Twitter sentiment analysis (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8e455e1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding, LSTM, Dense, Dropout  \n",
    "from keras.initializers import glorot_normal\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21c10851",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "file_path = r'C:\\Users\\92349\\Desktop\\Prodigy internship\\archive\\twitter_training.csv'\n",
    "\n",
    "df = pd.read_csv(file_path, header=None)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65646a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['ID', 'Brand', 'Sentiment', 'Text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2d80d52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands and i will murder yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>I am coming to the borders and I will kill you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands and i will kill you ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im coming on borderlands and i will murder you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID        Brand Sentiment  \\\n",
       "0  2401  Borderlands  Positive   \n",
       "1  2401  Borderlands  Positive   \n",
       "2  2401  Borderlands  Positive   \n",
       "3  2401  Borderlands  Positive   \n",
       "4  2401  Borderlands  Positive   \n",
       "\n",
       "                                                Text  \n",
       "0  im getting on borderlands and i will murder yo...  \n",
       "1  I am coming to the borders and I will kill you...  \n",
       "2  im getting on borderlands and i will kill you ...  \n",
       "3  im coming on borderlands and i will murder you...  \n",
       "4  im getting on borderlands 2 and i will murder ...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f62b5190",
   "metadata": {},
   "source": [
    "# Text Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "528782db",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def clean_text(text):\n",
    "    # Check if text is a string\n",
    "    if not isinstance(text, str):\n",
    "        return text\n",
    "    \n",
    "    # Remove URLs\n",
    "    text = re.sub(r'http\\S+', '', text)\n",
    "    \n",
    "    emoji_pattern = re.compile(\n",
    "        \"[\"\n",
    "        \"\\U0001F600-\\U0001F64F\"  # Emoticons\n",
    "        \"\\U0001F300-\\U0001F5FF\"  # Symbols & pictographs\n",
    "        \"\\U0001F680-\\U0001F6FF\"  # Transport & map symbols\n",
    "        \"\\U0001F700-\\U0001F77F\"  # Alchemical symbols\n",
    "        \"\\U0001F780-\\U0001F7FF\"  # Geometric shapes\n",
    "        \"\\U0001F800-\\U0001F8FF\"  # Miscellaneous Symbols and Arrows\n",
    "        \"\\U0001F900-\\U0001F9FF\"  # Supplemental Symbols and Pictographs\n",
    "        \"\\U0001FA00-\\U0001FA6F\"  # Extended-A\n",
    "        \"\\U0001FA70-\\U0001FAFF\"  # Extended-B\n",
    "        \"\\U00002702-\\U000027B0\"  # Dingbats\n",
    "        \"\\U000024C2-\\U0001F251\"\n",
    "        \"]+\"\n",
    "    )\n",
    "    text = emoji_pattern.sub(r'', text)\n",
    "    # Remove special characters and emojis\n",
    "    text = re.sub(r'[^\\w\\s]', '', text)\n",
    "    return text\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87f7828c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cleaned_Text'] = df['Text'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c95114c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Text</th>\n",
