Company_Rating_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Company Rating Analysis ",
      "provenance": [],
      "authorship_tag": "ABX9TyMhKHVnYjleyDSfDrLczmDN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shamreen20/Learn-NLP/blob/master/Company_Rating_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u86vHh9ge2N7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlYEi_u8kGBf",
        "colab_type": "text"
      },
      "source": [
        "# importing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5T_4suGbfV-2",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "7b7e6503-da38-43f6-8e7c-a41425e53bbf"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fcbe5c0a-3c25-4bf8-b55e-ca89b4cee655\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-fcbe5c0a-3c25-4bf8-b55e-ca89b4cee655\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving yelp.csv to yelp.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WngAW3zthBgr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "0b01510d-692a-4246-c7e0-e998b31a5f28"
      },
      "source": [
        "df = pd.read_csv(\"yelp.csv\")\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>business_id</th>\n",
              "      <th>date</th>\n",
              "      <th>review_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>text</th>\n",
              "      <th>type</th>\n",
              "      <th>user_id</th>\n",
              "      <th>cool</th>\n",
              "      <th>useful</th>\n",
              "      <th>funny</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
              "      <td>2011-01-26</td>\n",
              "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
              "      <td>5</td>\n",
              "      <td>My wife took me here on my birthday for breakf...</td>\n",
              "      <td>review</td>\n",
              "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
              "      <td>2011-07-27</td>\n",
              "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
              "      <td>5</td>\n",
              "      <td>I have no idea why some people give bad review...</td>\n",
              "      <td>review</td>\n",
              "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
              "      <td>2012-06-14</td>\n",
              "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
              "      <td>4</td>\n",
              "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
              "      <td>review</td>\n",
              "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
              "      <td>2010-05-27</td>\n",
              "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
              "      <td>5</td>\n",
              "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
              "      <td>review</td>\n",
              "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
              "      <td>2012-01-05</td>\n",
              "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
              "      <td>5</td>\n",
              "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
              "      <td>review</td>\n",
              "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              business_id        date  ... useful  funny\n",
              "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  ...      5      0\n",
              "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  ...      0      0\n",
              "2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  ...      1      0\n",
              "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  ...      2      0\n",
              "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  ...      0      0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUBIPZ1kiEKk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "64964c54-c7a7-4265-cef9-335314da8cae"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 10 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   business_id  10000 non-null  object\n",
