Twitter,_Basic_EDA,Cleaning_and_GloVe.ipynb

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      "source": [
        "### Basic Intro \n",
        "\n",
        "In this competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t.\n",
        "\n",
        "![](https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcTigQWzoYCNiDyrz1BN4WTf2X2k9OZ_yvW-FsmcIMsdS9fppNmh)"
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          "height": 279
        },
        "outputId": "9c47419b-601a-446b-e866-cfde2c62de3e"
      },
      "source": [
        "! pip install -q kaggle\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle competitions download -c nlp-getting-started"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "Downloading test.csv to /content\n",
            "  0% 0.00/411k [00:00<?, ?B/s]\n",
            "100% 411k/411k [00:00<00:00, 55.5MB/s]\n",
            "Downloading sample_submission.csv to /content\n",
            "  0% 0.00/22.2k [00:00<?, ?B/s]\n",
            "100% 22.2k/22.2k [00:00<00:00, 22.7MB/s]\n",
            "Downloading train.csv to /content\n",
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            "100% 965k/965k [00:00<00:00, 63.7MB/s]\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bC-L3OF5QpAE",
        "colab_type": "text"
      },
      "source": [
        "## What's in this kernel?\n",
        "- Basic EDA\n",
        "- Data Cleaning\n",
        "- Baseline Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLNMEGoZRc0_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "520c6166-815e-4fbb-b86c-e44caa5b17ae"
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14U-ivNtQpAI",
        "colab_type": "text"
      },
      "source": [
        "### Importing required Libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "q4W4f9HgQpAM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.util import ngrams\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from collections import defaultdict\n",
        "from collections import  Counter\n",
        "plt.style.use('ggplot')\n",
        "stop=set(stopwords.words('english'))\n",
        "import re\n",
        "from nltk.tokenize import word_tokenize\n",
        "import gensim\n",
        "import string\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from tqdm import tqdm\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding,LSTM,Dense,SpatialDropout1D\n",
        "from keras.initializers import Constant\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.optimizers import Adam\n",
        "\n"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YOs2qS5KQpAg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "#os.listdir('../input/glove-global-vectors-for-word-representation/glove.6B.100d.txt')"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1cYnwcIdQpAz",
        "colab_type": "text"
      },
      "source": [
        "## Loading the data and getting basic idea "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "UdsvVN_RQpA4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "8bdc0155-799f-4ed9-9d1f-9ea7636fcc76"
      },
      "source": [
        "tweet= pd.read_csv('/content/train.csv')\n",
        "test=pd.read_csv('/content/test.csv')\n",
        "tweet.head(3)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>All residents asked to 'shelter in place' are ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id keyword  ...                                               text target\n",
              "0   1     NaN  ...  Our Deeds are the Reason of this #earthquake M...      1\n",
              "1   4     NaN  ...             Forest fire near La Ronge Sask. Canada      1\n",
              "2   5     NaN  ...  All residents asked to 'shelter in place' are ...      1\n",
              "\n",
              "[3 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "i6SoAw5vQpBG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "b8f9b5a2-c551-405f-912e-7c2f20d47a13"
      },
      "source": [
        "print('There are {} rows and {} columns in train'.format(tweet.shape[0],tweet.shape[1]))\n",
        "print('There are {} rows and {} columns in test'.format(test.shape[0],test.shape[1]))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 7613 rows and 5 columns in train\n",
            "There are 3263 rows and 4 columns in test\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8mIiTd8QpBU",
        "colab_type": "text"
      },
      "source": [
        "## Class distribution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCDKjAVZQpBX",
        "colab_type": "text"
      },
      "source": [
        "Before we begin with anything else,let's check the class distribution.There are only two classes 0 and 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "mDmQHt5bQpBb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "f2aa45ef-0fd1-436e-a6e0-8e19eab58f05"
      },
      "source": [
        "x=tweet.target.value_counts()\n",
        "sns.barplot(x.index,x)\n",
        "plt.gca().set_ylabel('samples')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'samples')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRK2DjExQpBo",
        "colab_type": "text"
      },
      "source": [
        "ohh,as expected ! There is a class distribution.There are more tweets with class 0 ( No disaster) than class 1 ( disaster tweets)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1bfbmB_tQpBq",
