disaster_classify (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0012638e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import gensim\n",
    "import pandas as pd\n",
    "import re\n",
    "import keras\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "28cca0fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "020ff28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "063e0305",
   "metadata": {},
   "outputs": [
    {
     "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>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "    </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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id keyword location                                               text  \\\n",
       "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
       "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
       "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
       "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
       "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
       "\n",
       "   target  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f56adbb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "## number of sarcasm \n",
    "df_new = df.iloc[:,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7bedad2b",
   "metadata": {},
   "outputs": [
    {
     "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>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  target\n",
       "0  Our Deeds are the Reason of this #earthquake M...       1\n",
       "1             Forest fire near La Ronge Sask. Canada       1\n",
       "2  All residents asked to 'shelter in place' are ...       1\n",
       "3  13,000 people receive #wildfires evacuation or...       1\n",
       "4  Just got sent this photo from Ruby #Alaska as ...       1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "531ea641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_new['text'].str.len().hist() ## no. of characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3832633a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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napNkvqrmJtSl0+ZsGQc4ljPR2TIOcCyradLTO4eAzUPrm1qZJGkCJh36jwJbk2xJci5wPbB3wn2QpG5NdHqnqt5I8iHgi8A6YHdVPTPCpk44/TNFzpZxgGM5E50t4wDHsmpSVWu5f0nSBHlHriR1xNCXpI5MVeifTY9wSLI/yVNJnkgyv9b9WYkku5McSfL0UNn5SfYleb69b1jLPp6KZcbxi0kOtePyRJKr17KPpyrJ5iQPJXk2yTNJPtzKp/G4LDeWqTo2Sd6S5I+SfLWN47+08i1JHmk59rl2Ucvk+jUtc/rtEQ5/AvwbBjd1PQrcUFXPrmnHRpRkPzBXVVN3w0mSHwFeB+6sqne0sv8KHK2qW9sf5A1V9dG17OfJLDOOXwRer6pfXsu+rVSSi4CLqurxJP8YeAy4Fvgppu+4LDeW65iiY5MkwHlV9XqSNwF/AHwYuBn4QlXdneR/Al+tqtsn1a9pOtP3EQ5niKr6MnD0uOJtwJ62vIfBf9Iz2jLjmEpVdbiqHm/LfwU8x+AO+Gk8LsuNZarUwOtt9U3tVcB7gXtb+cSPyTSF/lKPcJi6fwhDCvjdJI+1R09Muwur6nBbfgm4cC07M6YPJXmyTf+c8dMhx0syC7wTeIQpPy7HjQWm7NgkWZfkCeAIsA/4U+DVqnqjNZl4jk1T6J9tfriqLmHwxNGb2lTDWaEGc4bTMW/47W4Hvgf4QeAw8N/XtDcrlOQ7gc8DP1tVfzlcN23HZYmxTN2xqaq/q6ofZPD0gUuB71/bHk1X6J9Vj3CoqkPt/QjwWwz+QUyzl9tc7LE52SNr3J+RVNXL7T/qt4DfYIqOS5s3/jzw2ar6QiueyuOy1Fim+dhU1avAQ8APAeuTHLsxduI5Nk2hf9Y8wiHJee0LKpKcB1wBPH3iT53x9gLb2/J24L417MvIjgVk8+NMyXFpXxreATxXVb8yVDV1x2W5sUzbsUkyk2R9W/4OBhehPMcg/N/fmk38mEzN1TsA7RKtX+XvH+HwybXt0WiS/AsGZ/cweBTG/56msSS5C3gPg0fEvgzcAvw2cA/wz4AXgeuq6oz+knSZcbyHwfRBAfuB/zA0J37GSvLDwO8DTwHfasUfZzAXPm3HZbmx3MAUHZskP8Dgi9p1DE6w76mqT7T//3cD5wNfAf5tVf3NxPo1TaEvSRrPNE3vSJLGZOhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjvx/C2ExLtl8+ZgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## no. of words \n",
    "count = []\n",
    "for plot in df_new.text:\n",
    "    words = plot.split()\n",
    "    count.append(len(words))\n",
    "    \n",
    "plt.hist(count)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "eb6154a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## creating a corpus of words for the analysis \n",
    "stop = set(nltk.corpus.stopwords.words('english'))\n",
    "corpus=[]\n",
    "p= df_new.text.str.split()\n",
    "p=p.values.tolist()\n",
    "\n",
    "corpus=[word.lower() for i in p for word in i if word not in stop]   ## lowering the word before adding to corpus\n",
    "\n",
    "from collections import Counter\n",
    "counter = Counter(corpus)\n",
    "freq = counter.most_common()\n",
    "\n",
    "x, y= [], []\n",
    "for word,count in freq[:30]:\n",
    "    if (word not in stop):\n",
    "        x.append(word)\n",
    "        y.append(count)\n",
    "        \n",
    "sns.barplot(x=y,y=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9e07f2f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## USING WORDCLOUD\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "stopwords = set(STOPWORDS)\n",
    "def show_wordcloud(data):\n",
    "    wordcloud = WordCloud(\n",
    "        background_color='white',\n",
    "        stopwords=stop,\n",
    "        max_words=100,\n",
    "        max_font_size=30,\n",
    "        scale=3,\n",
    "        random_state=1)\n",
    "   \n",
    "    wordcloud=wordcloud.generate(str(data))\n",
    "    fig = plt.figure(1, figsize=(12, 12))\n",
