Aryan_ML_Naive_Bayes.ipynb

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   "source": [
    "# Womens Clothing Reviews Sentiment Analysis"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MADE BY: Aryan Rastogi**\n",
    "\n",
    "**Reach me at :**\n",
    "\n",
    "**Github :** https://github.com/AryanRastogi7767\n",
    "\n",
    "**Gmail :** aryanrastogi97@gmail.com\n",
    "\n",
    "**Linkedin :** https://www.linkedin.com/in/aryan-rastogi-b38ba1190/\n",
    "\n",
    "**Kaggle :** https://www.kaggle.com/aryanrastogi7767"
   ]
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    "id": "Rh-4NI8sCC2C"
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   "source": [
    "**In this project I will use the Women's E-commerce Clothing Reviews from Kaggle to demonstrate the power of the Naive Bayes algorithm in Binary Classification.**\n",
    "\n",
    "**The primary focus of this project will be text analysis and therefore unnecessary columns will be removed and focus will be on the reviews column.**\n",
    "\n",
    "**Problem Type: Binary Classification**\n",
    "\n",
    "**Statement: Using the given data, develop a classifier that can predict if a review is Positeve or Negative.**"
   ]
  },
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    "This dataset includes 23486 rows and 10 feature variables. Each row corresponds to a customer review, and includes the variables:\n",
    "\n",
    "* Clothing ID: Integer Categorical variable that refers to the specific piece being reviewed.\n",
    "* Age: Positive Integer variable of the reviewers age.\n",
    "* Title: String variable for the title of the review.\n",
    "* Review Text: String variable for the review body.\n",
    "* Rating: Positive Ordinal Integer variable for the product score granted by the customer from 1 Worst, to 5 Best.\n",
    "* Recommended IND: Binary variable stating where the customer recommends the product where 1 is recommended, 0 is not recommended.\n",
    "* Positive Feedback Count: Positive Integer documenting the number of other customers who found this review positive.\n",
    "* Division Name: Categorical name of the product high level division.\n",
    "* Department Name: Categorical name of the product department name.\n",
    "* Class Name: Categorical name of the product class name."
   ]
  },
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   "source": [
    "## Approach to the Problem:\n",
    "\n",
    "* Importing the Libraries\n",
    "* Reading The Data\n",
    "* Understanding the Data\n",
    "* Checking for Null Values\n",
    "* Removing Unnecessary Columns\n",
    "* Removing Rows with Null Values\n",
    "* Creating the binary Sentiment Column(Target Variable)\n",
    "* Exploratory Data Analysis\n",
    "* Data Visualization\n",
    "* Preprocessing of the Review Text Column\n",
    "* Stemming the Reviews\n",
    "* Splitting The Data \n",
    "* Creating Feature Vectors from the Reviews\n",
    "* Training Multinomial Naive Bayes Classifiers\n",
    "* Analysing Performance and Predictions."
   ]
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  {
   "cell_type": "markdown",
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    "id": "TKaiHJM-CC2I"
   },
   "source": [
    "## Importing The Necessary Libraries and Functions"
   ]
  },
  {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "from wordcloud import WordCloud\n",
    "import seaborn as sns\n",
