auXJl3L2CLFi1wztGwYUMuuugiFixYsMu8Spq+OrCgaEwd4b3n4osvplOnTlx99dUFhr3++uscccQRBe7TXbp0Kbm5uQAsX76cxYsX07p1a44++miWLFnC0qVL2bFjB5MmTWLgwIEArFmzJm9Zzz//PF26dNmlHgMGDODVV19l06ZNbNq0iVdffZUBAwZkarXLrqxXZmrBq+TLVNXgim91elWlL7/80vfp08d36tTJd+7c2Y8bN857H65ennjiib59+/b+xBNPzLtyeffdd/vu3bv77t27++zsbF+vXj2/YcOGYufjvfeTJ0/2nTt39s45v3DhwmLr8sorr/jDDz/ct2vXzo8ZMyazK54hc+bM8YDv2rVr3naaOnWq9z5cNX7ggQcKjP/444/7zp07++7du/sjjzzST5kyJW/Y1KlTfYcOHXzbtm39qFGj8sr79u3ru3Tp4rOzs/3QoUP9t99+6733fuHChf7iiy/OG+/RRx/17dq18+3atfOPPfZYBtfae1/GGFHVAcqCYjV/VaXibiG57rrr8gLTmDFj/PXXX7/LtC+++KLv27dvifPx3vuPP/7YL1682Pfu3bvYoFjSLSimRihTjLDDZ1NtFXcLyQsvvMCwYcMAGDZsGM8///wu0yYvGpR0K0qnTp3o2LFjifXY3VtQTM1SJf0pGlNWyVtI1q5dS4sWLQBo3rw5a9euLTDuli1bmD59Ovfff3+J80lXUbeQzJ8/v5xrsvseP2JilS27Orpw8fkVOj9rKZpqr7hbSACcc3k3Fae89NJLHHvssRxwQMFOk0qajzEpFhRNtVbULSTNmjXLu8q5Zs0aDjrooALTTJo0aZf77Yq7FSUd1f0WElOxLCiaasv7om8hGThwYF4nAhMmTOD000/PG/b1118ze/bsAmXFzSddJd2CYmqfyu5ktidwH5ADrAIuJHQ4azlazC7mzp3LE088QdeuXfN6bhk9ejQ33ngj55xzDo8++iiHHXYYkydPzptmypQp9O/fn0aNGpU6n1NOOYUpU6ZwxRVXsG7dOk499VR69OjBjBkzWL16NZdccgnTpk0jKyuL+++/nwEDBvD9998zYsQIsrOzK3NTmErkvPeVtjARaQFsVtWtIjIGeI/QA3dv4GjgQlX9uYg8B9wArAVeUdVjReRK4DtVfST2x3gx0BS4TlXPF5GRwBeq+mQp1ShxhQufn6rrKnP/MOmxCy0FpXGhpUxf6kptKarqmsTbHUBHYo4WYK6I3BuHHayqSwBEJJmj5bo4PJWj5UAK5mi5CCgtKJpK1mbgF1VdhWpj6Yttq7oKphRVleL0MKA/cCMhsKVYjpZqpiJ6L3ngmgqoSC1REduzx33dK6AmtUdp27S652hBRPYjnBscTgiClqOlGquIw+fjLrWWYsrSF3f/qvXMn75ZATWpPWr0fYoxz/Mk4A5V/T9gCZajxRhTjVR2S/FcoBdwi4jcAjxAfo6WbcCwOF4qR0t94LZY9ggwUURGAC+r6kpgpYikcrR8CaTOSRpjTLlU6tXnasKuPpdBRewfdqElX0VcaLGrzwVV9NVnu3nbGGMSLCgaY0yCBUVjjEmwoGiMMQkWFI0xJsGCojHGJFhQNMaYBAuKxhiTYEHRGGMSLCgaY0yCBUVjjEmwoGiMMQkWFI0xJqFKet6uaJa8yhhTUWp8S1FEugMtVfV4YDFwVhVXyRhTg9X4oEhoISaTVx1bhXUxxtRwteHwudTkVcnEVcDPY86WItXBTnczzjLYVayKzkliCqoNQXEzpSSvKpS4yhhjilUbDp8teZUxpsLU+KCoqh8AqeRV2cCzVVsjY0xNVhcTVxljTLFqfEvRGGMqkgVFY4xJsKCYQSLSWkTWicgsEVkgIkenOd1D8W8fETk8/t9DRC7NZH1rgrhNvYj0jO9PE5Hbyzh9/zKMP1xELi9HVaut3d2GxcyzsYick3j/0G5Ws8pYUMy82araB7gC+F06E6jqz+O/fYDDY9kHqvpAJipYA30MXF/OaVsDuwRFEalr34Xd2YZFaQzkBcXEPlzj1Ib7FGuKD4AOIvIi4X7KNcCFwFHAfcAWQgC9XUQUOB4YDpwZf4EfA04DJgEXq+qlIuKAdwhP9fQHRgL1gT+r6pOVuG6V7RMgK9WKBhCRkyi0/iIyHrhXVT8SkXuBl4FLgR+LiABnAG8D84GvRWQqcDOwN/Csqt5VmStVydLdhkcCDwOrAQf8Pk47iRA/1gKDCdu1t4jMAi4DHldVEZG3gRNUdYeI/JZwy5wCjxDuL14DXKiq31fCOqelrv06VqXehFbKNFXtDSwChgCnAneoal/gztTIqroVGA/cpKoXJsoV6C4iWcAxwLuAB24BfkoIppeLSP1KWKeqdC9wXfzfkf76PwA8pap9VHUj0Aq4WlV/DcyNn00vwo/RXplcgWognW34W+A84HTC02MAm4B+sb+BVcAJhO06O27XjxPLmA6cHP8/AXgduBH4k6qeAPwD+I/MrF75WFDMvNSv56+AmcDCWL4Q6AD8BThFRP4OnJTmPN8g7GCDCb/YBxIOs1+Ny2gcy2otVX0baAe0oPj1T95v5oqZ1Wequin+/0MReR14k/ADdlCFV7waSXMbNlPVT1XVA+/HSZsAz4jIbOAU4OASFjMJGBxbnP9U1VygM3BH/F6cATSv4FXbLRYUMy/163ka4VezZyw/GlgCfK2qlwMXAWMLTZtDOJQpbBLh1/uHqvousJ7QQ1D/eP6yh6p+VeFrUv2MI/zYFLf+mwgtQYBu8W/hbboz8f/1wC+AvoQWUHGBtDYZR8nbcK2IdIinanrEac4DXo6t6umE7VTkvqqqnxGC7nDCfktczm/i96IXUK0uylhQrFwPA6fGX9iuhJ3k5yLyFjCLcLic9AZwjYjclyxU1Y8IO+jb8f1OYBTwmoi8Cfw9c6tQrbxE+CIWt/7jgbEi8izhnC3APwktwqdFpHGh+T0LTAEmAt9mturVRmnb8BbgyTjed4TgNxO4UkReIP+IZA2wl4g8IyIdCi1jKuEQ+a34/nfAVSLyhoi8AXTPyJqVkz3RYowplojsoao58er8m8AQVV1T2nQ1mbUUjTEl6RWPZOYDr9X2gAjWUjTGmAKspWiMMQkWFI0xJsGCojHGJNhjfiZtIjKfcH9lK1VdHcsuBCYAv1PVm9OYx7XAPcBFqjo+g9UtFxHpTHiGd5aqzopl44FhwNHxiaJM1+E8wo3U41R1c6aXZwqylqIpi6cIN+qemShL/T85kwuOjzVWhs7AbYTOOFIeAM4FPq+kOpwX69C4kpZnEuzqs0mbiBwCLAfmqGpvEdkHWAcsAz4i5MrZC/gCGKmqU+J01xKed/0X4fHGC4ktRRE5BvgD4Wb21cBtsSOC1sBSQocXW4CuqtqsiDodRLi5uBfhBuRPgNNUdZ2I3ETI4tiUkMvnUlX9InaTdRuhU4I+hOBzWazb0kKL6Et4GmMYoZW8Po4zD9gal/tHQibJW+OwQaq6TERaAn8iPJK5FXg8bpfvRWQZ4cbnR4ALCAH3Z4Qnam5LLH+5qrYu8gMxGWEtRZM2VV1BCFLHiUgzQq89exJaiQsJj8ndFEd/XET2FJHuhMPlrwiPlKWSjCEiBxB6rmlMeMphGTBRRHokFnsM8B7hyYqiDCUEnd8D1xB6I6ovIsOA0YT76+4iPOb3dKFpjwfuJ/RadBchwI+Lw54ltA4/pmg9gWnAhli3kwlP0HQHfh3HmQj0I/SC9CJwAyH4puxN6ClmKiDAfwLPkP+M8a8IXc6ZSmRB0ZTVZMJ+cwYFD52zCQHmD0AnYB9Cpwp94jh/jKlmH0vM6xhCnu4jCAGsX5z3CYlx3lfVG0rI1b0k/v0poXODp+Izu6fF8sGEx9eaA0fFQJzyB1W9j9Cyba2q35GfDfIjVZ2kqv8qZrnzVfUPifHHEFqFAG1iK7o3sC+h5ZfqX7BfYh47CV1u/SW+bx0f4Vwd37+kqi8Vs3yTIRYUTVk9TfgyX0BoHS0CDiEcEr9F6Olnahx3z8R0rtDfpMcJwSL1ejExbHUR4+dR1ZeBHxE6JjgOmCkiJyZGGZqY7wDyn4GG/BzhueR/F9I9n7Q5/s2Jf78GUn0CpjpGcMCHFFy33ybmsVVVt8XlJ6ezc1pVyIKiKZN41XkuoZXXiHDxJaURoTu0YxNls+LfX4vIfxF6A0p5hxCYTiK0FrsQzj22TLc+InIWoVW4ghCgIXRl9XL8fxghaPcGbolBqCSpbsSOF5Eh5e1TUVX/TVj3roTD9LaEH45+JUxWuA7DRKRPeZZvys+CoimPZCCcDLxG6PGnB6Hj3Bmpgar6IaEj0+aE82OzE8M2EgLaZ4RzeiMJLbllZajLFsJh/IOEW2meAp5R1QmEANuB/KvHbxU3k4S3Cb3AHE+4gNOkDHUp7HzgOeByQoeu7YAFaUz3EPAlcDuhJ3BTiezqs6kxRKRp4TJVXV8VdTG1l928bWqSdUWU1YWOYE0lsqBoapJ0zscZs1vs8NkYYxLsQosxxiRYUDTGmAQLisYYk2BB0RhjEiwoGmNMwv8DOnFBqScQB+IAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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5eXmcfPLJHHvssQDcfPPNrFy5kssvv5z8/HxEpLRMJ5xwAkuWhJ6WR44cyR/+8Afatm3LypUrueiii7L8DRljsi3jdwKLyGjC03X+DcxR1c4xfTbhAdrTgd6qukVE/kh4bOKZwCRVfUNEzgAOJDy1SlT1ZhFpXibfsXGeb6nqdk/hEZGhhKf2MGzYsMMLCrLR9bgxxtS8nJwcV9G4rD0QRlXXi8hKEdmL8Ki63VW1WETeBvqIyBvA4YQjhNmE54e+QTgf8CShi9XhcXb9gFmq+q2I1I/zbEzoqrW8ZT8CPBIHMxvxjDGmlshoABCRyYRmn0NE5GHCY+f+SnjI8g0x20jCVUC3AA/FQDEJOE1EZgDvquqbcX4vicgsQp/b58XpfwNMJmzYL6+usodnaJjqlOmjTWNM1SSxM7i0KmwBoPol8LdmzK6gwo2Z3QhmjDEJZQHAGGMSygKAMcYklAUAY4xJKAsAxhiTUBYAjDEmoSwAGGNMQlkAMMaYhLIAYIwxCWUBwBhjEsoCgDHGJJQFAGOMSSgLAMYYk1AWAIwxJqEsABhjTEJZADDGmISyAGCMMQllAcAYYxIqY88EFpGmwCtAB6CHqn4Y0/cHPgMOV9UPReRQwgPb6wMjVPVVEWkEjAFaAhNV9c447UigF7AAGKKqRSIyELgaWA8MUtXFmaqTMcbUJZk8AvgWOBEYXyb9OmBWyvBtwEVAf+DmmHYxMFlVjwSOFZEcEekC5Khqb+ATYICI1AeGA32A3wIjMlQXY4ypczIWAFS1SFWXp6aJyIGEh7IvTElurarzVHUdsEpEWhD28qfG8a8APcukTQGOANoBH6vqJlWdBeRlqj7GGFPXZKwJqAK/BO4AbkxJSw1Ca4FmwN7AunLSlu4gH0C98hYqIkOBoQDDhg2joKCg0oJOmDCh0jymagoLC2u6CMYkTk5OToXjshYAROQgAFVdICKpo7akfG4KrALWAE3ie1PgS0JZm1SQr8Tm8patqo8QzjNAOAKpVG5ubjrZTBV4n9ZXb4zJkmxeBdQF6CgiU4AC4CER+R6wVEQOEpHGQDNVXQHMBvrG6foCb5VJ60c4jzAPaC8iDUWkF/B+9qpjjDG1W0YDgIhMBo4DHgWaqGpvVe1PaNe/VFU3ANcDo4GX2do09BhwiojMBKar6mJVnQssE5EZQEfgBVUtAkYBrwO3xJcxxpg0uAQelqdVYedcpsuROAn8rRmzK6hwY2Y3ghljTEJZADDGmISyAGCMMQllAcAYYxLKAoAxxiSUBQBjjEkoCwDGGJNQFgCMMSahLAAYY0xCWQAwxpiEsgBgjDEJZQHAGGMSygKAMcYklAUAY4xJKAsAxhiTUBYAjDEmoSwAGGNMQlkAMMaYhKqfqRmLSFPCs387AD2AL4EJcZnFwIWq+qWIHAo8EtNHqOqrItIIGAO0BCaq6p1xniOBXsACYIiqFonIQOBqYD0wSFUXZ6pOxhhTl2TyCOBb4ERgfBwuAs5X1aOAkcAvYvptwEVAf+DmmHYxMFlVjwSOFZEcEekC5Khqb+ATYICI1AeGA32A3wIjMlgfY4ypUzJ2BKCqRcByESkZ3gAsiaM3AVvi59aqOg9ARFaJSAvCXn5JgHgF6An8AJga06YAFwJzgY9VdRMwS0TuzlR9jDGmrslYAKiIiDQEbiTs5cO2RyFrgWbA3sC6ctKW7iAfQL0KljkUGAowbNgwCgoKKi3nhAkTKs1jqqawsLCmi2BM4uTk5FQ4LusBgNDe/0DJXj9bjwQAmgKrgDVAk/jelHD+oH5MKy9fic3lLVBVH4nLBfDpFDI3NzedbKYKvE/rqzfGZElWrwISkRuAL1T1uZTkpSJykIg0Bpqp6gpgNtA3ju8LvFUmrR8wC5gHtBeRhiLSC3g/G/Uwxpi6wGVyr0xEJgP5hD34ycANwMw4+k1V/ZWIdAAeJjTf3KCqr4jInsAzQAtgkqreEed3F+GKooWEq4g2ichZwM+ADYSrgBZVUqy0KuycS7+iJi12BGBMjahwY5bRALCLsgBQQxL4WzNmV1DhxsxuBDPGmISyAGCMMQllAcAYYxLKAoAxxiSUBQBjjEkoCwDGGJNQFgCMMSahLAAYY0xCWQAwxpiEsgBgjDEJZQHAGGMSygKAMcYklAUAY4xJKAsAxhiTUBYAjDEmoSwAGGNMQlkAMMaYhMrYQ+FFpCnwCtAB6KGqH4rIQOBqYD3h8Y2LReRQwgPb6wMjVPVVEWkEjAFaAhNV9c44z5FAL2ABMERVi8qbZ6bqZIwxdUkmjwC+BU4ExgOISH1gONAH+C0wIua7DbgI6A/cHNMuBiar6pHAsSKSIyJdgBxV7Q18AgzYwTyNMcZUImMBQFWLVHV5SlI74GNV3aSqs4C8mN5aVeep6jpglYi0IOzlT43jXwF6lkmbAhyxg3kaY4ypRMaagMqxN7AuZbhefE8NQmuBZmXypqYt3UG+1HluQ0SGAkMBhg0bRkFBQaWFnTBhQqV5TNUUFhbWdBGMSZycnJwKx2UzAKwBmqQMb47vW1LSmgKrUvKuiWlfEsrapIJ8Zee5DVV9hHCeAcCnU9jc3Nx0spkq8D6tr94YkyXZDADzgPYi0hAQ4P2YvlREDgL+CzRT1RUiMhvoCzwR3y8BWhDa+58G+gGzdjBPY4wxlchoABCRyUA+cAjwMDAKeB3YAAyK2a4HRhOab26IaY8Bz4jIEGBSvLJnsYgsE5EZwELg7ngVUHnzNMYYUwmXwMPytCrsnMt0ORIngb81Y3YFFW7M7EYwY4xJKAsAxhiTUBYAjDEmodIKACJygYjsnzLcXER6Za5YxhhjMi3dI4AngW4pwwXAjOovjjHGmGzZ4WWgInIKcBrhLPLlInJ8HPUjwmWXxhhjaqnK7gP4ETCYcOnk0fFVYmyGymSMMSYLKgsAjwAvAXMIN2xNJQSD1ao6P8NlM8YYk0Fp3wgWu17eh5QO11R1YYbKlUl2I1gNsRvBjKkRFW7M0uoKQkSuBO4AvpeS7NOd3hhjzK4n3Q34TYSTvm8AxZkrjjHGmGxJNwAsBB5W1QczWRhjjDHZk24A+BcwQkRaA6tjmlfVezJTLGOMMZmWbgA4O75fn5LmAQsAxhhTS6UbAIaQ5tUzxhhjagd7HkAF7DLQ6pfA35oxu4Kdvgz0i3KSvaoe9J2LZIwxpkal2wTUkq17zt8ndCL3bUZKZIwxJivSCgCqumfJ5/gA9jv4Ds8SEJHdCA96P4hwWHIx4WHvdwJbgMtU9QMR+SHh4e+NgAdV9RkRqQc8CrQD3lHVn8d5/gw4E1gJnK+q66paLmOMSaJ0m4AOKzNNQ+Bc4OdVXF4+sLuq9haR3sBwwgPjTwQaAw8BJwC/JASF14EZIjIe6AcsUdUhIvKoiPQE5gGnAEfG8lwB3F7FMhljTCKluxevwNvx9SZwOfDZd1jeYsCJiAP2Br4BNqvq6tivULOYrxswTVWL47I7Ab0IndEBTAGOALoC01XVp6QZY4xJQ7rnAJ5m6zmAzcACQnNMVa0AioBPCP0K9QbuSxlfHJuYGqjqlpi2lhAY9gbWpZG2HREZCgwFGDZsGAUFBZUWdMKECWlXyqSnsLCwpotgTOLk5ORUOC7dcwCDYxv8wTHpM1Xd/B3KchxQrKqHiIgAvweapJZHVTeJSJGI7BaDQFNgFbAmJW9qWtsyaeWV/xFC19aQ5mWgubm56dbJpMkuAzVm15LuM4HbAx8DH8bXv2JaVTnCyVoIRwONgfoispeI7MvWDfjbQJ/YBfXhwEfAbKBvHN8PmBXzHVUmzRhjTBrSbQL6E9AKeDYOnwL8ka0b5HS9AgwWkenA7oSTwPWByYQ988tjvpGEZqdbgIdUdb2ITAJOE5EZwLuq+iaAiLwkIrMIfRSdV8XyGGNMYqV1J7CIrAN+rar3x+FhwG2q2mTHU+6S7E7gGmJNQMbUiJ27E5jQNNNXRCbH4QK2NuUYY4yphdINAI8BNwMnx2EH/CYjJTLGGJMV6QaAMcB/CXv+AC8T2vONMcbUUuneCPZ3YF9VHaiqA4H9sQBgjDG1WroBIIdw81eJLwG7UN4YY2qxdJuAvgCuFZFCQvv/NTHNGGNMLZVuABgJPAW8FIcd8JOMlMgYY0xWpP1EMBE5CjgpDk5S1TcyVqrMsvsAaojdB2BMjahwY2aPhKyABYDql8DfmjG7ggo3ZlV+qIsxxpi6wQKAMcYklAUAY4xJKAsAxhiTUBYAjDEmoSwAGGNMQlkAMMaYhLIAYIwxCZVuVxDVRkT6ACMIwec+YDlwJ7AFuExVPxCRHxIeCdkIeFBVn4kPpX8UaAe8o6o/j/P7GXAm4QE156vquuzWyBhjaqesHgGIyPcJHckdr6rHqOqLwK3AicC5hD6HAH5JCApHA1eIyPcI3VAsUdXeQCMR6SkiLQjPJz4SeA64Ipv1McaY2izbTUA9gfXAX0XkRRFpBWxW1dWquhBoFvN1A6apajGgQCegFzA1jp8CHAF0Baarqk9JM8YYk4ZsNwHtA7QFegB9gZuA1CabYhFpCDRQ1S0xbS0hMOydkndHacYYY9KQ7QCwBpilqptE5FVCAPgqtTxxXJGI7BaDQFPCQ+nXAE1ivtS0tmXStiMiQ4GhAMOGDaOgoKC8bNuYMGFClSpmKldYWFjTRTAmcXJyciocl+0A8DZwjYg4IB/4F3CgiOwFNGbrBvxtoI+IvAEcDlwHzCYcNbwB9AOeBD4Hhsdp+gGzyluoqj4CPBIH0+qSMjfXHnhW3aw3UGN2LVkNAKq6QkReBKYTNsRDCI+bnByHL49ZRxKuAroFeEhV14vIJOA0EZkBvKuqbwKIyEsiMgtYDZyXzfoYY0xtZs8DqIA9D6D6JfC3ZsyuwJ4HYIwxZlsWAIwxJqEsABhjTEJZADDGmISyAGCMMQllAcAYYxLKAoAxxiSUBQBjjEkoCwDGGJNQFgCMMSahLAAYY0xCWQAwxpiEsgBgjDEJZQHAGGMSygKAMcYklAUAY4xJKAsAxhiTUBYAjDEmobL9UHgAROQc4D5V/YGIDASuBtYDg1R1sYgcSniIe31ghKq+KiKNgDFAS2Ciqt4Z5zUS6AUsAIaoalH2a2SMMbVP1o8ARKQeMBBYJCL1geFAH+C3wIiY7TbgIqA/cHNMuxiYrKpHAseKSI6IdAFyVLU38AkwIGsVMcaYWq4mmoDOAZ4HtgDtgI9VdZOqzgLyYp7WqjpPVdcBq0SkBWEvf2oc/wrQs0zaFOCILNXBGGNqvaw2AcW9/zOB04BrgL2BdSlZ6sX31MC0FmhWJm9q2tIyaeUtdygwFGDYsGEUFBRUWtYJEyZUmsdUTWFhYU0XwZjEycnJqXBcts8BnA/8WVW3iAjAGqBJyvjN8X1LSlpTYFVK3jUx7UtC+ZuUybcdVX2EcE4BwKdT0Nzc3HSymSrwPq2v3hiTJdluAuoAXCAiUwjNP1cC7UWkoYj0At6P+ZaKyEEi0hhopqorgNlA3zi+L/BWmbR+wKws1cMYY2q9rB4BqOovSz6LiKrqZSJyFvA6sAEYFEdfD4wmNAndENMeA54RkSHAJFVdDCwWkWUiMgNYCNydlYoYY0wd4BJ4WJ5WhZ1zmS5H4iTwt2bMrqDCjZndCGaMMQllAcCYCixatIhjjjmGDh060LFjR+69914A5s6dS48ePcjPz0dEmDNnDgCffPIJPXv2ZPfdd+fuu7e2Rm7YsIFu3brRpUsXOnbsyA033LDdsq666ir23HPPCsty++2307ZtWw455BBefvnlaq6pSSzvfdJeaSE0FdmrGl+1zZIlS/w777zjvfd+3bp1vl27dv6jjz7yBQUFfvLkyd5771966SV/9NFHe++9X7ZsmZ8zZ47/9a9/7e+6667S+WzZssV/9dVX3nvvN23a5Lt16+bffPPN0vFvv/22P//8832jRo3KLcdHH33k8/Ly/IYNG/wXX3zh27Rp44uLizNRZVM3Vbg9tCMAYyrQqlUrDjvsMAAaN25M+/btKSwsxDnHunXhlpS1a9fSunVrAFq2bEnXrl1p0KDBNvNxzpXu3RcVFVFUVFR6jmnz5s384he/4M4776ywHBMmTODss89m991358ADD6Rt27alRx3G7Iwa6QvImNpmwYIFvPvuu3Tv3p1Ro0bRr18/rr32WrZs2cLs2bMrnX7z5s0cfvjhfP7551xxxRV0794dgPvvv59TTjmFVq1aVThtYWEhPXr0KB3Ozc21m+pMtbAjAGMq8fXXX3PGGWcwatQomjRpwoMPPsg999zDokWLuOeee7jooosqnUe9evWYO3cuixcvZs6cOXz44YcsWbKE559/niuvvDILtTBmexYAjNmBoqIizjjjDM477zxOP/10AJ566qnSzwMHDqxSc8xee+3FMcccw5QpU3j33Xf5/PPPadu2LQcccADffvstbdu23W6anJwcFi1aVDq8ePHiHd7eb0y6LAAYUwHvPRdddBHt27dn+PDhpemtW7dm+vTpAEybNo127drtcD7Lly9nzZo1AKxfv55XXnmFQw89