    "    plt.axis('off')\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.show()\n",
    "show_wordcloud(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f07c3954",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_new.text\n",
    "Y= df_new.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4a1548f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_list = []   ## removing stopwords\n",
    "for i in X:\n",
    "       \n",
    "    wor = i.split()\n",
    "    filtered_words = [w for w in wor if w not in stop]\n",
    "    i = filtered_words\n",
    "    i= ' '.join(i)\n",
    "    x_list.append(i)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "87717b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(x_list)\n",
    "y = np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2840eba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 22692 unique tokens.\n"
     ]
    }
   ],
   "source": [
    "## tokenize the word to create embedding\n",
    "vocab_size = 2000\n",
    "tk = keras.preprocessing.text.Tokenizer(num_words=vocab_size, filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n', lower=True, split=\" \")\n",
    "tk.fit_on_texts(x)\n",
    "x_new = tk.texts_to_sequences(x)\n",
    "word_index = tk.word_index  # index of unique words\n",
    "print('Found %s unique tokens.' % len(word_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b751416c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.985682385393407"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## avg tokens per sentence\n",
    "count = 0\n",
    "for i in range(len(x_new)):\n",
    "    count = count + len(x_new[i])\n",
    "    \n",
    "count/len(x_new)  ### 10 words per tweet avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7bcebc49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.model_selection import train_test_split\n",
    "max_len = 50       #length of sequence\n",
    "batch_size = 32\n",
    "epochs = 5\n",
    "max_features = 100\n",
    "x_new = pad_sequences(x_new, maxlen=max_len, padding='pre')\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_new, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "36d26857",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6090, 50)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28735ad4",
   "metadata": {},
   "source": [
    "## normal model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "dd595d44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_5 (Embedding)      (None, 50, 100)           200000    \n",
      "_________________________________________________________________\n",
      "lstm_16 (LSTM)               (None, 64)                42240     \n",
      "_________________________________________________________________\n",
      "dense_41 (Dense)             (None, 256)               16640     \n",
      "_________________________________________________________________\n",
      "dense_42 (Dense)             (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 259,137\n",
      "Trainable params: 259,137\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.models import Sequential\n",
    "def RNN():\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(vocab_size,max_features,input_length=max_len))\n",
    "    model.add(LSTM(64))\n",
    "    model.add(Dense(256,activation='relu',kernel_initializer=\"he_uniform\"))\n",
    "    \n",
    "    model.add(Dense(1,activation='sigmoid',kernel_initializer=\"glorot_uniform\"))\n",
    "    return model\n",
    "\n",
    "model = RNN()\n",
    "model.summary()\n",
    "model.compile(loss='binary_crossentropy',optimizer=keras.optimizers.Adam(0.001),metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f237296a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "191/191 [==============================] - 10s 40ms/step - loss: 0.6089 - accuracy: 0.6666 - val_loss: 0.4422 - val_accuracy: 0.8024\n",
      "Epoch 2/5\n",
      "191/191 [==============================] - 7s 35ms/step - loss: 0.3678 - accuracy: 0.8470 - val_loss: 0.4498 - val_accuracy: 0.7991\n",
      "Epoch 3/5\n",
      "191/191 [==============================] - 7s 38ms/step - loss: 0.3129 - accuracy: 0.8728 - val_loss: 0.4914 - val_accuracy: 0.7748\n",
      "Epoch 4/5\n",
      "191/191 [==============================] - 6s 33ms/step - loss: 0.2647 - accuracy: 0.8963 - val_loss: 0.5386 - val_accuracy: 0.7761\n",
      "Epoch 5/5\n",
      "191/191 [==============================] - 7s 35ms/step - loss: 0.2130 - accuracy: 0.9155 - val_loss: 0.6754 - val_accuracy: 0.7498\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x160124890>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping\n",
    "model.fit(x_train, y_train,validation_data= (x_test, y_test), epochs= epochs, batch_size = batch_size,)\n",
    "\n",
    "\n",
    "#callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "eb5ce4fb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48/48 [==============================] - 1s 12ms/step - loss: 0.6754 - accuracy: 0.7498\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6753894090652466, 0.7498358488082886]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "789ee623",