            " 1   date         10000 non-null  object\n",
            " 2   review_id    10000 non-null  object\n",
            " 3   stars        10000 non-null  int64 \n",
            " 4   text         10000 non-null  object\n",
            " 5   type         10000 non-null  object\n",
            " 6   user_id      10000 non-null  object\n",
            " 7   cool         10000 non-null  int64 \n",
            " 8   useful       10000 non-null  int64 \n",
            " 9   funny        10000 non-null  int64 \n",
            "dtypes: int64(4), object(6)\n",
            "memory usage: 781.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "68YFYa8-ifCU",
        "colab_type": "text"
      },
      "source": [
        "Here we are seeing that none of the column is empty and no column has messy data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7v3YthJViPfD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "3cee4081-d647-4d30-b4d3-c5df974c1c1e"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>stars</th>\n",
              "      <th>cool</th>\n",
              "      <th>useful</th>\n",
              "      <th>funny</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.777500</td>\n",
              "      <td>0.876800</td>\n",
              "      <td>1.409300</td>\n",
              "      <td>0.701300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.214636</td>\n",
              "      <td>2.067861</td>\n",
              "      <td>2.336647</td>\n",
              "      <td>1.907942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>57.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              stars          cool        useful         funny\n",
              "count  10000.000000  10000.000000  10000.000000  10000.000000\n",
              "mean       3.777500      0.876800      1.409300      0.701300\n",
              "std        1.214636      2.067861      2.336647      1.907942\n",
              "min        1.000000      0.000000      0.000000      0.000000\n",
              "25%        3.000000      0.000000      0.000000      0.000000\n",
              "50%        4.000000      0.000000      1.000000      0.000000\n",
              "75%        5.000000      1.000000      2.000000      1.000000\n",
              "max        5.000000     77.000000     76.000000     57.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V4vcljtQQPKZ",
        "colab_type": "text"
      },
      "source": [
        " make a new column for how much alphabet the text column have in each row"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knkL66wPiUg-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "8958c101-d2e7-4e9b-fca4-1054001ca1ad"
      },
      "source": [
        "df[\"text_length\"] = df[\"text\"].apply(len)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>business_id</th>\n",
              "      <th>date</th>\n",
              "      <th>review_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>text</th>\n",
              "      <th>type</th>\n",
              "      <th>user_id</th>\n",
              "      <th>cool</th>\n",
              "      <th>useful</th>\n",
              "      <th>funny</th>\n",
              "      <th>text_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
              "      <td>2011-01-26</td>\n",
              "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
              "      <td>5</td>\n",
              "      <td>My wife took me here on my birthday for breakf...</td>\n",
              "      <td>review</td>\n",
              "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
              "      <td>2011-07-27</td>\n",
              "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
              "      <td>5</td>\n",
              "      <td>I have no idea why some people give bad review...</td>\n",
              "      <td>review</td>\n",
              "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
              "      <td>2012-06-14</td>\n",
              "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
              "      <td>4</td>\n",