lBNPPJH//Oc/LFiwgAULFrDHHnvw+eefbzf9Kaecwrhx49i4cSPz589n3rx5dOvWrfoqapJrR2eI6+grvdPmu8BVM3XtVdvMmDHDA75z586+S5cuvkuXLv6ll17yM2bM8IcddpjPy8vz3bp186rqvfd+6dKlPicnxzdu3Ng3bdrU5+Tk+LVr1/r33nvP5+fn+86dO/uOHTv6m266qdzlpV4FNGHCBD9ixIjS4VtuucW3adPGH3zwwaVXIBnvFy5c6Pv06ePbt2/vO3To4EeNGuW99/7aa6/1hxxyiO/cubM/7bTT/OrVq0unee+993yPHj18hw4dfKdOnfz69eu9994fffTR/uCDDy5d18uWLfPee//kk0/6Fi1alKY/+uij5ZZFVX2nTp38QQcd5K+88kq/ZcuWzFY+fRVuD2t6Y2wBIEEvY6pbRZfqvvzyy76oqMh77/11113nr7vuOu+990VFRb5z585+7ty53nvvV6xYUXpJ7dFHH+3ffvvt7Zbx5JNP+iuuuKLSsnTt2tW/+eabfsuWLb5///67UqCucHtoVwGZWu3AU76o6SLUOfMntqnpIqStVatWpVdQpV6qe9xxx5Xm6dGjB+PHjwdg6tSp5OXl0aVLFwCaN29eLeVYunQp69atK71a64ILLuAvf/kLxx9/fLXMP1PsHIAxpk5IvVQ31RNPPFG6If7ss89wztGvXz8OO+yw7e6/uPDCC8nPz+d3v/tdaCKJXnjhBfLy8hgwYMA2J+RLFBYWbtOFfG25VNcCgDGm1it7qW6JW2+9lfr163PeeecBUFxczMyZMxk7diwzZ87kxRdf5NVXXwVg7NixfPDBB8yYMYMZM2YwZswYAE4++WQWLFjA+++/T0FBAYMGDcp+BTPEAoAxplYr71JdgNGjRzNp0iTGjh1beud1bm4uRx11FC1atGCPPfbghBNO4J///Cew9clZjRs35txzzy29vLd58+bsvvvuAFx88cW8884725UhJyeHxYsXlw7Xlkt1LQAYY2ot78u/VHfKlCnceeedTJw4kT322KM0vV+/fnzwwQd8++23FBcXM336dDp06EBxcTErVqwAQkCZNGkSnTp1AkL7fomJEyfSvn377crRqlUrmjRpwltvvYX3nqeffppTTz01U9WuNnYS2BhTa82aNYsxY8bQuXNn8vPzAbjtttu46qqr2LhxY+nzv3v06MFDDz3E3nvvzfDhw+natSvOOU444QROPPFEvvnmG/r160dRURGbN2+mb9++XHLJJQDcd999TJw4kfr169OsWTNGjx5duvz8/Hzmzp0LwAMPPMDgwYNZv349xx9//C5/AhjsgTAVsgfCVL9M/NbsKqDqV5uuAjJpsQfCGGOM2VZWm4BEpBtwL1AEFAIXAKcBVwPrgUGqulhEDgUeieUboaqvikgjYAzQEpioqnfGeY4EegELgCGqWpTNOhljKvf0oc/UdBHqnAs+OX+n55HtI4BFwLGqehRhg30qMBzoA/wWGBHz3QZcBPQHbo5pFwOTVfVI4FgRyRGRLkCOqvYGPgEGZKkexhhT62U1AKjqUlVdHwc3AYcAH6vqJlWdBeTFca1VdZ6qrgNWiUgLwl7+1Dj+FaBnmbQpwBHZqIcxxtQFNXIVkIjsDxwH/C/wg5RR9eJ7amBaCzQD9gbWlZO2tExaecsbCgwFGDZsWOmVATsyYcKENGpiqiITd0Y+eE21zzLxMrGe8u/tUu3zTLp019OO7kfIegAQkSaEtvzBhA1+k5TRm+P7lpS0psAqYE3MuyamfUkof5My+bajqo8QzilAmlcBpd7WbapHJq4COvIyuwqous2fWP03ML3649eqfZ5JV+vOAYhIfWAccJOqfgrMA9qLSEMR6QW8H7MuFZGDRKQx0ExVVwCzgb5xfF/grTJp/YBZWaqKMcbUetk+AjgH6A6MEJERwIPAKOB1YANQ0snG9cBowhHCDTHtMeAZERkCTFLVxcBiEVkmIjOAhcDd2amGMcbUfnYjWAXsRrDqZzeC1Q6ZuBHMLgOtflVoArIbwYwxxmzLAoAxxiSUBQBjjEkoCwDGGJNQFgCMMSahLAAYY0xCWQAwxpiEsgBgjDEJZQHAGGMSygKAMcYklAUAY4xJKAsAxhiTUBYAjDEmoSwAGGNMQlkAMMaYhLIAYIwxCWUBwBhjEirrD4XPBBEZCfQCFgBDVLWoZktkjDG7vlp/BCAiXYAcVe0NfAIMqOEiGWNMrVDrAwBhz39q/DwFOKIGy2KMMbVGXQgAewPr4ue1QLMaLIsxxtQadeEcwBqgSfzcFFhVNoOIDAWGxsGfquojlc3Ue19d5TMZNH9im5ougknDBZ+cX9NFMOWoCwFgNjAceBroB8wqmyFu8Cvd6BtjTJLU+iYgVZ0LLBORGUBH4IWaLZExxtQOzpo6jDEmmWr9EYAxxpjvxgKAMcYklAUAY4xJKAsAuwAROUBElovI6yIyR0S6pjndw/G9j4gcHD/ni8hlmSxvksV15UWkWxw+SURurOL0x1Uh/2ARGfYdipooO7teKpjnXiJyZsrwwztZzF2OBYBdx3RV7QNcCdyazgSq+tP4sQ9wcEybq6oPZqKAptS/gOu+47QHANsFABGx/+LO25n1Up69gNIAkPJ/qzPqwn0Adc1coJ2ITCTc2LYUuAA4DLgX+JYQLG4UEQV6A4OBM+LeyhPAScA44CJVvUxEHPAmoduM44DrgXrAH1X12SzWra74GKhfctQFICL9KfO9isho4G5V/VBE7gYmAZcBvUREgNOBmcA/gLUi8hLwG2AP4AVVvSOblaoD0l0vPwIeBZYADvh9nHYcYZu4DDiLsK6OFpHXgcuBp1VVRGQmcKyqbhKR3xHuPVLgMcJNqUuBC1R1cxbqvFNsr2PXczRhL3Gyqh4NfAScDZwI3KSqxwA3l2RW1fXAaOBXqnpBSroCXUSkPtATeAvwwAjgx4TAMUxE6mWhTnXR3cAv4mdH+t/rg8BzqtpHVVcBucBwVf05MCuu8+6EgP79TFagjkpnvfwOOBc4ldCVDMBqoCB2KlkIHEtYV9PjuvpXyjKmAMfHz8cCfwf+F7hPVY8F3gf+JzPVq14WAHYdJXsaVwGvAm/H9LeBdsCfgBNEZCzQP815TiP8QM8i7N38gNBUNDUuY6+YZqpIVWcCBwGtqPh7Tb3JxlUwq89VdXX8fLiI/B14jbAT0LLaC17Hpble9lHVz1TVA+/GSZsD40VkOnAC0HoHixkHnBWPJD5Q1WKgA3BT/A+fDvywmquWERYAdh0lexonEfYwusX0rsA8YK2qDgMuBEaWmbaIcIhb1jjCns7hqvoWsILQZfZx8XxDvqr+p9prkhyjCAG7ou91NWEPHyAvvpddV1tSPl8HXAocQ9gLrShomB0bxY7XyzIRaRebRvPjNOcCk+IR2BTCd1/u/0pVPycEmMGE/xhxOb+O/+HuQK04YWwBYNf0KHBi3BvpTPiR/VRE3gBeJzT5pJoGXCMi96YmquqHhB/4zDi8BbgFeEVEXgPGZq4KifBXwgaiou91NDBSRF4gnLsB+ICwp/+8iOxVZn4vAC8CzwBfZbbodVpl62UE8GzM9w1hQ/8q8DMRmcDWo+KlwPdFZLyItCuzjJcIzTxvxOFbgatFZJqITAO6ZKRm1cy6gjDGJIqINFDVonjl1WvA2aq6tKbLVRPsCMAYkzTd49H0P4BXkrrxBzsCMMaYxLIjAGOMSSgLAMYYk1AWAIwxJqGsKwhTa4nIFcD9cfBQVf00XtnxFOESvUZAAeEW/RaqumfNlDQ9ItKB0PfM66r6ekwbDQwCusa7uzNdhnMJN0+NUtU1mV6eqVl2BGBqs7PYeiNVSadd7YHzgc+A8wjX3V9J2Iju6joANxA69yvxIHAO8O8sleHcWIa9srQ8U4PsKiBTK4lIa2AR8GfgKGC1qnYSkbI/6AMJN8+1UNU9RWQw8CTh5roOwH7Ajap6r4j0IVwXPpmwAewEPKKqv6AcItKScENRd0Ig+hg4SVWXi8ivgKFAC2A2cJmqfhG7KL6BcFTSJy7nckKXH/PLLOIYwt2mgwh3hK+IeWYD6+Ny7wHWAr+N405T1QUikgPcR+gKZD3wNHC9qm4WkQWEm50eA35CCC4nE+5CviFl+V+q6gHl1d3UDXYEYGqrgYTf7/PA/wM6xiaU6+P4Nwh7zssrmP4Y4BFCfz13iEjDlHFHEwLLSuBaEdmvgnmcR9jA/h64htCTaz0RGQTcRrjO/A5CNxDPl5m2N6H5qmnMs5zQhQGEO4LPIXRvXJ5uhCC1knBX6/GEu467AD+PeZ4hNH/dC0wEfkkINCX2IPRc+RIgwCXAeLb2jXMV4cjJ1GEWAExtdSawidAHyz9i2lmEjr8A5qvqOFX9poLpn1DVPwFzgO8B+6SMm6iq96bMa/8K5jEvvv+Y0AHZc7GvmZNSynMLoWOww0SkWcq0f4jL+AI4IJZzVhz3YSz7fytY7j9U9Q8p+W8n7O0DHCgiexKCWGPCHn1JP/YFKfPYQuju+E9x+IDYdciSOPxXVf1rBcs3dYSdBDa1jojsS+ji2hG6yy5xJqF/l3Ssiu/F8b1emuNKqeokEelB2LD2B34pIqkb2fOAko34bmztD6jsMkp2xNJtj10T34vi+1qgpO/5krI64D3g2pTp1qZ8Xq+qG0SkbB2tTThB7AjA1EYDCRu42wlX+/wP4WErhxKaNrJCRAYQ9vYXsTUQtY5lgdB2vy9hb3yEqm6oZJYl3UL3FpGzv+vzAFT1a8J5j86EpqY2hIcKFexgsrJlGBTPiZg6zI4ATG10FmFP9R5VXQ4Q2/BPonofCViZb4EzCCeaNwDPAeNV9VsR+SGh6eVBYHEcV5mZhF4pjyKcW9h3J8p2PuGcwjCgAeFqqKfTmO5hQsC6MZbl9Z0og9nF2VVAxqRBRFqUTVPVFTVRFmOqix0BGJOe8q4msge2mFrNAoAx6Umn/dyYWsWagIwxJqHsKiBjjEkoCwDGGJNQFgCMMSahLAAYY0xCWQAwxpiE+v/aLccnRmHXkAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('*'*10,'Vader results','*'*10)\n",
        "print('precision :',precision_score(df['Actual_Sentiment'] , df['Vader_sentiment'] , average = 'weighted'))\n",
        "print('recall :',recall_score(df['Actual_Sentiment'] , df['Vader_sentiment'] , average = 'weighted'))\n",
        "print('f1_score :',f1_score(df['Actual_Sentiment'] , df['Vader_sentiment'] , average = 'weighted'))\n",
        "print(classification_report(df['Actual_Sentiment'] , df['Vader_sentiment']))\n",
        "plt.title('**********Confusion matrix of vader**********')\n",
        "plt.title('**********f1-score = 0.77**********')\n",
        "cm = confusion_matrix(df['Actual_Sentiment'], df['Vader_sentiment'] )\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm , display_labels = ['Negative','Neutral','Positive'])\n",
        "disp.plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "id": "tlMJDmt2vv8V",
        "outputId": "babdd25d-4547-4814-d245-6029da1268b3"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********** Vader results **********\n",
            "precision : 0.7915425044127081\n",
            "recall : 0.7644687645118512\n",
            "f1_score : 0.7773862751765922\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.53      0.51      0.52     28229\n",
            "     Neutral       0.08      0.13      0.10     13306\n",
            "    Positive       0.89      0.86      0.87    167346\n",
            "\n",
            "    accuracy                           0.76    208881\n",
            "   macro avg       0.50      0.50      0.50    208881\n",
            "weighted avg       0.79      0.76      0.78    208881\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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CKcRUdS5wbnnXwxgT/mzqtDHGhIkAJ31EJAvIxpiIYmtZGGNMmLDlN40xJkzYAvXGGBMmrA/ZGGPCRGnWsog0FpCNMREl2wKyMcaEB2shG2NMmLCZesYYEyZslIUxxoQJ67IwxpgwYcPejDEmTORYC9kYY8KDdVkYY0yYsC4LY4wJEzbszRhjwoS1kI0xJkxYQDbGmDCRk2sX9YwxJixYH7IxxoSJYHVZiEgS8AXQCjhLVReLyFXA/cB+oJeqbhCRlsA4nHg5WFVni0hlYDJQE5ihqiPdMkcA7YA1QG9VzS6qzOLqdPS2/Y0xR6VcnyegRwD2ARcB0wBEJAboB3QCHgMGu/mGATcD3YAn3bRbgJmqeg7QWUTqicgpQD1V7QAsA64socwiWUA2xkSUYAVkVc1W1a1+Sc2ApaqaparzgNZuel1VXaGq6cAOEUnGaQV/7m7/Aji7UNosoH0JZRbJuiyMMRHFW4qLeiKifi/Hqeq4ErJXA9L9Xufd3tr/gGlA9UJ5/dNSS8jnX2aRLCAbYyJKaS7qqaqUouhdQKLfa2/+IQ9IAnb45d3lpq3FiaeJxeQrXGaRLCAbYyJKCMchrwBOEJE4QIBFbnqqiDQFtgDVVXWbiHwPnA+Md3/2AZJx+ovfAroC80oos0gWkI0xEcUXxIAsIjOBNkAL4DXgeeAbIAPo5WYbBEzE6W4Y4qa9AbwtIr2BT9yRExtEZLOIfAesA0a5oyyKKrNIFpCNMRElmC1kVb2wiOQphfIsAToUStsDXFpEeQ8WkTalcJnFsYBsjIkowWwhhxsLyC7v36mHzlSWsrPDqk6+nJzyrsJBfD4fvszM8q7GARUrlHcNCvIB3hKvIUUkb64FZGOMCQs2ddoYY8KEdVkYY0yYsOU3jTEmTPh85V2D0LGAbIyJKNZlYYwxYaI0a1lEGgvIxpiIYl0WxhgTJqzLwhhjwsQxGZBF5LSSdlTVX4NfHWOMKdlR3GNRYgtZKfncS1xo2RhjQsF3jE6dfouj+8PIGBOBjskuC1W9sQzrYYwxATmmR1mISA3gVZxV8a8CbgO+UdWXQ1w3Y4w5yNHcQg5khPVYnNtfJ+LcW2oNcHsI62SMMcXzeQJ7RKBAAvIFwCi/10uAJqGpjjHGlMznC+wRiQIZh7wXqOU+j8bputgeshoZY0wJjtVRFnnew7mTqg/4xN3nmVBWyhhjihWhrd9ABBKQHwF2Axe5rz8B/h2yGhljTAmO5ot6hwzI7m2shwHT3aTlqpod2moZY0wxjuIW8iEv6olIZ5yRFb+5j9Ui0im01TLGmOJ4AnxEnkC6LN4AEoB3cAL4/wFvAk1DWC9jjClabnCKEZEoYDxOLPMAtwDJwEj3KHeo6u8iUhtn5nJlYKyqvi0i0cDrQDPgF1W9zy2zL9ATZ+DD9aqaXpo6BTLsrRIwQFVvUNXrcfqUbZU4Y0z5CN445DZAvKp2wIlr/YChONfLrgVGuPkexgnSHYG7RKQC0APY6O5bWUTOFpFk4GLgHGAKcFdpTy2Q1d4mA1eLyFKcAN4TmFraAxljTDAEcYzxBsAjIh6gGs4QX6+q7gR2ikh1N9+ZwAOqmisiCpwEtAM+dbfPAtoDVYE5quoTkVnApNJWKNDV3jzAbL/nHYAHS3swY4w5YqUIyG4AzTNOVcf5vd4GZAPLgAo4cW2M3/YcEYkDYlU1r6MkDaiOE8DTA0grFVvtzRgTWUox7E1VpYTNXYAcVW0hIgKMxlkiIk+MqmaJSLaIRLlBOQnYAezyy+ufdnyhtFKx1d6MMRHFE7xmoocDs463AVWAGBGp6j7PC6g/A51E5FvgdOAh4HucWcvfAl2BCcBKnH5o3LR5pa1QIKu9VQbuBU7GadYD+FT1itIezBhjjljwpk5/AdwoInOAeJxgGgPMxOkduNPNNwKnx+Bp4FVV3S8inwCXish3wAJVnQ8gIp+KyDxgJ3BdaSsU6LC3q90K5r0T1pVhjCkfQYo+qpqDE9sKa1coXyrOImuF972xiDKfA5473DoFMuztfOAl9/nVwDTg0cM9oDHGHBFfgI8IFEhATgAW4bSOK+H0p/QPZaWMMaZYR3FADqTLYgNOUF6FM0PPA6wPZaWOVrHxuYx6fzmxcT6iY3x8N7Mabz9blwdGr+HktrvZu9u5b+zoBxq7e/i444n1nHFeOpn7oxj9QGNWLq6UX16lBC+vzf6D+Z9V5ZXHGpb9CQVZv2fX0fb83ezaFsNtnVsAMPDVNdRvmglA5UQve9OjufOCFvn7pNTL4vVvlvP26FpMe7UmAJN+XML+PdHk5oI3x8M93ZuX/cmEyKU3bKDrlZvw+WDNn5V5blALqqdkMmD0MqpUzWblH1UYNaAFOdlR1KrxHe/Onc+2LXEAfPJOXT6bXofWZ+6iz4BV+WU2aLKPEf1PYP7s5PI6rdI5lhcXwrk7yB5gITAc57Nn4JEcVEQaA6uBtqr6k4j0AERVHy/F/s1V9fMA898IJKjqS4fKG0rZmR4e/mdzMvZFEx3jY/T0ZejXzsiZN4bVZ+7MagXyn3FeOnUbZ9L73BNpeepe7h66lvsuOSF/+w39N7L4x4QyPYdQ+nxKdWZMSObBFw583g+7vXH+81sf28je3QW/1N02ZCM/f1XloLIeuqop6TuOrgmlNWpmcvH1f3P7/wlZmdE88uwSOl64hTPO3cEHk+rx7f9qcveQFXS5fBMzp9QF4Nv/pTB26PEFyln0U1Xuufx0ABKSsnlz1s/8Oq/aQccLV0EcZRF2Alntbbbfy7OCeOwlOMNHrjyMfRvjjCEsEJD9xgqGKQ8Z+5xWcEyMj5gYX4lLCZ7dZRezp9cAPCxbkEBCopfqNbPZsSWW40/eS9XkbH75JpFmrfeVUf1Da/GPCdSqn1XMVh/nXryLh646sIRKjaq/svSXODL2BdLzdnSIjvYRVyGXnJwo4ivksmNrHK3b7mLEg84H9Zcf1uK6u9bmB+RDOafLNvS7amRmRIey2sF1LAZkESlpUQyfqiYd4bGX4oz5y/8+KSLdgEE4dyZ5UVXfFZGJwChVXSwio3DWY74DaOcO5r4cmAv8CKSJyKc4Fx0rAdNVdfgR1jOooqJ8vPjpUuo2zuTjt1JYvrAyPf61lRsf/Jvr+qayYF4VJgyvB0CN2tlsTY3L33frpjhq1M5i59YYbn10AyP7NuHUc0q1dknEOqntXnZujWHj6ngAKlTy0rDu/xg0uhZX3rG1YGafh2Hv/gU++HRyDf73To1yqHHwbd8Sz38nNGDS7B/Jyojm1++rsvKPKuzdHUOu1/lg37Y5jhq1MvP3ad9lGydJGn+vqci4EcexbVOFAmV27L6FDybVL9PzOFLHagt5B6H/LBqFMwX7I5y+6cHAeYAX+FZEilszYyywXlX7A4hIfaC9qu4UkUqq2tFdyelHEXmhuIOLyK3ArQA97ruETh06Bem0Svb3Pti0fB9drnuJ4zpcTE5OAn/tSMLjyaH9pW9xWvcUsirWpEZUI/719IWk72kGQOMWz3DTiKtITFhFVNRx3Ppyd2rVmEtC5bUMPLHUQx5LpazuURYft42Uhi8w8MN7C6Q3azSZ/ZmnM/DDrgAc12AKe3xt6PefFjSq9xFebzzNu3UDYPX2nWRlVyM2Jp1bnhhNp95dSdvd4qBjBZsnOrStzJjovbQ67iUWrnienJxKnHzOyzz+zslUq7mKAZN7AxAfu52U+n8zYHJvopNi+WP1Wfh8sdRp+TVjPvyJRSvuzC8vLmYXJ7RaQHrs/XS+JYK6d47FPmRVbRzqg6vqXBF5EqgDpADNOdANUdVN8w8Fxf1PrHQXBAE4XUSGALE4XRs1Szj+OGAcQJe4a3zfP7/k8E7kMF3bN53M/eOYPq52flrrs3ZzxW1/kJl4MSv/3Mii+VP4ZoYzJf6Nr1czqtdH3DJoAyeduZsWtWdQobKXmFgfqT//wYThoWvp+HJyQla2v1r1s3jyrR0Mu/TAkgJR0T7+8+sSHrysGdtSlwMw+oOVHN/mF1rUSSchwYsv18Os175nxoSCF6aufyCbjL2T8y/4hVJ01SP90liyc7puZfc5O3hh8DQAOl+8jxPafEydrjsYeeOb5Ho9tDwlnZi79jH81vEMmNyb4f+aDDjfzKbM/5Ph/xqfX94l1//NzuWVefHxt0Jab3+ddrQ7dKZDOYpbyOHQ+fY8zkzAbTiLfHRR1U5AG1XdhDPjJS/StHZ/ZuN0a+Tx7zd+COdC5HnA34TRStVJ1bOpnOgEtrj4XE7rsJv1qypQvWbeDVh8nN11F2uWO18rf/iiKv+4Yjvgo+Wpe9i7O5odW2IZ2bcJN5zdml7tT+aNp+sze3qNkAbj8nZah92sXxnPNr