   "metadata": {},
   "source": [
    "## keras tuner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f51009a6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'RandomSearch' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-51-b79c0fbcfca4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m tuner = RandomSearch(\n\u001b[0m\u001b[1;32m     33\u001b[0m     \u001b[0mbuild_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0mobjective\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'val_accuracy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'RandomSearch' is not defined"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,LSTM,Embedding\n",
    "from keras.layers import LeakyReLU, PReLU, ELU\n",
    "from keras.layers import Dropout\n",
    "\n",
    "## this is a build function we define\n",
    "def build_model(hp):\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(vocab_size,max_features,input_length=max_len))\n",
    "    for i in range(hp.Int('layers1',1,10)):\n",
    "        model.add(LSTM(units=hp.Int('unit_'+str(i),\n",
    "                                     min_value=32,\n",
    "                                     max_value=532,\n",
    "                                     step=32),return_sequences=True))\n",
    "    model.add(LSTM(units=hp.Int('unit_'+str(i),\n",
    "                                     min_value=32,\n",
    "                                     max_value=532,\n",
    "                                     step=32)))\n",
    "    for i in range(hp.Int('layers2',1,50)):\n",
    "        model.add(Dense(units=hp.Int('unit_'+str(i),\n",
    "                                     min_value=32,\n",
    "                                     max_value=532,\n",
    "                                     step=32),activation='relu',kernel_initializer=\"he_uniform\"))   \n",
    "\n",
    "    model.add(Dense(units=1,activation='sigmoid',kernel_initializer=\"glorot_uniform\"))\n",
    "    model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate',[1e-2,1e-3,1e-4])),\n",
    "                 loss=keras.losses.binary_crossentropy,\n",
    "                 metrics=['accuracy'])\n",
    "    return model\n",
    "                  \n",
    "tuner = RandomSearch(\n",
    "    build_model,\n",
    "    objective='val_accuracy',\n",
    "    max_trials=5,\n",
    "    executions_per_trial = 2,\n",
    "    directory='.',\n",
    "    project_name='my_project'\n",
    "    \n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "317e882d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## tuner tuned in collab\n",
    "tuner.search(x_train,y_train,\n",
    "            validation_data=(x_test,y_test),\n",
    "             epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e63585",
   "metadata": {},
   "source": [
    "## from kerastuner\n",
    "Results summary\n",
    "Results in ./my_project_4\n",
    "Showing 10 best trials\n",
    "Objective(name='val_accuracy', direction='max')\n",
    "Trial summary\n",
    "Hyperparameters:\n",
    "layers1: 3\n",
    "unit_0: 256\n",
    "layers2: 21\n",
    "learning_rate: 0.0001\n",
    "unit_1: 32\n",
    "unit_2: 32\n",
    "unit_3: 32\n",
    "unit_4: 32\n",
    "unit_5: 32\n",
    "unit_6: 32\n",
    "unit_7: 32\n",
    "unit_8: 32\n",
    "unit_9: 32\n",
    "unit_10: 32\n",
    "unit_11: 32\n",
    "unit_12: 32\n",
    "unit_13: 32\n",
    "unit_14: 32\n",
    "unit_15: 32\n",
    "unit_16: 32\n",
    "unit_17: 32\n",
    "unit_18: 32\n",
    "unit_19: 32\n",
    "unit_20: 32\n",
    "Score: 0.8095863461494446\n",
    "Trial summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dbbaa9d",
   "metadata": {},
   "source": [
    "Model: \"sequential\"\n",
    "_________________________________________________________________\n",
    "Layer (type)                 Output Shape              Param #   \n",
    "=================================================================\n",
    "embedding (Embedding)        (None, 50, 100)           200000    \n",
    "_________________________________________________________________\n",
    "lstm (LSTM)                  (None, 50, 256)           365568    \n",
    "_________________________________________________________________\n",
    "lstm_1 (LSTM)                (None, 50, 32)            36992     \n",
    "_________________________________________________________________\n",
    "lstm_2 (LSTM)                (None, 50, 32)            8320      \n",
    "_________________________________________________________________\n",
    "lstm_3 (LSTM)                (None, 32)                8320      \n",
    "_________________________________________________________________\n",
    "dense (Dense)                (None, 256)               8448      \n",
    "_________________________________________________________________\n",
    "dense_1 (Dense)              (None, 32)                8224      \n",