       "      <th>Cleaned_Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands and i will murder yo...</td>\n",
       "      <td>im getting on borderlands and i will murder yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>I am coming to the borders and I will kill you...</td>\n",
       "      <td>I am coming to the borders and I will kill you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands and i will kill you ...</td>\n",
       "      <td>im getting on borderlands and i will kill you all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im coming on borderlands and i will murder you...</td>\n",
       "      <td>im coming on borderlands and i will murder you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74677</th>\n",
       "      <td>9200</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Just realized that the Windows partition of my...</td>\n",
       "      <td>Just realized that the Windows partition of my...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74678</th>\n",
       "      <td>9200</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Just realized that my Mac window partition is ...</td>\n",
       "      <td>Just realized that my Mac window partition is ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74679</th>\n",
       "      <td>9200</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Just realized the windows partition of my Mac ...</td>\n",
       "      <td>Just realized the windows partition of my Mac ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74680</th>\n",
       "      <td>9200</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Just realized between the windows partition of...</td>\n",
       "      <td>Just realized between the windows partition of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74681</th>\n",
       "      <td>9200</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Just like the windows partition of my Mac is l...</td>\n",
       "      <td>Just like the windows partition of my Mac is l...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74682 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID        Brand Sentiment  \\\n",
       "0      2401  Borderlands  Positive   \n",
       "1      2401  Borderlands  Positive   \n",
       "2      2401  Borderlands  Positive   \n",
       "3      2401  Borderlands  Positive   \n",
       "4      2401  Borderlands  Positive   \n",
       "...     ...          ...       ...   \n",
       "74677  9200       Nvidia  Positive   \n",
       "74678  9200       Nvidia  Positive   \n",
       "74679  9200       Nvidia  Positive   \n",
       "74680  9200       Nvidia  Positive   \n",
       "74681  9200       Nvidia  Positive   \n",
       "\n",
       "                                                    Text  \\\n",
       "0      im getting on borderlands and i will murder yo...   \n",
       "1      I am coming to the borders and I will kill you...   \n",
       "2      im getting on borderlands and i will kill you ...   \n",
       "3      im coming on borderlands and i will murder you...   \n",
       "4      im getting on borderlands 2 and i will murder ...   \n",
       "...                                                  ...   \n",
