Tweets_Disaster_Analysis.ipynb

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      "language": "python",
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    "colab": {
      "name": "Tweets Disaster Analysis.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abdoulsn/NLP-Tweets-Disaster/blob/master/Tweets_Disaster_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xw2rZhc-J8Qg",
        "colab_type": "text"
      },
      "source": [
        "# Disaster Tweets EDA\n",
        "\n",
        "This notebook contains basic and/or complicated EDA."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "RoYrmbMoJ8Qn",
        "colab_type": "code",
        "outputId": "d921896a-9a9d-4267-fd32-9458478ff652",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import string\n",
        "import re\n",
        "!pip install -q wordcloud\n",
        "import wordcloud\n",
        "from nltk.corpus import stopwords\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "\n",
        "\n",
        "\n",
        "from nltk.util import ngrams\n",
        "from collections import defaultdict\n",
        "from collections import Counter\n",
        "from nltk.tokenize import word_tokenize\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from tqdm import tqdm\n",
        "# from spellchecker import SpellChecker\n",
        "\n",
        "# set color codes\n",
        "sns.set_color_codes('pastel')\n",
        "plt.style.use('ggplot')\n",
        "stop_words = set(stopwords.words('english'))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CAN1xSBJJ8Q5",
        "colab_type": "text"
      },
      "source": [
        "### Loading the dataset from [EnesGokce](https://raw.githubusercontent.com/EnesGokceDS/)  Repo (He was in my team)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "trusted": true,
        "id": "BllCIa56J8Q7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url1= 'https://raw.githubusercontent.com/EnesGokceDS/NLP_team_project/master/text_cleaned.csv' # With text_cleaned data\n",
        "url2='https://raw.githubusercontent.com/EnesGokceDS/NLP_team_project/master/text__testdata_cleaned.csv'\n",
        "url3= 'https://raw.githubusercontent.com/farmani60/NLP-Tweets-Disaster/master/data/sample_submission.csv'\n",
        "train = pd.read_csv(url1)\n",
        "test = pd.read_csv(url2)\n",
        "sample_submission = pd.read_csv(url3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "jD14ivm1J8RF",
        "colab_type": "code",
        "outputId": "960723a2-4778-4211-8f52-f9c96300190a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('Number of samples in train set: {}'.format(train.shape[0]))\n",
        "print('Number of samples in test set: {}'.format(test.shape[0]))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of samples in train set: 7552\n",
            "Number of samples in test set: 3237\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgx3Je49o1St",
        "colab_type": "code",
        "outputId": "165d773a-d802-47d6-ce77-3d7c463d1d86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train.sample(5).round(2)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>punctuation</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>upper</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>822</th>\n",
              "      <td>1235</td>\n",
              "      <td>blood</td>\n",
              "      <td>stars  reviewers dragon blood boxset lindsay...</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>80</td>\n",
              "      <td>7.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2330</th>\n",
              "      <td>3397</td>\n",
              "      <td>demolition</td>\n",
              "      <td>seven chinese christians detained amid widespr...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>73</td>\n",
              "      <td>7.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4373</th>\n",
              "      <td>6261</td>\n",
              "      <td>hijacking</td>\n",
              "      <td>hot funtenna hijacking computers send data sou...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>80</td>\n",
              "      <td>5.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5611</th>\n",
              "      <td>8045</td>\n",
              "      <td>refugees</td>\n",
              "      <td>newlyweds feed thousands syrian refugees inste...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>88</td>\n",
              "      <td>6.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7215</th>\n",
              "      <td>10376</td>\n",
              "      <td>weapons</td>\n",
              "      <td>liked youtube video dannyonpc  battlefield har...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>87</td>\n",
              "      <td>6.82</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id     keyword  ... char_count  avg_word\n",
              "822    1235       blood  ...         80      7.56\n",
              "2330   3397  demolition  ...         73      7.11\n",
              "4373   6261   hijacking  ...         80      5.58\n",
              "5611   8045    refugees  ...         88      6.33\n",
              "7215  10376     weapons  ...         87      6.82\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zaJ_qhJHJ8RM",
        "colab_type": "text"
      },
      "source": [
        "### Distribution of the Target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "pTqSM1yLJ8RP",
        "colab_type": "code",
        "outputId": "f26a6170-5c1e-4394-ab7d-661ad20eb4a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "df = train.groupby(['target'])['id'].count()\n",
        "df = df.reset_index()\n",
        "df = df.rename(columns={'id': 'number of samples'})\n",
        "fig, ax = plt.subplots(figsize=(4, 6))\n",
        "\n",
        "sns.barplot(x='target', y='number of samples', data=df)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f46b2e5ec18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QLbV8iTeJ8Re",
        "colab_type": "text"
      },
      "source": [
        "So, the classes are kind of balanced but we have about 1000 more samples in no disaster tweets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "mnaMD0bwJ8RX",
        "colab_type": "code",
        "outputId": "106ac585-a48e-4e46-e3d5-2bf1d0580513",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('Number of disaster tweets: {}'.format(df.loc[df['target']==1]['number of samples'].values[0]))\n",
        "print('Number of no disaster tweets: {}'.format(df.loc[df['target']==0]['number of samples'].values[0]))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of disaster tweets: 3229\n",
            "Number of no disaster tweets: 4323\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ICsBh4frxAv",
        "colab_type": "code",
        "outputId": "26dd9059-c5f6-4192-b92e-067e2fd19c46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "train.loc[train.text.isnull()]"
      ],
      "execution_count": 33,
      "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>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>punctuation</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>upper</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5084</th>\n",
              "      <td>7295</td>\n",
              "      <td>nuclear%20reactor</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id            keyword text  ...  word_count  char_count  avg_word\n",
              "5084  7295  nuclear%20reactor  NaN  ...           1           0       0.0\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8ww5Ia_r43a",