    "_________________________________________________________________\n",
    "dense_2 (Dense)              (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_3 (Dense)              (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_4 (Dense)              (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_5 (Dense)              (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_6 (Dense)              (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_7 (Dense)              (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_8 (Dense)              (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_9 (Dense)              (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_10 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_11 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_12 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_13 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_14 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_15 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_16 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_17 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_18 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_19 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_20 (Dense)             (None, 32)                1056      \n",
    "_________________________________________________________________\n",
    "dense_21 (Dense)             (None, 1)                 33        \n",
    "=================================================================\n",
    "Total params: 655,969\n",
    "Trainable params: 655,969\n",
    "Non-trainable params: 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "216d3261",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model(l1,n1,l2,n2=32):\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(vocab_size,max_features,input_length=max_len))\n",
    "    for i in range(l1):\n",
    "        model.add(LSTM(n1[i],return_sequences=True))\n",
    "        \n",
    "    model.add(LSTM(32))   \n",
    "    \n",
    "    for i in range(l2):\n",
    "        model.add(Dense(n2,activation='relu',kernel_initializer=\"he_uniform\"))\n",
    "    \n",
    "    model.add(Dense(units=1,activation='sigmoid',kernel_initializer=\"glorot_uniform\"))\n",
    "    model.compile(optimizer=keras.optimizers.Adam(0.0001),\n",
    "                 loss=keras.losses.binary_crossentropy,\n",
    "                 metrics=['accuracy'])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "edf1dc56",
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = 3\n",
    "n1 = [256,32,32]\n",
    "l2 = 20\n",
    "\n",
    "model_tuned = make_model(l1,n1,l2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1f82f36e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_4 (Embedding)      (None, 50, 100)           200000    \n",
      "_________________________________________________________________\n",
      "lstm_12 (LSTM)               (None, 50, 256)           365568    \n",
      "_________________________________________________________________\n",
      "lstm_13 (LSTM)               (None, 50, 32)            36992     \n",
      "_________________________________________________________________\n",
      "lstm_14 (LSTM)               (None, 50, 32)            8320      \n",
      "_________________________________________________________________\n",
      "lstm_15 (LSTM)               (None, 32)                8320      \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_27 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_28 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_29 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_30 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_31 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_33 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_34 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_35 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_36 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_37 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_38 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 640,353\n",
      "Trainable params: 640,353\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_tuned.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "679eb172",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "191/191 [==============================] - 63s 288ms/step - loss: 0.6840 - accuracy: 0.5775 - val_loss: 0.6756 - val_accuracy: 0.6382\n",
      "Epoch 2/5\n",
      "191/191 [==============================] - 48s 250ms/step - loss: 0.6490 - accuracy: 0.6321 - val_loss: 0.5483 - val_accuracy: 0.7905\n",
      "Epoch 3/5\n",
      "191/191 [==============================] - 48s 254ms/step - loss: 0.5042 - accuracy: 0.7945 - val_loss: 0.4482 - val_accuracy: 0.8070\n",
      "Epoch 4/5\n",
      "191/191 [==============================] - 44s 229ms/step - loss: 0.3956 - accuracy: 0.8364 - val_loss: 0.4591 - val_accuracy: 0.7997\n",
      "Epoch 5/5\n",