/umwcuO54ffxtJr7at+OCNFN57sSYzJiQTX9FLxcpeAOIrejm9427WLKtQXNERZWtqPC1P2U18BS/go81ZO1m3qhKLfqrKOV2cbpvzL93MD185XTRxMbvy92173nbW/1WpQHkdL9rCnJmh/6AKumN82FuofYwzeiMXZ2riFyKSC2zFWepzIjBZRPoAeVevfgf+LSLvA30KlTcd+MDNszvktS+F6jWzeeDZNURHgyfKx7efVOOn2VUZ/u6fJNXIxuOBv/6oxJiBDek3GX76KpEzzktj/HeLydwfxbP9G5f3KYTUgFfW0vrsPSRVz+FtXcLk0bX47N0adLxkF998WDWgMqql5DDkzTUARMf4+PqDaug3iSXvFCGWL0pk7ufJjJn2K16vh7+WJvC/qXX4eU51Hh61jBv6rmHV0gQ+m+5846pb8wvGzlC8OR52p8Xw7MAD3TY162aQXDuT338Obas+FDxhfNn+SHl8ZdU5GOa6xF0TVm/EwOl3MuyKV8q7GvnKqsuiNAZ+eG+Bro3yFuoui9JyuizGHzpjGZq1440j+sbq8Xh8xz0zOqC8fz34AL4IuyNqQC1kd03QVsBqVU0LbZWMMaZ4R/Moi0Bucnoq8BfOgvVniMhSEXk95DUzxpiiBO8WTmEnkIt6L+HM1PPg9PO+jbPgkDHGlL2j+KJeIAH5FJwLa3k2UsJQMmOMCSWPL7BHJAp0caGO7vPWwDXAmlBVyBhjSnI0j7IIJCCPxFmkHpx7TnkoYmFmY4wpExHa+g1EIIsLjReRv4AL3aRPVXVOaKtljDHFOIoDciCjLBrijLJ4yX2sdtOMMabMHet9yGs4+DPJF+C+xhhjAhRIUM27AytANaAtzm2cjDGm7EVo6zcQgfQh9/B/LSK3AJeFrEbGGFOCY3qUhYj0K5T/IqBNqCpkjDElOpZbyDiLyBf2brArYowxgQjmBTsR6YRzY4woYAzOKpMjcWYl36Gqv4tIbeAtoDIwVlXfFpFo4HWgGfCLqt7nltcXZ5XK7cD1qlqqW/oEEpBv8nvuBdao6tzSHMQYY4ImSAFZRCoCDwDdVTXLTZuD0wtQBXgVZ7jvwzhB+hvgOxGZBnQFNqpqbxF5XUTOBlYAFwPnANcCdwH/Lk2dSgzI7qfAXcBzqmqtYmNMuStNC1lE1O/lOPcuQXnOBvYDH4vIPuBOwOvefWiniFR3850JPKCquW55JwHtgE/d7bOA9jh3OZqjqj4RmQVMKu25lRiQVdUrIj7Axh0bY8JDKS7qqaqUsLkWcDxwFs6CaU8A/l0MOe7Sw7F+d7NPA6rjjDhLDyCtVAJZXGgb8ISITBORMe6j2BuHGmNMKAVxYsguYJ7bXTEbOBXwv71MjLst271pMkASzg2gd/nlLSmtVALpQ+7u/rzcL80H9C3twYwx5ogF76Lez8ADIuLBGTm2BGgiIlVx+pB3+OXrJCLfAqfj3Lfze5xW9bc4/ckTgJVA3qi0rsC80lao2IDsrl9xD87NQo0xJjwEKSCr6jYR+QCY45baG6jHgclwd7pZR+CMsngaeFVV94vIJ8ClIvIdsEBV5wOIyKciMg/n5szXlbZOJbWQGwOVVfXTEvIYY0yZCuawN1V9GXjZL2kVzgU7/zypwAWF0nIoYtVLVX0OeO5w63OoLouOIlLkPdRV9a3DPagxxhy2Y3hiyO3uw58H5y2xgGyMKXPH8tTp/wALy6AexhgTmGO4hfyxqk4tk5oYY0wAIvN+0oEpKSCvBfaWVUWMMSYgx2ILWVWblGVFjDEmEJF6N5BA2F0/jDGRxQKyMcaEh2N5lIUxxoQXayEbY0x4sD5kY4wJFxaQj35RSYmHzlSWoqPDqk6ehMrlXYWDeOJiiWnUoLyrke/T+R+XdxUKSM24iplL5pR3NYLOWsjGGBMu7KKeMcaEB2shG2NMuLCAbIwx4cHjO3ojsgVkY0xkOXrjsQVkY0xksT5kY4wJEzZ12hhjwoW1kI0xJjxYl4UxxoQLC8jGGBMegt1CFpFrgDGqmiIiVwH3A/uBXqq6QURaAuNw4uVgVZ0tIpWByUBNYIaqjnTLGgG0A9YAvVU1uzR1iQrWSRljTFnw5PoCegRCRKKBq4D1IhID9AM6AY8Bg91sw4CbgW7Ak27aLcBMVT0H6Cwi9UTkFKCeqnYAlgFXlvbcLCAbYyKLL8BHYK4B3sdZIaMZsFRVs1R1HtDazVNXVVeoajqwQ0SScVrBn7vbvwDOLpQ2C2hf2lOzLgtjTEQpzbA3EVG/l+NUdZzftmigJ3Ap8ABQDUj3yx/t/vRvuKYB1Qvl9U9LLZRWKhaQjTGRpRR9yKoqJWy+HpiqqrkiArAL8F/z1uv+9P8ISAJ2+OXd5aatxYmniYXylYp1WRhjIorHF9gjAK2AG0RkFk53xT3ACSISJyLtgEVuvlQRaSoiVYDqqroN+B44391+PvBDobSuwLzSnpu1kI0xkSVIiwup6sN5z0VEVfUOEbka+AbIAHq5mwcBE3G6MIa4aW8Ab4tIb+ATVd0AbBCRzSLyHbAOGFXaOllANsZElFBMnc7r2lDVKcCUQtuWAB0Kpe3B6XsuXM6DR1IPC8jGmIhiM/WMMSZc2HrIxhgTHqyFbIwx4cICsjHGhAdrIRtjTLjwHr0R2QKyMSaiWAvZGGPChY2yMMaY8GAtZGOMCRcWkI0xJjx47KKeMcaEB4/1IRtjTJg4euOxBeSydMl16+l6RSoej49Z0+vy0dsN8rdddsM6+jy4in92aE/6rjhqVvuel6f/hMcD+/ZG8/JTLVj9ZwLJtTJ4YNhSqtXIwueDWdPq8tE7DUo4anjrO+g3zmy3mV0747nr+o4Ftl12zSpuuXcp13TrQnpaHGd12MT1ty7Hl+vB6/WwN+aU/LwptfZz7yO/kVIrA58PhvQ7ky2bKnH/ows56dTt7NsTC8BzT5/CXyuSyvQcD8fo+xvw45eJVE3OYdzXywtsm/ZqCq8/WY+pv/9OUg0v389K5K1n6uDxQHSMj9uf+JuT2u4FoHv9U2jcMgOAmvWyeGLSagBG3deQRfMrU7mKs3Ra/+fX0fSk/ezeFc2z/RqQujae2PhcHnh2ff7+YcNayOZINTp+D12vSOX+a08nO9vDU68u4qc5NUhdX4nkWhmc1m4HWzbG5+fPyErhsd6nsic9FjlnO/cOWcb91wler4c3Rh3PqqVVqFgphzFTlF/nV2f9X5XL8ewO35ef1ueT9xvT77GFBdKTa+7n1DO3sSW1Yn7aQk3mh+9qAR4aN03nmTfGA6cD0O+xBUyZ2IyFP6dQoWIOvlxP/n7jXzqBeV/XLYOzCZ4uV+/g4pu28UzfhgXSt/wdy69zqlCzXlZ+2qkd9nB21+V4PPDXkgoMva0xb363DIC4CrmM/bJgQM/TZ/BGOvRIK5D23phaND1xP0PGr2HdinheHlSfEVNXBfnsjszRPMqiTO8YIiKNRWSriHwjIj+JyBkB7vea+7OTiDR3n7cRkTtCWd9ganDcPpb/nkhmRjS53igWa1Xan78VgFsfWsn4Z4/H5zsQRNL3NmNPutOqW7YokRq1MgHYuS2eVUurALB/XwzrVlcm2d0Wif5YWIPd7nn669P3Dya8fEKBb6cZ+2MA5z2qUNGbn96g8W6io30s/DklP19mZjSR7OSz9lKlmveg9Ncer8fNj27Ec+BXhYqVc/NfZ+yLKrCttNatiOeUc/YA0LBZJpvXx7Fza5i123y+wB4RqDxu4TRHVTvh3C5laCA7qOpt7tNOQHM3baGqjg1FBUNh7YrKnHTaLqokZRNfwYt02E5y7UzOOm8r27fEs/rPhGL37XJZKr/MrXFQes26+2nacjfLFiUWsVfkOqvDJrZvrcDqlQef19kdU3n1va95fPRPLF93CwD1Gu5l755YBv1bGTPpW3rfvYSoqAN/kDfctpyXJs+hT98/iIk9OMhFiu9nJZJcO5umJx7chTDvf0nc3KElg284jn7PrstPz8qM4u5uzenboxnf/69gV83E4XW4/R8teHVIXbIynSjepFUG82Y6+ZYtqMTmDXFsSz34A7M8eby+gB6RqDw/+hYCzURkBs4NAVOBG4DTgBeAfTjB+3H3zrEdgBuBK0SkJzAe6AG8B9zs3n7FA8zHuR13F5xbr0QDL6rqu2V4bgdZv7oy749vyNPjFpK5P5q/liUQG5fL1besZdBtbYrdr/UZO+lyeSoP3nBagfQKFXMY9Nxixo1oxv69YdaCOQLx8V569lrJo33bFrl9/pw6zJ9ThxPbbOeRkdOB44iOzuXEU3Zwb68ObNlckQFP/cr5F63n848bMnFsS3ZujycmNpd7BvzOVf9axbvjm5ftSQVBxj4P771Yi3+/W3T3QfvuabTvnsbvP1Rm0sg6+d0Mk39aQnKdbFLXxvHwVcfT+IT91G2cxU2PbKR6zRyyszy88FADpr5ck+v7bebquzczdnA97ji/BU1O2M/xJ+0nKtzuvBmZsTYg5fmX3BFoDIxQ1VdFZDDwT5ybDT6hqjNFJP9XQVX3i8hE56l+IiKd3HQVkTEiEgOciXOzQR8wGDgP586x34rIVFUt0DwSkVuBWwF63Hw5nc7tFMrzBSA1A/DAyR2nkZWdSMPaH/P2N0sAiI/LYuLspSxY9hg16tfgqamdaXXcGBavfIy7xtTOL8NDDice/zw703tw7g3dOPeGkFebUP5VxsdtJaXuch4e25NKFdbTpOlcJs/8xdkWm8mEGcqCPx8nO6dqgf0qV3qQQePuomL8ZrLZS6/BNwFQo1oDrjppJade2LNA/qSENpzafiYNzyiYHiypGV2DWt7WzN3k+D4nNeMe1v+5g43rZtLnH7UA2LlpL7d3OYvHPryEqimV8vdJbgN/r53CnxvvoEK1hlDtOed3rhYc33YO+mt3zqjdBJJgk9vTdfplG5n1+u9O/WPhuuFOus/no/+5U/DUup3UjLignFO9IJRhw96Cq6OIfAPsAWYDP7vpPwPtgZeBR0XkOuAdYGYAZX4FdAYuAt4FUnC6Nj53t1d10zb576Sq44BxAN1SbvP98Oakwz2ngCRVzyJtRxwptTN4etxvPHLdaezdfeAu5RNmzafvP08gfddHDPlPDxokDePRm09g6W+f+ZXi44GhS/n2j1jGjdwMhLbOeTwJobtoWLP2PoaMSmfEHVPdlAOjLcb/dzb33SSkp31Onfp7Sd1QCfDQtHkabV/PYuitM4mKghcmbmTsI5NJ3xXPfYMWsmJZVT6dPpVqNTLYub0C4KPPfUv4MzOKiWOnFlWNI/bp/I+DWp4nPo4YTxPqVLibOm1g2u8Htt1wZite/N8ikmrM4+/VcdRtnIXHAysWVSQ3uwnN6vRn1ZbR1K7an7h4H2nbo1n9a3N63TODOhUy2b45hhq1cvD54MOv6tGiVS51KnzMnrRo4ivmEhvnY+Y71WlzdgLHpfQL4lmtOPIiLCAH1RxVvRJARPrjtGp/Ac7A+d9KU9W7RSTOTfcPyNk4XRCFvQf0B45X1b5uy3oZ0EVVs0QkVlWzQ3dKgRn07GISq2aTk+PhlaHN2Lu7+L65hnU+IiEumzsf/ROAXK+Hvv8UWp2axj8u3szqPyvz4vvOZ9mkMceh3x3cxxwJHnriV04+bTuJVbOY9NGXvPNGcz7/uGGRedt3SqVz9w14c6LIzIxi6dq7gN/IzYU3X2zFsBd/wOOBlcuS+Owjp4wHH19AUjVnRMLqFYm8NPLksjq1I/LvOxqxaH4CaTtiuO70VvzrgU10u3ZHkXnnflqVL6dVIyYG4ivmMnDsWjwe2LhyF88Mbo4nCny5cPVdm2nU3GkWj7i7EWnbY/D5oOmJ+7l3RCrgXNQbdV9DPECjFhncP3p9WZ1y4EJwk9Nw4fGV4aeNiDQGRvkF5CScVnAVYDNwPXAncDnOh8V0VR3t3qJbRORsYASwAPgA6KGq/d2yFgKzVHWA+7orMBDnv2+rqpb4PbVbym1h9bE7YFIvhvcqm9ZvIELZQj5cD4/t6deqLn/BbiEfqdSMl6hT4e7yrkYBUbVXHMEYEPB4PL4u8nhAeT/Xx/H5D12KAGXaQlbVNcCVfq/TcC7M+Xveffjvl3eL7vnAuX6bvvHL06bQPp8B/t/1jTFHg9yjt4l89FyeN8YcG4IUj0XkTJwRXdnA3zijvC4F7gf2A71UdYOItMS51hQDDFbV2SJSGZgM1ARmqOpIt8wROKO81gC9S9tVGm4DWowxpkQeny+gRwDWA51V9VycAHoJ0A9nvsNjOCO1AIYBNwPdgCfdtFuAmap6DtBZROqJyClAPVXtgHMNK783IFAWkI0xkSVIM/VUNVVV97svs4AWwFJVzVLVeUBrd1tdVV2hqunADhFJxmkF543i+gI4u1DaLJxRY6ViXRbGmMhSioEI7qSyPOPcoa6F8zTCmUg2AGd4bJ68EV3+Ddc0oDpQDUgvIi21UFqpWEA2xkSWUkyLzhsQUBwRScTpC74RJwD7z9fPm0jm32udBOwAdrl5d7lpa3HiaWKhfKViXRbGmIgSrD5kd3bvezgzg5fjzIM4QUTiRKQdsMjNmioiTUWkClBdVbcB3wPnu9vPx5kh7J/WFZhX2nOzFrIxJrIEb+7ENUBbYLC7dMNYnCG33wAZQC833yBgIk4Leoib9gbwtoj0Bj5R1Q3ABhHZLCLfAeuAUaWtkAVkY0xkyQ1OQFbVyTjdFYVNKZRvCc7iZv5pe3CGyBUu88EjqZMFZGNMZLG1LIwxJkxYQDbGmDDhtanTxhgTHnwWkI0xJjxYl4UxxoSJII2yCEcWkI0xkcVayMYYEyYsIBtjTJjweg+dJ0JZQDbGRBZrIRtjTJiwgGyMMWHCRlkYY0x48NnEEGOMCRM2ddoYY8JErgVkY4wJD3ZRzxhjwoPPWsjGGBMmrIVsjDFhwoa9GWNMePDZ1GljjAkTNg7ZGGPCg8+6LIwxJkwcxS1kj+8ovmJpjDm6eDyeNUCjALOv9fl8jUNXm+CzgGyMMWEiqrwrYIwxxmEB2RhjwoQFZGOMCRMWkI0xJkxYQDbGmDBhAdkYY8KEBeQgE5HGIuITkTPd1z1E5PEjLLOqiPT0e/3aEVYzbBzp++Xu36UU+W8UkbsPo6rlwj2/rSLyjYj8JCJnBLjfa+7PTiLS3H3eRkTuCGV9zZGxgBwaS4CHglheVSA/IKvqbUEsOxwcyfvVGDgoIIvI0fS7PUdVOwH3AEMD2cHvd6QT0NxNW6iqY0NRQRMcNnU6NJYCMXktEwAR6QYMAqKBF1X1XRE5FXgd2Ah4gNHuvu/h/N9sBq4G7gA6isg3wJ3AW6oqIjIX6KyqWSLyFDAPUOANIBFIBW5Q1XBfHivQ92siMEpVF4vIKOATnPemnYgIcDkwF/gRSBORT4FHgUrAdFUdXpYnFQILgWYiMgNIwv3/BU4DXgD24QTvx0VEgQ7AjcAV7jes8UAPnN+vm1X1DhHxAPOBdjgfbAXe8zI8N4O1kENpFPCg+9wDDAb+gfNHcreIRANPAdcClwDV3Lw7gQtUtQPwN9AZGIvbSlLVJX7HmAV0d593Br4EBgBjVLUzsAi4LDSnF3SBvF9FGQtMcd+bHUB9oJ+q3gfMU9WOQFucoFQxlCdQBjrifCOY6Z7XH8A/gYuAJ1T1PODJvMyquh+YCDyiqjf4pStwiojEAGcDPwA+An/PTYhYQA4RVZ0LNAXqACk4Xxs/B2bjdEGkALVU9U9V9QEL3F1rANNEZA5wIVC3hMO8B1zttrR/V9UcoBXwhNuavhyoHeRTC4kA3y//ef6eYopaqao73eeni8iXwNc4gaxm0CteNvK+Hd2L83787Kb/DDQDXgYuFJF3gG4BlvkVzof41Ti/R8W956YMWUAOredx/oi2AcuALm5fYBtV3QRsFpFm7tfGNu4+1wKfuC2gWTiBJxvna2QBqroSJ4DdiPNHhXucgW6LsS0QSRcAn6fk92snTgsYoLX7s/B7478U2EPA7cB5ON82igvi4S7v21EPnN+JM930M4AVQJqq3g3cBIwotG+Rvzs4vy/XAqer6g8U/56bMmQBObQ+xvljyAWeBr4Qka+Bd9ztg4F33Xx7cf54ZgN9ReQjDrRQUoGKIjJNRJoVOsanON0S37qvhwL3i8hXIvIVcEpIziw0DvV+TQRGiMh0nP5SgN9xWsLvi0jVQuVNBz4A3gZ2h7bqZeZ14CL3G9TJOIH1NhH5FvgG5z3y9xXwgIi84J+oqotxGgFz3dfFveemDNlqb+VIRGJVNdsdEfA18E9VTS3vehljyoe1kMtXW7dl8yPwhQVjY45t1kI2xpgwYS1kY4wJExaQjTEmTFhANsaYMGFTp00BItIYWO2XtBNnssBdqrr9CMvuDzwD3KSqE0VkDZCsqgkl7JM3pfdDVV1YyuNdCbyPM4vt8ULbOuGMbHnZHcNbUjk+4A9VPamUxz+s/cyxy1rIpjgLcCYOfI0zm2tkUZmOcHrtPUCvQ+RpBwzhwMQZY45a1kI2xdnoLuizEGcKdlsAt1WbAkwCrsOZ1lsReBZnosJGYEjewjRuq3gAsIUDU37zvAgkA9NFJA54AudDoKab9zGcFjXABBGZADQBKgJj3DrtBJ5X1efc412Ls0hTFs7iQwERkefd80nEmdU3QlX9ZznGishbwBXAHOA6Vd0pIicUVxdjSstayKY4sSKSAlzqvl7nt60Szhob/XEC7Sc4ax8MBdYAb7tr756CE1A34UyLPr+E4w1wH38AdwO/4izLmTdj7FXgGpyg9xHOmh0jccZwPysi/ycitYA3cWb6DcVZjCdQS3FWOuuPs8reyyLS0G97c2C9W5/uwGB3cZ4i61KK4xqTz1rIpjhdcIItOC3GgYW291LVNBG5CKjuPob5be8M5C37+ZyqvikiDXCWwyzK/+EsHnS1quZPc3Zb6NcBP6rqeyJyIs6COuCslpfnApwGRgVgvKqOExEvzlKkgTgO54Ogkl9aKw58EG1Q1UFuS/5mnHWGW5RQl48DPK4x+Swgm+L8iNNi3AEsUdVMv217VTWtUP63gMl+r9fgLAsJBxb1OZzFfYqbufQZzpKdeTbhrBZX6uOJSEuchYgW4pzzFUBvnOAeiKLqYkypWUA2xdmmqrMDyDcfJ2h3w+n3jcFZBP0pnMVuAO5z1+u4qYRyPgYEmCIi04DW7prGeUtpdheRfcB/cVY4OwdnIaZ9OF0hH+AExgzgJhFZh7NyXGlUBBpQdNdKfREZhtPnHeWe2/IS6rK4lMc2xvqQzZFxF4XvAawEhuO0MPcBa1T1N5xF52vjjKiYU0JRw93HScArOHfBAJgB/ILTav2Pu+bzJTh3R3kUJ/BXwVkPejNOd0IU8DDOwuuBnMMy4DmcpT3vBv5XRLY/3e09cZbAfKqkugRyXGMKs7UsjDEmTFgL2RhjwoQFZGOMCRMWkI0xJkxYQDbGmDBhAdkYY8KEBWRjjAkTFpCNMSZM/D8fq68rYPtVWAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('*'*10,'Affin results','*'*10)\n",
        "print('precision :' ,precision_score(df['Actual_Sentiment'] , df['Afinn_sentiment'] , average = 'weighted'))\n",
        "print('recall :',recall_score(df['Actual_Sentiment'] , df['Afinn_sentiment'] , average = 'weighted'))\n",
        "print('f1_score :',f1_score(df['Actual_Sentiment'] , df['Afinn_sentiment'] , average = 'weighted'))\n",
        "print(classification_report(df['Actual_Sentiment'] , df['Afinn_sentiment']))\n",
        "plt.title('**********Confusion matrix of Affin**********')\n",
        "plt.title('**********f1-score = 0.75**********')\n",
        "cm = confusion_matrix(df['Actual_Sentiment'], df['Afinn_sentiment'] )\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm , display_labels = ['Negative','Neutral','Positive'])\n",
        "disp.plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "id": "-HGR3oOB4TDg",
        "outputId": "35912895-0d3b-48ae-91c3-db4b838c6a22"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********** Affin results **********\n",
            "precision : 0.776222628516565\n",
            "recall : 0.7304015204829544\n",
            "f1_score : 0.7508699284149124\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.50      0.40      0.45     28229\n",
            "     Neutral       0.09      0.19      0.12     13306\n",
            "    Positive       0.88      0.83      0.85    167346\n",
            "\n",
            "    accuracy                           0.73    208881\n",
            "   macro avg       0.49      0.47      0.47    208881\n",
            "weighted avg       0.78      0.73      0.75    208881\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('*'*10,'TextBlob results','*'*10)\n",