        "colab_type": "code",
        "outputId": "5dd6518b-3c73-4126-ebb7-5d49c4aa4c05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 48
        }
      },
      "source": [
        "train= train.dropna()\n",
        "train.loc[train.text.isnull()]"
      ],
      "execution_count": 34,
      "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>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>punctuation</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>upper</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, keyword, text, target, stopwords, punctuation, hastags, numerics, upper, word_count, char_count, avg_word]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGSIbcXvJ8Rg",
        "colab_type": "text"
      },
      "source": [
        "### Number of Characters in Tweets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "cwSEr4yaJ8Rj",
        "colab_type": "code",
        "outputId": "9a0eefc5-cef8-4056-eaba-4ec4a454a656",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "tweet_len = train.loc[train['target']==1]['text'].str.len()\n",
        "ax1.hist(tweet_len, color='blue')\n",
        "ax1.set_title('disaster tweets')\n",
        "tweet_len = train.loc[train['target']==0]['text'].str.len()\n",
        "ax2.hist(tweet_len, color='red')\n",
        "ax2.set_title('not disaster tweets')\n",
        "\n",
        "fig.suptitle('Number of characters in tweets')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0xYTVvNJ8Rr",
        "colab_type": "text"
      },
      "source": [
        "Both diaster and not disaster tweets have almost same distribution on number of characters. 70 to 90 characters are most common in both groups."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yn5X1ACCJ8Ru",
        "colab_type": "text"
      },
      "source": [
        "### Number of Words in Tweets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "-o2BTxxVJ8Rv",
        "colab_type": "code",
        "outputId": "94ff558b-effe-46b6-a14c-5a6fd25fe5be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
        "tweet_len = train.loc[train['target']==1]['text'].str.split().map(lambda x: len(x))\n",
        "ax1.hist(tweet_len, color='blue')\n",
        "ax1.set_title('disaster tweets')\n",
        "\n",
        "tweet_len = train.loc[train['target']==0]['text'].str.split().map(lambda x: len(x))\n",
        "ax2.hist(tweet_len, color='red')\n",
        "ax2.set_title('not disaster tweets')\n",
        "\n",
        "fig.suptitle('Number of words in tweets')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8-cs6GLJ8R2",
        "colab_type": "text"
      },
      "source": [
        "It seems not disaster tweets have slightly more words. This might be used as a feature!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKlrdGQfJ8R3",
        "colab_type": "text"
      },
      "source": [
        "### Average Word Length in Tweets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "5r4x5fvQJ8R6",
        "colab_type": "code",
        "outputId": "4330134c-236a-46da-c671-80a894f26f68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "words = train.loc[train['target']==1] ['text'].str.split().apply(lambda x: [len(i) for i in x])\n",
        "sns.distplot(words.map(lambda x: np.mean(x)), ax=ax1, color='blue')\n",
        "\n",
        "\n",
        "words = train.loc[train['target']==0] ['text'].str.split().apply(lambda x: [len(i) for i in x])\n",
        "sns.distplot(words.map(lambda x: np.mean(x)), ax=ax2, color='red')\n",
        "ax2.set_title('not disaster tweets')\n",
        "\n",
        "fig.suptitle('Average length of words in tweets')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpc62HcgJ8SA",
        "colab_type": "text"
      },
      "source": [
        "Disaster and not disaster have almost same distribution over average length of words in them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "VL6ao5OCJ8SD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# funcion to make corpus\n",
        "def make_corpus(df, target):\n",
        "    corpus = []\n",
        "    for x in df.loc[df['target']==target]['text'].str.split():\n",
        "        for i in x:\n",
        "            corpus.append(i)\n",
        "    return corpus"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y6f1Jo6aJ8SJ",
        "colab_type": "text"
      },
      "source": [
        "### Stop Words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "MLlhbLgEJ8SL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# make a corpus of not disaster tweets\n",
        "not_disaster_corpus = make_corpus(train, target=0) # stop words for not disaster tweets\n",
        "dic = defaultdict(int)\n",
        "\n",
        "for word in not_disaster_corpus:\n",
        "    if word in stop_words:\n",
        "        dic[word] += 1\n",
        "        \n",
        "# top 10 stop words in not disaster tweets\n",
        "not_disaster_top_stop_words = sorted(dic.items(), key=lambda x: x[1], reverse=True)[:10]\n",
        "\n",
        "# make a corpus of disaster tweets\n",
        "disaster_corpus = make_corpus(train, target=1) # stop words for disaster tweets\n",
        "dic = defaultdict(int)\n",
        "\n",
        "for word in disaster_corpus:\n",
        "    if word in stop_words:\n",
        "        dic[word] += 1\n",
        "        \n",
        "# top 10 stop words in disaster tweets\n",
        "disaster_top_stop_words = sorted(dic.items(), key=lambda x: x[1], reverse=True)[:10]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8D_fQVFafNXV",
        "colab_type": "code",
        "outputId": "83e0fa2b-20e8-4007-f307-6609620ab814",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(disaster_top_stop_words)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "kmKefXN6J8SQ",
        "colab_type": "code",
        "outputId": "06166be4-d128-4e5b-f760-ca1e1d4f3ab1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "#This part can be added to data cleaning section before cleaning stopwords\n",
        "\n",
        "'''\n",
        "x1, y1 = zip(*disaster_top_stop_words)\n",
        "x2, y2 = zip(*not_disaster_top_stop_words)\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "ax1.bar(x1, y1, color='blue')\n",
        "ax1.set_title('disaster tweets')\n",
        "\n",
        "ax2.bar(x2, y2, color='red')\n",
        "ax2.set_title('not disaster tweets')\n",
        "\n",
        "fig.suptitle('Top 10 stop words')\n",
        "\n",
        "plt.show()\n",
        "'''"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nx1, y1 = zip(*disaster_top_stop_words)\\nx2, y2 = zip(*not_disaster_top_stop_words)\\n\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\\n\\nax1.bar(x1, y1, color='blue')\\nax1.set_title('disaster tweets')\\n\\nax2.bar(x2, y2, color='red')\\nax2.set_title('not disaster tweets')\\n\\nfig.suptitle('Top 10 stop words')\\n\\nplt.show()\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rp91kfMiJ8SY",
        "colab_type": "text"
      },
      "source": [
        "In both classes `the` is the most frequent stop word followed by `a` in class not disaster and `in` in class disaster."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sf_OQUT5J8SZ",
        "colab_type": "text"
      },
      "source": [
        "### Punctuations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "2O75wENsJ8Sa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "punctuations = string.punctuation\n",
        "\n",
        "dic_punc_not_disaster = defaultdict(int)\n",
        "\n",
        "for word in not_disaster_corpus:\n",
        "    if word in punctuations:\n",