      "191/191 [==============================] - 42s 220ms/step - loss: 0.3616 - accuracy: 0.8586 - val_loss: 0.4517 - val_accuracy: 0.8096\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x15728f6d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tuned.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=5,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fead8df9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n",
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "## new way, json and h5py\n",
    "# serialize model to JSON\n",
    "model_json = model_tuned.to_json()\n",
    "with open(\"model_disaster_pre.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model_disaster.h5\")\n",
    "print(\"Saved model to disk\")\n",
    "\n",
    "## did not work\n",
    "\n",
    "## new way, json and h5py\n",
    "# serialize model to JSON\n",
    "model_json = model_tuned.to_json()\n",
    "with open(\"model_disaster_tuned.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model_tuned.save_weights(\"model_disaster_tuned.h5\")\n",
    "print(\"Saved model to disk\")\n",
    "\n",
    "## did not work"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcee2f65",
   "metadata": {},
   "source": [
    "## ON DATA WITH NO LABEL GIVEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fb88aabd",
   "metadata": {},
   "outputs": [
    {
     "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>location</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Just happened a terrible car crash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Heard about #earthquake is different cities, s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>there is a forest fire at spot pond, geese are...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apocalypse lighting. #Spokane #wildfires</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id keyword location                                               text\n",
       "0   0     NaN      NaN                 Just happened a terrible car crash\n",
       "1   2     NaN      NaN  Heard about #earthquake is different cities, s...\n",
       "2   3     NaN      NaN  there is a forest fire at spot pond, geese are...\n",
       "3   9     NaN      NaN           Apocalypse lighting. #Spokane #wildfires\n",
       "4  11     NaN      NaN      Typhoon Soudelor kills 28 in China and Taiwan"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_csv('test.csv')\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c43b2756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "from keras.models import model_from_json\n",
    "# load json and create model\n",
    "json_file = open('model_disaster_pre.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "# load weights into new model\n",
    "loaded_model.load_weights(\"model_disaster.h5\")\n",
    "print(\"Loaded model from disk\")\n",
    "\n",
    "loaded_model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c03f33f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 12800 unique tokens.\n"
     ]
    }
   ],
   "source": [
    "stop = set(nltk.corpus.stopwords.words('english'))\n",
    "X = test_data.text\n",
    "x_list = []   ## removing stopwords\n",
    "for i in X:\n",
    "       \n",
    "    wor = i.split()\n",
    "    filtered_words = [w for w in wor if w not in stop]\n",
    "    i = filtered_words\n",
    "    i= ' '.join(i)\n",
    "    x_list.append(i)\n",
    "    \n",
    "x = np.array(x_list)\n",
    "\n",
    "    \n",
    "## tokenize the word to create embedding\n",
    "vocab_size = 2000\n",
    "tk = keras.preprocessing.text.Tokenizer(num_words=vocab_size, filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n', lower=True, split=\" \")\n",
    "tk.fit_on_texts(x)\n",
    "x_new = tk.texts_to_sequences(x)\n",
    "word_index = tk.word_index  # index of unique words\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.model_selection import train_test_split\n",
    "max_len = 50       #length of sequence\n",
    "batch_size = 32\n",
    "epochs = 5\n",
    "max_features = 100\n",
    "x_test = pad_sequences(x_new, maxlen=max_len, padding='pre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e41659d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3263, 50)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3e6b7020",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = loaded_model.predict(x_test)\n",
    "y_pred_new = model_tuned.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6a275720",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Just happened a terrible car crash\n",
      "prediction:  0\n",
      "------\n",
      "Heard about #earthquake is different cities, stay safe everyone.\n",
      "prediction:  0\n",
      "------\n",
      "there is a forest fire at spot pond, geese are fleeing across the street, I cannot save them all\n",
      "prediction:  1\n",
      "------\n",
      "Apocalypse lighting. #Spokane #wildfires\n",
      "prediction:  0\n",
      "------\n",
      "Typhoon Soudelor kills 28 in China and Taiwan\n",
      "prediction:  1\n",