    "from bs4 import BeautifulSoup\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB,GaussianNB\n",
    "from nltk.corpus import stopwords\n",
    "from tqdm import tqdm\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from sklearn.metrics import roc_curve, auc, confusion_matrix, classification_report\n",
    "import sklearn.metrics as mt\n",
    "from plotly import tools\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    "colab_type": "code",
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     "timestamp": 1600525511042,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download(\"stopwords\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Clothing ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended IND</th>\n",
       "      <th>Positive Feedback Count</th>\n",
       "      <th>Division Name</th>\n",
       "      <th>Department Name</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>767</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>Intimates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1080</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1077</td>\n",
       "      <td>60</td>\n",
       "      <td>Some major design flaws</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1049</td>\n",
       "      <td>50</td>\n",
       "      <td>My favorite buy!</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>847</td>\n",
       "      <td>47</td>\n",
       "      <td>Flattering shirt</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Clothing ID  Age  ...   Division Name Department Name  Class Name\n",
       "0           0          767   33  ...       Initmates        Intimate   Intimates\n",
       "1           1         1080   34  ...         General         Dresses     Dresses\n",
       "2           2         1077   60  ...         General         Dresses     Dresses\n",
       "3           3         1049   50  ...  General Petite         Bottoms       Pants\n",
       "4           4          847   47  ...         General            Tops     Blouses\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Womens Clothing E-Commerce Reviews.csv\") # reading the data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23486 entries, 0 to 23485\n",
      "Data columns (total 11 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   Unnamed: 0               23486 non-null  int64 \n",
      " 1   Clothing ID              23486 non-null  int64 \n",
      " 2   Age                      23486 non-null  int64 \n",
      " 3   Title                    19676 non-null  object\n",
      " 4   Review Text              22641 non-null  object\n",
      " 5   Rating                   23486 non-null  int64 \n",
      " 6   Recommended IND          23486 non-null  int64 \n",
      " 7   Positive Feedback Count  23486 non-null  int64 \n",
      " 8   Division Name            23472 non-null  object\n",
      " 9   Department Name          23472 non-null  object\n",
      " 10  Class Name               23472 non-null  object\n",
      "dtypes: int64(6), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DsPV-AdfCC3G"
   },
   "source": [
    "**We can see that the given columns have a few null values that need to be handled.**\n",
    "* Title\n",
    "* Review Text\n",
    "* Division Name\n",
    "* Department Name \n",
    "* Class Name"
   ]
  },
  {
   "cell_type": "code",
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    "colab_type": "code",