        "        dic_punc_not_disaster[word] += 1\n",
        "        \n",
        "dic_punc_disaster = defaultdict(int)\n",
        "\n",
        "for word in disaster_corpus:\n",
        "    if word in punctuations:\n",
        "        dic_punc_disaster[word] += 1\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZhP6lzfeQac",
        "colab_type": "code",
        "outputId": "e6d9a8c6-039a-4266-f4d5-1ccc04d2dc00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "#print('Number of disaster tweets: {}'.format(train.loc[train['target']==1]['text']))\n",
        "tweet_len = train.loc[train['target']==1]['text'].str.len()\n",
        "print(tweet_len)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0        32\n",
            "2        53\n",
            "6        28\n",
            "7       103\n",
            "15       56\n",
            "       ... \n",
            "7511     67\n",
            "7512     68\n",
            "7521     31\n",
            "7528     35\n",
            "7541     31\n",
            "Name: text, Length: 3229, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYnVw48B7ruh",
        "colab_type": "code",
        "outputId": "29248e2d-fb5d-43ab-9806-58e6437580cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "\n",
        "#df_train['text'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]))\n",
        "tweet_punc = train.loc[train['target']==1]['text'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]))\n",
        "print(tweet_punc)\n",
        "ax1.hist(tweet_punc, color='blue')\n",
        "ax1.set_title('disaster tweets')\n",
        "\n",
        "tweet_punc = train.loc[train['target']==0]['text'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]))\n",
        "ax2.hist(tweet_punc, color='red')\n",
        "ax2.set_title('not disaster tweets')\n",
        "\n",
        "fig.suptitle('Number of punctuation in tweets')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0       0\n",
            "2       0\n",
            "6       0\n",
            "7       0\n",
            "15      0\n",
            "       ..\n",
            "7511    0\n",
            "7512    0\n",
            "7521    0\n",
            "7528    0\n",
            "7541    0\n",
            "Name: text, Length: 3229, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HoCYhoXfJ8Sl",
        "colab_type": "text"
      },
      "source": [
        "### Common Words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "O_9TaRZqJ8Sn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "disaster_counter = Counter(disaster_corpus)\n",
        "disaster_most_common_words = disaster_counter.most_common()\n",
        "\n",
        "x1 = []\n",
        "y1 = []\n",
        "\n",
        "for word, count in disaster_most_common_words[:40]:\n",
        "    if (word not in stop_words) & (word not in punctuations):\n",
        "        x1.append(word)\n",
        "        y1.append(count)\n",
        "        \n",
        "not_disaster_counter = Counter(not_disaster_corpus)\n",
        "not_disaster_most_common_words = not_disaster_counter.most_common()\n",
        "\n",
        "x2 = []\n",
        "y2 = []\n",
        "\n",
        "for word, count in not_disaster_most_common_words[:40]:\n",
        "    if (word not in stop_words) & (word not in punctuations):\n",
        "        x2.append(word)\n",
        "        y2.append(count)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "AtSGvpwTJ8Ss",
        "colab_type": "code",
        "outputId": "5fa36e96-e1bc-4491-8ed2-cca74c28e606",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        }
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(30, 10))\n",
        "\n",
        "sns.barplot(x1, y1, ax=ax1)\n",
        "ax1.set_title('disaster tweets')\n",
        "\n",
        "sns.barplot(x2, y2, ax=ax2)\n",
        "ax2.set_title('not disaster tweets')\n",
        "\n",
        "fig.suptitle('Common Words in Tweets')\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 2160x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LDtW04lSJ8Sx",
        "colab_type": "text"
      },
      "source": [
        "Lots of cleaning needed!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zi_NQQUpJ8Sy",
        "colab_type": "text"
      },
      "source": [
        "### Ngram Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-WfvRIjJ8S0",
        "colab_type": "text"
      },
      "source": [
        "We analyze bigrams (n = 2) and trigrams (n = 3) over tweets. Let's check the most common ngrams in tweets. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "MJlDg6XhJ8S1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_top_tweet_ngrams(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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "D5uPZo1ZJ8S7",
        "colab_type": "code",
        "outputId": "7f1880cf-a302-4505-c496-c55871daaccb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(28, 5))\n",
        "\n",
        "top_tweet_bigrams = get_top_tweet_ngrams(train['text'], n=2)[:10]\n",
        "top_tweet_trigrams = get_top_tweet_ngrams(train['text'], n=3)[:10]\n",
        "\n",
        "x1, y1 = map(list, zip(*top_tweet_bigrams))\n",
        "x2, y2 = map(list, zip(*top_tweet_trigrams))\n",
        "\n",
        "sns.barplot(y1, x1, ax=ax1)\n",
        "sns.barplot(y2, x2, ax=ax2)\n",
        "ax1.set_title('Top 10 bigrams')\n",
        "ax2.set_title('Top 10 trigrams')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2016x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9QN0_9iIJ8TB",
        "colab_type": "text"
      },
      "source": [
        "A lot of cleaning is needed here too!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tPNfDHR5J8TC",
        "colab_type": "text"
      },
      "source": [
        "### Data Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FdHl5SWYJ8TE",
        "colab_type": "text"
      },
      "source": [
        "* Spelling Correction\n",
        "* Removing Punctuations\n",
        "* Removing html tags\n",
        "* Removing Emojis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "RMSRUFkQJ8TG",
        "colab_type": "code",
        "outputId": "fbfaef95-3a9a-4724-e9ea-2b67647bb245",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#df = pd.concat([train, test])\n",
        "df= train \n",
        "df.head()"
      ],
      "execution_count": 49,
      "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>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>punctuation</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>upper</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>bbcmtd wholesale markets ablaze</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>32</td>\n",
              "      <td>6.999998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>always try bring heavy metal rt</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>32</td>\n",
              "      <td>4.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>africanbaze breaking newsnigeria flag set abla...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>53</td>\n",
              "      <td>6.571428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>52</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>crying set ablaze</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "      <td>4.999998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>plus side look sky last night ablaze</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>37</td>\n",
              "      <td>4.285714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id keyword  ... char_count  avg_word\n",