      "------\n",
      "We're shaking...It's an earthquake\n",
      "prediction:  0\n",
      "------\n",
      "They'd probably still show more life than Arsenal did yesterday, eh? EH?\n",
      "prediction:  1\n",
      "------\n",
      "Hey! How are you?\n",
      "prediction:  0\n",
      "------\n",
      "What a nice hat?\n",
      "prediction:  0\n",
      "------\n",
      "Fuck off!\n",
      "prediction:  0\n",
      "------\n",
      "No I don't like cold!\n",
      "prediction:  0\n",
      "------\n",
      "NOOOOOOOOO! Don't do that!\n",
      "prediction:  0\n",
      "------\n",
      "No don't tell me that!\n",
      "prediction:  0\n",
      "------\n",
      "What if?!\n",
      "prediction:  0\n",
      "------\n",
      "Awesome!\n",
      "prediction:  0\n",
      "------\n",
      "Birmingham Wholesale Market is ablaze BBC News - Fire breaks out at Birmingham's Wholesale Market http://t.co/irWqCEZWEU\n",
      "prediction:  1\n",
      "------\n",
      "@sunkxssedharry will you wear shorts for race ablaze ?\n",
      "prediction:  0\n",
      "------\n",
      "#PreviouslyOnDoyinTv: Toke MakinwaÛªs marriage crisis sets Nigerian Twitter ablaze... http://t.co/CMghxBa2XI\n",
      "prediction:  0\n",
      "------\n",
      "Check these out: http://t.co/rOI2NSmEJJ http://t.co/3Tj8ZjiN21 http://t.co/YDUiXEfIpE http://t.co/LxTjc87KLS #nsfw\n",
      "prediction:  0\n",
      "------\n",
      "PSA: IÛªm splitting my personalities.\n",
      "\n",
      "?? techies follow @ablaze_co\n",
      "?? Burners follow @ablaze\n",
      "prediction:  0\n",
      "------\n",
      "beware world ablaze sierra leone &amp; guap.\n",
      "prediction:  0\n",
      "------\n",
      "Burning Man Ablaze! by Turban Diva http://t.co/hodWosAmWS via @Etsy\n",
      "prediction:  0\n",
      "------\n",
      "Not a diss song. People will take 1 thing and run with it. Smh it's an eye opener though. He is about 2 set the game ablaze @CyhiThePrynce\n",
      "prediction:  0\n",
      "------\n",
      "Rape victim dies as she sets herself ablaze: A 16-year-old girl died of burn injuries as she set herself ablazeÛ_ http://t.co/UK8hNrbOob\n",
      "prediction:  1\n",
      "------\n",
      "SETTING MYSELF ABLAZE http://t.co/6vMe7P5XhC\n",
      "prediction:  0\n",
      "------\n",
      "@CTVToronto the bins in front of the field by my house wer set ablaze the other day flames went rite up the hydro pole wonder if it was him\n",
      "prediction:  0\n",
      "------\n",
      "#nowplaying Alfons - Ablaze 2015 on Puls Radio #pulsradio http://t.co/aA5BJgWfDv\n",
      "prediction:  0\n",
      "------\n",
      "'Burning Rahm': Let's hope City Hall builds a giant wooden mayoral effigy 100 feet tall &amp; sets it ablaze. http://t.co/kFo2mksn6Y @John_Kass\n",
      "prediction:  1\n",
      "------\n",
      "@PhilippaEilhart @DhuBlath hurt but her eyes ablaze with insulted anger.\n",
      "prediction:  0\n",
      "------\n",
      "Accident cleared in #PaTurnpike on PATP EB between PA-18 and Cranberry slow back to #traffic http://t.co/SL0Oqn0Vyr\n",
      "prediction:  1\n",
      "------\n",
      "Just got to love burning your self on a damn curling wand... I swear someone needs to take it away from me cuase I'm just accident prone.\n",
      "prediction:  0\n",
      "------\n",
      "I hate badging shit in accident\n",
      "prediction:  0\n",
      "------\n",
      "#3: Car Recorder ZeroEdgeå¨ Dual-lens Car Camera Vehicle Traffic/Driving History/Accident Camcorder  Large Re... http://t.co/kKFaSJv6Cj\n",
      "prediction:  0\n",
      "------\n",
      "Coincidence Or #Curse? Still #Unresolved Secrets From Past http://t.co/7VG8Df9pLE #accident\n",
      "prediction:  1\n",
      "------\n",
      "@Traffic_SouthE @roadpol_east Accident on A27 near Lewes is it Kingston Roundabout rather than A283\n",
      "prediction:  1\n",
      "------\n",
      "@sakuma_en If you pretend to feel a certain way the feeling can become genuine all by accident. -Hei (Darker than Black) #manga #anime\n",
      "prediction:  1\n",
      "------\n",
      "For Legal and Medical Referral Service @1800_Injured Call us at: 1-800-465-87332 #accident #slipandfall #dogbite\n",
      "prediction:  0\n",
      "------\n",
      "There's a construction guy working on the Disney store and he has huge gauges in his ears ?? ...that is a bloody accident waiting to happen\n",
      "prediction:  0\n",
      "------\n",
      "@RobynJilllian @WlSDOMTEETHS I feel like I'm going to do it on accident. Teesha is gonna come out??\n",
      "prediction:  0\n",
      "------\n",
      "On the #M42 northbound between junctions J3 and J3A there are currently delays of 10 mins due to an accident c... http://t.co/LwI3prBa31\n",
      "prediction:  1\n",
      "------\n",
      "@DaveOshry @Soembie So if I say that I met her by accident this week- would you be super jelly Dave? :p\n",
      "prediction:  0\n",
      "------\n",
      "ACCIDENT - HIT AND RUN - COLD at 500 BLOCK OF SE VISTA TER GRESHAM OR [Gresham Police #PG15000044357] 10:35 #pdx911\n",