        "colab_type": "text"
      },
      "source": [
        "## Exploratory Data Analysis of tweets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUobJtnLQpBr",
        "colab_type": "text"
      },
      "source": [
        "First,we will do very basic analysis,that is character level,word level and sentence level analysis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VllndBIxQpBt",
        "colab_type": "text"
      },
      "source": [
        "### Number of characters in tweets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "g5v3ji8YQpBu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "e2aa81d7-223a-4635-d93e-c5d729a86158"
      },
      "source": [
        "fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,5))\n",
        "tweet_len=tweet[tweet['target']==1]['text'].str.len()\n",
        "ax1.hist(tweet_len,color='red')\n",
        "ax1.set_title('disaster tweets')\n",
        "tweet_len=tweet[tweet['target']==0]['text'].str.len()\n",
        "ax2.hist(tweet_len,color='green')\n",
        "ax2.set_title('Not disaster tweets')\n",
        "fig.suptitle('Characters in tweets')\n",
        "plt.show()\n"
      ],
      "execution_count": 43,
      "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": "oZvswuYbQpB2",
        "colab_type": "text"
      },
      "source": [
        "The distribution of both seems to be almost same.120 to 140 characters in a tweet are the most common among both."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kSHm55nQpB4",
        "colab_type": "text"
      },
      "source": [
        "### Number of words in a tweet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "fGPdy6jhQpB5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "5b8c64e4-c71e-4d7d-8e2a-34194d9c87ae"
      },
      "source": [
        "fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,5))\n",
        "tweet_len=tweet[tweet['target']==1]['text'].str.split().map(lambda x: len(x))\n",
        "ax1.hist(tweet_len,color='red')\n",
        "ax1.set_title('disaster tweets')\n",
        "tweet_len=tweet[tweet['target']==0]['text'].str.split().map(lambda x: len(x))\n",
        "ax2.hist(tweet_len,color='green')\n",
        "ax2.set_title('Not disaster tweets')\n",
        "fig.suptitle('Words in a tweet')\n",
        "plt.show()\n"
      ],
      "execution_count": 44,
      "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": "-eZ2shK3QpCB",
        "colab_type": "text"
      },
      "source": [
        "###  Average word length in a tweet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Lnj8V3dOQpCD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "f219eb8e-f67c-4029-f7f9-0323aaf60ac1"
      },
      "source": [
        "fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,5))\n",
        "word=tweet[tweet['target']==1]['text'].str.split().apply(lambda x : [len(i) for i in x])\n",
        "sns.distplot(word.map(lambda x: np.mean(x)),ax=ax1,color='red')\n",
        "ax1.set_title('disaster')\n",
        "word=tweet[tweet['target']==0]['text'].str.split().apply(lambda x : [len(i) for i in x])\n",
        "sns.distplot(word.map(lambda x: np.mean(x)),ax=ax2,color='green')\n",
        "ax2.set_title('Not disaster')\n",
        "fig.suptitle('Average word length in each tweet')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0.98, 'Average word length in each tweet')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "v9zcgb_iQpCN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_corpus(target):\n",
        "    corpus=[]\n",
        "    \n",
        "    for x in tweet[tweet['target']==target]['text'].str.split():\n",
        "        for i in x:\n",
        "            corpus.append(i)\n",
        "    return corpus"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtTbh5UZQpCV",
        "colab_type": "text"
      },
      "source": [
        "### Common stopwords in tweets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKk2oJfoQpCY",
        "colab_type": "text"
      },
      "source": [
        "First we  will analyze tweets with class 0."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "EmiWxCLZQpCa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corpus=create_corpus(0)\n",
        "\n",
        "dic=defaultdict(int)\n",
        "for word in corpus:\n",
        "    if word in stop:\n",
        "        dic[word]+=1\n",
        "        \n",
        "top=sorted(dic.items(), key=lambda x:x[1],reverse=True)[:10] \n"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "XDKYPMscQpCk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "52853f77-ace6-474e-e3df-806f2bb5005a"
      },
      "source": [
        "x,y=zip(*top)\n",
        "plt.bar(x,y)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 10 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5EwvKOkRQpCt",
        "colab_type": "text"
      },
      "source": [
        "Now,we will analyze tweets with class 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "h99c2482QpCv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "4f701f98-d520-407e-98a8-9bc0ff9cb373"
      },
      "source": [
        "corpus=create_corpus(1)\n",
        "\n",
        "dic=defaultdict(int)\n",
        "for word in corpus:\n",