              "0  48  ablaze  ...         32  6.999998\n",
              "1  49  ablaze  ...         32  4.333333\n",
              "2  50  ablaze  ...         53  6.571428\n",
              "3  52  ablaze  ...         17  4.999998\n",
              "4  53  ablaze  ...         37  4.285714\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xiNgWnxdJ8TL",
        "colab_type": "text"
      },
      "source": [
        "### Removing urls"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hs3vtpOj5tlF",
        "colab_type": "code",
        "outputId": "994c4e7d-3c9f-43cf-88fb-5060b71955f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 7551 entries, 0 to 7551\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   id           7551 non-null   int64  \n",
            " 1   keyword      7551 non-null   object \n",
            " 2   text         7551 non-null   object \n",
            " 3   target       7551 non-null   int64  \n",
            " 4   stopwords    7551 non-null   int64  \n",
            " 5   punctuation  7551 non-null   int64  \n",
            " 6   hastags      7551 non-null   int64  \n",
            " 7   numerics     7551 non-null   int64  \n",
            " 8   upper        7551 non-null   int64  \n",
            " 9   word_count   7551 non-null   int64  \n",
            " 10  char_count   7551 non-null   int64  \n",
            " 11  avg_word     7551 non-null   float64\n",
            "dtypes: float64(1), int64(9), object(2)\n",
            "memory usage: 766.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RU4vbRYGEUtP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['text'] = df['text'].astype(str)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13WTXVdMEbwA",
        "colab_type": "code",
        "outputId": "110a0c45-127e-4585-ff8a-838930057652",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 7551 entries, 0 to 7551\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   id           7551 non-null   int64  \n",
            " 1   keyword      7551 non-null   object \n",
            " 2   text         7551 non-null   object \n",
            " 3   target       7551 non-null   int64  \n",
            " 4   stopwords    7551 non-null   int64  \n",
            " 5   punctuation  7551 non-null   int64  \n",
            " 6   hastags      7551 non-null   int64  \n",
            " 7   numerics     7551 non-null   int64  \n",
            " 8   upper        7551 non-null   int64  \n",
            " 9   word_count   7551 non-null   int64  \n",
            " 10  char_count   7551 non-null   int64  \n",
            " 11  avg_word     7551 non-null   float64\n",
            "dtypes: float64(1), int64(9), object(2)\n",
            "memory usage: 766.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "WpDK9616J8TN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def remove_url(text):\n",
        "    url = re.compile(r'https?://\\S+|www\\.\\S+')\n",
        "    return url.sub(r'', text)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fxYXKfq1DaPm",
        "colab_type": "code",
        "outputId": "09adf5b8-72c0-491a-b739-745a4654ed99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "df.sample()"
      ],
      "execution_count": 54,
      "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>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>punctuation</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>upper</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3155</th>\n",
              "      <td>4575</td>\n",
              "      <td>emergency%20plan</td>\n",
              "      <td>bc costs less sick people using emergency room...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>70</td>\n",
              "      <td>6.888888</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id           keyword  ... char_count  avg_word\n",
              "3155  4575  emergency%20plan  ...         70  6.888888\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "9G_uCumPJ8TR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# remove all urls from df\n",
        "df['text'] = df['text'].apply(lambda x: remove_url(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xQ8FxAiMJ8TV",
        "colab_type": "text"
      },
      "source": [
        "### Removing HTML Tags"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "3PTM5HUAJ8TX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def remove_html(text):\n",
        "    html=re.compile(r'<.*?>')\n",
        "    return html.sub(r'',text)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "9vbxCFdkJ8Tc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# remove all html tags from df\n",
        "df['text'] = df['text'].apply(lambda x: remove_html(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyvH42FZJ8Th",
        "colab_type": "text"
      },
      "source": [
        " ### Removing Emojis\n",
        " \n",
        " Emojis can be indactors of some emotionals that can be related to being disaster or not. So, later we should decide if we want to remove them from tweets or not."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "zhun21WHJ8Ti",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reference : https://gist.github.com/slowkow/7a7f61f495e3dbb7e3d767f97bd7304b\n",
        "def remove_emoji(text):\n",
        "    emoji_pattern = re.compile(\"[\"\n",
        "                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
        "                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
        "                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
        "                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
        "                           u\"\\U00002702-\\U000027B0\"\n",
        "                           u\"\\U000024C2-\\U0001F251\"\n",
        "                           \"]+\", flags=re.UNICODE)\n",
        "    return emoji_pattern.sub(r'', text)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "pi0rfYdPJ8Tl",
        "colab_type": "code",
        "outputId": "884dd16e-ab77-4e0a-aaf8-8da5bfbab404",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "remove_emoji(\"Omg another Earthquake 😔😔\")"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Omg another Earthquake '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Nusv4z57J8Tr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# remove all emojis from df\n",
        "df['text'] = df['text'].apply(lambda x: remove_emoji(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flnXDR5IJ8Tu",
        "colab_type": "text"
      },
      "source": [
        "### Removing Punctuations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "m0bWTvOfJ8Tw",
        "colab_type": "code",
        "outputId": "7845edfb-1fcc-4b72-f7bc-ae50f652ca72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def remove_punct(text):\n",
        "    table=str.maketrans('','',string.punctuation)\n",
        "    return text.translate(table)\n",
        "\n",
        "example=\"I am a #king\"\n",
        "print(remove_punct(example))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I am a king\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "k1PgCtVSJ8T0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# remove all punctuations from df\n",
        "df['text'] = df['text'].apply(lambda x: remove_punct(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WM5At_LCJ8T4",
        "colab_type": "text"
      },
      "source": [
        "### Spelling Correction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MdYXqxFoJ8T4",
        "colab_type": "text"
      },
      "source": [