        "print('precision :',precision_score(df['Actual_Sentiment'] , df['TextBlob_Analysis'] , average = 'weighted'))\n",
        "print('recall :',recall_score(df['Actual_Sentiment'] , df['TextBlob_Analysis'] , average = 'weighted'))\n",
        "print('f1_score :',f1_score(df['Actual_Sentiment'] , df['TextBlob_Analysis'] , average = 'weighted'))\n",
        "print(classification_report(df['Actual_Sentiment'] , df['TextBlob_Analysis']))\n",
        "plt.title('**********Confusion matrix of TextBlob**********')\n",
        "plt.title('**********f1-score = 0.75**********')\n",
        "cm = confusion_matrix(df['Actual_Sentiment'], df['TextBlob_Analysis'] )\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm , display_labels = ['Negative','Neutral','Positive'])\n",
        "disp.plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "id": "m5y9tQOivzgj",
        "outputId": "a03de74b-90c2-4d17-8d8e-912b190889d4"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********** TextBlob results **********\n",
            "precision : 0.7641089719652175\n",
            "recall : 0.7545253038811572\n",
            "f1_score : 0.7576058461076254\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.51      0.37      0.42     28229\n",
            "     Neutral       0.06      0.09      0.08     13306\n",
            "    Positive       0.86      0.87      0.87    167346\n",
            "\n",
            "    accuracy                           0.75    208881\n",
            "   macro avg       0.48      0.44      0.46    208881\n",
            "weighted avg       0.76      0.75      0.76    208881\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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nzWVhkqTZ6eVbNHuAdc3yGcDuZztyzkcCtwAjpZRH5r48SdJMTRnwpZS7gf05513ACuC2nPOWpvsy4CXATTnnnTnn0+arUEnS9PT0NclSyuVdTSNN+7XAtXNdlCRp9jzRSZKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCMuAlKSgDXpKCWtTLSjnn64HVwD5gQyllrGk/BLgZOAG4q5Ry2fyUKUmarimP4HPOq4ChUspa4D5gfUf32cBDTd/inPNr5qdMSdJ09TJFsxrY0SxvB9b02CdJGqBepmiWAQ83yweA5V19P52gD4Cc80ZgY3N3pJSydWalxjI0NDToEhYMx6LNsWhzLGavl4AfBZY0y0uBx3vsA6AJdENdkvqslymaPcC6ZvkMYHePfZKkAZoy4EspdwP7c867gBXAbTnnLU337cAxTd8vSyl7561SSdK0pKqqBl2DJGkeeKKTJAVlwEtSUAa8JAXV06UKZsLLG7RNMhbnAJuAMeqxuHRgRfbJRGPR0X8lsL6UkgdQXl9NNhY557dTnz/yHOAD0b/AMMnfyOHArdRfx34GOL+Usn9QdfZDznkpcAdwInBKKeWejr5p5ee8HMF7eYO2Kcbi28CaUsqpwNE559ChNsVYkHM+Elg5iNr6bbKxyDm/CDgXeH0p5fSDINwne12cBdxTSjkN2AZc3P8K++5J4E3Al8fpm1Z+ztcUjZc3aJvw9y2l/LCU8kxz92ng132urd+m2veXAjf1taLBmWwszgSeAu7IOX8u53xEv4vrs8nG4gFgcbO8DHisj3UNRCllrJTy6ATd08rP+Qr4yS5hMOXlDYLp5XIOJwNHl1K+2c/CBmDCsWj+LV0Z/Wi1w2SvixcCRwFvAPYCl/S3tL6bbCzuB07MOd8LvBf4fJ9rW2imlZ/zFfCjzOLyBsGMMsnvm3N+MXAD8K6+VjUYo0w8FpcBN/a5nkEaZfK/kTtLKRXwdeoTDCMbZeKxeBfwjVLKCuAvgGv6W9qCM8o08nO+At7LG7RN+Ps2c863UF+E7ZEB1NZvk+3744FNOeftwAk556v7XVyfTTYWu4GTmuWTgB/0rarBmGwsEu1pmceoQ+1gNq38nLczWXPOHwVOAX4IvBu4sZQyknNeBHwWeCnwrVLKn8xLAQvIJGNxDfW/nfc3q36wlPIfAyqzLyYai651ykHyLZoJxyLn/JfU861PAReUUkLPPU/yN7IU+CLwPOAQ4OJSyvcGV2l/5Jy/Sv3m/iCwBXjNTPLTSxVIUlCe6CRJQRnwkhSUAS9JQRnwkhSUAS9JQRnwkhSUAS9JQf0flA5tA6XC5i8AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I81JnqgXzHwY",
        "outputId": "c3200ccd-8d4e-4939-bd12-b92a5dfcc414"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['asin', 'reviewerID', 'reviewerName', 'reviewText', 'overall',\n",
              "       'reviewTime', 'summary', 'main_cat', 'Actual_Sentiment',\n",
              "       'TextBlob_Subjectivity', 'TextBlob_Polarity', 'TextBlob_Analysis',\n",
              "       'Vader_Polarity', 'Vader_score', 'Vader_sentiment', 'Afinn_score',\n",
              "       'Afinn_sentiment'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Actual_Sentiment'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddOc8Q-S1aB9",
        "outputId": "52af5489-39e7-412d-9046-b522ecde4743"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Positive    167346\n",
              "Negative     28229\n",
              "Neutral      13306\n",
              "Name: Actual_Sentiment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classification Model"
      ],
      "metadata": {
        "id": "ERnA0nsw-8V8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Capestone:\n",
        "  def __init__(self):\n",
        "    # Creating user-defined function to clean the data\n",
        "    def cleaning(text):\n",
        "      text = text.lower()\n",
        "      text = re.sub(r'https?\\S+ | www\\S+' , ' ' , text)\n",
        "      text = re.sub(r'[^a-z]+' , ' ' , text)\n",
        "      text = re.sub(r'\\s+' , ' ' , text)\n",
        "      return text\n",
        "    # Data Cleaning\n",
        "    df['reviewText'] = df['reviewText'].apply(cleaning)\n",
        "    df_3_class = df.copy()\n",
        "\n",
        "    # Separating dataframe based on sentiment to handle imbalance dataset\n",
        "    df_3_class_pos  = df_3_class[df_3_class['Actual_Sentiment'] == 'Positive']\n",
        "    df_3_class_neg  = df_3_class[df_3_class['Actual_Sentiment'] == 'Negative']\n",
        "    df_3_class_neu  = df_3_class[df_3_class['Actual_Sentiment'] == 'Neutral']\n",
        "\n",
        "    # Oversampling to handle imbalanced dataset\n",
        "    df_3_class_neu2 = df_3_class_neu.sample(n=167346 , random_state = 42 , replace = True)\n",
        "    df_3_class_neg2 = df_3_class_neg.sample(n=167346 , random_state = 42 , replace = True)\n",
        "\n",
        "    # Concating Oversampled DataFrames\n",
        "    df_3_class_2 = pd.concat([df_3_class_pos,df_3_class_neg2,df_3_class_neu2])\n",
        "    self.df_3_class = df_3_class_2.copy()\n",
        "\n",
        "  def fastcat(self):\n",
        "    \n",
        "    # Adding __label__ as prefix for fasttext input\n",
        "    self.df_3_class['Target_2.0'] = '__label__' + self.df_3_class['Actual_Sentiment'].astype(str)\n",
        "    self.df_3_class['Target_review_2.0'] = self.df_3_class['Target_2.0'] + ' ' + self.df_3_class['reviewText']\n",
        "\n",
        "    # Train-Test Split\n",
        "    train_3 , test_3 = train_test_split(self.df_3_class[['Target_2.0' , 'Target_review_2.0']], test_size=0.2)\n",
        "\n",
        "    # CSV of train\n",
        "    train_3.to_csv(\"electronics_train_3.txt\", columns=[\"Target_review_2.0\"], index=False, header=False)\n",
        "\n",
        "    # CSV of test file\n",
        "    test_3.to_csv(\"electronics_test_3.txt\", columns=[\"Target_review_2.0\"], index=False, header=False)\n",
        "\n",
        "    # Model training\n",
        "    model_3 = fasttext.train_supervised(input=\"electronics_train_3.txt\")\n",
        "\n",
        "    # Model Testing\n",
        "    model_3.test(\"electronics_test_3.txt\")\n",
        "    test_num , precision_3 ,recall_3 = model_3.test(\"electronics_test_3.txt\")\n",
        "    f1_score_3 = (2*precision_3*recall_3)/(precision_3+recall_3)\n",
        "    print('*'*10,'fasttext results','*'*10)\n",
        "    print('precision',precision_3)\n",
        "    print('Recall',recall_3)\n",
        "    print('f1_score',f1_score_3)\n",
        "    \n",
        "    confu_3 = pd.DataFrame()\n",
        "    confu_3['labeled'] = test_3['Target_2.0']\n",
        "    \n",
        "    from sklearn.metrics import confusion_matrix\n",
        "    confu_3[\"predicted_3\"] = test_3[\"Target_review_2.0\"].apply(lambda x: model_3.predict(x)[0][0])\n",
        "    # predict the data\n",
        "    \n",
        "    plt.title('**********Confusion matrix of fasttext**********')\n",
        "    plt.title('**********f1-score = 0.78**********')\n",
        "    cm = confusion_matrix(confu_3[\"labeled\"], confu_3[\"predicted_3\"])\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm , display_labels = ['Negative','Neutral','Positive'])\n",
        "    disp.plot();\n",
        "    print(classification_report(confu_3[\"labeled\"], confu_3[\"predicted_3\"]))\n",
        "    return model_3\n",
        "\n",
        "\n",
        "\n",
        "  def checkStationarity(self,df,col1='reviewTime',col2='Vader_score',rt='M'):\n",
        "    tsdf=df[[col1,col2]]\n",
        "    tsdf.set_index(col1,inplace=True)\n",
        "    tsdf=tsdf.resample(rt).mean()\n",
        "    tsdf['Time']=tsdf.index\n",
        "\n",
        "    idx = np.where(tsdf.isnull()==True)\n",
        "    index = idx[0][-1]+1\n",
        "    tsdf = tsdf.iloc[index:,:]\n",
        "\n",
        "    pvalue = adfuller(tsdf[col2])[1]\n",
        "    \n",
        "    if(pvalue>0.05):\n",
        "        print('p-value =', pvalue, 'Data is not Stationary')   \n",
        "    else:\n",
        "        print('p-value =', pvalue, 'Data is Stationary')\n",
        "\n",
        "    return tsdf\n",
        "     \n",
        "\n",
        "\n",
        "  def shift_trail(self,df):\n",
        "    ob = Capestone()\n",
        "    tsdf = ob.checkStationarity(df=df)\n",
        "    for i in range(1,3):\n",
        "      print('shifting of', i)\n",
        "      shift_data = tsdf['Vader_score']-tsdf['Vader_score'].shift(i)\n",
        "      shift_data.dropna(inplace=True)\n",
        "      pvalue = adfuller(shift_data)[1]\n",
        "    \n",
        "      if(pvalue>0.05):\n",
        "          print('p-value =', pvalue, 'Data is not Stationary')   \n",
        "      else:\n",
        "          print('p-value =', pvalue, 'Data is Stationary')\n",
        "\n",
        " \n",
        "  def pacf_acf(self,df,stamp,col1='reviewTime',col2='Vader_score',rt='M'):\n",
        "    # b = Capestone_T1()\n",
        "    # metrics , train = b.model(model=ARIMA(),df=df)\n",
        "\n",
        "    ob = Capestone()\n",
        "    tsdf = ob.checkStationarity(df=df)\n",
        "\n",
        "    idx = np.where(tsdf.isnull()==True)\n",
        "    index = idx[0][-1]+1\n",
        "    tsdf = tsdf.iloc[index:,:] #10\n",
        "\n",
        "    from darts import TimeSeries\n",
        "    series=TimeSeries.from_dataframe(tsdf,'Time',col2)\n",
        "    train,test=series.split_before(pd.Timestamp(stamp))\n",
        "\n",
        "    plot_pacf(train);plot_acf(train)\n",
        " \n",
        "  def model(self,model,df,stamp,col1='reviewTime',col2='Vader_score',rt='M'):\n",
        "\n",
        "    tsdf=df[[col1,col2]]\n",
        "    #tsdf[col1]=tsdf[col1].astype('datetime64[ns]')\n",
        "    tsdf.set_index(col1,inplace=True)\n",
        "    tsdf=tsdf.resample(rt).mean()\n",
        "    tsdf['Time']=tsdf.index\n",
        "\n",
        "    idx = np.where(tsdf.isnull()==True)\n",
        "    index = idx[0][-1]+1\n",
        "    tsdf = tsdf.iloc[index:,:] #10\n",
        "\n",
        "    from time import time\n",
        "    from darts import TimeSeries\n",
        "    start=time()\n",
        "    series=TimeSeries.from_dataframe(tsdf,'Time',col2)\n",
        "    train,test=series.split_before(pd.Timestamp(stamp))\n",
        "    model.fit(train)\n",
        "    forecast=model.predict(len(test)+60)\n",
        "    plt.figure(figsize=(12,7))\n",
        "    new_train = train.append(forecast[0:1])\n",
        "    new_train.plot(label='Historical Trend')\n",