              "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
              "      <td>review</td>\n",
              "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
              "      <td>2010-05-27</td>\n",
              "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
              "      <td>5</td>\n",
              "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
              "      <td>review</td>\n",
              "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
              "      <td>2012-01-05</td>\n",
              "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
              "      <td>5</td>\n",
              "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
              "      <td>review</td>\n",
              "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              business_id        date  ... funny  text_length\n",
              "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  ...     0          889\n",
              "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  ...     0         1345\n",
              "2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  ...     0           76\n",
              "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  ...     0          419\n",
              "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  ...     0          469\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20VN8-RXlemd",
        "colab_type": "text"
      },
      "source": [
        "# Explore the data by visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_my35VolCMw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "012cfaa6-eefc-471b-9b85-6c12c36b8abd"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RSKZnMDWmDTV",
        "colab_type": "text"
      },
      "source": [
        "Use FacetGrid from the seaborn library to create a grid of 5 histograms of text length based off of the star ratings. Reference the seaborn documentation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdVJ_vBYlcI0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "9767806c-c38c-400c-82a9-2097a9772d2c"
      },
      "source": [
        "m = sns.FacetGrid(df,col=\"stars\")\n",
        "m.map(plt.hist,\"text_length\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f61f4924978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VziR/kOSQJH/cWjt/o2sAWIkOPwwnPzB/cghsBRva0KiqQ5K8JcnTk9ya5Jqquqy1duNG1rEZ+aQVADYTl93D/MkhW5H3/bhs9BUapyTZ01q7JUmq6pIkz0mioTFnswruPK31Lw1NIWbNL0SYv0X6v9SLVAtspEV67/vdzEZZpH9fbeb3a7XWNu5kVb+UZGdr7SX98S8neWJr7dypY85OcnZ/+NgkN60w7VFJ7lqHcodYpFqSxapHLctbqZ67Wms7D3ayVWZobH8WG0ktS1ukWhL5mbZI9ahlaYtUSyI/0xapHrUsbZFqSeRn2iLVo5blLVI9M8vPwjU0Bsx5bWttx6xqXItFqiVZrHrUsrx51uPPYnlqWdoi1ZLIz7RFqkctS1ukWhL5mbZI9ahlaYtUSyI/0xapHrUsb5HqmWUtG/0pJ7clOXbq8TF9DAAAAOCgbXRD45okJ1TV8VV1vyRnJLlsg2sAAAAARm5DFwVtrX2vqs5N8qFMPrb1wtbaF9c47QVrr2xmFqmWZLHqUcvy5lmPP4vlqWVpi1RLIj/TFqketSxtkWpJ5GfaItWjlqUtUi2J/ExbpHrUsrxFqmdmtWzoGhoAAAAAs7DRt5wAAAAArJmGBgAAADA6o25oVNXOqrqpqvZU1a51OseFVXVHVd0wNXZkVV1VVTf3r0f08aqqN/d6dlfVyVPPOasff3NVnTWwlmOr6iNVdWNVfbGqXjGveqrqAVX1mar6Qq/lt/r48VX16X7O9/bFX1NV9++P9/TvHzc113l9/KaqeuaQP5s+zyFV9fmqunwBatlbVddX1XVVdW0fm8v75gA1ys+c6pGfFWuRn8jPAWqRnwPXIj+RnwPUIj8HrkV+Ij8HqEV+DlzLfPLTWhvllsmiov87yaOT3C/JF5KcuA7neUqSk5PcMDX2xiS7+v6uJL/T909L8pdJKsnPJfl0Hz8yyS396xF9/4gBtRyd5OS+/5AkX0py4jzq6XMe1vfvm+TT/RyXJjmjj78tya/1/ZcmeVvfPyPJe/v+if1nd/8kx/ef6SEDf1avSvLuJJf3x/OsZW+So/Ybm8v7Rn7kR37kR37kR37kR37kR37kZzPmZ6Zv/o3ckvx8kg9NPT4vyXnrdK7j9gv0TUmO7vtHJ7mp7789yZn7H5fkzCRvnxq/13FrqOuDSZ4+73qSPCjJ55I8McldSQ7d/2eUySfb/HzfP7QfV/v/3KaPW2UNxyS5Osm/SHJ5n3sutfTnLhXohXjfyI/8yI/8yI/8yI/8yI/8yI/8bIb8jPmWk0cm+erU41v72EbY3lq7ve9/Pcn2FWqaea39MqGfyaQzOJd6+iVO1yW5I8lVmXT0vt1a+94S8/7wnP37dyf5iVnVkuT3k/xGkh/0xz8xx1qSpCX5cFV9tqrO7mNzf99MkR/5mSY/qyM/8jNNflZHfuRnmvysjvzIzzT5yaQ7wxq01lpVtY08Z1UdluTPk7yytfb3VTWXelpr309yUlUdnuQDSR63EefdX1U9O8kdrbXPVtWp86hhCU9urd1WVQ9PclVV/d30N+fxvllE8iM/y5CfgyA/8rMM+TkI8iM/y5CfgyA/8rOMueRnzFdo3Jbk2KnHx/SxjfCNqjo6SfrXO1aoaWa1VtV9Mwnzxa2198+7niRprX07yUcyuazp8Kra1yibnveH5+zff1iSb86olicl+cWq2pvkkkwuu/qDOdWSJGmt3da/3pHJX3anZM4/p/3Ij/zsIz+rJz/ys4/8rJ78yM8+8rN68iM/+8jP1IlHuWVydcktmSxesm9RnMev07mOy73vIXtT7r24yRv7/um59+Imn+njRyb5ciYLmxzR948cUEcleWeS399vfMPrSbItyeF9/4FJPp7k2Un+LPdeiOalff+c3Hshmkv7/uNz74VobsnAhWj6fKfmR4vizKWWJA9O8pCp/b9OsnNe7xv5kR/5kR/5kR/5kR/5kR/5kZ/NmJ+Zv/k3cstkddQvZXLv0mvW6RzvSXJ7kn/K5B6eF2dyv9HVSW5O8r/2/SH3H8hbej3XJ9kxNc+vJNnTtxcNrOXJmdybtDvJdX07bR71JHlCks/3Wm5I8pt9/NFJPtPn/bMk9+/jD+iP9/TvP3pqrtf0Gm9K8qw1/rymAz2XWvp5v9C3L+57b87rfSM/8iM/8iM/8iM/8iM/8iM/8rMZ81P9SQAAAACjMeY1NAAAAIAtSkMDAAAAGB0NDQAAAGB0NDQAAACA0dHQAAAAAEZHQwMAAAAYHQ2Nkamqw6vqpQOfe1JVnbbCMS+sqj8cVt2K8z5i6vHeqjpq1ueBA5EfGE5+YDj5geHkhwPR0Bifw5MMCnSSk5IcMNDr6IVJHrHSQbDO5AeGkx8YTn5gOPlhWRoa43N+ksdU1XVV9aaq+vWquqaqdlfVbyVJVT2vqq6uiaOr6ktV9agkr0/ygv7cF6x0oqraVlV/3ue/pqqe1MdfV1UXVtVHq+qWqnr51HP+c1XdVFWfqKr3VNWrq+qXkuxIcnE/9wP74S+rqs9V1fVV9bhZ/0HBEuQHhpMfGE5+YDj5YXmtNduItiTHJbmh7z8jyQVJKpPm1OVJntK/964k5/axM/vYC5P84Qrz//CYJO9O8uS+/6gkf9v3X5fkr5PcP8lRSb6Z5L5JfjbJdUkekOQhSW5O8ur+nI8m2TF1nr1JXtb3X5rkj+f9Z2vb/Jv82GzDN/mx2YZv8mOzDd/kx3ag7dAwZs/o2+f748OSnJDkY0leluSGJJ9qrb1n4Py/kOTEqtr3+KFVdVjfv6K1dk+Se6rqjiTbkzwpyQdba/+Y5B+r6n+uMP/7+9fPJvlXA2uEoeQHhpMfGE5+YDj54V40NMatkvy31trbl/jeMUl+kGR7Vd2ntfaDAfPfJ8nP9YD+6KSTgN8zNfT9DHsv7Ztj6PNhLeQHhpMfGE5+YDj54V6soTE+38nkcqYk+VCSX9nXNayqR1bVw6vq0CQXJjkzyd8medUSzz0YH86k05k+/0krHP/JJP+yqh7Qa3r2MnXDvMgPDCc/MJz8wHDyw7I0NEamtfbNJJ+sqhuSPD2T+7z+pqquT/K+TELzn5J8vLX2iUzC/JKq+udJPpLJJVQHtShOkpcn2dEX3Lkxya+uUNs1SS5LsjvJXya5Psnd/dvvSPK2/RbFgQ0lPzCc/MBw8gPDyQ8HUn1REpiJqjqstfbdqnpQJveynd1a+9y864IxkB8YTn5gOPmB4eRnvty3w6xdUFUnZrLS70XCDKsiPzCc/MBw8gPDyc8cuUJji6qqFyV5xX7Dn2ytnTOPemBM5AeGkx8YTn5gOPnZnDQ0AAAAgNGxKCgAAAAwOhoaAAAAwOhoaAAAAACjo6EBAAAAjM7/B5M6jxXLsZNbAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FG8tR1G0TWHe",
        "colab_type": "text"
      },
      "source": [
        "we are seeing highest rating has highest vote"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YX--7ASZ5ETm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "cd2ef33f-29c7-4e04-be23-dd505cd006a6"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   business_id  10000 non-null  object\n",
            " 1   date         10000 non-null  object\n",
            " 2   review_id    10000 non-null  object\n",
            " 3   stars        10000 non-null  int64 \n",
            " 4   text         10000 non-null  object\n",
            " 5   type         10000 non-null  object\n",
            " 6   user_id      10000 non-null  object\n",
            " 7   cool         10000 non-null  int64 \n",
            " 8   useful       10000 non-null  int64 \n",
            " 9   funny        10000 non-null  int64 \n",
            " 10  text_length  10000 non-null  int64 \n",
            "dtypes: int64(5), object(6)\n",
            "memory usage: 859.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TzA77vYh-ZPF",
        "colab_type": "text"
      },
      "source": [
        "Create a countplot for each type of stars"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OvPD38JD9BJ0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "2ccf6097-a398-42b6-a68a-2a8899e03176"
      },
      "source": [
        "sns.countplot(x=\"stars\", data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61f17625c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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909+6cegWerHxIxcN3YKkH2MeWUiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6DhEWSx5M8kGRTkg2tdkyS9UkebT+PbvUkuTbJVJLNSU4aomdJms+GPLJ4e1WtqKrJtnwFcEdVLQfuaMsA5wDL27QauG7WO5WkeW4unYY6D7ihzd8AnD9Wv7FG7gaOSrJwiAYlab4aKiwK+KskG5OsbrXjqmp7m/8ucFybXwQ8Mbbt1lb7/yRZnWRDkg07d+7sq29JmpeGekX526pqW5KfANYn+eb4yqqqJPVSdlhVa4A1AJOTky9pW0nS/g1yZFFV29rPHcAXgJOB7+0+vdR+7mjDtwFLxjZf3GqSpFky62GR5DVJjtw9D5wFPAisA1a1YauAL7b5dcBF7a6oU4Cnx05XSZJmwRCnoY4DvpBk9+f/56r6UpL7gJuTXAJ8G3hXG38bcC4wBTwLXDz7LUvS/DbrYVFVjwFvnqb+JHDGNPUCLp2F1iRJ+zCXbp2VJM1RhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE4HTVgkWZnkkSRTSa4Yuh9Jmk8OirBIsgD4OHAOcAJwYZIThu1KkuaPgyIsgJOBqap6rKr+N3ATcN7APUnSvJGqGrqHTkneCaysqn/Zlt8DvLWqLhsbsxpY3RZ/Cnhk1ht9sWOB7w/dxBzhd7GH38Uefhd7zIXv4h9W1cR0Kw6d7U76UlVrgDVD9zEuyYaqmhy6j7nA72IPv4s9/C72mOvfxcFyGmobsGRseXGrSZJmwcESFvcBy5MsS3I4cAGwbuCeJGneOChOQ1XV80kuA24HFgBrq2rLwG3NxJw6LTYwv4s9/C728LvYY05/FwfFBW5J0rAOltNQkqQBGRaSpE6GRQ+SrE2yI8mDQ/cypCRLktyZ5KEkW5K8b+iehpLkiCT3JvlG+y4+NHRPQ0uyIMnXk9w6dC9DSvJ4kgeSbEqyYeh+9sVrFj1I8vPAM8CNVfUzQ/czlCQLgYVVdX+SI4GNwPlV9dDArc26JAFeU1XPJDkMuAt4X1XdPXBrg0nyb4BJ4HVV9YtD9zOUJI8Dk1U19AN5++WRRQ+q6qvArqH7GFpVba+q+9v8j4CHgUXDdjWMGnmmLR7Wpnn7P7Uki4F3AP9p6F40M4aFZkWSpcCJwD3DdjKcdtplE7ADWF9V8/a7AP4D8G+Bvxu6kTmggL9KsrG9tmhOMizUuySvBW4BLq+qHw7dz1Cq6oWqWsHoDQQnJ5mXpyiT/CKwo6o2Dt3LHPG2qjqJ0Vu1L22nseccw0K9aufnbwE+XVWfH7qfuaCqfgDcCawcupeBnAr8cjtXfxNwepJPDdvScKpqW/u5A/gCo7dszzmGhXrTLupeDzxcVR8bup8hJZlIclSbfzVwJvDNYbsaRlV9oKoWV9VSRq/u+XJVvXvgtgaR5DXt5g+SvAY4C5iTd1EaFj1I8hngr4GfSrI1ySVD9zSQU4H3MPqf46Y2nTt0UwNZCNyZZDOjd52tr6p5fcuoADgOuCvJN4B7gb+sqi8N3NO0vHVWktTJIwtJUifDQpLUybCQJHUyLCRJnQwLSVInw0LqQZLLk/y9ofuQDhRvnZV68HLeJJpkQVW90F9X0st3UPwNbmkua0/e3szonU8LgD8Hjmf0EN73q+rtSa4D3gK8GvhcVV3Ztn0c+CyjJ7r/fZKfAH4DeB54qKoumO3fR5qOYSG9ciuB71TVOwCSvB64GHj72JHFv6uqXUkWAHckeVNVbW7rnmwvkiPJd4BlVfXc7teDSHOB1yykV+4B4Mwkf5Dkn1fV09OMeVeS+4GvA28EThhb99mx+c3Ap5O8m9HRhTQnGBbSK1RV/wM4iVFo/F6S3x1fn2QZ8JvAGVX1JuAvgSPGhvzN2Pw7gI+3/d2XxKN/zQmGhfQKJTkeeLaqPgV8hNE/9D8CjmxDXscoEJ5Ochyjv1sw3X4OAZZU1Z3AbwOvB17bc/vSjPi/FumV+1ngI0n+Dvg/wL8Cfg74UpLvtAvcX2f0SvIngK/tYz8LgE+1ax4Brm1/+0IanLfOSpI6eRpKktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnf4vLFXNunVcWF8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euGi7cEETzsS",
        "colab_type": "text"
      },
      "source": [
        "4 star has highest count"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yM8GzJHHMTsr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "217d261d-8e02-441a-9c61-b8e03c03854c"
      },
      "source": [
        " stars = df.groupby(\"stars\").mean()\n",
        " stars"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cool</th>\n",
              "      <th>useful</th>\n",
              "      <th>funny</th>\n",
              "      <th>text_length</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stars</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.576769</td>\n",
              "      <td>1.604806</td>\n",
              "      <td>1.056075</td>\n",
              "      <td>826.515354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.719525</td>\n",
              "      <td>1.563107</td>\n",
              "      <td>0.875944</td>\n",
              "      <td>842.256742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.788501</td>\n",
              "      <td>1.306639</td>\n",
              "      <td>0.694730</td>\n",
              "      <td>758.498289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.954623</td>\n",
              "      <td>1.395916</td>\n",
              "      <td>0.670448</td>\n",
              "      <td>712.923142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.944261</td>\n",
              "      <td>1.381780</td>\n",
              "      <td>0.608631</td>\n",
              "      <td>624.999101</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           cool    useful     funny  text_length\n",
              "stars                                           \n",
              "1      0.576769  1.604806  1.056075   826.515354\n",
              "2      0.719525  1.563107  0.875944   842.256742\n",
              "3      0.788501  1.306639  0.694730   758.498289\n",
              "4      0.954623  1.395916  0.670448   712.923142\n",
              "5      0.944261  1.381780  0.608631   624.999101"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNYvUzR2UX6U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "e7ffea51-ca90-49da-d785-a0f8573d8e73"
      },
      "source": [
        "stars.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cool</th>\n",
              "      <th>useful</th>\n",
              "      <th>funny</th>\n",
              "      <th>text_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cool</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.743329</td>\n",
              "      <td>-0.944939</td>\n",
              "      <td>-0.857664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>useful</th>\n",