        "    if word in stop:\n",
        "        dic[word]+=1\n",
        "\n",
        "top=sorted(dic.items(), key=lambda x:x[1],reverse=True)[:10] \n",
        "    \n",
        "\n",
        "\n",
        "x,y=zip(*top)\n",
        "plt.bar(x,y)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 10 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "trusted": false,
        "id": "4qy1hH3DQpC6",
        "colab_type": "text"
      },
      "source": [
        "In both of them,\"the\" dominates which is followed by \"a\" in class 0 and \"in\" in class 1."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "trusted": false,
        "id": "y0FpODbwQpC8",
        "colab_type": "text"
      },
      "source": [
        "### Analyzing punctuations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvyQCLgsQpC-",
        "colab_type": "text"
      },
      "source": [
        "First let's check tweets indicating real disaster."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "4F6FsCnwQpC_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "bbd6d271-914a-4646-eead-428602356c59"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "corpus=create_corpus(1)\n",
        "\n",
        "dic=defaultdict(int)\n",
        "import string\n",
        "special = string.punctuation\n",
        "for i in (corpus):\n",
        "    if i in special:\n",
        "        dic[i]+=1\n",
        "        \n",
        "x,y=zip(*dic.items())\n",
        "plt.bar(x,y)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 18 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GnCmGutFQpDF",
        "colab_type": "text"
      },
      "source": [
        "Now,we will move on to class 0."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "s2WC3pFyQpDJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "f9f7d77f-0aa7-45dc-ef55-696f300f4278"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "corpus=create_corpus(0)\n",
        "\n",
        "dic=defaultdict(int)\n",
        "import string\n",
        "special = string.punctuation\n",
        "for i in (corpus):\n",
        "    if i in special:\n",
        "        dic[i]+=1\n",
        "        \n",
        "x,y=zip(*dic.items())\n",
        "plt.bar(x,y,color='green')"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 20 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTlTSOu_QpDR",
        "colab_type": "text"
      },
      "source": [
        "### Common words ?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "nhkdCsufQpDV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "counter=Counter(corpus)\n",
        "most=counter.most_common()\n",
        "x=[]\n",
        "y=[]\n",
        "for word,count in most[:40]:\n",
        "    if (word not in stop) :\n",
        "        x.append(word)\n",
        "        y.append(count)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "7YmAOmhBQpDe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "66dd2547-3d2b-4694-d8d5-d75c55062aa9"
      },
      "source": [
        "sns.barplot(x=y,y=x)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3752159438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGNQTShxQpDl",
        "colab_type": "text"
      },
      "source": [
        "Lot of cleaning needed !"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OUiKEMkjQpDn",
        "colab_type": "text"
      },
      "source": [
        "### Ngram analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJfOrOc0QpDo",
        "colab_type": "text"
      },
      "source": [
        "we will do a bigram (n=2) analysis over the tweets.Let's check the most common bigrams in tweets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YQSGMao0QpDr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_top_tweet_bigrams(corpus, n=None):\n",
        "    vec = CountVectorizer(ngram_range=(2, 2)).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[:n]"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "FpIaiAwvQpDw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "c76c23ee-5be3-4260-dd96-116d1fd244ec"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "top_tweet_bigrams=get_top_tweet_bigrams(tweet['text'])[:10]\n",
        "x,y=map(list,zip(*top_tweet_bigrams))\n",
        "sns.barplot(x=y,y=x)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f37520dec50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viYL78EvQpD4",
        "colab_type": "text"
      },
      "source": [
        "We will need lot of cleaning here.."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sgWuqwxFQpD6",
        "colab_type": "text"
      },
      "source": [
        "## Data Cleaning\n",
        "As we know,twitter tweets always have to be cleaned before we go onto modelling.So we will do some basic cleaning such as spelling correction,removing punctuations,removing html tags and emojis etc.So let's start."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "M2E-XMrAQpD8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "ffc6e95e-d6db-4955-fac6-d54512056050"
      },
      "source": [
        "df=pd.concat([tweet,test])\n",
        "df.shape"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10876, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yrELELVQpEB",
        "colab_type": "text"
      },
      "source": [