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     "timestamp": 1600525751798,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Clothing ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended IND</th>\n",
       "      <th>Positive Feedback Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23486.000000</td>\n",
       "      <td>23486.000000</td>\n",
       "      <td>23486.000000</td>\n",
       "      <td>23486.000000</td>\n",
       "      <td>23486.000000</td>\n",
       "      <td>23486.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11742.500000</td>\n",
       "      <td>918.118709</td>\n",
       "      <td>43.198544</td>\n",
       "      <td>4.196032</td>\n",
       "      <td>0.822362</td>\n",
       "      <td>2.535936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6779.968547</td>\n",
       "      <td>203.298980</td>\n",
       "      <td>12.279544</td>\n",
       "      <td>1.110031</td>\n",
       "      <td>0.382216</td>\n",
       "      <td>5.702202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5871.250000</td>\n",
       "      <td>861.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11742.500000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17613.750000</td>\n",
       "      <td>1078.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23485.000000</td>\n",
       "      <td>1205.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>122.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0   Clothing ID  ...  Recommended IND  Positive Feedback Count\n",
       "count  23486.000000  23486.000000  ...     23486.000000             23486.000000\n",
       "mean   11742.500000    918.118709  ...         0.822362                 2.535936\n",
       "std     6779.968547    203.298980  ...         0.382216                 5.702202\n",
       "min        0.000000      0.000000  ...         0.000000                 0.000000\n",
       "25%     5871.250000    861.000000  ...         1.000000                 0.000000\n",
       "50%    11742.500000    936.000000  ...         1.000000                 1.000000\n",
       "75%    17613.750000   1078.000000  ...         1.000000                 3.000000\n",
       "max    23485.000000   1205.000000  ...         1.000000               122.000000\n",
       "\n",
       "[8 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
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    {
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pRUmrUy3anQ6sC0yVtHeLc2fZnmF7RjrWiIgJos/1Xl1Qu3O1vbj8uwQ4hyrtop3zFgBLgc2BNwC32L7L9uPA2cBr6rYtIiLGgc6GSHRE3eD+qZKe0/8e2Jlq0tJAx08vmcJIeiHwEuBWquHgbSWtrOpB7U5Us7YiImKi68HOte4z17WBc8rEpSnA/7N9QVli85/A84FzJc2z/SbgdcBhZdJSH/CRMsv4bklnAXOBZcCfGXjBb0RExJimIZbqjFnZFSciYuxY9thiDX3U8Dz89Q/V+nu/8iHf7VjbBpL4w4iIGNu6NLRbR+3OVdKtwN+BJ4BltmdI2hL4DtVa1mVUw79XSvoUsFfDtV9KNXQ8FfgB1TCzqUKVT6jbtoiIGAe6NOO3jk4lNH0F+KLt88tG6l8BdrB9LHAsgKS3AYfavlfSCsAnbM8tE6T+JOnips1qIyJiIurBEIlOBfe3s7HsnlQZjdi+0/bc8v7vVDOFm/fTi4iI6AmdSmg6BLiwpC5NomnNqqSVqbbvObi5MkkbAK8ArmjxXRKaIiImmgk6LPw624slrQVcLOl6YHeqId+fSNoD+B5VUES/twG/t31vY0WSVgF+Ahxi+8HmCzXuyZfZwhERE4N7cEJTpxKa9qVKWQL4Mc9MbZpJGRLuJ2k5qo71NNtnExERARMv/nCQhKY7gO3LYa8Hbmo4Z9Xy3c8aykR1d7vA9tfqtCkiIsaZzm6W3hGdSmhaCpxQog4fpTwnLd5BtevNQw1lrwX2Aa6RNK+UfbbssRcREdFTanWutm8GtmhR/jvgVQOccypNm6CX40c9QSMiInrABJ3QFBNUNkuPiFHRgxOaanWuklYDTqLaNs7AB2xfJumjwEFUqU3n2v50Of5wYP9S/jHbFw5WT522ReelY4uIUTEB71xPAC6wvbuk5YGVJe1ItfH5Frb/UZboIGlTqlnCm1FtiP4/kja2/USremq2K0ZB7lwjIlobdudaZv3+M7AfgO3HgMckHQgcbfsfpXxJOWU34IxSfoukhcDWkq5rVc9w2xWjJx1bRIyKCRZ/OB24CzhF0p8lnVSW42wMbCfpCkm/kbRVOX4acHvD+YtK2UD1RERETLh1rlOAVwLftv0K4CHgsFL+PGBb4FPAmWUd67Ot5xkkHSBpjqQ5fX0PtTokIiLGGff11Xp1Q53OdRGwyHZ/BvBZVJ3kIuBsV64E+oA1gcXA+g3nr1fKBqrnGWzPsj3D9ozkCkdETBAT6c7V9v8Ct0vapBTtBFwH/BTYEUDSxsDywN3AbGCmpBUkTQc2Aq4cpJ6IiIieVHe28EeB08oM35uB91MN654s6VqqiUn72jYwX9KZVB3nMuCgMlN4oHoiIiJ6cimOqn6v92RXnIiIsWPZY4s7lrK39JO71fp7v8pxPxv1BMAkNEVExNjWg3eudXfF2UTSvIbXg5IOkXSUpKtL2UWS1m06bytJyyTt3lC2r6SbymvfOu2KiIjxw32u9eqGusH9NwBbAkiaTDX79xzgPtufL+UfA44APtxw3DHARf31SHoe8AVgBlX84Z8kzbZ9X532RUREdEPtzdIb7AT8xfZfbT/YUD6VqsPs91GqTdGXNJS9CbjY9r2lQ70Y2GUE2xYREb2qB5fijOQz15nA6f0fJH0ZeB/wAE8tzZlGtZ/rjsBWDecOlN4UERETXQ/uijMid65lCc2uwI/7y2x/zvb6wGnAwaX468Bn7OEFRSahKSJiAurBO9eRGhZ+MzDX9t9afHca8K7yfgZwhqRbgd2Bb0l6OwOnNz1NEpoiIqIXjNSw8J48fUh4I9s3lY+7AdcD2J7ecMypwC9s/7RMaPo/klYvX+8MHD5CbYsOyZZzETEqenApTu3Otexg80bgQw3FR5c4wz7gr5SZwgOxfa+ko4A/lqIv2b63btuis9KxRcRo6MWwo9qdq+2HgDWayt41wOGNx+zX9Plk4OS67YmIiHFmIt65RkREdFQPdq61JzRJOlTSfEnXSjpd0oqSppfN0hdK+lGZTYyk4xvSnG6UdH9TXc+VtEjSiXXbFRER0S114w+nAR8DZtjeHJhMtd71GOB42xsC9wH7A9g+1PaWtrcE/hM4u6nKo4BL67QpIiLGl16MPxyJpThTgJUkTQFWBu4EXk+16TnA94G3tziveYbxq4C1aYhFjIiImHDrXG0vBo4DbqPqVB8A/gTcb3tZOewZaUuSXghMB35VPk8Cvgp8sk57IiJiHOqr+eqCusPCq1OtY50OrEuVI9xOJvBM4KyGzdI/Apxne9EQ10tCU0REjHl1Zwu/AbjF9l0Aks4GXgusJmlKuXttlbY0Ezio4fOrge0kfQRYBVhe0lLbhzWeZHsWMAuyWXpExETRreemddTtXG8DtpW0MvAI1c44c4BfU8UbngHsC/ys/wRJLwFWBy7rL7O9V8P3+1FNkHpaxxpjTxKaImJUTLTO1fYVks4C5gLLgD9T3VmeS5Uh/B+l7HsNp80EznAvRm7E06Rji4hR0Xub4qBe7eMyLBwRMXYse2yxOlX3fe/eodbf+9V/fEnH2jaQJDTFsGVYOCKitXSuMWzp2CJiVPTgsHCn4g93kjS3xBz+TtKG5dgVShziwhKPuEEpX07S9yVdI2mBpGw3FxERQG8mNNW6c22IP9zU9iOSzqSasPRZYDfbC8rymn8H9qOKQbzP9oaS+mMS3wO8G1jB9svKzOPrJJ1u+9Y67YvOyrBwRIyKHrxzHYlh4f74w8ep4g/vAAw8t3y/aimDKnDiyPL+LOBESSrHTy0RiisBjwEPjkDbYpzrdgefzj2i8zzROlfbiyX1xx8+Alxk+yJJ/wqcJ+kRqk5y23LKNOD2cu4ySQ9Q7QV7FlXHeydVB31oq83SJR0AHACgyasyadLUOs2PmtKxRES0NuLxh5L2Bg4F3mJ7PeAU4GtDVLU18ESpYzrwCUkvaj7I9izbM2zPSMcaETFB9GC2cKfiD7ewfUU55kfABeX9YmB9YFEZAl4VuAd4L3CB7ceBJZJ+D8wAbq7Zvuigbg/JQu6eIyaCXhwWrjtb+Mn4w/LsdCfgOmBVSRuXY94ILCjvZ1PFIUIVj/irktR0G9U2dUiaSjWMfH3NtkVExHgw0e5cB4k/XAT8RFIf1WbpHyinfA/4b0kLgXupZhYDfBM4RdJ8QMAptq+u07aIiIhuSfxhRETU1sn4w7veuH2tv/fPv/g3g7ZN0i7ACcBk4CTbR7c4Zg+q1S4GrrL93sHqTEJTDFueuUbEaOjkM1dJk6lGT99INer6R0mzbV/XcMxGwOHAa23fJ2mtoeqtO1v44yWZab6kQ0rZkZIWl3SmeZLe0nTOCyQtlfTJ8nl9Sb+WdF2p5+N12hQREeOL++q9hrA1sND2zbYfo9oqdbemYz4IfNP2fQC2lwxV6bDvXCVtXi64NVXowwWSflG+Pt72cQOc+jXg/IbPy4BP2J4r6TnAnyRd3PhfDRERMYG53ohzY0ZCMcv2rPL+yfyFYhGwTVMVG5d6fk81dHyk7QsYRJ1h4ZcCV9h+uFz0N8A7BztB0tuBW4CH+sts30kVHoHtv0taQPVj07mOcRmSjYheUDrSWUMeOLApwEbADsB6wKWSXmb7/oFOqDMsfC2wnaQ1Sh7wW6jWsAIcLOlqSSeXoAkkrQJ8BvjiQBWWIP9XAFcM8P0BkuZImtPX91CrQyIiYpzp8LBwf/5Cv/VKWaNFwGzbj9u+BbiRqrMd0LDvXEso/zHARVR3ovOoUpa+DRxFNaPqKOCrVEtxjqQaLl5aLYl9utL5/gQ4xHbLXOHG//rIbOHuy4SmiBgN7uvoXud/BDaSNJ2qU51JFWzU6KfAnlRLRtekGiYeNOSo7jrX71GtXUXS/wEW2f5b//eS/gvofw67DbC7pK8AqwF9kh61faKk5ag61tNsn12nTTF60rFFxGjo5GzhknN/MHAh1fPUk23Pl/QlYI7t2eW7nSVdR3UT+Snb9wxWb611rpLWsr1E0guo7mC3BVYqz1GRdCiwje2ZTecdCSy1fVxJdvo+cK/tQ9q9du5cIyLGjk6uc1386tfX+ns/7bJfdfTWt5W661x/ImkN4HHgINv3S/pPSVtSDQvfCnxoiDpeC+wDXCNpXin7rO3zarYtIiKiK+oOCz9jXND2Pm2cd2TD+99RRR5GREQ8Qy8G9yehKSIixrQOT2jqiCGX4pTlNEskXdtQ9jxJF0u6qfzbv9xmr7IE5xpJf5C0RSnfpCGxaZ6kB/sTncr3H5V0fUlo+konfmhERPQmu96rG9pZ53oqsEtT2WHAL21vBPyyfIYqIGJ72y+jWoYzC8D2Dba3tL0l8CrgYeAcAEk7UkVNbWF7M2CgZKeIiIieMGTnavtSqu3hGu1GNcOX8u/by7F/6M9eBC6nWozbbCfgL7b/Wj4fCBxt+x+ljiEzGyMiYuJwn2q9umG4CU1r9y+3Af4XWLvFMfvz9AzhfjOB0xs+b0yV9HSFpN9I2mqYbYqIiHGoFzvX2hOabFvS00a1y1Dv/sDrmsqXB3al2rqnsQ3Po1ojuxVwpqQXucUC3MbwZU1elUmTptZtftSQhKaIGA29uO34cDvXv0lax/adktYBnhzKlfRy4CTgzS0SLN4MzG1McaLKbDy7dKZXSuoD1gTuar5o4g/HlnRsETEaxuVs4QHMBvYt7/cFfgbVXq3A2cA+tm9scd6ePH1IGKrMxh3L+RsDywN3D7NdERERXTfknauk06m22VlT0iLgC8DRVMO3+wN/BfYohx8BrAF8q4TzL7M9o9QzlWqn9+bEppOBk8tSn8eAfVsNCcfYk2HhiBgNrrmfazfUyhbupgwLR0SMHZ3MFl646Ztq/b3f8LoLey5bOCIioqP6evDOta1nrgOkNL27JCr1SZrRdPzhkhZKukHSmwarp5S3THyKiIiwVevVDe1OaDqVZ6Y0XQu8E7i0sVDSplRrWTcr53xL0uRB6oGBE58iIiJ6Tluda6uUJtsLbN/Q4vDdgDNs/8P2LcBCYOuB6mk45xmJTxERERMyRKKFaVTRh/0WlbLBtJP4FBERE1AvzrsdcxOaWiU+9UtC09gyFpbidFuWAkV0Xi+GSHSic10MrN/web1SNpgBE58aJaFpbEnHEhHR2nATmgYzG5gpaQVJ04GNgCvbOOcZiU8RERF9Vq1XN7S7FOd04DJgE0mLJO0v6R0lsenVwLmSLgSwPR84E7gOuAA4yPYTA9VTLnE08EZJNwFvKJ8jIiJ6cilOEpoiIqK2TiY0Xb3B22r9vX/5rT9PQlNERESjJDRV5XtJmtfw6pO0ZVN9s5tTmkr5JyRZ0pp1flREREQ3jXhCk+3TbG9pe0tgH+AW2/P6v5f0TmBp8wUkrQ/sDNzWduujq7q9FKfb14+I0dGLz1zbGha2famkDZrKFgCUreUGsidwRv8HSasA/0a1VvXMpmOPBz5NZgr3jJXW3a6rHVyWAkVMDL04NajTz1zfQxVt2O8o4KvAw40HSdoNWGz7qiE66xhj0sFFRKf14jPXjnWukrYBHrZ9bfm8JfBi24c23gVLWhn4LNWQ8FB1JqEpImKC6cXN0jsRItFvJnB6w+dXAzMk3Qr8DthY0iXAi4HpwFXlu/WAuZL+qblC27Nsz7A9Ix1r93X7mWe3rx8RMZCO3LlKmgTsATw5Zmj728C3y/cbAL+wvUP5eq2Gc28FZti+uxNti5HT7SHhbl8/IkZHLw4Lj3hCU/HPwO22bx75JkdExETimq9uSEJTRETU1smEpj+s865af+9fc+dPRv3Wt5PPXGOc6/Yzz25fPyJiIG09c5V0MvBWYIntzUvZscDbgMeAvwDvt31/wzkvoArvP9L2cZI2AX7UUO2LgCNsf73MJP4OsCKwDPiI7aF20okuyzrXiBgNvThbuN0JTacCJwI/aCi7GDjc9jJJxwCHA59p+P5rwPn9H2zfAGwJIGky1R6v55SvvwJ80fb5kt5SPu/wbH9MjK5H7vhtVzu4bl8/IkZHX7cbMAxtDQvbvhS4t6nsItvLysfLqZbQACDp7cAtwPwBqtwJ+Ivtv/ZXBzy3vF8VuKOt1kdExLhnVOvVDSO1FOcDlCHfEnH4GeCNwCcHOL55DewhwJesuNcAACAASURBVIWSjqPq8F8zQu2KDstzz4jotL4enL5au3OV9Dmq56SnlaIjgeNtL20VZShpeWBXqmHkfgcCh9r+iaQ9gO9RbZrefG4SmsaQDMlGRLRWq3OVtB/VRKed/NSanm2A3SV9BVgN6JP0qO0Ty/dvBuba/ltDVfsCHy/vfwyc1Op6tmcBsyBLcSIiJoq+Lg3t1jHszlXSLlS72Gxv+8kgftvbNRxzJLC0oWOFaqecxiFhqJ6xbg9cArweuGm47YqIiPGlW89N62h3Kc7pVLN31yypTF+gGtZdAbi4DP9ebvvDQ9QzlepZ7IeavvogcIKkKcCjlKHfiIiIXpwtnISmiIiorZMJTRetPbPW3/ud/3ZGEpoiIiJ6Xac3S4+IiKilF4eFh7xzlXSypCWSrm0oO0rS1ZLmSbpI0rqlfK9Sfo2kP0jaYrB6Svmxkq4v550jabWR/IEREdHb+mq+uqGdYeFTgV2ayo61/XLbWwK/AI4o5bdQzR5+GXAUZdnMIPVAFaO4ue2XAzfy9PWvERExwfViQtOQnesA0YcPNnycStkyz/YfbN9Xyp8WidiqnlI+YIxiREREL6qzzvXLwPuAB4AdWxyyPw3B/W16MkZxgGsmoSkiYoLp671lrsOfLWz7c7bXp4o9PLjxO0k7UnWun2l1bistYhRbXXOW7Rm2Z6RjjYiYGPpQrVc3jMRSnNOAd/V/kPRyqvjC3Wzf004FDTGKe7lXF95OQN0O7e/29SNidLjmqxuGNSwsaSPb/RGFuwHXl/IXAGcD+9i+sc26WsYoxtjX7eD+bl8/IkZHLy7FGbJzHSD68C2SNqH6zX8F+mMPjwDWAL5VIhGX2Z4xUD22v0e1CfuzilGMiIgYyxJ/GBERtXUy/vCsdfaq9fd+9ztPG/UHr0loioiIMa0X76TamtD0LFOaVi9JS1dLulLS5k11TZb0Z0m/aCiTpC9LulHSAkkfG6kfGBERvW28JjTBs0tp+iwwryQuvQ84oem8jwMLmsr2A9YHXmL7pcAZbbYrIiLGuT7Ve3VDW53rs0lpAjYFflWOuR7YQNLaAJLWA/6FaqlOowOBL9nuK+cteXY/IyIiYngk7SLpBkkLJR02yHHvkmRJM4aqs9Y61zKUezuwF0/duV4FvLN8vzXwQp6KNPw61bKb5jv1FwPvkTRH0vmSNqrTrhgd3V5n2u3rR8To6GSIhKTJwDeBN1PdHO4padMWxz2HauT1inbaXKtzHSCl6WhgNUnzgI8CfwaekPRWYIntP7WoagXg0bJs57+Ak1tdT9IBpQOe09f3UJ2mR0REj+hwiMTWwELbN9t+jOqx5G4tjjsKOAZ4tJ02j9Rs4dOA86jWrj4IvB+qiUpUO+XcDLwH2FXSW4AVgedK+qHtvYFFVOETAOcAp7S6iO1ZlJ12shRnbOj23WO3r58gi4jO6/Bz02nA7Q2fFwHbNB4g6ZXA+rbPlfSpdiqtE9w/UErTasDD5b8A/hW4tHS4h5cXknYAPlk6VoCfUoX/3wJsT7X1XPSAbnYuj9zx23RuETGkxk1filnlZq2dcycBX6OaeNu2tjrXZ5nS9FLg+5IMzKcK8B/K0cBpkg4FllJ1yjHGdbtj6/b1I2J01F1O0zjq2cJiqtUq/dYrZf2eA2wOXFJSBP8JmC1pV9tzBrpmEpoiIqK2TiY0nTJt71p/79+/+IcDtk3SFKrR0p2oOtU/Au+1PX+A4y+hGnkdsGOFJDRFRMQY18lnrraXSToYuBCYDJxse76kLwFzbM8eTr3DTmhq+O4TZd3Pmg1lO5TkpvmSftNQfmgpu1bS6ZJWLOXTJV1R1hj9