              "      <td>-0.743329</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.894506</td>\n",
              "      <td>0.699881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>funny</th>\n",
              "      <td>-0.944939</td>\n",
              "      <td>0.894506</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.843461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>text_length</th>\n",
              "      <td>-0.857664</td>\n",
              "      <td>0.699881</td>\n",
              "      <td>0.843461</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 cool    useful     funny  text_length\n",
              "cool         1.000000 -0.743329 -0.944939    -0.857664\n",
              "useful      -0.743329  1.000000  0.894506     0.699881\n",
              "funny       -0.944939  0.894506  1.000000     0.843461\n",
              "text_length -0.857664  0.699881  0.843461     1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8f30ZPH5U20z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "8b2c4828-9a88-4f6f-df82-d9e04a8120ab"
      },
      "source": [
        "sns.heatmap(stars.corr(),annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61ecc886d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iYIav-REZq2u",
        "colab_type": "text"
      },
      "source": [
        "# NLP Classification tasks\n",
        "Make thing a little easier, only grab reviews that were either 1 star or 5 stars."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWxTiSt4W6Z9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "bbaeab25-1e84-4e8c-eea6-22fe6adabb64"
      },
      "source": [
        "df_class = df[(df[\"stars\"]==1) | (df[\"stars\"]==5)]\n",
        "df_class.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>business_id</th>\n",
              "      <th>date</th>\n",
              "      <th>review_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>text</th>\n",
              "      <th>type</th>\n",
              "      <th>user_id</th>\n",
              "      <th>cool</th>\n",
              "      <th>useful</th>\n",
              "      <th>funny</th>\n",
              "      <th>text_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
              "      <td>2011-01-26</td>\n",
              "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
              "      <td>5</td>\n",
              "      <td>My wife took me here on my birthday for breakf...</td>\n",
              "      <td>review</td>\n",
              "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
              "      <td>2011-07-27</td>\n",
              "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
              "      <td>5</td>\n",
              "      <td>I have no idea why some people give bad review...</td>\n",
              "      <td>review</td>\n",
              "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
              "      <td>2010-05-27</td>\n",
              "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
              "      <td>5</td>\n",
              "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
              "      <td>review</td>\n",
              "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
              "      <td>2012-01-05</td>\n",
              "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
              "      <td>5</td>\n",
              "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
              "      <td>review</td>\n",
              "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>zp713qNhx8d9KCJJnrw1xA</td>\n",
              "      <td>2010-02-12</td>\n",
              "      <td>riFQ3vxNpP4rWLk_CSri2A</td>\n",
              "      <td>5</td>\n",
              "      <td>Drop what you're doing and drive here. After I...</td>\n",
              "      <td>review</td>\n",
              "      <td>wFweIWhv2fREZV_dYkz_1g</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>1565</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              business_id        date  ... funny  text_length\n",