        "### Removing urls"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "tkUmu4hVQpEC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "example=\"New competition launched :https://www.kaggle.com/c/nlp-getting-started\""
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "L0V8m7FJQpEI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "92d9c781-81dd-4dcd-cc00-4948411f1043"
      },
      "source": [
        "def remove_URL(text):\n",
        "    url = re.compile(r'https?://\\S+|www\\.\\S+')\n",
        "    return url.sub(r'',text)\n",
        "\n",
        "remove_URL(example)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'New competition launched :'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "57ovzp1bQpEb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['text']=df['text'].apply(lambda x : remove_URL(x))"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1QAZTudQpEf",
        "colab_type": "text"
      },
      "source": [
        "### Removing HTML tags"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "RjAkxv56QpEg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "example = \"\"\"<div>\n",
        "<h1>Real or Fake</h1>\n",
        "<p>Kaggle </p>\n",
        "<a href=\"https://www.kaggle.com/c/nlp-getting-started\">getting started</a>\n",
        "</div>\"\"\""
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "m5sebV5jQpEm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "7e8e53cd-6688-41f0-e0c7-bca9ca070d00"
      },
      "source": [
        "def remove_html(text):\n",
        "    html=re.compile(r'<.*?>')\n",
        "    return html.sub(r'',text)\n",
        "print(remove_html(example))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Real or Fake\n",
            "Kaggle \n",
            "getting started\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "udP0v7QTQpEs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['text']=df['text'].apply(lambda x : remove_html(x))"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pti5HMNvQpEv",
        "colab_type": "text"
      },
      "source": [
        "### Romoving Emojis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "iHcROeAeQpEx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "dd70489d-880e-4065-e4f9-4774094e47ec"
      },
      "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)\n",
        "\n",
        "remove_emoji(\"Omg another Earthquake 😔😔\")"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Omg another Earthquake '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "0t62fMo0QpE1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['text']=df['text'].apply(lambda x: remove_emoji(x))\n"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WjUopv1gQpE5",
        "colab_type": "text"
      },
      "source": [
        "### Removing punctuations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "bXgtJXa3QpE6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "a47f3ea7-b60b-47da-9cad-8d0f7d07d515"
      },
      "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": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I am a king\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "a6SGISBFQpE_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['text']=df['text'].apply(lambda x : remove_punct(x))"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aos9_cFXQpFG",
        "colab_type": "text"
      },
      "source": [
        "### Spelling Correction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQNIQVG1QpFH",
        "colab_type": "text"
      },
      "source": [
        "Even if I'm not good at spelling I can correct it with python :) I will use `pyspellcheker` to do that."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-output": true,
        "trusted": true,
        "id": "8cKeJDWXQpFH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "outputId": "94e35137-5e3e-4714-c792-61f5d9ece0b3"
      },
      "source": [
        "!pip install pyspellchecker"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pyspellchecker in /usr/local/lib/python3.6/dist-packages (0.5.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ejSxEgBPQpFL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "1f94243c-a725-4444-f1f3-316b843cf664"
      },
      "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": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'correct me please'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "l7vfPdP5QpFO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df['text']=df['text'].apply(lambda x : correct_spellings(x)#)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LFqMwwfVQpFS",
        "colab_type": "text"
      },
      "source": [
        "## GloVe for Vectorization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "977UMdh8QpFS",
        "colab_type": "text"
      },
      "source": [
        "Here we will use GloVe pretrained corpus model to represent our words.It is available in 3 varieties :50D ,100D and 200 Dimentional.We will try 100 D here."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWfSFarqTJ-6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "e62c0125-0b62-422f-e4aa-34c825652fdb"
      },
      "source": [
        "nltk.download('punkt')"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "bsWDQdSNQpFT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "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.isalpha()==1) & (word not in stop))]\n",