SNLyw/kxEREx/nQ6ocn2ebY3tv1i218uZUe06lht7zDUXSvUS2hC0vrAzsBtDWWrAd8CdrW9GfDuUj4N+Bgww/bmVP+FMLOcdgxwvO0Ngfto7zltRETEmNTWsLDtSyVt0OKr46lCIX7WUPZe4Gzbt5VzG9OWpgArSXocWBm4oyzXeX05D+D7wJHAt9v+FdEV3V4GA5nUFDERjMv9XAciaTdgse2rygyqfhsDy5WHvs8BTrD9A9uLJR1HdZf7CHCR7YvKcPL9tpeV8xdRrTuKMS4dW0SMBncpH7iOYXWuklamCujfeYA6X0U182ol4DJJlwN3Ua2HnQ7cD/xY0t7ABc/iuk+uVdLkVZk0aepwmh8RET1kIt25vpiqk+y/a10PmFuyhBcB99h+CHhI0qXAFuW8W2zfBSDpbOA1VOlOq0maUu5em9cYPSkJTRER0QuGlS1s+xrba9newPYGVB3qK23/L9Xz19dJmlLucLeh2mLuNmBbSSuX56w7AQtcLbT9NbB7qX5fnv4MNyIiJrBxu59rSWi6DNhE0iJJA87mtb2Aaqj3auBK4CTb19q+AjgLmAtcU67dn5jxGeDfJC0E1gC+N8zfExER40yHg/s7IglNERFRWycTmk54Qb2Epo/fNnBCU6ckoSkiIsa0cTuhSdLJQP9+rJuXsiOBD1LNAgb4rO3zGs55AXAdcKTt4waqp+k6nwCOA55v++7h/qgYHWNhnWu3ZTlSRLTS7p3rqcCJwA+ayo/v7zhb+Bpwfpv1tEx7ihhKOreI8a8X71zbmtBk+1Lg3nYrlfR2qr1Zn7arwBD19Kc95VlqREQ8qRcnNNV95nqwpPcBc4BP2L5P0ipUs3/fCHyynUoGSXuKGFS3h6Zz5xzReZ3cFadT6nSu3waOovoPg6OArwIfoMoFPt720nY6yiHSnpqPTULTGJKOJSJGQy8OCw+7c7X9t/73kv4L+EX5uA2wu6SvAKsBfZIetX3iAFUNmPZUQikar5mEpoiIGPPqBPevY/vO8vEdwLUAtrdrOOZIYOkgHSu2rwHWajjnVqpt6TJbeIzr9pAs5O45YiLoxTupdpfinA7sAKwpaRHwBWAHSVtS/e5bgQ8Npx7bSWPqUenYImI09PVg99rufq57tigeslO0fWQb9TSfs0E7bYruGwt3rt2W/8CI6LwJ9cw1Ih1LRERr7Qb3nyxpiaRrm8o/Kul6SfPLBCYkrSHp15KWSmr5rFXS7Ma6JG0p6XJJ8yTNKVvXRUREjOt1rqfSlKwkaUeqzc+3sP0PSf2Tkh4FPg9sXl5PI+mdwNKm4q8AX7R9vqS3lM87tP8zohsyLJy794jRMG6HhW1fKmmDpuIDgaNt/6Mcs6T8+xDwO0kbNtdTAib+jWqt6pmNlwCeW96vCtzR/k+IbknHEhGjYaKFSGwMbCfpy1R3q5+0/cchzukPm3i4qfwQ4EJJx1ENVb+mRrsiImIc6cXZwm09cx3AFOB5wLbAp4AzNUgkU1m282Lb57T4+kDgUNvrA4cywExkSQeUZ7Jz+voeqtH0iIiIzqnTuS4CznblSqph8TUHOf7VwIwSEvE7YGNJl5Tv9gXOLu9/DLSc0GR7lu0Ztmck+jAiYmLoxQlNdTrXnwI7AkjaGFgeGDBVyfa3ba9b1rG+DrjR9g7l6zuA7cv71wM31WhXRESMI301X91QJ6HpZODksqTmMWBf2y7H30o1QWn5sv3czravG+QSHwROkDSF6vntAcP7ORERMd704jPXOglNAHsPcPwGQ9R3Kw3LdGz/DnhVO22JiIiJpfe61iQ0RQ1jYZ1rlgNFxFiUzjUiIsa0cRsiIelk4K3AEtubN5R/FDgIeAI41/anJS0HnAS8stT/A9v/txx/KPCvVHf51wDvt/2opNOAGcDjwJXAh2w/PkK/MTokd40RMRp68Zlru7OFTwV2aSxoij/cDDiufPVuYAXbL6N6jvohSRtImgZ8jGqv1s2BycDMcs5pwEuAlwErUXXAERER43cpju1LgXubilvGH1L9lqll5u9KVDOJHyzfTQFWKt+tTIk5tH1eWS9rqjvX9Yb/kyIiIrqrzjrX/vjDKyT9RtJWpfws4CHgTuA24Djb99peTHV3e1v57gHbFzVWWIaU9wEuaHXBJDRFREw8vbjOtRPxh1tTPYNdF5gOfELSiyStTjWMPL18N1VS81KebwGX2m45DTUJTRERE49r/q8b6swWfjL+ELhSUn/84XuBC8qEpCWSfk81WcnALbbvApB0NlVA/w/L5y8Azwc+VKNNERExzvTibOFOxB/eRhVhiKSpVHe215fybSWtXO5wdwIWlOP+FXgTsKftXvz/MSIiOqQP13p1Q1uda4k/vAzYRNIiSftTxR++qMQfnsFT8YffBFaRNB/4I3CK7attX0H1PHYu1TKcScCsconvAGsDl0maJ+mIkfuJERERo0slDrjnTFl+Wm82PCJiHFr22OKObWl+4AZ71Pp7/+1bzxz17daT0BTDlvjDiBgNvRgiMWTn2iqdSdKPgE3KIasB99vesnz3cuC7VLvi9AFblRSmLwPvA1a3vUrTNfYAjqSa9HSV7feOwG+LDkvHFhGjoRcn4rRz53oqcCLwg/4C2+/pfy/pq8AD5f0Uqtm/+9i+StIaVJGGAD8v9Txtr1ZJGwGHA6+1fZ+ktYb9ayIiYtzp1nKaOobsXG1fKmmDVt+VWb97UGYHAzsDV9u+qpx7T0M9l5dzmqv5IPBN2/eV45Y0HxAREdFL6izFAdgO+Jvt/rvRjQFLulDSXEmfbqOOjYGNJf1e0uWSdhnyjIiImDB6MaGp7oSmPYHTm+p7HbAV8DDwS0l/sv3LIdqwEbADVabwpZJeZvv+5gMlHQAcAKDJq5KUpoiI8a8Xh4WHfedanq++E/hRQ/EiqvjCu20/DJxHtfXcYBYBs20/bvsW4EaqzvYZEn8YETHx9OKda51h4TcA19te1FB2IfCyksI0BdgeuG6Ien5KddeKpDWpholvrtGuiIiIrhqycx0gnQmqvVgbh4Qpk5K+RpXMNA+Ya/vcUs9XJC0CVi71HFlOuxC4R9J1wK+BTzVOhIqIiImtz6716oYkNEVERG2dTGja+4XvrPX3/od/PTsJTREREY16MaGp3eD+kyUtKSH9/WVblqUz88oG5luX8t0kXd1Q/rqGc54o5fMkzW4on142XV8o6UeSlh/JHxkREb2rF/dzbXdC06lA8/rTrwBfLLGHR5TPAL8EtijlHwBOajjnEdtblteuDeXHAMfb3hC4D9ifiIiIHtVW52r7UuDe5mKq/GCAVYE7yrFL/dSD3KnluAGVlKfXU21HB/B94O3ttCsiIsa/XlyKU+eZ6yHAhZKOo+qkX9P/haR3AP8XWAv4l4ZzVpQ0B1gGHG37p8AaVMH/y8oxi4BpNdoVERHjyLh95jqAA4FDba8PHAp8r/8L2+fYfgnVHehRDee80PYM4L3A1yW9+NlcUNIB5TnunL6+h2o0PSIiesV4fubayr7A2eX9j4Gtmw8ow8kvKuEQ2F5c/r0ZuAR4BXAPsFoJnYAqAnFxqwsmoSkiYuLpxWHhOp3rHVQJTFA9M70JQNKG5Tkqkl4JrEAVErG6pBVK+ZrAa4HryvPZXwO7l7r2BX5Wo10RERFtk7SLpBvKipXDWnz/b5KuKythfinphUPV2dYz15LStAOwZklZ+gLVVnEnlDvORymB+sC7gPdJehx4BHiPbUt6KfBdSX1UnfrRtvujET8DnCHpP4A/0zDEHBERE1snw44kTQa+CbyRas7PHyXNbuifoOqXZth+WNKBVKtj3vPM2p7SVudqe88BvnpVi2OPoVpa01z+B+BlA9R/My2GlSMiIjo8oWlrYGHph5B0BrAbDbn4tn/dcPzlwN5DVZqEphi2R+74bbebwErrbtftJkREh3X4uek04PaGz4uAbQY5fn/g/KEqrZPQtIWkyyRdI+nnkp7b8N3hZez6Bklvaig/VNJ8SddKOl3Sik3X+Yakpe20KSIioh2NK03K64Chz2pZz97ADODYoY5t9871VOBE4AcNZScBn7T9G0kfAD4FfF7SplQ75mwGrAv8j6SNgX8CPgZsavsRSWeW404tjZ4BrN5me2IMyF1jRIyGustpbM8CZg3w9WJg/YbPLVesSHoD8Dlge9v/GOqa7T5zvVTSBk3FGwOXlvcXU20d93mqseozysVvkbSQakz7tnK9lcpkp5UpqU7lgfKxVOtf39FOm6L7MiwcEaOhw89c/whsJGk6Vac6k6ovepKkVwDfBXaxvaSdSussxZlP1ZECvJunev5W49fTyhrX46g62TuBB2xfVI45GJht+84a7YmIiHHIdq3XEHUvo+qDLgQWAGfani/pS5L6M/CPBVYBfty88cxA6kxo+gDwDUmfB2YDjw12sKTVqTrj6cD9pZF7A7+i6px3GOqCZZz8AABNXpUESXRX7hojYjR0OgjC9nnAeU1lRzS8f8OzrXPYnavt64GdAcoz1f4M4YHGr98A3GL7rnLO2VR5xPcBGwILS/bEypIWlh1ymq/55Lh5NkuPiIixatjDwpLWKv9OAv4d+E75ajYwU9IKZQx7I+BKquHgbSWtXBKcdgIW2D7X9j/Z3sD2BsDDrTrWiIiYmHoxW7hOQtMqkg4qh5wNnAJQxqrPpFqAuww4yPYTwBWSzgLmlvI/M/DsrYiICKA3d8VRJ2OlOinDwhERY8eyxxarU3XvtN7Otf7e/3LRRR1r20CS0BQREWNaL965jnhCk6TlJH2/lC+QdPhg9ZTyLSVdXqY4z5GUnOGIiOhZ7U5oOhXYpansJOAw2y8DzqFKaIJqWc0KpfxVwIcaAiha1QPVDgNftL0lcET5HBER0ZMTmtrqXMum5/c2FTcnNL2r/3BgatmKbiWq9a8PDlJP/zn92cSrUpKbIiIi+uxar26o88y1P6Hppzw9oemsUn4nVcThobZbdaiNDgEulHQcVYf/mhrtilGS+MOIGA2998S1MwlNWwNPUIX2rw78VtL/9O+VN4ADqTrhn0jag2qz9GckYiShaWxJxxYR0VonEpreC1xg+3FgiaTfU23RM1jnui/w8fL+x1TPc1tdMwlNY0juXCNiNPTibOFhd66S1rK9pEVC023A64H/ljQV2Bb4+hDV3QFsD1xSzr1puO2K0ZOOLSJGw7jtXJ9NQhPwTeAUSfMBAafYvnqgemx/D/ggcEKZBPUoZeg3IiKiF8OOktAUERG1dTKhaet1t6/19/7KO34z6glNdfZzjYiIiBYSfxgREWNat4Ig6hjyzlXS+pJ+Lek6SfMlfbyUP0/SxZJuKv+uXsp3kPRAiTKcJ+mIhrpuLbGI8yTNaSg/VtL1kq6WdI6k1TrxYyMiovfYrvXqhnaGhZcBn7C9KdXM34MkbQocBvzS9kbAL8vnfr+1vWV5fampvh1L+YyGsouBzW2/HLgROJyIiAiq2cJ1Xt0wZOdq+07bc8v7vwMLgGlUKUzfL4d9H3j7cBth+yLby8rHy4H1hltXREREtz2rCU0lgP8VwBXA2rbvLF/9L7B2w6GvlnSVpPMlbdZQbuAiSX8qaUutfAA4f4DrH1B2zZnT1/fQs2l6RET0qF4cFm57QpOkVYCfAIfYflB6amazbUvq/wVzgRfaXirpLVTZwxuV715ne7GktYCLJV1fwvz7r/E5qmHo01q1IQlNERETTy+GSLS7n+tyVB3rabbPLsV/k7RO+X4dYAmA7QdtLy3vzwOWk7Rm+by4/LuEapu6rRuusR/wVmAv9+ri24iIGHHjcss5Vbeo3wMW2P5aw1ezqTKBKf/+rBz/T+Ucyqbnk4B7JE2V9JxSPpUql/ja8nkX4NPArrYfHokfFhER48N43XLutcA+wDWS5pWyzwJHA2dK2h/4K7BH+W534EBJy4BHgJll2Hht4JzS704B/p/tC8o5JwIrUA0VA1xu+8O1f110VIL7IyJaS/xhRETU1sn4w83W3qbW3/v5f7ti1OMPk9AUERFjWreGdusYsnOVtD7wA6qlNgZm2T5B0ruBI4GXAlvbnlOOfyPVkPHyVBuof8r2r8p3e1INKZtqm7m9bd9dvvsocBDVRuvn2v70CP7O6IAMC0fEaOjF+MN27lz7E5rmlglJf5J0MdVkpHcC3206/m7gbbbvkLQ5cCEwrWwndwKwqe27JX0FOBg4UtKOVKEUW9j+R1mqE2NcOraIGA3j8s61BEXcWd7/XdICYJrtiwEa17uWY/7c8HE+sJKkFYA+qv1dp0q6B3gusLAcdyBwtO1/lDqW1PlRERER3VQnoakd7wLm2v6H7cepOtFrqIaEN6Va4gOwMbCdpCsk/UbSVs+mXRERMX6Ny3Wu/ZoTmto4fjPgGOBD5fNyVJ3rK4B1gat5KqB/CvA8qo0BPkW1xOcZs7sSfxgRMfH04jrXOglNgx2/HlUC0/ts/6UUbwlg+y8lgelM4DXlu0XA2a5cSTWEvGZzvbZn2Z5he8akSVPbaXpERPS4cXnnOkhC00DHrwacCxxm+/cNXy0GNpX0/PL5jVQ77ECVP7xjOX9jqpnGd7f7I6I7uj1buNvXj4gYyJAhEpJeB/yW6llpXyn+LFWi0n8CzwfuB+bZfpOkf6ca7r2poZqdbS+R9GHg48DjVKlO+9m+R9LywMlUd7ePAZ/sX74zkIRIRESMHZ0MkZi+xha1/t7fcs9Vox4ikYSmiIiorZOd6wvXeHmtv/d/vefqJDRFREQ06sWbwHaeua4v6deSrpM0X9LHS/mxkq6XdLWkc8qz1v5zDpe0UNINkt40WD3lu+dJuljSTeXf1TvxY2NkdfuZZ7evHxGjow/XenVDO89c1wHWaUxoAt4OrAf8yvYySccA2P6MpE2B06n2al0X+B+qdaxrtarH9nUlrele20dLOgxY3fZnBmtXhoXHhm52cEmIihg