              "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  ...     0          889\n",
              "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  ...     0         1345\n",
              "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  ...     0          419\n",
              "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  ...     0          469\n",
              "6  zp713qNhx8d9KCJJnrw1xA  2010-02-12  ...     4         1565\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZr6gB4Fahjd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df_class.text\n",
        "y = df_class.stars"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfHip9PIbNW0",
        "colab_type": "text"
      },
      "source": [
        "# Apply countVectorizer\n",
        "countVectorizer is Bag of words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHGdR7gybLTh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sN1qaoLJb3wn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cv = CountVectorizer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sthhiAfb-px",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = cv.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHNHzfnbc_2x",
        "colab_type": "text"
      },
      "source": [
        "# Apply Train_test_split\n",
        "split the data into training and testing form"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ay3JdBMucH3g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h76egTVDdXZe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3, random_state=101)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2XHNnyyyd4z8",
        "colab_type": "text"
      },
      "source": [
        "# Training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ntEA43jdvEL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0Dhi2GQeRd-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "NB = MultinomialNB()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IAryaixeVcE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ac6a49f8-8ec7-449a-c908-6f7d71f9e4f0"
      },
      "source": [
        "NB.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JbB9FXSofUA1",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwkIVaLMfV1X",
        "colab_type": "text"
      },
      "source": [
        "# Predict the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5sIlpSRec9t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fc0a522b-a76a-4c4f-d97e-353a65a50c53"
      },
      "source": [
        "pred = NB.predict(X_test)\n",
        "pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 1, 5, ..., 5, 5, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxkq5wOLfSUI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IXwCSwVgCEh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "confusion_m = confusion_matrix(y_test,pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQNH46SYgLta",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "730a9be6-12b5-43f1-cc77-15f54b68f9fa"
      },
      "source": [
        "confusion_m"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[159,  69],\n",
              "       [ 22, 976]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Azn7E00kgOzi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fo1VPt3gXPP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "report = classification_report(y_test,pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIfHpp7pgssJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "b8212f1b-01d7-4618-eaf6-fc76e13aa357"
      },
      "source": [
        "print(report)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.88      0.70      0.78       228\n",
            "           5       0.93      0.98      0.96       998\n",
            "\n",
            "    accuracy                           0.93      1226\n",
            "   macro avg       0.91      0.84      0.87      1226\n",
            "weighted avg       0.92      0.93      0.92      1226\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bK4MF_cQgxvK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}