        "    #test.plot(label='predicted')\n",
        "    forecast.plot(label='Forecast Trend')\n",
        "    plt.xticks(fontsize=14)\n",
        "    plt.yticks(fontsize=14)\n",
        "    plt.ylabel('Sentiment Score')\n",
        "    df=pd.DataFrame({'model':[model],'mse':[mse(forecast,test)],'rmse':[rmse(forecast,test)],'mape':[mape(forecast,test)],'execution_time':[time()-start]})\n",
        "    return df\n",
        "   "
      ],
      "metadata": {
        "id": "nWnXXki9kYo_"
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ob1 = Capestone()\n",
        "ob1.fastcat()"
      ],
      "metadata": {
        "id": "3h1vKNQn-qT6",
        "outputId": "57bacd55-c282-40ef-a5eb-df818854263e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********** fasttext results **********\n",
            "precision 0.783324037925265\n",
            "Recall 0.783324037925265\n",
            "f1_score 0.7833240379252648\n",
            "                   precision    recall  f1-score   support\n",
            "\n",
            "__label__Negative       0.80      0.80      0.80     33526\n",
            " __label__Neutral       0.70      0.73      0.71     33317\n",
            "__label__Positive       0.85      0.82      0.83     33565\n",
            "\n",
            "         accuracy                           0.78    100408\n",
            "        macro avg       0.78      0.78      0.78    100408\n",
            "     weighted avg       0.79      0.78      0.78    100408\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fasttext.FastText._FastText at 0x7fd97d4e1520>"
            ]
          },
          "metadata": {},
          "execution_count": 160
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Positive Sentiment Forecast"
      ],
      "metadata": {
        "id": "xndQmCS16Vu9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pos_df = df[df['Vader_sentiment']=='Positive']\n",
        "neg_df = df[df['Vader_sentiment']=='Negative']\n",
        "neu_df = df[df['Vader_sentiment']=='Neutral']\n",
        "\n",
        "pos_df['Time'] = pos_df.index\n",
        "neg_df['Time'] = neg_df.index\n",
        "neu_df['Time'] = neu_df.index"
      ],
      "metadata": {
        "id": "Lr6RJi1Ps3g2"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ob1 = Capestone()\n",
        "ob1.checkStationarity(df=pos_df,col1='reviewTime',col2='Vader_score',rt='M') "
      ],
      "metadata": {
        "id": "Wtk6NngFcVKu",
        "outputId": "3dd32821-e876-4c37-b53c-5888ce253e96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        }
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p-value = 0.8582107530100551 Data is not Stationary\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Vader_score       Time\n",
              "reviewTime                        \n",
              "2005-12-31     0.802957 2005-12-31\n",
              "2006-01-31     0.926013 2006-01-31\n",
              "2006-02-28     0.747836 2006-02-28\n",
              "2006-03-31     0.776058 2006-03-31\n",
              "2006-04-30     0.883300 2006-04-30\n",
              "...                 ...        ...\n",
              "2018-06-30     0.659726 2018-06-30\n",
              "2018-07-31     0.673614 2018-07-31\n",
              "2018-08-31     0.654475 2018-08-31\n",
              "2018-09-30     0.663749 2018-09-30\n",
              "2018-10-31     0.771550 2018-10-31\n",
              "\n",
              "[155 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd7687ef-3d17-4835-a2cf-b713fed9455d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Vader_score</th>\n",
              "      <th>Time</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reviewTime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-12-31</th>\n",
              "      <td>0.802957</td>\n",
              "      <td>2005-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-01-31</th>\n",
              "      <td>0.926013</td>\n",
              "      <td>2006-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-02-28</th>\n",
              "      <td>0.747836</td>\n",
              "      <td>2006-02-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-03-31</th>\n",
              "      <td>0.776058</td>\n",
              "      <td>2006-03-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-04-30</th>\n",
              "      <td>0.883300</td>\n",
              "      <td>2006-04-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-30</th>\n",
              "      <td>0.659726</td>\n",
              "      <td>2018-06-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-07-31</th>\n",
              "      <td>0.673614</td>\n",
              "      <td>2018-07-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-08-31</th>\n",
              "      <td>0.654475</td>\n",
              "      <td>2018-08-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-30</th>\n",
              "      <td>0.663749</td>\n",
              "      <td>2018-09-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-31</th>\n",
              "      <td>0.771550</td>\n",
              "      <td>2018-10-31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>155 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd7687ef-3d17-4835-a2cf-b713fed9455d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd7687ef-3d17-4835-a2cf-b713fed9455d 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-bd7687ef-3d17-4835-a2cf-b713fed9455d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob2 = Capestone()\n",
        "ob2.shift_trail(df=pos_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "piG1scZ1B3rW",
        "outputId": "f4df7e70-fe78-4ccf-b1b7-28b54f3fc54e"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p-value = 0.8582107530100551 Data is not Stationary\n",
            "shifting of 1\n",
            "p-value = 4.03787818149174e-07 Data is Stationary\n",
            "shifting of 2\n",
            "p-value = 2.40269999189333e-06 Data is Stationary\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob3 = Capestone()\n",
        "ob3.pacf_acf(df=pos_df,stamp='20180930')"
      ],
      "metadata": {
        "id": "kVTJqGX5cjp3",
        "outputId": "6e0fc249-2ec8-460f-a5db-8364e731b98a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p-value = 0.8582107530100551 Data is not Stationary\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-164-663817b3da9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mob3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCapestone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mob3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpacf_acf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstamp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'20180930'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-159-329143eeed0b>\u001b[0m in \u001b[0;36mpacf_acf\u001b[0;34m(self, df, stamp, col1, col2, rt)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtsdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m     \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m     \u001b[0mtsdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtsdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: index -1 is out of bounds for axis 0 with size 0"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob3 = Capestone()\n",
        "ob3.model(ARIMA(4,1,14),df=pos_df ,col1 = 'reviewTime', col2 = 'Vader_score',stamp='20180930') "
      ],
      "metadata": {
        "id": "X41oaUHgso_b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "exp_model = ExponentialSmoothing(seasonal_periods=12)\n",
        "ob3.model(exp_model , df=pos_df ,col1 = 'reviewTime', col2 = 'Vader_score',stamp='20181031') "
      ],
      "metadata": {
        "id": "y3GbMuQq381u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ob3.model(Theta() , df=pos_df ,col1 = 'reviewTime', col2 = 'Vader_score',stamp='20181031') "
      ],
      "metadata": {
        "id": "pzaljjEk5p25"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ob3.model(Prophet() , df=pos_df ,col1 = 'reviewTime', col2 = 'Vader_score',stamp='20181031') "
      ],
      "metadata": {
        "id": "CYKBn4wE58YJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Negative Sentiment Forecast"
      ],
      "metadata": {
        "id": "C_D62yX46hX8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ob1.checkStationarity(df=neg_df,col1='reviewTime',col2='Vader_score',rt='M') "
      ],
      "metadata": {
        "id": "nNOTSy_g6kbw",
        "outputId": "5fba19f6-6c48-459d-8c74-218e0fb22ff9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        }
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p-value = 0.5999713846035494 Data is not Stationary\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Vader_score       Time\n",
              "reviewTime                        \n",
              "2008-02-29    -0.651360 2008-02-29\n",
              "2008-03-31    -0.416357 2008-03-31\n",
              "2008-04-30    -0.466162 2008-04-30\n",
              "2008-05-31    -0.575443 2008-05-31\n",
              "2008-06-30    -0.461470 2008-06-30\n",
              "...                 ...        ...\n",
              "2018-05-31    -0.475170 2018-05-31\n",
              "2018-06-30    -0.474670 2018-06-30\n",
              "2018-07-31    -0.488317 2018-07-31\n",
              "2018-08-31    -0.461141 2018-08-31\n",
              "2018-09-30    -0.506736 2018-09-30\n",
              "\n",
              "[128 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bfaaccf0-9bef-4b04-882d-4bf5c087baab\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Vader_score</th>\n",
              "      <th>Time</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reviewTime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2008-02-29</th>\n",
              "      <td>-0.651360</td>\n",
              "      <td>2008-02-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-03-31</th>\n",
              "      <td>-0.416357</td>\n",
              "      <td>2008-03-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-04-30</th>\n",
              "      <td>-0.466162</td>\n",
              "      <td>2008-04-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-05-31</th>\n",
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              "    <tr>\n",
              "      <th>2008-06-30</th>\n",
              "      <td>-0.461470</td>\n",
              "      <td>2008-06-30</td>\n",
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              "    <tr>\n",
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              "      <th>2018-05-31</th>\n",
              "      <td>-0.475170</td>\n",
              "      <td>2018-05-31</td>\n",
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              "    <tr>\n",
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              "      <td>-0.474670</td>\n",
              "      <td>2018-06-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-07-31</th>\n",
              "      <td>-0.488317</td>\n",
              "      <td>2018-07-31</td>\n",
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              "    <tr>\n",