       "74677  Just realized that the Windows partition of my...   \n",
       "74678  Just realized that my Mac window partition is ...   \n",
       "74679  Just realized the windows partition of my Mac ...   \n",
       "74680  Just realized between the windows partition of...   \n",
       "74681  Just like the windows partition of my Mac is l...   \n",
       "\n",
       "                                            Cleaned_Text  \n",
       "0      im getting on borderlands and i will murder yo...  \n",
       "1      I am coming to the borders and I will kill you...  \n",
       "2      im getting on borderlands and i will kill you all  \n",
       "3      im coming on borderlands and i will murder you...  \n",
       "4      im getting on borderlands 2 and i will murder ...  \n",
       "...                                                  ...  \n",
       "74677  Just realized that the Windows partition of my...  \n",
       "74678  Just realized that my Mac window partition is ...  \n",
       "74679  Just realized the windows partition of my Mac ...  \n",
       "74680  Just realized between the windows partition of...  \n",
       "74681  Just like the windows partition of my Mac is l...  \n",
       "\n",
       "[74682 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab24fe3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df.drop(['ID', 'Brand'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0b7e608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Sentiment</th>\n",
       "      <th>Text</th>\n",
       "      <th>Cleaned_Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands and i will murder yo...</td>\n",
       "      <td>im getting on borderlands and i will murder yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Positive</td>\n",
       "      <td>I am coming to the borders and I will kill you...</td>\n",
       "      <td>I am coming to the borders and I will kill you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands and i will kill you ...</td>\n",
       "      <td>im getting on borderlands and i will kill you all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Positive</td>\n",
       "      <td>im coming on borderlands and i will murder you...</td>\n",
       "      <td>im coming on borderlands and i will murder you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sentiment                                               Text  \\\n",
       "0  Positive  im getting on borderlands and i will murder yo...   \n",
       "1  Positive  I am coming to the borders and I will kill you...   \n",
       "2  Positive  im getting on borderlands and i will kill you ...   \n",
       "3  Positive  im coming on borderlands and i will murder you...   \n",
       "4  Positive  im getting on borderlands 2 and i will murder ...   \n",
       "\n",
       "                                        Cleaned_Text  \n",
       "0  im getting on borderlands and i will murder yo...  \n",
       "1  I am coming to the borders and I will kill you...  \n",
       "2  im getting on borderlands and i will kill you all  \n",
       "3  im coming on borderlands and i will murder you...  \n",
       "4  im getting on borderlands 2 and i will murder ...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e043fb",
   "metadata": {},
   "source": [