      "prediction:  0\n",
      "------\n",
      "@Calum5SOS this happened on accident but I like it http://t.co/QHmXuljSX9\n",
      "prediction:  0\n",
      "------\n",
      "Please donate and spread the word! A training accident left the pole-vaulter Kira GrÌ_nberg a paraplegic http://t.co/6MpnyCl8PK\n",
      "prediction:  1\n",
      "------\n",
      "Please like and share our new page for our Indoor Trampoline Park Aftershock opening this fall!! http://t.co/UgXhHErrxS\n",
      "prediction:  1\n",
      "------\n",
      "@bxckylynch foi no ROH Aftershock: Las Vegas procura no pirate bay que tem\n",
      "prediction:  0\n",
      "------\n",
      "Schoolboy ÛÒ Aftershock (Original Mix)\n",
      "Excision &amp; Skism ÛÒ SEXisM (Far Too Loud Remix)\n",
      "Firebeatz Schella ÛÒ Dear New... http://t.co/JQLzUA6YzQ\n",
      "prediction:  1\n",
      "------\n",
      "320 [IR] ICEMOON [AFTERSHOCK] | http://t.co/THyzOMVWU0 | @djicemoon | #Dubstep #TrapMusic #DnB #EDM #Dance #IcesÛ_ http://t.co/83jOO0xk29\n",
      "prediction:  0\n",
      "------\n",
      "'When the aftershock happened (Nepal) we were the last int'l team still there; in a way we were 1st responders.' Chief Collins @LACo_FD\n",
      "prediction:  1\n",
      "------\n",
      "320 [IR] ICEMOON [AFTERSHOCK] | http://t.co/gRPeF7yAWG | @djicemoon | #Dubstep #TrapMusic #DnB #EDM #Dance #IcesÛ_ http://t.co/GGmvzT58vE\n",
      "prediction:  0\n",
      "------\n",
      "Stop saying 'I Wish' and start saying 'I Will'. ÛÒ Unknown\n",
      "prediction:  0\n",
      "------\n",
      "I want to go to Aftershock in October because it has all the bands I listen to and #NXT! Can't afford it yet though. #gradschoolapps\n",
      "prediction:  0\n",
      "------\n",
      "'We are still living in the aftershock of Hiroshima people are still the scars of history.' - Edward Bond http://t.co/engTl5wrGp\n",
      "prediction:  1\n",
      "------\n",
      "320 [IR] ICEMOON [AFTERSHOCK] | http://t.co/THyzOMVWU0 | @djicemoon | #Dubstep #TrapMusic #DnB #EDM #Dance #IcesÛ_ http://t.co/83jOO0xk29\n",
      "prediction:  0\n",
      "------\n",
      "Aftershock https://t.co/Ecy4U623nO\n",
      "prediction:  0\n",
      "------\n",
      "'There is no victory at bargain basement prices.' Dwight David Eisenhower\n",
      "prediction:  1\n",
      "------\n",
      "Bo2 had by far the best competitive maps imo hope bo3 is the same #InVahnWeTrust\n",
      "prediction:  0\n",
      "------\n",
      "Brass and Copper in Cataclysm &amp; AfterShock!!! http://t.co/uxYZyaygTy\n",
      "prediction:  1\n",
      "------\n",
      "@JadeForMKX You should be happy I don't use Aftershock. That variation counters your play style hard.\n",
      "prediction:  0\n",
      "------\n",
      "Aftershock https://t.co/38Nhq9moEf\n",
      "prediction:  0\n",
      "------\n",
      "When carelessness leads to an aviation accident the victim has the right to seek compensation for damages. http://t.co/eqAG6rz1vO\n",
      "prediction:  1\n",
      "------\n",
      "@rewind_music found out about you guys today(regarding the accident on the airplane lol) &amp; became a fan! Sending love &amp; support from Cali~?\n",
      "prediction:  0\n",
      "------\n",
      "A girl who died in an airplane accident fifteen years ago\n",
      "prediction:  1\n",
      "------\n",
      "@Mintechan Hihow are you? There is Keio line on the stationright? BTW do you know the airplane accident near Chofu airport this week?\n",
      "prediction:  0\n",
      "------\n",
      "Mexican airplane accident in Ocampo Coahuila MX on 7/29/25 killed Coahuila government SubSecretariat Francisco Garcia Castells age 42.\n",
      "prediction:  0\n",
      "------\n",
      "Horrible Accident  Man Died In Wings of AirplaneåÊ(29-07-2015) http://t.co/5ZRKZdhODe\n",
      "prediction:  0\n",
      "------\n",
      "@god if an accident were to happen on this airplane idc if the rest of my luggage is completely destroyed just please save my makeup\n",
      "prediction:  0\n",
      "------\n",
      "Horrible Accident Man Died In Wings of Airplane (29-07-2015) http://t.co/hG8u2kR1Rq\n",
      "prediction:  0\n",
      "------\n",
      "#UPDATE: Picture from the Penn Twp. airplane accident. http://t.co/6JfgDnZRlC\n",
      "prediction:  1\n",
      "------\n",
      "See how a judge ruled in this 2009 accident at #JFK Airport? involving Korean Air?.\n",
      "\n",
      "http://t.co/Yh1cGlN3rl http://t.co/6F5ShPKjOB\n",
      "prediction:  1\n",
      "------\n",
      "@thugIauren I had myself on airplane mode by accident ??\n",
      "prediction:  0\n",
      "------\n",
      "36 years ago today baseball lost one of its greats in an airplane accident. RIP Captain. #Yankees @yankees http://t.co/iNKU28vjJj\n",
      "prediction:  0\n",
      "------\n",
      "Experts in France begin examining airplane debris found on Reunion Island: French air accident experts on Wedn... http://t.co/miw2i0pQxz\n",