              "      <th>2018-08-31</th>\n",
              "      <td>-0.461141</td>\n",
              "      <td>2018-08-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-30</th>\n",
              "      <td>-0.506736</td>\n",
              "      <td>2018-09-30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>128 rows × 2 columns</p>\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\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",
              "    [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-bfaaccf0-9bef-4b04-882d-4bf5c087baab 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-bfaaccf0-9bef-4b04-882d-4bf5c087baab');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob2.shift_trail(df=neg_df) "
      ],
      "metadata": {
        "id": "tPDg0UJa6kWl",
        "outputId": "fc48c53c-f177-4754-f4fb-96c8ec7b0784",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p-value = 0.5999713846035494 Data is not Stationary\n",
            "shifting of 1\n",
            "p-value = 1.302029627100839e-09 Data is Stationary\n",
            "shifting of 2\n",
            "p-value = 4.3335347742480673e-07 Data is Stationary\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob3.pacf_acf(df=neg_df,stamp='20180930')"
      ],
      "metadata": {
        "id": "yQBOE-JR6kQk",
        "outputId": "ab76ac55-2613-4362-bf05-9a35c87f1958",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        }
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob3.model(ARIMA(5,1,2),df=neg_df ,col1 = 'reviewTime', col2 = 'Vader_score',stamp='20180930')"
      ],
      "metadata": {
        "id": "AtLxDWCX6kK_",
        "outputId": "b87c1154-7971-49c0-c535-2e4d2f8a436c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        }
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            model       mse      rmse      mape  execution_time\n",
              "0  ARIMA(5, 1, 2)  0.001689  0.041092  8.824734        0.645718"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>mse</th>\n",
              "      <th>rmse</th>\n",
              "      <th>mape</th>\n",
              "      <th>execution_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ARIMA(5, 1, 2)</td>\n",
              "      <td>0.001689</td>\n",
              "      <td>0.041092</td>\n",
              "      <td>8.824734</td>\n",
              "      <td>0.645718</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3134a1a-4e61-47ae-ad6a-eeca6a756b25')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-d3134a1a-4e61-47ae-ad6a-eeca6a756b25 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-d3134a1a-4e61-47ae-ad6a-eeca6a756b25');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob3.model(ExponentialSmoothing(seasonal_periods=12),df=neg_df ,col1 = 'reviewTime', col2 = 'Vader_score',stamp='20180930')"
      ],
      "metadata": {
        "id": "7tyx1QIm6kF6",
        "outputId": "3d9dcb69-fee6-48a1-f1fd-d486c3496386",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        }
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               model       mse      rmse  \\\n",
              "0  ExponentialSmoothing(trend=ModelMode.ADDITIVE,...  0.003354  0.057912   \n",
              "\n",
              "        mape  execution_time  \n",
              "0  12.902921        0.229928  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-896d9c13-d7c2-42f1-820c-5cd90d3890d7\">\n",
              "    <div 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>model</th>\n",
              "      <th>mse</th>\n",
              "      <th>rmse</th>\n",
              "      <th>mape</th>\n",
              "      <th>execution_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ExponentialSmoothing(trend=ModelMode.ADDITIVE,...</td>\n",
              "      <td>0.003354</td>\n",
              "      <td>0.057912</td>\n",
              "      <td>12.902921</td>\n",
              "      <td>0.229928</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-896d9c13-d7c2-42f1-820c-5cd90d3890d7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-896d9c13-d7c2-42f1-820c-5cd90d3890d7 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-896d9c13-d7c2-42f1-820c-5cd90d3890d7');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob3.model(Prophet(),df=neg_df ,col1 = 'reviewTime', col2 = 'Vader_score',stamp='20180930')"
      ],
      "metadata": {
        "id": "0jbh0xN96kBP",
        "outputId": "8a9bebb8-bda1-469b-f880-855538a8b37d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        }
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnnn1ywxm/zr8p7dhh.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnnn1ywxm/cpmekqwf.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.9/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=66169', 'data', 'file=/tmp/tmpnnn1ywxm/zr8p7dhh.json', 'init=/tmp/tmpnnn1ywxm/cpmekqwf.json', 'output', 'file=/tmp/tmpnnn1ywxm/prophet_model6xy98ai_/prophet_model-20230320055802.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "05:58:02 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "05:58:02 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     model       mse      rmse      mape  execution_time\n",
              "0  Prophet  0.003048  0.055205  12.22609         0.35769"
            ],
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fda0a3a5-548e-4a74-a837-3ad094db0500');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neutral Sentiment Forecast"
      ],
      "metadata": {
        "id": "xhOarY9w9A6i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ob1.checkStationarity(df=neu_df,col1='reviewTime',col2='Vader_score',rt='M') "
      ],
      "metadata": {
        "id": "IRIIUKp56jzI",
        "outputId": "ead8ccfd-0f0d-4e62-b62a-219b2133db86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        }
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p-value = 0.30737998276064205 Data is not Stationary\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Vader_score       Time\n",
              "reviewTime                        \n",
              "2008-06-30    -0.001380 2008-06-30\n",
              "2008-07-31     0.000000 2008-07-31\n",
              "2008-08-31     0.000000 2008-08-31\n",
              "2008-09-30     0.000000 2008-09-30\n",
              "2008-10-31     0.000000 2008-10-31\n",
              "...                 ...        ...\n",
              "2018-05-31     0.000885 2018-05-31\n",
              "2018-06-30    -0.000147 2018-06-30\n",
              "2018-07-31     0.000806 2018-07-31\n",
              "2018-08-31     0.000846 2018-08-31\n",
              "2018-09-30     0.000000 2018-09-30\n",
              "\n",
              "[124 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0d188f7-9f26-4547-8cfc-3910b4ce96b6\">\n",
              "    <div 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>Vader_score</th>\n",
              "      <th>Time</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reviewTime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2008-06-30</th>\n",
              "      <td>-0.001380</td>\n",
              "      <td>2008-06-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-07-31</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2008-07-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-08-31</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2008-08-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-09-30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2008-09-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-10-31</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2008-10-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-05-31</th>\n",
              "      <td>0.000885</td>\n",
              "      <td>2018-05-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-30</th>\n",
              "      <td>-0.000147</td>\n",
              "      <td>2018-06-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-07-31</th>\n",
              "      <td>0.000806</td>\n",
              "      <td>2018-07-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-08-31</th>\n",
              "      <td>0.000846</td>\n",
              "      <td>2018-08-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2018-09-30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>124 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0d188f7-9f26-4547-8cfc-3910b4ce96b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c0d188f7-9f26-4547-8cfc-3910b4ce96b6 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-c0d188f7-9f26-4547-8cfc-3910b4ce96b6');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob2.shift_trail(df=neu_df) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWexli5nHHWV",
        "outputId": "783ebe8b-b083-4752-b55a-ae6166b28dd3"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p-value = 0.30737998276064205 Data is not Stationary\n",
            "shifting of 1\n",
            "p-value = 6.310859825805149e-05 Data is Stationary\n",
            "shifting of 2\n",
            "p-value = 0.0006557564011513352 Data is Stationary\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob3.pacf_acf(df=neu_df,stamp='20180930')"
      ],
      "metadata": {
        "id": "BfJ7jBIV6jjl",
        "outputId": "26efaca8-8a42-49f6-e6aa-c3c25c4d3ba5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        }
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob3.model(ARIMA(3,1,2),df=neu_df ,col1 = 'reviewTime', col2 = 'Vader_score',stamp='20180930')"
      ],
      "metadata": {
        "id": "JaqWZ_kZ9n9E",
        "outputId": "d6d06f06-06db-4a3e-8df4-d1b8e6406e20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        }
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            model           mse      rmse   mape  execution_time\n",
              "0  ARIMA(3, 1, 2)  2.536131e-07  0.000504  100.0        0.349462"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e6892c23-7b90-46de-90dc-c1aa270c8d06\">\n",
              "    <div 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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>mse</th>\n",
              "      <th>rmse</th>\n",
              "      <th>mape</th>\n",
              "      <th>execution_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ARIMA(3, 1, 2)</td>\n",
              "      <td>2.536131e-07</td>\n",
              "      <td>0.000504</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.349462</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6892c23-7b90-46de-90dc-c1aa270c8d06')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e6892c23-7b90-46de-90dc-c1aa270c8d06 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-e6892c23-7b90-46de-90dc-c1aa270c8d06');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob3.model(ExponentialSmoothing(seasonal_periods=12),df=neu_df ,col1 = 'reviewTime', col2 = 'Vader_score',stamp='20180930')"
      ],
      "metadata": {
        "id": "OKHew5vC9oN7",
        "outputId": "f10bade1-46a1-4adb-cd01-f06eceb93147",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        }