        "Couldn't install spellchecker package on kaggle teminal!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "M1gAqdylJ8T6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from spellchecker import SpellChecker\n",
        "\n",
        "# spell = SpellChecker()\n",
        "# def correct_spellings(text):\n",
        "#     corrected_text = []\n",
        "#     misspelled_words = spell.unknown(text.split())\n",
        "#     for word in text.split():\n",
        "#         if word in misspelled_words:\n",
        "#             corrected_text.append(spell.correction(word))\n",
        "#         else:\n",
        "#             corrected_text.append(word)\n",
        "#     return \" \".join(corrected_text)\n",
        "        \n",
        "# text = \"corect me plese\"\n",
        "# correct_spellings(text)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "MmBSaEqcJ8T-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df['text'] = df['text'].apply(lambda x : correct_spellings(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SsURyvu1J8UB",
        "colab_type": "text"
      },
      "source": [
        "### Glove for Vectorization\n",
        "\n",
        "Loot at what this is and try to utulize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "k1-sUzs3J8UD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_corpus(df):\n",
        "    corpus = []\n",
        "    for tweet in tqdm(df['text']):\n",
        "        words = [word.lower() for word in word_tokenize(tweet) if ((word not in stop_words) & (word.isalpha()==1))]\n",
        "        corpus.append(words)\n",
        "    return corpus"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "9fM70nNIJ8UG",
        "colab_type": "code",
        "outputId": "0fc27ed4-43ad-4176-cdf9-b5e59e0608e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "corpus = create_corpus(df)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 7551/7551 [00:00<00:00, 7952.98it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "AlZOOJRLJ8UK",
        "colab_type": "code",
        "outputId": "2da0d498-4dea-45e6-9d5f-b35471b9173e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "! ls ../input/nlp-getting-started"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ls: cannot access '../input/nlp-getting-started': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "AgiVJU5uJ8UO",
        "colab_type": "code",
        "outputId": "1cb97161-ff12-4a3e-f0d8-c9238231ca3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "!wget http://nlp.stanford.edu/data/glove.6B.zip #We need to explore the usage of this"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-16 02:57:44--  http://nlp.stanford.edu/data/glove.6B.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n",
            "--2020-05-16 02:57:44--  https://nlp.stanford.edu/data/glove.6B.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n",
            "--2020-05-16 02:57:44--  http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘glove.6B.zip’\n",
            "\n",
            "glove.6B.zip        100%[===================>] 822.24M  2.12MB/s    in 6m 28s  \n",
            "\n",
            "2020-05-16 03:04:12 (2.12 MB/s) - ‘glove.6B.zip’ saved [862182613/862182613]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "AxxmP0OMJ8US",
        "colab_type": "code",
        "outputId": "541d1561-0ffd-4041-9d5c-5af7bf3b1af3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "! ls"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "glove.6B.zip  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "TjR3-Vo8J8UW",
        "colab_type": "code",
        "outputId": "0f528c07-4733-4e72-ddae-7a962964bfc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.sample(5)"
      ],
      "execution_count": 70,
      "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>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>punctuation</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>upper</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5225</th>\n",
              "      <td>7517</td>\n",
              "      <td>oil%20spill</td>\n",
              "      <td>national briefing west california spring oil s...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>103</td>\n",
              "      <td>6.923076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4502</th>\n",
              "      <td>6446</td>\n",
              "      <td>injured</td>\n",
              "      <td>terrorists attack police post  spos injured ht...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>65</td>\n",
              "      <td>7.124999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1353</th>\n",
              "      <td>1995</td>\n",
              "      <td>bush%20fires</td>\n",
              "      <td>drought fuels bush fires jamaica   jamaicaobse...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>61</td>\n",
              "      <td>7.428570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4175</th>\n",
              "      <td>5975</td>\n",
              "      <td>hazard</td>\n",
              "      <td>pm hazard high traffic area  deans bridge rd</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>46</td>\n",
              "      <td>4.499999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6793</th>\n",
              "      <td>9774</td>\n",
              "      <td>trapped</td>\n",
              "      <td>hollywood movie trapped miners released chile</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>45</td>\n",
              "      <td>6.666666</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id       keyword  ... char_count  avg_word\n",
              "5225  7517   oil%20spill  ...        103  6.923076\n",
              "4502  6446       injured  ...         65  7.124999\n",
              "1353  1995  bush%20fires  ...         61  7.428570\n",
              "4175  5975        hazard  ...         46  4.499999\n",
              "6793  9774       trapped  ...         45  6.666666\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uoz9iJyky9BW",
        "colab_type": "code",
        "outputId": "131603e6-7c95-4fb8-9662-7f404724dfe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "# Now, let's take a look at the punctuation number for tweets\n",
        "\n",
        "plt.figure(figsize=(12,10), dpi= 100 )\n",
        "# sns.set(font_scale=1)\n",
        "\n",
        "with sns.axes_style('white'):\n",
        "    g = sns.catplot( 'punctuation', data= df, aspect= 2, kind='count', \n",
        "                    hue='target', hue_order=df['target'].value_counts().sort_values(ascending=False).index)\n",
        "    g.set_ylabels('Number of Tweets')\n",
        "    g.set_xlabels('Number of Punction')\n",