      "prediction:  1\n",
      "------\n",
      "#BreakingNews Experts in France begin examining airplane debris found on Reunion Island: French air accident e...  http://t.co/3XIcUvlvlJ\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction:  1\n",
      "------\n",
      "Experts in France begin examining airplane debris found on Reunion Island: French air accident experts on Wedn...  http://t.co/KuBsM16OuD\n",
      "prediction:  1\n",
      "------\n",
      "Twelve feared killed in Pakistani air ambulance helicopter crash http://t.co/zdsug0UjS7\n",
      "prediction:  0\n",
      "------\n",
      "http://t.co/Q5XTkaKM08 I'M DYING SEND AN AMBULANCE @M__Belly @brightasastar_\n",
      "prediction:  0\n",
      "------\n",
      "AMBULANCE SPRINTER AUTOMATIC FRONTLINE VEHICLE CHOICE OF 14 LEZ COMPLIANT | eBay http://t.co/7X3PDDbT0Z\n",
      "prediction:  1\n",
      "------\n",
      "@disneyxrowbrina the ambulance isn't even parked well like its nearly on top of someone's car I'm laughing\n",
      "prediction:  0\n",
      "------\n",
      "Shot 12 times. Found dead in cuffs after being involved in a car accident. Officers told ambulance not to treat him. https://t.co/MEUDJwaaNg\n",
      "prediction:  1\n",
      "------\n",
      "@margaretcho Call me a fag and I'm going to call you an ambulance :) #RainbowPower\n",
      "prediction:  0\n",
      "------\n",
      "New Nanotech Device Will Be Able To Target And Destroy Blood Clots http://t.co/MnmyJXQ9go #science\n",
      "prediction:  0\n",
      "------\n",
      "@vballplaya2296 want me to send you  some medic supplies from my dad's ambulance?\n",
      "prediction:  0\n",
      "------\n",
      "2 held with heroin in ambulance http://t.co/d9sOwi1G21 http://t.co/OPxmdPIwAu\n",
      "prediction:  0\n",
      "------\n",
      "http://t.co/B2FaSrt1tN Twelve feared killed in Pakistani air ambulance helicopter crash http://t.co/O8BJXnEPQm\n",
      "prediction:  0\n",
      "------\n",
      "Why should a helicopter ambulance ride to transfer to a hospital 21 miles away cost $29800? http://t.co/7ZqIreY7Te http://t.co/9Qp3PqPwZv\n",
      "prediction:  0\n",
      "------\n",
      "so privileged and proud to wear this uniform.?? #NHS #Ambulance #GayUK #Uniform #Proud #Privileged #WhatsYourEmergency http://t.co/0BkmuhYSFx\n",
      "prediction:  1\n",
      "------\n",
      "The bartender at work described a drunk man as annihilated @kdunning1919 @hsnowberger @gabrielasmith29. 16 more days\n",
      "prediction:  0\n",
      "------\n",
      "Cop pulls drunk driver to safety SECONDS before his car is hit by train. http://t.co/cTL5xzIHxAåÊ http://t.co/1kDOZTD9mv via @ViralSpell\n",
      "prediction:  0\n",
      "------\n",
      "Cop pulls drunk driver to safety SECONDS before his car is hit by train. http://t.co/cSsc8HcsXnåÊ http://t.co/jBlGyLCsAy via @ViralSpell\n",
      "prediction:  0\n",
      "------\n",
      "@RosieGray Now in all sincerety do you think the UN would move to Israel if there was a fraction of a chance of being annihilated?\n",
      "prediction:  1\n",
      "------\n",
      "@johnboywest The only 'stage' the drama queen can stand on is a drama stage &amp; certainly never the world stage; he would be annihilated.\n",
      "prediction:  0\n",
      "------\n",
      "@_drodrolagi #handplacementgoals \n",
      "Bro we annihilated that hashtag this week.\n",
      "prediction:  1\n",
      "------\n",
      "How do I step outside for 5 seconds and get annihilated  by mosquitoes?\n",
      "prediction:  0\n",
      "------\n",
      "@jacksfilms #yiayplan well first we strike dreamworks and the minions will be annihilated.\n",
      "prediction:  0\n",
      "------\n",
      "'If your nature appropriates it love will burn you until you become annihilated in your beloved...'  https://t.co/sMlwjunD09\n",
      "prediction:  1\n",
      "------\n",
      "@NinaHoag - 'if you shred my Psych work our friendship would be annihilated'\n",
      "prediction:  0\n",
      "------\n",
      "@thehill this is 1 example of y the Conservatives annihilated Burton v Wiimington Prkng Auth while Liberals stood by &amp;have done nothing\n",
      "prediction:  0\n",
      "------\n",
      "Aug 3 1915ÛÓKAISERJAEGERS WIPED OUT.; Francis Joseph's Crack Regiment Annihilated on Carso Plateau.\n",
      "http://t.co/D1sPSwl66H\n",
      "prediction:  1\n",
      "------\n",
      "They should all die! All of them! Everything annihilated!\n",
      "prediction:  0\n",
      "------\n"
     ]
    }
   ],
   "source": [
    "for i in range(100):\n",
    "    input1 = test_data.text[i]\n",
    "    print(input1)\n",
    "    \n",
    "    op = model_tuned.predict(x_test[i].reshape(1,50))\n",
    "    if op>0.5:\n",
    "        op=1\n",
    "    else: \n",
    "        op = 0\n",
    "\n",
    "    print(\"prediction: \",op)\n",
    "    print('------')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e68f161c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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