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               model           mse      rmse  \\\n",
              "0  ExponentialSmoothing(trend=ModelMode.ADDITIVE,...  3.666792e-07  0.000606   \n",
              "\n",
              "    mape  execution_time  \n",
              "0  100.0        0.248441  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f746ee80-9129-40f0-b19c-e0dfe9e618db\">\n",
              "    <div 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>model</th>\n",
              "      <th>mse</th>\n",
              "      <th>rmse</th>\n",
              "      <th>mape</th>\n",
              "      <th>execution_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ExponentialSmoothing(trend=ModelMode.ADDITIVE,...</td>\n",
              "      <td>3.666792e-07</td>\n",
              "      <td>0.000606</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.248441</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f746ee80-9129-40f0-b19c-e0dfe9e618db')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-f746ee80-9129-40f0-b19c-e0dfe9e618db 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-f746ee80-9129-40f0-b19c-e0dfe9e618db');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ob3.model(Prophet(),df=neu_df ,col1 = 'reviewTime', col2 = 'Vader_score',stamp='20180930')"
      ],
      "metadata": {
        "id": "14IrllBH9oSH",
        "outputId": "204a2568-4903-4275-ced5-b7eb0ba59242",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        }
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnnn1ywxm/epoxq30o.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnnn1ywxm/38acqfri.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.9/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=192', 'data', 'file=/tmp/tmpnnn1ywxm/epoxq30o.json', 'init=/tmp/tmpnnn1ywxm/38acqfri.json', 'output', 'file=/tmp/tmpnnn1ywxm/prophet_modeluwicfi5c/prophet_model-20230320055838.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "05:58:38 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "05:58:38 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     model           mse     rmse   mape  execution_time\n",
              "0  Prophet  7.833191e-08  0.00028  100.0        0.343093"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
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              "      <th>rmse</th>\n",
              "      <th>mape</th>\n",
              "      <th>execution_time</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Prophet</td>\n",
              "      <td>7.833191e-08</td>\n",
              "      <td>0.00028</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.343093</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eeafb79b-cdd4-4c05-a51a-f2ecaab82f13')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eeafb79b-cdd4-4c05-a51a-f2ecaab82f13 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-eeafb79b-cdd4-4c05-a51a-f2ecaab82f13');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tabulate import tabulate\n",
        "\n",
        "tabel = [['fasttext' , 78.46] ,\n",
        "    ['Vader sentiment' , 77.77] , \n",
        "    ['Textblob Sentiment' , 75.76] ,\n",
        "    ['Affin Sentiment' , 75.08]]\n",
        "\n",
        "print(tabulate( tabel , headers = ['Models' , 'f1_score'] , tablefmt = 'grid'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6_cQTP-wXhx",
        "outputId": "0c8728b4-bcf0-47b2-8129-9c5e0f4e3b4a"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+------------+\n",
            "| Models             |   f1_score |\n",
            "+====================+============+\n",
            "| fasttext           |      78.46 |\n",
            "+--------------------+------------+\n",
            "| Vader sentiment    |      77.77 |\n",
            "+--------------------+------------+\n",
            "| Textblob Sentiment |      75.76 |\n",
            "+--------------------+------------+\n",
            "| Affin Sentiment    |      75.08 |\n",
            "+--------------------+------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabe_pos = [['ARIMA(4,1,14)' , 0.006,0.081] ,\n",
        "    ['Exponential Smoothing' , 0.021 , 0.146] , \n",
        "    ['Theta' , 0.013 , 0.114] ,\n",
        "    ['Prophet' , 0.026 , 0.163]]\n",
        "\n",
        "print(tabulate( tabe_pos , headers = ['Models(For Positive forecast)' , 'MSE' , 'RMSE'] , tablefmt = 'grid'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TgL64CUZnqrT",
        "outputId": "aea462fc-10eb-4493-ed1e-24ac6f8ee39e"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------------------------------+-------+--------+\n",
            "| Models(For Positive forecast)   |   MSE |   RMSE |\n",
            "+=================================+=======+========+\n",
            "| ARIMA(4,1,14)                   | 0.006 |  0.081 |\n",
            "+---------------------------------+-------+--------+\n",
            "| Exponential Smoothing           | 0.021 |  0.146 |\n",
            "+---------------------------------+-------+--------+\n",
            "| Theta                           | 0.013 |  0.114 |\n",
            "+---------------------------------+-------+--------+\n",
            "| Prophet                         | 0.026 |  0.163 |\n",
            "+---------------------------------+-------+--------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabe_neg = [['ARIMA(5,1,2)' , 0.001,0.041] ,\n",
        "    ['Exponential Smoothing' , 0.003 , 0.057] , \n",
        "    ['Prophet' , 0.003 , 0.055]]\n",
        "\n",
        "print(tabulate( tabe_neg , headers = ['Models(For Negative forecast)' , 'MSE' , 'RMSE'] , tablefmt = 'grid'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTOuL2UKp9rA",
        "outputId": "98df6307-2fae-431a-e816-6a323a5411ed"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------------------------------+-------+--------+\n",
            "| Models(For Negative forecast)   |   MSE |   RMSE |\n",
            "+=================================+=======+========+\n",
            "| ARIMA(5,1,2)                    | 0.001 |  0.041 |\n",
            "+---------------------------------+-------+--------+\n",
            "| Exponential Smoothing           | 0.003 |  0.057 |\n",
            "+---------------------------------+-------+--------+\n",
            "| Prophet                         | 0.003 |  0.055 |\n",
            "+---------------------------------+-------+--------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabe_neu = [['ARIMA(3,1,2)' , 0.000,0.000] ,\n",
        "    ['Exponential Smoothing' , 0.000 , 0.000] , \n",
        "    ['Prophet' , 0.000 , 0.00]]\n",
        "\n",
        "print(tabulate( tabe_neu , headers = ['Models(For Neutral forecast)' , 'MSE' , 'RMSE'] , tablefmt = 'grid'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rSD_5jPmrOQ2",
        "outputId": "40efb8ba-5467-48db-b470-c426788a2ea5"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------------------+-------+--------+\n",
            "| Models(For Neutral forecast)   |   MSE |   RMSE |\n",
            "+================================+=======+========+\n",
            "| ARIMA(3,1,2)                   |     0 |      0 |\n",
            "+--------------------------------+-------+--------+\n",
            "| Exponential Smoothing          |     0 |      0 |\n",
            "+--------------------------------+-------+--------+\n",
            "| Prophet                        |     0 |      0 |\n",
            "+--------------------------------+-------+--------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Customer segmentation\n",
        "- We can’t treat every customer the same way with the same content, same channel, same importance. They will find another option which understands them better.\n",
        "- Customers who use your platform have different needs and they have their own different profile. You should adapt your actions depending on that.\n",
        "- You can do many different segmentations according to what you are trying to achieve. If you want to increase retention rate, you can do a segmentation based on the similarities between the customers\n",
        "- But there are very common and useful segmentation methods as well. Now we are going to implement one of them to our business: RFM.\n",
        "1. **Recency: How recently customers made their purchase.**\n",
        "2. **Frequency: For simplicity, we’ll count the number of times each customer made a purchase.**\n",
        "3. **Monetary: How much money they spent in total.**\n",
        "\n",
        "**As there is no currency related columns in data so monetary analyis is not possiable** "
      ],
      "metadata": {
        "id": "lFjViGya18dw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EiJ-sKg-15Xg",
        "outputId": "28172631-511e-4e9e-acb9-0f9de5631a22"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['asin', 'reviewerID', 'reviewerName', 'reviewText', 'overall',\n",
              "       'reviewTime', 'summary', 'main_cat', 'Actual_Sentiment',\n",
              "       'TextBlob_Subjectivity', 'TextBlob_Polarity', 'TextBlob_Analysis',\n",
              "       'Vader_Polarity', 'Vader_score', 'Vader_sentiment', 'Afinn_score',\n",
              "       'Afinn_sentiment'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CS_df = pd.DataFrame(df['reviewerID'].unique())\n",
        "CS_df.columns = ['reviewerID']"
      ],
      "metadata": {
        "id": "c5DB8pxP15QF"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Max_purchase = df.groupby('reviewerID').reviewTime.max().reset_index()\n",
        "Max_purchase.columns = ['reviewerID','MaxPurchaseDate']"
      ],
      "metadata": {
        "id": "D5d3Hk-k15Jd"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Recency"
      ],
      "metadata": {
        "id": "_K1aXmkGe1Fd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Max_purchase['Recency'] = (Max_purchase['MaxPurchaseDate'].max() - Max_purchase['MaxPurchaseDate']).dt.days"
      ],
      "metadata": {
        "id": "W8_Pv_9n141_"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CS_df = pd.merge(CS_df, Max_purchase, on='reviewerID')\n",
        "CS_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kGVqy0AIe42j",
        "outputId": "8317e12b-b2e7-419c-9f8a-ef1e3430070a"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       reviewerID MaxPurchaseDate  Recency\n",
              "0   AJIC7H0QB0HQ8      2013-11-01     1796\n",
              "1  A24UCU88FE1FPN      2015-12-08     1029\n",
              "2  A3C5MTRFKQYHMW      2016-08-18      775\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Frequency"
      ],
      "metadata": {
        "id": "EIf6fl0OfbLT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
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        "id": "OOMaOEZymx3m",
        "outputId": "899bd8d3-1240-4fff-c363-073d217a6000"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['asin', 'reviewerID', 'reviewerName', 'reviewText', 'overall',\n",
              "       'reviewTime', 'summary', 'main_cat', 'Actual_Sentiment',\n",
              "       'TextBlob_Subjectivity', 'TextBlob_Polarity', 'TextBlob_Analysis',\n",