        "    g.set_titles('Distrubition of Number of Punction for Tweets')"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0Jk+erPr162vo0KHavHmzEhIS5O/vr9mzZztVDwAAAEDlceicg9jYWD355JPq3r27cnNzNXXqVG3dulX//Oc/HV5RQUGBoqOjFRERoV69ekmSrr/+etv8yMhIPfXUU5Iu7Cm4+GRnq9Uqs9ns8LoAAAAAOM+hPQedOnXSJ598olatWmnw4MFq3ry54uLi1KFDB4dWYhiGpkyZohYtWigqKso2PTMz0/bzli1b1Lp1a0mSxWLR+vXrlZ+fryNHjigtLc3hdQEAAABwjUN7DnJycmQ2m/XEE0+4tJKvv/5aCQkJatOmjfr37y9JiomJ0bp167Rv3z5JUrNmzTR9+nRJUuvWrfXAAw+ob9++8vDw0NSpU7lSEQAAAFDFHAoH9957r1q0aKEuXbrozjvvVOfOndWoUSOHV9K5c2f99NNPl00PDQ21u8zTTz+tp59+2uF1AAAAAKgYhw4r2rVrl55//nn5+vrq/fffl8ViUUREhO2bfgAAAABXPofCgbe3t0JCQvT444/rySef1MMPP6xff/1VGzdurOr+AAAAALiJQ4cVzZkzR7t375bValVwcLA6d+6sjz/+WK1atarq/gBUojxruvKzjtud7+XXRN7mQDd2BAAAahKHwsFHH32k66+/XkOHDtWdd96p9u3b2+6UDODKkZ91XPtnxNqd32bKbMIBAAC1mENb+Lt27dL333+vXbt26e2339bevXvVqlUrdenSRc8880xV9wgAAADADRwKB56engoODlaLFi1000036auvvlJ8fLx27dpFOAAAAACuEmWekBwRESFJeuWVV/Tggw+qW7dueu+991S/fn0tXLhQKSkpbmkSAAAAQNUrc8/BsWPHJEkNGzbU5MmTFRwcLG9vb7c0BgAAAMC9HDqsaPTo0VXdBwAAAIBqVmY4yM3N1SOPPFJmgRUrVlRqQwAAAACqR5nhwNPTU0OGDHFXLwAAAACqUbnhYODAge7qBQAAAEA1KjMcGIbhrj4AlKGsOxsX123s5m4AAMDVqsxw8NRTT7mrDwBlKOvOxqYX3nJzNwAA4GpV5n0OnnzySXf1AQAAAKCalRkOAAAAANQehAMAAAAAksoIBw899JDt53/84x9uaQYAAABA9bF7QnJaWpry8vLk7e2tpUuX6m9/+5s7+wKuClxlCAAAXEnshoOePXuqd+/eatasmfLy8uzeKZk7JAP2cZUhAABwJbEbDmbNmqXdu3fr2LFj+v7777lTMgAAAHCVK/M+B507d1bnzp1VUFDAnZIBAACAq1yZ4eB3Q4YM0c6dOxUfH6/MzEz5+/urf//+uuuuu6q6PwAAAOCqkZOTo08//dTuIfuVZcuWLbrxxhvVqlUrp5Zz6FKmq1ev1pgxY9SkSROFhYXJ399f48aN06pVq1xqFgAAAKiNcnJytHLlSocfbxiGiouLnV7Pli1bdODAAaeXc2jPweLFi7Vs2TK1bdvWNu2BBx5QdHT0JZc8BQAAAGDf66+/rsOHD6t///7q2rWrfvrpJ+Xk5KiwsFDPPfec7r//fh09elQjR45Ux44d9eOPP2rRokWKj4/XJ598osaNGyswMFDt2rXTyJEjdfjwYU2bNk3Z2dm65ppr9PLLL+vUqVPaunWrUlJS9Pbbb+vNN9/Un/70J4f6cygcnDx5Ui1btrxkWosWLXTq1Cnn/yIAAABALTVu3Dj93//9nxISElRYWKjc3FzVr19fJ06c0MMPP6yePXtKkg4dOqRXX31VnTp1UmpqqjZt2qRPPvlEBQUFGjRokNq1aydJ+vvf/65p06bpxhtv1Hfffadp06bp/fffl8ViUffu3dWnTx+n+nMoHNx+++2aPXu2xo8fLx8fH507d07z5s1TcHCwk38OAAAAANKFQ4bmzZunXbt2qU6dOrJarfrtt98kSU2bNlWnTp0kSXv27FHPnj3l7e0tb29v9ejRQ5J09uxZffPNN3ruuedsNfPz8yvUk0PhYNq0aRo7dqw6d+6shg0b6tSpUwoODtbrr79eoZUDAAAAtdWnn36qEydOaM2aNapbt64sFovy8vIkSddee225yxuGIV9fXyUkJFRaTw6dkOzv768VK1YoMTFR77zzjhITE/Xhhx/KbDY7tJL09HQNHz5cffv2VXh4uJYvXy7pwuFKUVFR6tWrl6KiomyHKRmGoVdeeUVhYWGKiIjQjz/+6OLTAwAAAGqOevXq6ezZs5Kk06dPy8/PT3Xr1tVXX32lY8eOlbrM7bffri+++EJ5eXk6e/astm3bJkmqX7++mjdvrs8++0zShW3offv2XbYeZzgUDn4XEBCgDh06KCAgwKmVeHh4KDY2Vhs2bNDHH3+sjz76SAcOHNCiRYsUEhKiTZs2KSQkRIsWLZIkJScnKy0tTZs2bdLLL7+sl156yan1AQAAADVRo0aNdPvtt6tfv37at2+ffvjhB0VERCghIUEtWrQodZkOHTrIYrHowQcf1BNPPKE2bdqoQYMGkqQ5c+YoLi5ODz74oMLDw7VlyxZJUt++fbVkyRINGDBAhw8fdrg/hw4rqih/f3/5+/tLupBwWrRoIavVqsTERH3wwQeSpAEDBmj48OGaMGGCEhMTNWDAAJlMJnXq1Ek5OTm2+ysAAAAAVzJHDs1ft27dJb8/9thjGj16tM6fP69HHnnEdkJyUFCQlixZctnyd9xxhzZs2OB0b24JBxc7evSo9u7dq44dOyorK8u2wd+kSRNlZWVJkqxW6yV7JwICAmS1WgkHAAAAqJWmTp2qAwcOKC8vTwMHDrSFg8pWbjgoLi7Wzp07dccdd8jLy6tCKzt79qyio6M1efJk1a9f/5J5JpNJJpOpQvUBAACAq5G7LgRUbjioU6eOnnnmGX3zzTcVWlFBQYGio6MVERGhXr16SZL8/PxshwtlZmaqcePGkiSz2ayMjAzbshkZGQ6f/AzAdSd8rtPBQ8dLnefv66PARvVLnQcAAK4ODh1W1KVLF3377be2a606yzAMTZkyRS1atFBUVJRtusViUXx8vEaNGqX4+HjbTR8sFos+/PBDhYeH67vvvlODBg04pAhwg6zcIsXGJ5c6b86wboQDAACucg6Fg6ZNm+qJJ55Qz549FRAQcMnhPxffdMGer7/+WgkJCWrTpo369+8vSYqJidGoUaM0ZswYxcXFqWnTplqwYIEkKTQ0VElJSQoLC5OPj49mzpzpynMDAAAA4ASHwkFeXp7uv/9+SRdOFnZW586d9dNPP5U67/d7HlzMZDLpxRdfdHo9AAAAAFznUDiYNWtWVfcBAAAAoBIlJydrxowZKi4uVmRkpEaNGlXuMg5fyvTnn3/W559/rqysLE2dOlUHDx5Ufn6+2rZtW6GmAQAAgKtZnjVd+VmlX/DDFV5+TeRtDizzMUVFRZo+fbqWLVsms9msIUOGyGKxqFWrVmUu51A4+OyzzzRt2jT16tVL69at09SpU3X27Fm9/vrreu+99xx+IgAAAEBtk591XPtnxFZavTZTZpcbDlJTU3XDDTcoKChIkhQeHq7ExMRyw0EdRxpYuHCh3nvvPU2fPl0eHh6SpLZt22rfvn2OLA4AAADAjf54U2Gz2ezQucMOhYMTJ07o5ptvliTblYq4aRkAAABwdXEoHLRr104JCQmXTFu/fr06dOhQJU0BAAAAcN0fbypstVoduqmwQ+ccTJkyRSNHjlRcXJzOnTunkSNH6pdfftHSpUtd7xgAAABAlWjfvr3S0tJ05MgRmc1mrV+/Xq+//nq5yzkUDlq2bKnPPvtMX3zxhbp3767AwEB1795d9erVq3DjAAAAACqXp6enpk6dqscff1xFRUUaPHiwWrduXf5yjq7Ax8dHd9xxh5o3by6z2UwwAAAAABzg5ddEbabMrtR6jggNDVVoaKhTtR0KB7/++qvGjx+v7777Tr6+vsrJyVHHjh01Z84cNWvWzKkVAgAAALWJtzmw3EuP1hQOnZD8/PPPq127dtq1a5d27NihlJQU3XbbbYqNrbzrtQIAAACoXg7tOfjxxx+1dOlS1a1bV5JUr149jR8/Xl27dq3S5gAAAAC4j0N7Djp16qTU1NRLpv3www8KDg6ukqYAAAAAuJ/dPQdvvPGG7eegoCCNGjVK3bt3V0BAgDIyMpSUlKR+/fq5pUkAAAAAVc9uOLj4pgmS1KtXL0kX7pbs5eWlsLAw5eXlVW13AAAAANzGbjiYNWuWO/sAAAAAUIkmTZqkbdu2yc/PT+vWrXNoGYfvc3D+/HkdOnRI586du2T67bff7lyXAK54edZ05Wcdtzvfy6/JFXPJNgAAqlp69hll5pyvtHr+vj4KbFS/3McNGjRIjz76qJ5//nmHazsUDuLj4zV9+nTVrVtX11xzjW26yWTStm3bHF4ZgKtDftZx7Z9h/1LGbabMJhwAAFAiM+e8JnyUXGn15gzr5lA46NKli44ePepUbYfCwZw5c/Tmm2/qnnvucao4AAAAgCuHQ5cyrVu3ru68886q7gUAAABANXJoz8Fzzz2n2bNn69lnn1Xjxo2ruicAV7gTPtfp4KHSz0lw9DhJAADgfg6FgxtvvFELFy7URx99ZJtmGIZMJpP27t1bZc0BuDJl5RYpNr70YysdPU4SAAC4n0PhYOLEierfv7/69u17yQnJqDn4phYAAAAXi4mJUUpKirKzs9WtWzeNHj1akZGRZS7jUDg4efKknnvuOZlMpkppFJWPb2oBAABqJn9fH80Z1q1S6zli3rx5Ttd2KBwMGjRICQkJGjBggNMruFrxTT0AAAAcEdio/hWzbehQOEhNTdWKFSv09ttv6/rrr79k3ooVK6qksZqOb+oBAABwtXEoHDz00EN66KGHqroXAAAAANXIoXAwcODACq1k0qRJ2rZtm/z8/LRu3TpJ0ptvvqlVq1bZLo0aExOj0NBQSdK7776ruLg41alTRy+88ILuu+++Cq0fAAAAQPkcCgdxcXF25w0ZMqTc5QcNGqRHH31Uzz///CXTR4wYoZEjR14y7cCBA1q/fr3Wr18vq9WqqKgobdy4UR4eHo60CgAAAMBFDoWDhISES37/7bffdOTIEQUHBzsUDrp06aKjR4861FBiYqLCw8Pl5eWloKAg3XDDDUpNTVVwcLBDywMAAABwjUPh4IMPPrhsWlxcnH7++ecKrXzFihWKj4/XbbfdptjYWDVs2FBWq1UdO3a0PcZsNstqtVZoPTUZVz0CAABATeFQOCjNoEGDdNddd112qJCjhg4dqmeeeUYmk0lvvPGGZs+erVmzZrnazhWLqx4BAACgpnAoHBQXF1/y+/nz5/XJJ5+oQYMGLq/44kuiRkZG6qmnnpJ0YU9BRkaGbZ7VapXZbHZ5PXAOezIAAABqL4fCwa233nrZ3ZHNZrNefvlll1ecmZkpf39/SdKWLVvUunVrSZLFYtG4ceMUFRUlq9WqtLQ0dejQweX1wDnsyQAAAKi9HAoHiYmJl/zu4+NjuwSpI2JiYpSSkqLs7Gx169ZNo0ePVkpKivbt2ydJatasmaZPny5Jat26tR544AH17dtXHh4emjp1KlcqAgAAANzAoXDQrFmzCq1k3rx5l02LjIy0+/inn35aTz/9dIXWCQAAAMA5ZYaD4cOHX3Y40cVMJpOWL19e6U0BAAAAcL8yw8GDDz5Y6nSr1aoPPvhAubm5VdIUAAAAAPcrMxz88dCf7OxsLVq0SKtWrVLfvn317LPPVmlzAAAAANzHoXMOzpw5o8WLF2vFihXq3r271q5dqz/96U9V3RsAAAAANyozHOTm5mr58uVaunSpunbtqo8++sh2yVEAqAx51nTlZ5V+bw0vvybyNge6uSMAAGqvMsOBxWJRcXGxHn/8cd1222367bff9Ntvv13ymJCQkCptEMDVLT/ruPbPiC11XpspswkHAAC4UZnh4JprrpEkrVy5stT5JpPpsnsgAAAAALgylRkOtm7d6q4+AAAAAFSzOtXdAAAAAICagXAAAAAAQBLhAAAAAEAJwgEAAAAASYQDAAAAACUIBwAAAAAkEQ4AAAAAlCAcAAAAAJBEOAAAAABQgnAAAAAAQBLhAAAAAEAJwgEAAAAASYQDAAAAACUIBwAAAAAkEQ4AAAAAlCAcAAAAAHrEVKYAAB6USURBVJAkeVZ3AwBgzwmf63Tw0PFS5/n7+iiwUX03dwQAwNWNcACgxsrKLVJsfHKp8+YM60Y4AACgknFYEQAAAABJhAMAAAAAJdwSDiZNmqSQkBD169fPNu3kyZOKiopSr169FBUVpVOnTkmSDMPQK6+8orCwMEVEROjHH390R4sAAABAreeWcDBo0CAtXrz4kmmLFi1SSEiINm3apJCQEC1atEiSlJycrLS0NG3atEkvv/yyXnrpJXe0CAAAANR6bgkHXbp0UcOGDS+ZlpiYqAEDBkiSBgwYoC1btlwy3WQyqVOnTsrJyVFmZqY72gQAAABqtWo75yArK0v+/v6SpCZNmigrK0uSZLVaFRAQYHtcQECArFZrtfQIAAAA1CY14oRkk8kkk8lU3W0AAAAAtVq1hQM/Pz/b4UKZmZlq3LixJMlsNisjI8P2uIyMDJnN5mrpEQAAAKhNqi0cWCwWxcfHS5Li4+PVs2fPS6YbhqFvv/1WDRo0sB1+BAAAAKDquOUOyTExMUpJSVF2dra6deum0aNHa9SoURozZozi4uLUtGlTLViwQJIUGhqqpKQkhYWFycfHRzNnznRHiwAAAECt55ZwMG/evFKnL1++/LJpJpNJL774YlW3BAAAAOAPasQJyQAAAACqH+EAAAAAgCTCAQAAAIAShAMAAAAAktx0QjIAVLU8a7rys46XOs/Lr4m8zYFu7ggAgCsP4QDAVSE/67j2z4gtdV6bKbMJBwAAOIDDigAAAABIIhwAAAAAKEE4AAAAACCJcAAAAACgBOEAAAAAgCSuVgQAl+CSqACA2oxwAAAX4ZKoAIDajMOKAAAAAEhizwGAWuCEz3U6eKj0Q4X8fX0U2Ki+mzsCAKBmIhwAuOpl5RYpNj651HlzhnUjHAAAUILDigAAAABIIhwAAAAAKEE4AAAAACCJcAAAAACgRK06IbmsK5ZIXLUEAAAAtVutCgdlXbFE4qolAAAAqN04rAgAAACAJMIBAAAAgBKEAwAAAACSCAcAAAAAShAOAAAAAEiqAVcrslgsqlevnurUqSMPDw+tWbNGJ0+e1NixY3Xs2DE1a9ZMCxYsUMOGDau7VQAAAOCqViP2HCxfvlwJCQlas2aNJGnRokUKCQnRpk2bFBISokWLFlVzhwAAAMDVr0aEgz9KTEzUgAEDJEkDBgzQli1bqrkjAAAA4OpX7YcVSdLIkSNlMpn08MMP6+GHH1ZWVpb8/f0lSU2aNFFWVlY1dwgA3GUdAHD1q/ZwsHLlSpnNZmVlZSkqKkotWrS4ZL7JZJLJZKqm7gDg/+Mu6wCAq121H1ZkNpslSX5+fgoLC1Nqaqr8/PyUmZkpScrMzFTjxo2rs0UAAACgVqjWcHDu3DmdOXPG9vN///tftW7dWhaLRfHx8ZKk+Ph49ezZszrbBAAAAGqFaj2sKCsrS88++6wkqaioSP369VO3bt3Uvn17jRkzRnFxcWratKkWLFhQnW0CAAAAtUK1hoOgoCB98sknl01v1KiRli9fXg0dAQAAALVXtZ9zAAAAAKBmIBwAAAAAkEQ4AAAAAFCCcAAAAABAEuEAAAAAQIlqv0MyAFyN8qzpys86bne+l18TeZsD3dgRAADlIxwAQBXIzzqu/TNi7c5vM2U24QAAUOMQDgCgGpzwuU4HD5W+Z8Hf10eBjeq7uSMAAAgHAFAtsnKLFBufXOq8OcO6EQ4AANWCE5IBAAAASCIcAAAAAChBOAAAAAAgiXAAAAAAoAThAAAAAIAkwgEAAACAEoQDAAAAAJIIBwAAAABKEA4AAAAASOIOyQAAuFV69hll5pwvdZ6/rw93xwZQrQgHAFBL5FnTlZ91vNR5Xn5N5G0OdHNHtVNmznlN+Ci51HlzhnUjHACoVoQDAKgl8rOOa/+M2FLntZkyu0aFg7K+XZf4hh0AqgrhAABqsLK+7Zcq7xv/Ez7X6eCh0tdTHRviZX27LvENOwBUFcIBANRgZX3bL0nXv/KOTlbCRn1WbpFi4znUBQBqO8IBAFzBqmOjvqy9GaeuC1BWganUeRwKBAA1H+EAAOCUsvZmmF54S7Hxe0qdxx6I6sPJ6AAcRTgAAOAqdyWdjA6gehEOAABQ5Z78XVat4rqNXeoPANyBcAAAqBY1bQO6vJO/nfmGvbxDrwCgpqrR4SA5OVkzZsxQcXGxIiMjNWrUqOpuCQBQSdy1Ac0dictW1mVsJf5GQG1TY8NBUVGRpk+frmXLlslsNmvIkCGyWCxq1apVdbcGALiCcEfispV1xSvp0r9RZR165a5DuDjZumbi/6xmq7HhIDU1VTfccIOCgoIkSeHh4UpMTCQcAAAuUd6GZmUdolTTbhRXHXtEKuu+G5V5/47KOtm6sjZY3VHHmVo1MYhdSf9nzta6GpgMwzCqu4nSfP7559q+fbtmzJghSYqPj1dqaqqmTp1a6uNvvvlmd7YHAAAA2Pz000/V3UKlqLF7Dpx1tfyHAAAAANWlTnU3YI/ZbFZGRobtd6vVKrPZXI0dAQAAAFe3GhsO2rdvr7S0NB05ckT5+flav369LBZLdbcFAAAAXLVq7GFFnp6emjp1qh5//HEVFRVp8ODBat26dXW3BQAAAFy1auwJyQAAAADcq8YeVgQAAADAvQgHAAAAACTV4HMOKltycrJmzJih4uJiRUZGatSoUU7XmDRpkrZt2yY/Pz+tW7fO5V7S09M1ceJEZWVlyWQy6aGHHtJf//pXl2rl5eXpkUceUX5+voqKitS7d29FR0e7VOv3czvMZrPeffddl2pIksViUb169VSnTh15eHhozZo1LtXJycnRCy+8oP3798tkMmnmzJkKDg52qsbBgwc1duxY2+9HjhxRdHS0RowY4XQ/7733nlavXi2TyaQ2bdpo1qxZ8vb2drqOJC1fvlyrV6+WYRiKjIx0uJ/SxuDJkyc1duxYHTt2TM2aNdOCBQvUsGFDp+t89tln+sc//qGff/5Zq1evVvv27V3q59VXX9UXX3yhunXr6k9/+pNmzZolX19fl2otWLBAiYmJqlOnjvz8/DRr1qxyr1pW1ut06dKlevXVV7Vjxw41blz2jbFKq/Pmm29q1apVtmVjYmIUGhrqUj8ffPCBVqxYIQ8PD4WGhmrixIlO1xkzZox++eUXSdLp06fVoEEDJSQklFnHXq29e/fqxRdfVF5enjw8PPTSSy+pQ4cOTtfZt2+fXnzxRZ07d07NmjXT3LlzVb9+2Tfjsvee6OzYtlfH2bFtr46zY9teHVfGdXmfG46ObXt1nB3bZfXjzNi2V8fZsW2vjrPj2l4dV8a1vc/nDz/8UMuXL9fhw4cdei+yV2fcuHH64YcfVLduXbVv317Tp09X3bp1na4zefJk/fDDDzIMQzfddJNmzZqlevXqOV3HMAwtWLBAn3/+uerUqaOhQ4fqL3/5S5nPTSp9m8GVz7XS6rjyeVTatkdGRobTn49XNKMWKCwsNHr27GkcPnzYyMvLMyIiIoz/+7//c7pOSkqK8cMPPxjh4eEV6sdqtRo//PCDYRiGcfr0aaNXr14u9WMYhlFcXGycOXPGMAzDyM/PN4YMGWJ88803LtVaunSpERMTY4waNcql5X/Xo0cPIysrq0I1DMMwJk6caKxatcowDMPIy8szTp06VaF6hYWFxt13320cPXrU6WUzMjKMHj16GOfPnzcMwzCio6ONf//73y718dNPPxnh4eHGuXPnjIKCAuOvf/2rkZaW5tCypY3BV1991Xj33XcNwzCMd99913jttddcqnPgwAHj559/Nh599FEjNTXV5X62b99uFBQUGIZhGK+99ppD/dirdfr0advPy5cvN/7+97+7VMcwDOPXX381HnvsMaN79+4Ojc/S6ixcuNBYvHixI0+nzDo7duww/vrXvxp5eXmGYRjGb7/95lKdi82aNct48803Xe4pKirK2LZtm2EYhrFt2zbj0UcfdanOoEGDjJ07dxqGYRirV6825s+fX24de++Jzo5te3WcHdv26jg7tu3VcWVcl/W54czYtlfH2bFtr46zY9uRz0NHxra9Os6Oa3t1XBnX9j6ff/zxR+PIkSMOf1baq7Nt2zajuLjYKC4uNsaOHWusWLHCpToXj8eZM2faXnPO1omLizMmTJhgFBUVGYbh2PuaYZS+zeDK51ppdVz5PCpt28OVz8crWa04rCg1NVU33HCDgoKC5OXlpfDwcCUmJjpdp0uXLuUmV0f4+/urXbt2kqT69eurRYsWslqtLtUymUy2hF9YWKjCwkKZTCan62RkZGjbtm0aMmSIS31UttOnT2vXrl22fry8vBz69rksO3bsUFBQkJo1a+bS8kVFRcrNzVVhYaFyc3Pl7+/vUp2ff/5ZHTp0kI+Pjzw9PdWlSxdt2rTJoWVLG4OJiYkaMGCAJGnAgAHasmWLS3VatmypFi1aOPgs7Ne599575el5Yadkp06dLrlfibO1Lv5m7vz58w6NbXuv01mzZmnChAkOvz4q6/VeWp2VK1dq1KhR8vLykiT5+flVqB/DMPTZZ5+pX79+LvdkMpl09uxZSRdef46M79LqpKWlqUuXLpKke+65x6Gxbe890dmxba+Os2PbXh1nx7a9Oq6M67I+N5wZ25X1+WOvjrNju7x+HB3b9uo4O67t1XFlXNv7fL711lvVvHnzcpcvr05oaKhMJpNMJpM6dOhQ7v+jvTq/j0fDMJSbm+tyPytXrtSzzz6rOnUubFo68r5mjyufa6Vx9jVrb9vDlc/HK1mtCAdWq1UBAQG2381ms8sb45Xt6NGj2rt3rzp27OhyjaKiIvXv319333237r77bpdqzZw5UxMmTLC9qCtq5MiRGjRokD7++GOXlj969KgaN26sSZMmacCAAZoyZYrOnTtXoZ7Wr1/v8MbTH5nNZj322GPq0aOH7r33XtWvX1/33nuvS7XatGmjr7/+WtnZ2Tp//rySk5Md3oAuTVZWlu0Dr0mTJsrKynK5VmX797//rW7dulWoxvz58xUaGqpPP/1Uzz33nEs1tmzZIn9/f7Vt27ZCvUjSihUrFBERoUmTJunUqVMu1UhLS9Pu3bsVGRmpRx99VKmpqRXqaffu3fLz89ONN97oco3JkyfrtddeU2hoqF599VXFxMS4VKd169a2L18+//xzpaenO7X8xe+JFRnblfHeWlYdZ8f2H+tUZFxfXKsiY/uPPbk6ti+uU5GxXdrf2pWxfXGdiozri+u4Oq4r4/O5vDoFBQVKSEjQfffd53KdSZMm6Z577tHBgwc1fPhwl+ocOXJEGzZs0KBBg/T4448rLS3N4ef3x20GV1/7ZW17OPKarYptjytRrQgHNdXZs2dtx/uVd+xiWTw8PJSQkKCkpCSlpqZq//79Ti3/xRdfqHHjxrrttttc7uFiK1eu1Nq1a/Wvf/1LK1as0K5du5yuUVhYqP/9738aOnSo4uPj5ePjo0WLFrncU35+vrZu3ao+ffq4tPypU6eUmJioxMREbd++XefPn3fo2O7StGzZUo8//rhGjhypxx9/XG3btq20UPb7t0g1wdtvvy0PDw89+OCDFaozduxYJSUlKSIiQh9++KHTy58/f17vvvuuy8HiYkOHDtXmzZuVkJAgf39/zZ4926U6RUVFOnXqlFatWqWJEydqzJgxMipwVel169a5HHx/t3LlSk2aNElJSUmaNGmSpkyZ4lKdGTNm6KOPPtKgQYN09uxZ2zfIjijrPdGZsV1Z76326jg7tkur4+q4vriWh4eHy2P7jz25Orb/WMfVsW3vb+3s2P5jHVfH9R/ruDquK/r57EidadOmqXPnzurcubPLdWbNmqXt27erZcuW2rBhg0t18vPz5e3trTVr1uihhx7S5MmTHXpu5W0zOPraL6uOo6/Zyt72uFLVinBgNpsv+WbWarWWe/JXVSsoKFB0dLQiIiLUq1evSqnp6+urrl27avv27U4tt2fPHm3dulUWi0UxMTH66quvNH78eJf7+P1v6+fnp7CwMJe+FQ0ICFBAQIDtW40+ffrof//7n8s9JScnq127drr++utdWv7LL79U8+bN1bhxY9WtW1e9evXSN99843I/kZGRWrNmjVasWKGGDRtW6BtfPz8/ZWZmSpIyMzPLPbnNHdasWaNt27Zp7ty5lRZWIiIiHD786mKHDx/W0aNH1b9/f1ksFmVkZGjQoEE6fvy407Wuv/56eXh4qE6dOoqMjNT333/vdA3pwmskLCzMdjhAnTp1lJ2d7VKtwsJCbd68WX379nVp+d+tXbvW9l70wAMPuLw3o2XLllq6dKnWrFmj8PBwBQUFObRcae+JroztynpvtVfH2bFdXj/OjOs/1nJ1bJfWkytju7Q6roxte38jZ8d2aXVcGdel1XF1XP/O1c/n8ur84x//0IkTJzRp0qQK9+Ph4aHw8HCn3mcvrvP7/70khYWF6aeffnKoRmnbDK689u1tezjzmq3sbY8rVa0IB+3bt1daWpqOHDmi/Px8rV+/XhaLpdr6MQxDU6ZMUYsWLRQVFVWhWidOnFBOTo4kKTc3V19++aXTx8WNGzdOycnJ2rp1q+bNm6e77rpLc+fOdamfc+fO6cyZM7af//vf/7p0Z+smTZooICBABw8elHThfIGWLVu61JN04ZCi8PBwl5dv2rSpvvvuO50/f16GYVS4n993kf7666/atGmTIiIiXK5lsVgUHx8vSYqPj1fPnj1drlUZkpOTtXjxYr399tvy8fGpUK2Ld0snJia6dMznzTffrB07dmjr1q3aunWrAgICtGbNGjVp0sTpWr9/WEkXDlVy9a7t999/v3bu3ClJ+uWXX1RQUKBGjRq5VOv31/zFh066wt/fXykpKZKkr776yuXA+vvYLi4u1ttvv60///nP5S5j7z3R2bFdWe+t9uo4O7bt1XFlXJdWy5Wxba8nZ8e2vTrOju2y/s+cGdv26jg7ru3VcWVcV8bnc1l1Vq9erf/85z+aN2+eQ3ufS6tz00036dChQ5IuPPetW7eW26O9fi7+v09JSXHoPcTeNoOzr317dZx9zVb2tseVqtbcITkpKUkzZ860Xa7z6aefdrpGTEyMUlJSlJ2dLT8/P40ePVqRkZFO19m9e7ceeeQRtWnTxvaCduSSiKXZt2+fYmNjVVRUJMMw1KdPH/3tb39zus7vdu7cqaVLl7p8KdMjR47o2WeflXTh0Il+/fq59LeWLlxaccqUKSooKFBQUJBmzZrl0gmi586dU48ePbRlyxY1aNDApV4kaeHChdqwYYM8PT11yy23aMaMGU4dMnGxYcOG6eTJk/L09NSkSZMUEhLi0HKljcH7779fY8aMUXp6upo2baoFCxbouuuuc7rOddddp5dfflknTpyQr6+vbrnlFi1ZssTpOosWLVJ+fr6th44dO2r69OkuPbfk5GT98ssvMplMatasmaZNm1buXr/yXqcWi0VxcXHlfhNVWp2UlBTt27dPktSsWTNNnz693BMcS6vTv39/TZ48Wfv27VPdunU1ceLEcseAvecVGxurjh07aujQoWUuX16tm266STNnzlRhYaG8vb314osvlnuoYWl1zp07p48++kjShW8Px40bV+63dfbeEzt06ODU2LZXJz8/36mxba/OK6+84tTYtlcnLi7O6XHtyOeGI2PbXp1169Y5Nbbt1QkJCXFqbJf1vJwZ2/bq1KtXz6lxba9OWlqa0+Pa3ufz+++/r8WLF+u3335T48aNFRoaqhkzZjhd59Zbb1XTpk1tJweHhYWV+flfWp1nnnlGw4YN09mzZ2UYhm6++WZNmzatzMPx7PWTk5Oj8ePHKz09Xddee62mTZtW7rkw9rYZsrOznXrt26sTFhbm9OdRadseKSkpTn8+XslqTTgAAAAAULZacVgRAAAAgPIRDgAAAABIIhwAAAAAKEE4AAAAACCJcAAAAACgBOEAAGqI2NhYzZ8/v1rWbRiGJk2apC5dumjIkCHV0oMjgoODdeTIkepuAwCuWoQDALDDYrEoJCRE586ds01bvXq1hg8fXo1dVY2vv/5a//3vf5WUlKS4uLjL5q9Zs0a33HKLgoODdfvtt6t///764osvqrSn4cOHa/Xq1ZdM++abb5y+Oy0AwHGEAwAoQ3Fxsd5///3qbsNpRUVFTj3+2LFjatasma699lq7j+nUqZO++eYb7d69W0OGDNGYMWN06tSpirYKAKhBCAcAUIaRI0dq6dKlysnJuWze0aNHdfPNN6uwsNA27eJvu9esWaM///nPmjlzpjp37qyePXtqz549WrNmjUJDQxUSEqK1a9deUjM7O1tRUVEKDg7Wo48+qmPHjtnm/fzzz4qKitKdd96p3r17a8OGDbZ5sbGxevHFF/XEE0+oU6dO2rlz52X9Wq1WPfXUU7rzzjsVFhamVatWSbqwN+SFF17Qt99+q+DgYC1cuLDMv0mdOnU0ePBg5ebm6vDhw5cdDrVz505169bN9rvFYtGSJUsUERGhO+64Q2PGjFFeXp5t/pYtW9S/f3/dfvvtuv/++5WcnKz58+dr9+7dmj59uoKDg213Nb355pt16NAhSdLp06c1ceJE3XXXXerRo4f++c9/qri42Pa3Hzp0qF599VV16dJFFotFSUlJZT4vAIDkWd0NAEBNdtttt+nOO+/UkiVLNHbsWKeXT01NVWRkpHbu3KmFCxcqJiZGPXr00ObNm5WSkqLRo0erV69eqlevniTp008/1aJFi9SxY0e99tprGj9+vFauXKlz587pscceU3R0tP71r39p//79ioqKUps2bdSqVStJ0rp167Ro0SK9++67KigouKyXmJgYtW7dWtu3b9fBgwcVFRWloKAgRUZGysPDQ6tXr9bKlSvLfU6FhYVavXq1rr32Wt14440O/R0+++wzLV68WN7e3ho6dKht4z01NVXPP/+8Fi5cqJCQEB0/flxnzpxRt27dtGfPHj344IOKjIwstebLL7+s06dPa8uWLTp58qRGjhypJk2a2B6fmpqqgQMH6quvvtLHH3+sKVOmaPv27TKZTA71DAC1EXsOAKAc0dHR+vDDD3XixAmnl23evLkGDx4sDw8P9e3bV+np6Xr22Wfl5eWle++9V15eXjp8+LDt8d27d1eXLl3k5eWlsWPH6ttvv1V6erq2bdumZs2aafDgwfL09NStt96q3r176/PPP7ct27NnT91xxx2qU6eOvL29L+kjPT1de/bs0fjx4+Xt7a1bbrlFkZGRSkhIcPi5fPfdd+rcubPuuecerV+/Xm+99ZYaNGjg0LLDhw+X2WzWddddpx49emjv3r2SpLi4OA0ePFj33HOP6tSpI7PZrJYtW5Zbr6ioSBs2bNC4ceNUv359NW/eXFFRUfrkk09sj2natKkeeugheXh4aODAgTp+/Lh+++03h58vANRG7DkAgHK0adNG3bt316JFixzacL2Yn5+f7edrrrlGknT99dfbpnl7e+vs2bO23wMCAmw/16tXTw0bNlRmZqaOHTum1NRUde7c2Ta/qKhIDz74oO33wMBAu31kZmaqYcOGql+/vm1a06ZN9cMPPzj8XDp27OjQnoXSNGnSxPazj4+PMjMzJV0ILaGhoU7Xy87OVkFBgZo2bWqb1rRpU1mtVtvvF/+dfXx8JOmSk8sBAJcjHACAA6KjozVw4EA99thjtmm/n7ybm5tr2+g+fvx4hdaTkZFh+/ns2bM6deqU/P39FRgYqC5dumjZsmUu1fX399epU6d05swZW6/p6ekym80V6le6sOGdm5tr+92Zb+cDAwMv2XPiqEaNGqlu3br69ddfbYdVVdbzAYDajMOKAMABN9xwg/r27asPPvjANq1x48Yym81KSEhQUVGR4uLiKnwN/qSkJO3evVv5+fl644031LFjRwUGBqp79+5KS0tTfHy8CgoKVFBQoNTUVP38888O1Q0MDFRwcLDmzZunvLw87du3T3FxcZfseXDVLbfcoqSkJJ08eVLHjx/X8uXLHV52yJAhWrNmjXbs2KHi4mJZrVbbc7r++uvt/j09PDzUp08fzZ8/X2fOnNGxY8e0bNmySnk+AFCbEQ4AwEHPPvvsZYelvPzyy1qyZIm6du2qAwcOKDg4uELr6Nevn9566y117dpVP/74o+bMmSNJql+/vpYsWaINGzbovvvu07333qu5c+cqPz/f4drz5s3TsWPHdN999+lvf/ubRo8erbvvvrtC/UpS//791bZtW1ksFj322GPq27evw8t26NBBs2bN0syZM3XHHXfo0Ucf1a+//ipJ+stf/qKNGzeqS5cueuWVVy5b9u9//7t8fHx0//33a9iwYerXr58GDx5c4ecDALWZyTAMo7qbAAAAAFD92HMAAAAAQBLhAAAAAEAJwgEAAAAASYQDAAAAACX+X/t1TAAAAIAwaP1T+xgDWiAHAABAJQcAAMDJAQAAUMkBAABwA3lvq/jJFtxIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 762.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ncf143AIrrn",