    "# Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad117517",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df.drop_duplicates()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "25ae15f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neutral     29321\n",
       "Negative    21238\n",
       "Positive    19139\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['Sentiment'] = df['Sentiment'].replace('Irrelevant', 'Neutral')\n",
    "\n",
    "df['Sentiment'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8e4fb529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Positive', 'Neutral', 'Negative'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sentiment'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd047e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2f3fb36c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sentiment_Label'] = label_encoder.fit_transform(df['Sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f079213a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fdd5adda",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(subset=['Cleaned_Text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "83d81608",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer= Tokenizer()\n",
    "\n",
    "Comments= df['Cleaned_Text'].tolist()\n",
    "tokenizer.fit_on_texts(Comments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b0e4e2d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The max length in Comments is: 166\n",
      "The Unique word in the Comments are: 40027\n"
     ]
    }
   ],
   "source": [
    "sequences= tokenizer.texts_to_sequences(Comments)\n",
    "print('The max length in Comments is:',max([len(x) for x in sequences]))\n",
    "print('The Unique word in the Comments are:',len(tokenizer.word_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe0f4f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_pad_sequences= pad_sequences(sequences,maxlen=70 ,padding= 'pre')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ff702e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= input_pad_sequences\n",
    "y= df['Sentiment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "855d8a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "le= LabelEncoder()\n",
    "y= le.fit_transform(y)\n",
    "\n",
    "y= to_categorical(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "342a9a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55756, 70)\n",
      "(13939, 70)\n",
      "(55756, 3)\n",
      "(13939, 3)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f23a0ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.initializers import glorot_normal\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# Apply Xavier/Glorot initialization to the Embedding layer\n",
    "model.add(Embedding(40758, 200, input_length=70, embeddings_initializer=glorot_normal()))\n",
    "\n",
    "model.add(LSTM(100, return_sequences=True,dropout=0.6 ))\n",
    "\n",
    "\n",
    "model.add(LSTM(100, return_sequences=True,dropout=0.6 ))\n",
    "\n",
    "model.add(LSTM(100, return_sequences=True,dropout=0.6 ))\n",
    "\n",
    "\n",
    "model.add(LSTM(100))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(3, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0e2c3920",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer= 'adam',loss= 'categorical_crossentropy',metrics= ['accuracy'])\n",