              "       'Vader_Polarity', 'Vader_score', 'Vader_sentiment', 'Afinn_score',\n",
              "       'Afinn_sentiment'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tx_frequency = df.groupby('reviewerID').reviewTime.count().reset_index()\n",
        "tx_frequency.columns = ['reviewerID','Frequency']"
      ],
      "metadata": {
        "id": "bsQnHo5Xe48U"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CS_df = pd.merge(CS_df, tx_frequency, on='reviewerID')"
      ],
      "metadata": {
        "id": "h7YfCmH8e5GZ"
      },
      "execution_count": 117,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "CS_df.head()"
      ],
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          "height": 206
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        "id": "h1Icv6MSf_mj",
        "outputId": "826945a2-cc76-46dd-a56b-d2ed62156116"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       reviewerID MaxPurchaseDate  Recency  Frequency\n",
              "0   AJIC7H0QB0HQ8      2013-11-01     1796          1\n",
              "1  A24UCU88FE1FPN      2015-12-08     1029          1\n",
              "2  A3C5MTRFKQYHMW      2016-08-18      775          1\n",
              "3  A253WC2BIJB7ET      2010-08-17     2968          1\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\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",
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              "\n",
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              "          document.querySelector('#df-90018799-b038-46a1-8615-fec29593238d 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-90018799-b038-46a1-8615-fec29593238d');\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",
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              "  "
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          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "CS_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QZU50DFf_13",
        "outputId": "4d8d26fd-6db1-4d53-ff33-41821075305a"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "reviewerID         0\n",
              "MaxPurchaseDate    0\n",
              "Recency            0\n",
              "Frequency          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K_means"
      ],
      "metadata": {
        "id": "JyaC3gb2lMWy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num = CS_df.select_dtypes(exclude = ['object','datetime64[ns]'])\n",
        "cat = CS_df.select_dtypes(include = 'object')"
      ],
      "metadata": {
        "id": "5XDcLGdVf_83"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bf5LMIKilIZh",
        "outputId": "61713f51-115a-4a70-f253-c52c478c9904"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Recency', 'Frequency'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PLH-ONSSlIiD",
        "outputId": "3c6c9c35-7cc0-4a77-ed15-83d32727cc32"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['reviewerID'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CS_df1 = CS_df.copy()\n",
        "CS_df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pfQh1rOIlIn7",
        "outputId": "3a530bfd-3a69-42e0-f9ad-bec4bd522525"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       reviewerID MaxPurchaseDate  Recency  Frequency\n",
              "0   AJIC7H0QB0HQ8      2013-11-01     1796          1\n",
              "1  A24UCU88FE1FPN      2015-12-08     1029          1\n",
              "2  A3C5MTRFKQYHMW      2016-08-18      775          1\n",
              "3  A253WC2BIJB7ET      2010-08-17     2968          1\n",
              "4  A3SMSPKX6LL5U5      2018-06-12      112          1"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A3SMSPKX6LL5U5</td>\n",
              "      <td>2018-06-12</td>\n",
              "      <td>112</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1363920c-a1c7-42b0-99c6-01b29a4d87e4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-1363920c-a1c7-42b0-99c6-01b29a4d87e4 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-1363920c-a1c7-42b0-99c6-01b29a4d87e4');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling the data\n",
        "%time\n",
        "Scaler = MinMaxScaler()\n",
        "for i in num.columns:\n",
        "    CS_df1[i] = Scaler.fit_transform(np.array(CS_df[i]).reshape(-1, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lqw0Xyi_lIy3",
        "outputId": "878f430c-6650-4932-9234-92a7193fa46b"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 4 µs, sys: 0 ns, total: 4 µs\n",
            "Wall time: 6.44 µs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting optimum cluster number\n",
        "%%time\n",
        "X = CS_df1.drop(['reviewerID','MaxPurchaseDate'], axis = 1).values\n",
        "WCSS = []\n",
        "for i in range(2, 10):\n",
        "    kmeans = KMeans(n_clusters = i).fit(X)\n",
        "    WCSS.append(kmeans.inertia_)\n",
        "\n",
        "plt.figure(figsize = (15, 5))\n",
        "plt.plot(range(2, 10), WCSS)\n",
        "plt.grid(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "4MOVWSJelJA7",
        "outputId": "dabb4795-60a5-4924-e77a-9fa91044d15f"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 53.2 s, sys: 21.4 s, total: 1min 14s\n",
            "Wall time: 20.4 s\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interpretation\n",
        "- From the above elbow curve we can take k as 4 because there is sharp bend at k = 4"
      ],
      "metadata": {
        "id": "EhrYiqMFprjB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating Model and fitting it\n",
        "%%time\n",
        "model = KMeans(n_clusters = 3, random_state = 42).fit(X)\n",
        "pred = model.fit_predict(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XTl66N9wpsxH",
        "outputId": "9db84a69-7baf-4093-9838-f83046bd5c2e"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 10.5 s, sys: 3.98 s, total: 14.5 s\n",
            "Wall time: 3.9 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CS_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBKuidVnpsRD",
        "outputId": "7fd1bfa8-2ab6-49de-b38f-0a641bdd5f21"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['reviewerID', 'MaxPurchaseDate', 'Recency', 'Frequency'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "color1 = ['yellow' , 'green' , 'red']\n",
        "l = [None,\"Churned_Recently\", 'Target', 'Churned_old']\n",
        "sns.scatterplot(x = CS_df['Recency'] , y = CS_df['Frequency'] , hue = pred , palette = color1)\n",
        "plt.legend(labels = l)\n",
        "plt.title('recency v/s frequency', fontsize = 15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "aij_068HxNAW",
        "outputId": "a6a1a4af-a4d0-41f8-a83f-3a3d74399b22"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interpretation\n",
        "- From the above clustering result we can see cleary 4 types of clusters are there\n",
        "\n",
        "  - **Old_churned: Customer who have left shopping from amazon long back**.\n",
        "  - **Churned_Recently: Customer who have left shopping from amazon recently**.\n",
        "  - **Target : The customers are likely to churn in order to prevent churn we need to target these customers.**"
      ],
      "metadata": {
        "id": "9Iks4sJn2GEF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df1[['reviewerID','title','overall']]"
      ],
      "metadata": {
        "id": "eY4Pc1AV-BpR"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTotal no of ratings :\",df2.shape[0])\n",
        "print(\"Total No of reviewers   :\", len(np.unique(df2.reviewerID)))\n",
        "print(\"Total No of products  :\", len(np.unique(df2.title)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0kNjCW7g2ErH",
        "outputId": "969dd069-6b8b-43cb-c042-e696057eaeae"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Total no of ratings : 209836\n",
            "Total No of reviewers   : 201220\n",
            "Total No of products  : 17564\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion\n",
        "## EDA\n",
        "\n",
        "  - Count of reviews increasing over the period of time\n",
        "  - Positive Sentiment is Dominating\n",
        "\n",
        "## SENTIMENTAL ANALYSIS\n",
        "\n",
        "  - Model is able to Automate Sentiment Predictions\n",
        "\n",
        "## Time Series Analysis\n",
        "\n",
        "  - Model is abel to future forecast sentiment of reviewers.\n",
        "\n",
        "## CLUSTERING\n",
        "\n",
        "  - Model is able to create, customer segmentations"
      ],
      "metadata": {
        "id": "BrDG-DSvEkAp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tkinter import *\n",
        "from PIL import ImageTk,Image\n",
        "\n",
        "ob1 = Capestone_T1()\n",
        "model_3 = ob1.fastcat()\n",
        "def myfun():\n",
        "    x = e1.get()\n",
        "    #y = sent.polarity_scores(x)\n",
        "    y = model_3.predict(x)\n",
        "    a = y[0]\n",
        "    if a[0]=='__label__Positive':\n",
        "        l3.config(text = 'Your Predicted Sentiment of given review is Positive')\n",
        "    elif a[0]=='__label__Negative':\n",
        "        l3.config(text = 'Your Predicted Sentiment of given review is Negative')\n",
        "    else:\n",
        "        l3.config(text = 'Your Predicted Sentiment of given review is Neutral')\n",
        "\n",
        "window = Tk()\n",
        "\n",
        "# Title\n",
        "window.title('Welcome to sentiment Analysis')\n",
        "\n",
        "# Windowsize\n",
        "window.minsize(width = 200 , height = 200)\n",
        "\n",
        "# Background\n",
        "#window.configure(background='black')\n",
        "\n",
        "# label\n",
        "# l1 = Label(window , text = 'Amazon customer review sentiment identifier',font=50 , bg = 'pink' , fg = 'black')\n",
        "# l1.pack()\n",
        "\n",
        "# Image\n",
        "img = ImageTk.Image.open('Amazon Logo.png')\n",
        "resized_img = img.resize((1000,350))\n",
        "p1 = ImageTk.PhotoImage(resized_img)\n",
        "l2 = Label(window , image=p1)\n",
        "l2.pack()\n",
        "\n",
        "# label: Enter the text\n",
        "l1 = Label(window , text = 'Enter the review text:',font=50 , fg = 'black')\n",
        "l1.pack(pady=30)\n",
        "\n",
        "# Entry\n",
        "e1 = Entry(window, width = 50 , bd = 5 , font = (\"Calibri\",20))\n",
        "e1.pack(pady=15, ipady = 10)\n",
        "\n",
        "# Button\n",
        "b1 = Button(window , text = 'Click here to Analyze' ,font = 50, bg = 'green' , fg = 'black' , width = 30 , height = 3 , command = myfun)\n",
        "b1.pack(pady=10)\n",
        "\n",
        "# label 3\n",
        "l3 = Label(window , text = 'Your Result',font=20 , fg = 'orange')\n",
        "l3.pack(pady=10)\n",
        "\n",
        "window.mainloop()"
      ],
      "metadata": {
        "id": "_uAdKPgFhf5J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nwffdD3ux8ns"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "machine_shape": "hm",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}