        "colab_type": "text"
      },
      "source": [
        "Until number of punction is 5, no disaster tweets have has more number of tweets. In the other words,  no disaster tweets use less punction.\n",
        "\n",
        "\n",
        "##### **Keep in mind:**  \n",
        "    Number of disaster tweets: 3229\n",
        "    Number of no disaster tweets: 4323"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mLKr4GYLjFv",
        "colab_type": "code",
        "outputId": "87f585dc-e99e-4155-b366-7b974b0e1633",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "df.sample()"
      ],
      "execution_count": 72,
      "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>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>punctuation</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>upper</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4377</th>\n",
              "      <td>6267</td>\n",
              "      <td>hijacking</td>\n",
              "      <td>hot funtenna hijacking computers send data sou...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>80</td>\n",
              "      <td>5.583333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id    keyword  ... char_count  avg_word\n",
              "4377  6267  hijacking  ...         80  5.583333\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNFk_RWDGMM6",
        "colab_type": "code",
        "outputId": "376ca6ac-61f4-4d2b-cde8-6cac2e439573",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "\n",
        "import plotly.express as px\n",
        "fig = px.histogram(df, x=\"char_count\",color=\"target\", marginal=\"rug\",\n",
        "                   title='Distiribtion of number of character',\n",
        "                   labels={'char_count':'Distiribtion of number of character'})\n",
        "fig.show()\n"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
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              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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              "\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYkJS1mVS9g6",
        "colab_type": "code",
        "outputId": "753577f8-4821-4c1f-e99d-4459af0b3a0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
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      },
      "source": [
        "df.groupby('word_count').numerics.agg(['sum','count']).plot(kind='area') # I need to figure out how to make this plot bigger\n",
        "\n",
        "#This may not be a meaningful plot\n",
        "\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f46ab132668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MubkonxTfY-T",
        "colab_type": "code",
        "outputId": "ebde73c9-fc1e-4fc3-cb26-f40093e6b102",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        }
      },
      "source": [
        "df.groupby('word_count').numerics.agg(['sum','count'])"
      ],
      "execution_count": 75,
      "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>sum</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>word_count</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>21</td>\n",
              "      <td>456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>30</td>\n",
              "      <td>510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>53</td>\n",
              "      <td>615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>80</td>\n",
              "      <td>726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>75</td>\n",
              "      <td>691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>138</td>\n",
              "      <td>761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>100</td>\n",
              "      <td>767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>139</td>\n",
              "      <td>654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>127</td>\n",
              "      <td>594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>129</td>\n",
              "      <td>366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>150</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>61</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>29</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>41</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>19</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            sum  count\n",
              "word_count            \n",
              "1             0     15\n",
              "2             0    101\n",
              "3             4    218\n",
              "4             3    276\n",
              "5             8    352\n",
              "6            21    456\n",
              "7            30    510\n",
              "8            53    615\n",
              "9            80    726\n",
              "10           75    691\n",
              "11          138    761\n",
              "12          100    767\n",
              "13          139    654\n",
              "14          127    594\n",
              "15          129    366\n",
              "16          150    230\n",
              "17           61    110\n",
              "18           29     41\n",
              "19           41     45\n",
              "20           19     12\n",
              "21            6      6\n",
              "22            1      1\n",
              "23            0      1\n",
              "24            3      1\n",
              "25            6      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1SmByveZJgY",
        "colab_type": "code",
        "outputId": "9804386d-cb59-4922-ecbf-89e1fbeda2b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "# Now, let's take a look at the pet species for each claim\n",
        "\n",
        "plt.figure(figsize=(12,10), dpi= 100 )\n",
        "# sns.set(font_scale=1)\n",
        "\n",
        "with sns.axes_style('white'):\n",
        "    g = sns.catplot( 'hastags', data= df, aspect= 2, kind='count', \n",
        "                    hue='target', hue_order=df['target'].value_counts().sort_values(ascending=False).index)\n",
        "    g.set_ylabels('Number of Tweets')\n",
        "    g.set_xlabels('Number of Hastag')\n",
        "    g.set_titles('Distrubition of Number of Hastag for Tweets')"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 762.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LppvbENVrN5",
        "colab_type": "code",
        "outputId": "ddd0bc3f-6fbb-48eb-c50f-0bab2864b66a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "\n",
        "import plotly.express as px\n",
        "fig = px.histogram(df, x=\"hastags\",color=\"target\", marginal=\"rug\",\n",
        "                   title='Distiribtion of number of character',\n",
        "                   labels={'char_count':'Distiribtion of number of character'})\n",
        "fig.show()\n"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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