        "        corpus.append(words)\n",
        "    return corpus\n",
        "        \n",
        "        "
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Em7N5oFGQpFX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "83c2cabf-618a-41de-838e-2717efe9ab6d"
      },
      "source": [
        "corpus=create_corpus(df)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 10876/10876 [00:01<00:00, 6289.18it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6azVTHkTyLg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "dfb9b3eb-696c-4e65-8897-283a8ee06d12"
      },
      "source": [
        "! wget http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-05 11:21:40--  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  1.92MB/s    in 6m 30s  \n",
            "\n",
            "2020-08-05 11:28:10 (2.11 MB/s) - ‘glove.6B.zip’ saved [862182613/862182613]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "to4vNYvsVf5Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "f73230b2-1f6a-4baa-b206-1577998257eb"
      },
      "source": [
        "! unzip /content/glove.6B.zip"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/glove.6B.zip\n",
            "  inflating: glove.6B.50d.txt        \n",
            "  inflating: glove.6B.100d.txt       \n",
            "  inflating: glove.6B.200d.txt       \n",
            "  inflating: glove.6B.300d.txt       \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "x6pthER9QpFa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embedding_dict={}\n",
        "with open('/content/glove.6B.100d.txt','r') as f:\n",
        "    for line in f:\n",
        "        values=line.split()\n",
        "        word=values[0]\n",
        "        vectors=np.asarray(values[1:],'float32')\n",
        "        embedding_dict[word]=vectors\n",
        "f.close()"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "M_wWpefiQpFe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "MAX_LEN=50\n",
        "tokenizer_obj=Tokenizer()\n",
        "tokenizer_obj.fit_on_texts(corpus)\n",
        "sequences=tokenizer_obj.texts_to_sequences(corpus)\n",
        "\n",
        "tweet_pad=pad_sequences(sequences,maxlen=MAX_LEN,truncating='post',padding='post')"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "2y5lfu0XQpFh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "02cf5d61-0506-4d76-cc0e-d3e872532d8f"
      },
      "source": [
        "word_index=tokenizer_obj.word_index\n",
        "print('Number of unique words:',len(word_index))"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of unique words: 20342\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "NZ3hFv6gQpFl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "946381f8-7a04-437c-8afb-8fa8fb8ef92c"
      },
      "source": [
        "num_words=len(word_index)+1\n",
        "embedding_matrix=np.zeros((num_words,100))\n",
        "\n",
        "for word,i in tqdm(word_index.items()):\n",
        "    if i > num_words:\n",
        "        continue\n",
        "    \n",
        "    emb_vec=embedding_dict.get(word)\n",
        "    if emb_vec is not None:\n",
        "        embedding_matrix[i]=emb_vec\n",
        "            "
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 20342/20342 [00:00<00:00, 467386.84it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mcCjWxVQpFr",
        "colab_type": "text"
      },
      "source": [
        "## Baseline Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "eTrLnv7GQpFr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "outputId": "95c32f62-96a2-4062-874e-367779d72bca"
      },
      "source": [
        "model=Sequential()\n",
        "\n",
        "embedding=Embedding(num_words,100,embeddings_initializer=Constant(embedding_matrix),\n",
        "                   input_length=MAX_LEN,trainable=False)\n",
        "\n",
        "model.add(embedding)\n",
        "model.add(SpatialDropout1D(0.2))\n",
        "model.add(LSTM(64, dropout=0.2, recurrent_dropout=0.2))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "\n",
        "optimzer=Adam(learning_rate=1e-5)\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer=optimzer,metrics=['accuracy'])\n",
        "\n"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "54P7-ichQpFy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "d072a838-dd55-4456-946b-f344d87e6e31"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 50, 100)           2034300   \n",
            "_________________________________________________________________\n",
            "spatial_dropout1d (SpatialDr (None, 50, 100)           0         \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 64)                42240     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 2,076,605\n",
            "Trainable params: 42,305\n",
            "Non-trainable params: 2,034,300\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "B8XG9WnPQpF1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train=tweet_pad[:tweet.shape[0]]\n",
        "test=tweet_pad[tweet.shape[0]:]"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "XG91thDZQpF3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "0c4c3b5d-9144-49c2-cc15-050f96c9f790"
      },
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(train,tweet['target'].values,test_size=0.15)\n",