    "callback = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7e227617",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1743/1743 [==============================] - 670s 377ms/step - loss: 0.0847 - accuracy: 0.9691 - val_loss: 0.2909 - val_accuracy: 0.9212\n",
      "Epoch 2/10\n",
      "1743/1743 [==============================] - 645s 370ms/step - loss: 0.0742 - accuracy: 0.9728 - val_loss: 0.3228 - val_accuracy: 0.9204\n",
      "Epoch 3/10\n",
      "1743/1743 [==============================] - 634s 364ms/step - loss: 0.0692 - accuracy: 0.9743 - val_loss: 0.3250 - val_accuracy: 0.9191\n",
      "Epoch 4/10\n",
      "1743/1743 [==============================] - 604s 347ms/step - loss: 0.0643 - accuracy: 0.9766 - val_loss: 0.2981 - val_accuracy: 0.9232\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=10, batch_size=32,\n",
    "    callbacks= callback,\n",
    "\n",
    "    validation_data=(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3285a1f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17ed2298340>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EFgAAYDwCCwAAMB6BBQAAGK9bgWXTpk2aMGGCEhISlJ2drQMHDnTYdseOHVqwYIHS0tKUkpKinJwcvfXWWyFttm7dKpvN1m6qq6vrTvcAAMAAE3VgKSwsVH5+vtatW6fi4mLNnz9fixYtUklJSdj2+/fv14IFC7R7924dPXpU3/zmN7V48WIVFxeHtEtJSVFpaWnIlJCQ0L1vBQAABhSbZVlWNG+YM2eOZs2apc2bNwfnXXXVVbr99ttVUFAQ0TquvvpqLV++XE888YSkQIUlPz9fFy9ejKYrITwej9xutyorK5WSktLt9QAAgL4T6fE7qgpLQ0ODjh49qry8vJD5eXl5OnToUETr8Pv9qqqq0ogRI0LmV1dXKysrS2PHjtV3vvOddhWYturr6+XxeEImAAAwMEUVWCoqKuTz+ZSenh4yPz09XWVlZRGt47nnnlNNTY2WLVsWnDd16lRt3bpVb7zxhrZt26aEhATNmzdPn332WYfrKSgokNvtDk6ZmZnRfBUAANCPdOukW5vNFvLasqx288LZtm2bnnzySRUWFmrUqFHB+XPnztUPfvADzZgxQ/Pnz9frr7+uyZMn64UXXuhwXWvXrlVlZWVwOn36dHe+CgAA6Afiommcmpoqh8PRrppSXl7erurSVmFhoe655x7953/+p2655ZZO29rtdl1//fWdVlhcLpdcLlfknQcAAP1WVBUWp9Op7OxsFRUVhcwvKipSbm5uh+/btm2bVq5cqVdffVXf/va3u/wcy7J07NgxZWRkRNM9AAAwQEVVYZGkNWvWaMWKFZo9e7ZycnK0ZcsWlZSUaNWqVZICQzVnzpzRK6+8IikQVu666y5t3LhRc+fODVZnEhMT5Xa7JUlPPfWU5s6dqyuvvFIej0e//vWvdezYMb344os99T0BAEA/FnVgWb58uc6fP6/169ertLRU06dP1+7du5WVlSVJKi0tDbkny0svvSSv16vVq1dr9erVwfl33323tm7dKkm6ePGi7r//fpWVlcntdmvmzJnav3+/brjhhsv8egAAYCCI+j4spuI+LAAA9D+9ch8WAACAWCCwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIwXF+sOAABgOp/Pp8bGxlh3o1+Kj4+Xw+G47PUQWAAA6IBlWSorK9PFixdj3ZV+bdiwYRo9erRsNlu310FgAQCgA81hZdSoUUpKSrqsA+5gZFmWamtrVV5eLknKyMjo9roILAAAhOHz+YJhZeTIkbHuTr+VmJgoSSovL9eoUaO6PTzESbcAAITRfM5KUlJSjHvS/zVvw8s5D4jAAgBAJxgGunw9sQ0JLAAAwHgEFgAA0KHx48drw4YNse4GJ90CADDQ3HTTTbruuut6JGj88Y9/VHJy8uV36jIRWAAAGGQsy5LP51NcXNcxIC0trQ961DWGhAAAGEBWrlypffv2aePGjbLZbLLZbNq6datsNpveeustzZ49Wy6XSwcOHNCJEye0ZMkSpaena8iQIbr++uv19ttvh6yv7ZCQzWbTb37zGy1dulRJSUm68sor9cYbb/T69yKwAAAQIcuyVNvgjclkWVZEfdy4caNycnJ03333qbS0VKWlpcrMzJQkPfLIIyooKNBf//pXXXvttaqurtZtt92mt99+W8XFxVq4cKEWL16skpKSTj/jqaee0rJly/TRRx/ptttu0/e//31duHDhsrdvZxgSAgAgQpcafZr2xFsx+ezj6xcqydn1YdvtdsvpdCopKUmjR4+WJH388ceSpPXr12vBggXBtiNHjtSMGTOCr3/xi19o586deuONN/Tggw92+BkrV67UHXfcIUl6+umn9cILL+j999/Xrbfe2q3vFoluVVg2bdqkCRMmKCEhQdnZ2Tpw4ECHbXfs2KEFCxYoLS1NKSkpysnJ0Vtvtf/L3r59u6ZNmyaXy6Vp06Zp586d3ekaAADowOzZs0Ne19TU6JFHHtG0adM0bNgwDRkyRB9//HGXFZZrr702+HNycrKGDh0avP1+b4m6wlJYWKj8/Hxt2rRJ8+bN00svvaRFixbp+PHjGjduXLv2+/fv14IFC/T0009r2LBhevnll7V48WIdOXJEM2fOlCQdPnxYy5cv189//nMtXbpUO3fu1LJly3Tw4EHNmTPn8r8lAAA9IDHeoePrF8bssy9X26t9/v7v/15vvfWWnn32WU2aNEmJiYn63ve+p4aGhk7XEx8fH/LaZrPJ7/dfdv86E3Vgef7553XPPffo3nvvlSRt2LBBb731ljZv3qyCgoJ27dteUvX0009r165d+u///u9gYNmwYYMWLFigtWvXSpLWrl2rffv2acOGDdq2bVu0XQQAoFfYbLaIhmVizel0yufzddnuwIEDWrlypZYuXSpJqq6u1qlTp3q5d90T1ZBQQ0ODjh49qry8vJD5eXl5OnToUETr8Pv9qqqq0ogRI4LzDh8+3G6dCxcujHidAACgxfjx43XkyBGdOnVKFRUVHVY/Jk2apB07dujYsWP68MMPdeedd/Z6paS7ogosFRUV8vl8Sk9PD5mfnp6usrKyiNbx3HPPqaamRsuWLQvOKysri3qd9fX18ng8IRMAAJAefvhhORwOTZs2TWlpaR2ek/KrX/1Kw4cPV25urhYvXqyFCxdq1qxZfdzbyHSrrtX2IUaWZUX0YKNt27bpySef1K5duzRq1KjLWmdBQYGeeuqpKHoNAMDgMHnyZB0+fDhk3sqVK9u1Gz9+vN55552QeatXrw553XaIKNzl1RcvXuxWP6MRVYUlNTVVDoejXeWjvLy8XYWkrcLCQt1zzz16/fXXdcstt4QsGz16dNTrXLt2rSorK4PT6dOno/kqAACgH4kqsDidTmVnZ6uoqChkflFRkXJzczt837Zt27Ry5Uq9+uqr+va3v91ueU5OTrt17tmzp9N1ulwupaSkhEwAAGBginpIaM2aNVqxYoVmz56tnJwcbdmyRSUlJVq1apWkQOXjzJkzeuWVVyQFwspdd92ljRs3au7cucFKSmJiotxutyTpoYce0o033qhnnnlGS5Ys0a5du/T222/r4MGDPfU9AQBAPxb1jeOWL1+uDRs2aP369bruuuu0f/9+7d69W1lZWZKk0tLSkJN7XnrpJXm9Xq1evVoZGRnB6aGHHgq2yc3N1WuvvaaXX35Z1157rbZu3arCwkLuwQIAACRJNivShxMYzuPxyO12q7KykuEhAMBlq6ur08mTJ4N3dkf3dbYtIz1+8/BDAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAADDA3HTTTcrPz++x9a1cuVK33357j62vOwgsAADAeAQWAAAGkJUrV2rfvn3auHGjbDabbDabTp06pePHj+u2227TkCFDlJ6erhUrVqiioiL4vt/97ne65pprlJiYqJEjR+qWW25RTU2NnnzySf37v/+7du3aFVzfu+++2+ffq1tPawYAYFCyLKmxNjafHZ8k2WxdNtu4caM+/fRTTZ8+XevXr5ck+Xw+feMb39B9992n559/XpcuXdJPf/pTLVu2TO+8845KS0t1xx136J/+6Z+0dOlSVVVV6cCBA7IsSw8//LD++te/yuPx6OWXX5YkjRgxole/ajgEFgAAItVYKz09Jjaf/bOzkjO5y2Zut1tOp1NJSUkaPXq0JOmJJ57QrFmz9PTTTwfb/du//ZsyMzP16aefqrq6Wl6vV9/97neDj9q55pprgm0TExNVX18fXF8sEFgAABjgjh49qr1792rIkCHtlp04cUJ5eXm6+eabdc0112jhwoXKy8vT9773PQ0fPjwGvQ2PwAIAQKTikwKVjlh9djf5/X4tXrxYzzzzTLtlGRkZcjgcKioq0qFDh7Rnzx698MILWrdunY4cOaIJEyZcTq97DIEFAIBI2WwRDcvEmtPplM/nC76eNWuWtm/frvHjxysuLvyh32azad68eZo3b56eeOIJZWVlaefOnVqzZk279cUCVwkBADDAjB8/XkeOHNGpU6dUUVGh1atX68KFC7rjjjv0/vvv6/PPP9eePXv0wx/+UD6fT0eOHNHTTz+tDz74QCUlJdqxY4fOnTunq666Kri+jz76SJ988okqKirU2NjY59+JwAIAwADz8MMPy+FwaNq0aUpLS1NDQ4P+8Ic/yOfzaeHChZo+fboeeughud1u2e12paSkaP/+/brttts0efJkPfbYY3ruuee0aNEiSdJ9992nKVOmaPbs2UpLS9Mf/vCHPv9ONsuyrD7/1F7g8XjkdrtVWVmplJSUWHcHANDP1dXV6eTJk5owYYISEhJi3Z1+rbNtGenxmwoLAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAOjEALmYNqZ6YhsSWAAACCM+Pl6SVFsbo6czDyDN27B5m3YHt+YHACAMh8OhYcOGqby8XJKUlJQkm80W4171L5Zlqba2VuXl5Ro2bJgcDke310VgAQCgA6NHj5akYGhB9wwbNiy4LbuLwAIAQAdsNpsyMjI0atSomDw/ZyCIj4+/rMpKMwILAABdcDgcPXLQRfdx0i0AADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIzXrcCyadMmTZgwQQkJCcrOztaBAwc6bFtaWqo777xTU6ZMkd1uV35+frs2W7dulc1mazfV1dV1p3sAAGCAiTqwFBYWKj8/X+vWrVNxcbHmz5+vRYsWqaSkJGz7+vp6paWlad26dZoxY0aH601JSVFpaWnIlJCQEG33AADAABR1YHn++ed1zz336N5779VVV12lDRs2KDMzU5s3bw7bfvz48dq4caPuuusuud3uDtdrs9k0evTokAkAAECKMrA0NDTo6NGjysvLC5mfl5enQ4cOXVZHqqurlZWVpbFjx+o73/mOiouLO21fX18vj8cTMgEAgIEpqsBSUVEhn8+n9PT0kPnp6ekqKyvrdiemTp2qrVu36o033tC2bduUkJCgefPm6bPPPuvwPQUFBXK73cEpMzOz258PAADM1q2Tbm02W8hry7LazYvG3Llz9YMf/EAzZszQ/Pnz9frrr2vy5Ml64YUXOnzP2rVrVVlZGZxOnz7d7c8HAABmi4umcWpqqhwOR7tqSnl5ebuqy+Ww2+26/vrrO62wuFwuuVyuHvtMAABgrqgqLE6nU9nZ2SoqKgqZX1RUpNzc3B7rlGVZOnbsmDIyMnpsnQAAoP+KqsIiSWvWrNGKFSs0e/Zs5eTkaMuWLSopKdGqVaskBYZqzpw5o1deeSX4nmPHjkkKnFh77tw5HTt2TE6nU9OmTZMkPfXUU5o7d66uvPJKeTwe/frXv9axY8f04osv9sBXBAAA/V3UgWX58uU6f/681q9fr9LSUk2fPl27d+9WVlaWpMCN4trek2XmzJnBn48ePapXX31VWVlZOnXqlCTp4sWLuv/++1VWVia3262ZM2dq//79uuGGGy7jqwEAgIHCZlmWFetO9ASPxyO3263KykqlpKTEujsAACACkR6/eZYQAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjdSuwbNq0SRMmTFBCQoKys7N14MCBDtuWlpbqzjvv1JQpU2S325