        "print('Shape of train',X_train.shape)\n",
        "print(\"Shape of Validation \",X_test.shape)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of train (6471, 50)\n",
            "Shape of Validation  (1142, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "v-KGnLirQpF8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "34bf2248-9a65-4c5e-af8c-d2f8f698f401"
      },
      "source": [
        "history=model.fit(X_train,y_train,batch_size=4,epochs=15,validation_data=(X_test,y_test))"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "1618/1618 [==============================] - 423s 262ms/step - loss: 0.6909 - accuracy: 0.5713 - val_loss: 0.6878 - val_accuracy: 0.5648\n",
            "Epoch 2/15\n",
            "1618/1618 [==============================] - 419s 259ms/step - loss: 0.6126 - accuracy: 0.6738 - val_loss: 0.5451 - val_accuracy: 0.7601\n",
            "Epoch 3/15\n",
            "1618/1618 [==============================] - 417s 258ms/step - loss: 0.5331 - accuracy: 0.7543 - val_loss: 0.5202 - val_accuracy: 0.7732\n",
            "Epoch 4/15\n",
            "1618/1618 [==============================] - 417s 258ms/step - loss: 0.5205 - accuracy: 0.7594 - val_loss: 0.5067 - val_accuracy: 0.7837\n",
            "Epoch 5/15\n",
            "1618/1618 [==============================] - 414s 256ms/step - loss: 0.5041 - accuracy: 0.7707 - val_loss: 0.4984 - val_accuracy: 0.7855\n",
            "Epoch 6/15\n",
            "1618/1618 [==============================] - 414s 256ms/step - loss: 0.5015 - accuracy: 0.7716 - val_loss: 0.4895 - val_accuracy: 0.7933\n",
            "Epoch 7/15\n",
            "1618/1618 [==============================] - 418s 258ms/step - loss: 0.4973 - accuracy: 0.7793 - val_loss: 0.4852 - val_accuracy: 0.7925\n",
            "Epoch 8/15\n",
            "1618/1618 [==============================] - 419s 259ms/step - loss: 0.4868 - accuracy: 0.7826 - val_loss: 0.4827 - val_accuracy: 0.7933\n",
            "Epoch 9/15\n",
            "1618/1618 [==============================] - 420s 260ms/step - loss: 0.4916 - accuracy: 0.7803 - val_loss: 0.4784 - val_accuracy: 0.7951\n",
            "Epoch 10/15\n",
            "1618/1618 [==============================] - 422s 261ms/step - loss: 0.4913 - accuracy: 0.7759 - val_loss: 0.4753 - val_accuracy: 0.7977\n",
            "Epoch 11/15\n",
            "1618/1618 [==============================] - 422s 261ms/step - loss: 0.4812 - accuracy: 0.7864 - val_loss: 0.4745 - val_accuracy: 0.7951\n",
            "Epoch 12/15\n",
            "1618/1618 [==============================] - 420s 260ms/step - loss: 0.4801 - accuracy: 0.7887 - val_loss: 0.4743 - val_accuracy: 0.7951\n",
            "Epoch 13/15\n",
            "1618/1618 [==============================] - 420s 260ms/step - loss: 0.4780 - accuracy: 0.7874 - val_loss: 0.4722 - val_accuracy: 0.7986\n",
            "Epoch 14/15\n",
            "1618/1618 [==============================] - 418s 258ms/step - loss: 0.4766 - accuracy: 0.7852 - val_loss: 0.4719 - val_accuracy: 0.7942\n",
            "Epoch 15/15\n",
            "1618/1618 [==============================] - 424s 262ms/step - loss: 0.4780 - accuracy: 0.7847 - val_loss: 0.4690 - val_accuracy: 0.7977\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8zGwXIqyQpGA",
        "colab_type": "text"
      },
      "source": [
        "## Making our submission"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "JoSyPhezQpGB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample_sub=pd.read_csv('/content/sample_submission.csv')"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "eRd3XqkQQpGG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pre=model.predict(test)\n",
        "y_pre=np.round(y_pre).astype(int).reshape(3263)\n",
        "sub=pd.DataFrame({'id':sample_sub['id'].values.tolist(),'target':y_pre})\n",
        "sub.to_csv('submission.csv',index=False)\n"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "l8Ek-5B5QpGK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ee0196c8-1bec-4d32-de95-0f6bea91690c"
      },
      "source": [
        "sub.head()"
      ],
      "execution_count": 87,
      "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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  target\n",
              "0   0       1\n",
              "1   2       1\n",
              "2   3       1\n",
              "3   9       1\n",
              "4  11       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "trusted": true,
        "id": "DcrNxFzXQpGN",
        "colab_type": "text"
      },
      "source": [
        "<font size='5' color='red'>  if you like this kernel,please do an upvote.</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "m4BU5fyOQpGO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "outputId": "b74a35ef-64d8-49bf-8872-f3400a5c5ed6"
      },
      "source": [
        "! kaggle competitions submit -c nlp-getting-started -f submission.csv -m \"First Submit\""
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "100% 22.2k/22.2k [00:02<00:00, 8.36kB/s]\n",
            "Successfully submitted to Real or Not? NLP with Disaster Tweets"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3b7U3ee9xzwN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}