Wfnx+23fbt2zVt2jS5XC5NmzZNO3fu7E7XAADAABR1YCksLFR+fr7WrVun4uJizZ8/X4sWLVJJSUnY9vX19UpLS9O6des0Y8aMsG0OHz6s5cuXa8WKFfrwww+1YsUKLVu2TEeOHIm2ewAAYACyWZZlRfOGOXPmaNasWdq8eXNw3lVXXaXbb79dBQUFnb73pptu0nXXXacNGzaEzF++fLk8Ho/efPPN4Lxbb71Vw4cP17Zt2yLql8fjkdvtVmVlpVJSUiL/QgAAIGYiPX5HVWFpaGjQ0aNHlZeXFzI/Ly9Phw4d6l5PFaiwtF3nwoULO11nfX29PB5PyAQAAAamqAJLRUWFfD6f0tPTQ+anp6errKys250oKyuLep0FBQVyu93BKTMzs9ufDwAAzNatk25tNlvIa8uy2s3r7XWuXbtWlZWVwen06dOX9fkAAMBccdE0Tk1NlcPhaFf5KC8vb1chicbo0aOjXqfL5ZLL5er2ZwIAgP4jqgqL0+lUdna2ioqKQuYXFRUpNze3253Iyclpt849e/Zc1joBAMDAEVWFRZLWrFmjFStWaPbs2crJydGWLVtUUlKiVatWSQoM1Zw5c0avvPJK8D3Hjh2TJFVXV+vcuXM6duyYnE6npk2bJkl66KGHdOONN+qZZ57RkiVLtGvXLr399ts6ePBgD3xFAADQ30UdWJYvX67z589r/fr1Ki0t1fTp07V7925lZWVJCtworu09WWbOnBn8+ejRo3r11VeVlZWlU6dOSZJyc3P12muv6bHHHtPjjz+uiRMnqrCwUHPmzLmMrwYAAAaKqO/DYiruwwIAQP/TK/dhAQAAiAUCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMF/XTmgebH/2/o/prqUepQ1yBaaiz5echLqW1ep3sYnMCANAbOMJ24dT52uDUlcR4R/tAM8Sp1KGuVvMCr4e64mSz2frgGwAA0P8RWLrw0g+yVeapU0V1fWCqqte56oaW19X1qqhq0KVGny41+nT6wiWdvnCpy/U64+xKaw4wXVRv3InxhBsAwKBGYOnCuJFJGjcyqct2NfXeYIA5V9U+0LS8blB1vVcNXr/OXLykMxe7DjfxDptGJrcPNKlDnEprU70ZnuSU3U64AQAMLASWHpLsilOyK05ZI5O7bHupwRcSYJorN82vz7Wq5njqvGr0WSrz1KnMU9fluh12m0YkO1sCzRBX05BUm7Az1KmRyS45CDcAgH6AwBIDiU6HMkckKXNE15Wbeq9P51sPQVW1CjTVDa2CTr2+qm2Uz2/pXFW9zlXVd7lum00akeQMOxzVfK5NWtPrkUOcindwURkAIDYILIZzxTk0ZliixgxL7LJto8+vCzUNOlfVcfWmOdycr2mQZUnnaxp0vqZBn3zZdV+GJcW3hJngeTZtz8MJvHbFOXrg2wMAEEBgGUDiHXalpyQoPSWhy7Y+v6ULNeHPtTnXpnpzvqZBPr+li7WNuljbqP8r77ovQxPigtWZrqo3iU7CDQCgcwSWQcphtyltaKBC0hW/39LFS42trpLqvHrT6LNUVedVVZ1Xn1fUdLn+ZKej1aXfodWatDavk50OrpgCgEGIwIIu2ZtO5B2R7NTk9KGdtrUsS55L3lbn2TSHmpZAc65V9abe61dNg08152v1twjudZMQb+/wxn2tqzepQ1xKSeBeNwAwUBBY0KNsNpvcSfFyJ8Vr0qghnba1LEvV9d521ZpzHVRvaht8qmv064uvLumLryK7101qsrPD6k3wKqqme91wOTgAmIvAgpix2WwamhCvoQnxmpDa9eXgtQ3eNldJtb3HTcu5N1VN97o5W1mns5VdXw4eZ7dpZJhLv9PavE4d4tLwJCeXgwNAHyOwoN9IcsZp3Mi4iG7kV9foCwkwrQPNuTbVm8pLjfL6LX3pqdeXnq4vB7fbpBHJbW/cF756MyLZqTguBweAy0ZgwYCUEO/Q2OFJGju863DT4PXrfE3bq6TCV2++qm2Q31Jw3sdlVZ2u22aThic5w964LzU58MDMJKdDiU6HkpqmRGeckuID81xxds7DAQARWAA54+zKcCcqw931vW68zfe66aB6E3g0Q+DnCzX18lvShZoGXahp0KdfVkfdN7stUFlKdDqUGO8ICTeJ8XGtQk5z4IlTQnyr8BPvCL4/tG2cEuMdDG0B6DcILEAU4hx2jUpJ0KgI73XzVW1Du2rNuabX52vqVVvvU22jV7UNPl1q8AX+bPSpweuXJPktqbreq+p6b698H2ecPRBk4lsFmdbhplUoCglCzlaVoTahKJHqEIBeQGABeonDbgsOAWl0dO/1+vyBJ4A3hZhAkPG2/Nwq3FxqaDM/7DyvLjX4A/MbfbKswOc0eP1q8Pp1UY298v0T4x0hQSbJGT7cJLWq+rSuAoVWlKgOAYMZgQUwUJzDrqEOu4YmxPf4ui3LUr3X3xRovGFD0aWmMNQSekKrQK1DUetgdanBpwZfoDrk81u9Wh1yNVeHOghA4YbMms8Pap7X0ZCZ00F1CDANgQUYZGw2mxLiA0M8I5KdPb5+r8/fFGh87ULRpXDzg/PaD421VIda5jVXh+q9ftV7/fqqtneqQ0nxoUEnMd7e4ZBZ63kJ8e2rQ0nxoaGKe/4A0SOwAOhRcQ67Uhx2pfRSdaiu0d8Sbhp9rSpCYYbMmofGWoWiS43+9hWjBq8uNfrU6AukIZ/fUlW9V1W9VB1KaA4/XQ2ZNYWdJKdDCcHzhcIPmTWHImccl9FjYCKwAOg3bDZbYCjH6dDIXlh/o8/fKsh4OzyPKGRemKGxkCGzVtWhZnWNftU1NvTCNwjcBDGxTXUoqU0oSmx3vlD4IbNwoYrqEGKFwAIATeIddrkT7XIn9nx1yO+3VOdtXREKM2TWPDTWGGZoLEwAaj3P6w9Uh7x9WB1qfd5P2/OBWleHwp04TXUI0SKwAEAfsNttTQfq3vm12+D1tw83jW2GxtqEpbBDZq2qR3WNMagOtakEhasOtb/CLHx1qPXl+FSH+j8CCwAMAM44u5xxfVMdal0Zan0eUdvqUEdDZp1Wh+q8qqozrzrU1QnVVId6H4EFANCpPqkOtQ0yHVSH2t1/yLDqUEv1p+PqUNgbMXZwQnViPNWhZgQWAEBMBatD6tvqULtzhPphdajdidEdVIfCnmjdz6pDBBYAwIDVV9Wh2jA3XQxXHapt9Kou5GqycDdwDMyra/QHP8eU6tDdOeM1bmTXD5XtlX7G5FMBABgAYlkdqm30NYWfzqtDoUHq8qpD3742g8ACAABamFgdGhPBU+17C4EFAIBBqDerQ72hf5xpAwAABjUCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGGzBPa7YsS5Lk8Xhi3BMAABCp5uN283G8IwMmsFRVVUmSMjMzY9wTAAAQraqqKrnd7g6X26yuIk0/4ff7dfbsWQ0dOlQ2m63H1uvxeJSZmanTp08rJSWlx9Y7ELGtosP2ihzbKnJsq8ixrSLXm9vKsixVVVVpzJgxsts7PlNlwFRY7Ha7xo4d22vrT0lJYYeOENsqOmyvyLGtIse2ihzbKnK9ta06q6w046RbAABgPAILAAAwHoGlCy6XS//wD/8gl8sV664Yj20VHbZX5NhWkWNbRY5tFTkTttWAOekWAAAMXFRYAACA8QgsAADAeAQWAABgPAILAAAwHoFF0qZNmzRhwgQlJCQoOztbBw4c6LT9vn37lJ2drYSEBF1xxRX613/91z7qaexFs63effdd2Wy2dtPHH3/chz2Ojf3792vx4sUaM2aMbDab/uu//qvL9wzW/SrabTWY96uCggJdf/31Gjp0qEaNGqXbb79dn3zySZfvG4z7Vne21WDdtzZv3qxrr702eFO4nJwcvfnmm52+Jxb71KAPLIWFhcrPz9e6detUXFys+fPna9GiRSopKQnb/uTJk7rttts0f/58FRcX62c/+5n+7u/+Ttu3b+/jnve9aLdVs08++USlpaXB6corr+yjHsdOTU2NZsyYoX/5l3+JqP1g3q+i3VbNBuN+tW/fPq1evVrvvfeeioqK5PV6lZeXp5qamg7fM1j3re5sq2aDbd8aO3asfvnLX+qDDz7QBx98oG9961tasmSJ/vKXv4RtH7N9yhrkbrjhBmvVqlUh86ZOnWo9+uijYds/8sgj1tSpU0PmPfDAA9bcuXN7rY+miHZb7d2715JkffXVV33QO3NJsnbu3Nlpm8G8X7UWybZiv2pRXl5uSbL27dvXYRv2rYBIthX7Vovhw4dbv/nNb8Iui9U+NagrLA0NDTp69Kjy8vJC5ufl5enQoUNh33P48OF27RcuXKgPPvhAjY2NvdbXWOvOtmo2c+ZMZWRk6Oabb9bevXt7s5v91mDdry4H+5VUWVkpSRoxYkSHbdi3AiLZVs0G877l8/n02muvqaamRjk5OWHbxGqfGtSBpaKiQj6fT+np6SHz09PTVVZWFvY9ZWVlYdt7vV5VVFT0Wl9jrTvbKiMjQ1u2bNH27du1Y8cOTZkyRTfffLP279/fF13uVwbrftUd7FcBlmVpzZo1+vrXv67p06d32I59K/JtNZj3rT//+c8aMmSIXC6XVq1apZ07d2ratGlh28ZqnxowT2u+HDabLeS1ZVnt5nXVPtz8gSiabTVlyhRNmTIl+DonJ0enT5/Ws88+qxtvvLFX+9kfDeb9KhrsVwEPPvigPvroIx08eLDLtoN934p0Ww3mfWvKlCk6duyYLl68qO3bt+vuu+/Wvn37OgwtsdinBnWFJTU1VQ6Ho12FoLy8vF16bDZ69Oiw7ePi4jRy5Mhe62usdWdbhTN37lx99tlnPd29fm+w7lc9ZbDtVz/+8Y/1xhtvaO/evRo7dmynbQf7vhXNtgpnsOxbTqdTkyZN0uzZs1VQUKAZM2Zo48aNYdvGap8a1IHF6XQqOztbRUVFIfOLioqUm5sb9j05OTnt2u/Zs0ezZ89WfHx8r/U11rqzrcIpLi5WRkZGT3ev3xus+1VPGSz7lWVZevDBB7Vjxw698847mjBhQpfvGaz7Vne2VTiDZd9qy7Is1dfXh10Ws32qV0/p7Qdee+01Kz4+3vrtb39rHT9+3MrPz7eSk5OtU6dOWZZlWY8++qi1YsWKYPvPP//cSkpKsn7yk59Yx48ft377299a8fHx1u9+97tYfYU+E+22+tWvfmXt3LnT+vTTT63//d//tR599FFLkrV9+/ZYfYU+U1VVZRUXF1vFxcWWJOv555+3iouLrb/97W+WZbFftRbtthrM+9WPfvQjy+12W++++65VWloanGpra4Nt2LcCurOtBuu+tXbtWmv//v3WyZMnrY8++sj62c9+ZtntdmvPnj2WZZmzTw36wGJZlvXiiy9aWVlZltPptGbNmhVy2dvdd99tfeMb3whp/+6771ozZ860nE6nNX78eGvz5s193OPYiWZbPfPMM9bEiROthIQEa/jw4dbXv/5163/+539i0Ou+13x5ZNvp7rvvtiyL/aq1aLfVYN6vwm0nSdbLL78cbMO+FdCdbTVY960f/vCHwd/raWlp1s033xwMK5Zlzj5ls6ymM2UAAAAMNajPYQEAAP0DgQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxvv/gxvu5NFPrd4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(history.history['loss'],label= 'train')\n",
    "plt.plot(history.history['val_loss'],label= 'test')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "56fcea4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(history.history['accuracy'],label= 'train')\n",
    "plt.plot(history.history['val_accuracy'],label= 'test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "415f5618",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "436/436 [==============================] - 61s 140ms/step - loss: 0.2909 - accuracy: 0.9212\n",
      "Test Loss: 0.2909\n",
      "Test Accuracy: 92.12%\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy= model.evaluate(X_test,y_test)\n",
    "print(f'Test Loss: {loss:.4f}')\n",
    "print(f'Test Accuracy: {accuracy*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad60b7f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9055cf4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb3cbe7d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}