7OjksvN7zNq/1937RvdeOvWHhQRKaLmo47HKqreagijE8o6Qt3SJpIVX28GWt6gGuK+fsUM7/PnAJMGjnGmNDOriI6LReHBZ+Vs9cB0lo+gDwo/J+GlVn229RKRusnrVLJw7wv1SbBERERPRktnDthCZJn6MK9z+tTj39SsBEy/8nk9A0tnT7mWe3rx8Ro6MXQyTaunMdKKFJ0n7AW4Gd/NR9+2Jg/YbT1ytlgyU9/U3SOrbvLM94Wwb3254FzII8cx0Luj0k3O3rR8To6MVh4WEnNEnaBfg0sKvthxtOmQ3MlLSCpOnARsCVQyQ9zQb2Le/3BX423B8UERHRbXUSmr5BldJ0Tym73PaHyzmfo3oOu4xq+Pf8geqxfZ6kNaiyhl9Aldy0h+17B2tX7lwjIsaOTs4Wfv6qm9T6e3/XAzckoald6VzHhizFiQjobOe65nM3rvX3/u4Hbxx7S3EiBpMOLiI6rRdnCw/ZuUpaH/gB1fIYA7NsnyDpWOBtVEH7fwHeb/t+SXtR7cna7+XAK23Pk3QJsA7wSPluZ9tPTl6S9C7gLGAr23Nq/7rouNy5RkQ804gnNDWd+zLgp7ZfXD5fQrXjzTM6zlL3uVTbzR08VOeaYeHue+SO33a1g+v29SPiKZ0cFl59lQ1r/b2/b+nCUR8WHnK2sO07bc8t7/9OtQfrNNsX2V5WDrucqrNttidwRpttOQo4Bni0zeOjy7rdsXX7+hExOnoxW7gTCU2N3kMVbdjoFElPUK13/Q/blvRKYH3b50r61DNqiTErw8IR0Wm9OPG27c712SY0SdoGeNj2tQ3Fe9leXIaAfwLsI+mHwNeA/dpowwHAAQCavCqTJk1tt/nRIengIqLTenFCU1vxh20kNO3lZ/6nxUyq3XGeZHtx+ffvwP+j2jnnOcDmwCWSbgW2BWZLmtHcDtuzbM+wPSMda/d1O36w29ePiBhIO7OFh0po2r4poQlJk4A9gO0ayqYAq9m+u3TWbwX+x/YDwJoNx13CAJOeYmzp9l1rt68fEaOjW/nAdbQzLPxaYB/gGknzSlljQtPFVf/7VEIT8M/A7bZvbqhnBeDC0rFOptrn9b/q/4SIiBjPenFYOAlNERFRWyeX4qy44gtq/b1/9NHbktAUERHRqBeHhdvezzUiImI8krSLpBskLZR0WIvvV5D0o/L9FWVZ6qDSuUZExJhmu9ZrMJImA98E3gxsCuwpadOmw/YH7rO9IXA8VeDRoDIsHLUkRCIiOq3Dc4O2Bhb2T8CVdAZV+NF1DcfsBhxZ3p8FnChJLZagPimda9SSDi4iOq3DT1ynAbc3fF4EbDPQMSVP/wFgDeDugSrt2c617sw0SQfYnjVS7enFNnT7+mOhDd2+/lhoQ7evPxbaMNGv///bO/d4Tedy/78/M8bZOISxxQg5pEFs5FQ5pBKphIhfUUmljEMqVGTvVNrZOaSGjSI57SjHhBwip2GGcUgYlEOYwmBiHD6/P77fe9YzyzNravfc33t57uv9ej0v6/7ea7mumVnPc93f73Vdn2u4+DAnevF5T1b3y5xQ95+1zTnXz8z9W2qnaR+atg/N+9C0fWjeh6btQ/M+tN0+DA8faqFT3S+/OgPrI8DyHdfL5TW6fU8WRFoU+OtQNtscXIMgCILgZmAVSStKmpck3Xv+oO85H/hE/noH0rjVIU+rX7fHwkEQBEHwr5JzqF8ALiWpB55s+05JhwMTbZ9PkgA+TdJ9wN9IAXhI2hxch0NuoWkfmrYPzfvQtH1o3oem7UPzPrTdPgwPHxrB9sXAxYPWvtHx9QvAjv/M//N1K38YBEEQBMOVyLkGQRAEQY+J4BoEQRAEPaYVwVWJt0vaPr/enufUBi1D0mvyJt3W+hlJi0laP78WbciH8f/IWj8jaQFJqzXswwqS3t3hzyJN+tNP9H1wlfQe4F6SdNX78+ubwL35Xik/VpP0fUkX5dd/Nf3GagJJq0q6QtId+XotSV8r6MJB/+Ba35HFx38CPEgqXjkReFDSybkFoSSf6LK2eynjksZIOknSJfl6DUmfKmj/A8Bk4Nf5+m2SBrd/1O3DniQpvwl5aTnglyV96Gf6vqBJ0t3A1rYfHLS+InCx7bcU8GEj4FzSL/EkQMA6wJ7A9rZvqNuH7McY4AhgLFu1LAAAH/9JREFUWdtbZ3HqjWyfVMJ+9uFq4EBggu118todtsfVbHdr0oPVTsBZHbdGA2vY3qBO+1382b7L8jPAFNtP1GTzcGBl4LO2n81ri5BEyx+y/fU67A7yYRfgY8CmQKcw9SLAq7a3rNuH7MclwCnAIbbXzsIAk2yvWcj+LcAWwFUd74Mppexne5NJuro3NuVDP9OGVpx5SFqRg3kEGFXIh28Au9i+qmPtl5J+CxxKmsZQgp+QP1Dy9R9JgaZYcAUWtH3ToFP5lwvYfRSYCGwH3NKx/iywXwH7g/kUsBFwZb7ejOTXipIOt31aDTa3BzawPaNasP2spM8DNwC1B1fg98BjwJLA9zvWnwVuL2C/YknbZ0s6CGb1Or5S0P5Ltp8Z9D4ovdN50fbMyof8gNHfu62CtCG4ngzcnCcdVOLMy5OagEsFlZUHBVYAbF8tqWRvWdMfKADTJK1MfhNL2oH0YVsrtm8DbpN0he3ZHrby8fxTdfswiHmAt9h+PPswBjiVJBh+DVBHcH21M7BW2H5OUpEPVdsPAQ+RHiya5HlJb2Dg93BD0slBKe6U9DFgpKRVgH1IDx4luVrSwcACkrYCPg9cUNiHvqXvc662v006hhLpDb1R/nrXfK8Ezw5x7/lCPkDzHygAe5OOx1eX9AiwL/C5gvavkLRTdSHpAOC8gvYrlq8Ca+aJvPY34KWabFrS4pKWGPwCXq3JZldyYeG9kp6RNF3Ss5KmF3Rhf5Kk3cqSriM92HyxoP0vAm8FXgTOAKaT3gsl+SrwJDAF2IskolCy/qGv6fuc63BA0hPAmd1uATvZHlPIj3WBY4FxwB3AUsAOtksex1W+LASMqHJ/Be3+G6mY5wVgDHA3cIDt5wr7cTwwFjgnL32ElL44ELjQ9uY12HyQFES7Vcrb9kq9tjmEL/cBH7B9dymbXXyYB1iN9Pdxj+26HmqCFhLBtQCSulVGzsL2Twv60sgHiqT9h7pv+6gSfmRf9iZVCL8K7Gy79HEcuRXsI8Ameek64BdzEwPvFyRdZ3uTuX9nbfZHAtsAb6IjPVbq91DSesDBXeyvVcJ+9mFb4D+AFbIPSi54dCkf+pk25Fwbp2TwHIouFaqrKg39ra1CtYOh+ueKBRRJl5OKm8aRcu8nSbrG9pdK+QDpE4zUBvG/pWxKGjvUfdt/KuULMFHSWaTWjxc7fDi3kP0LSKcXUyh8JJ45nXRK0ZR9gB+QitymtOWhriQRXAsg6QKGCCC2tyvkShMVqgDY/iaApE1sX9d5T1LJHcxxtqtevqdzm9TBBe0Dsx50vgssTdoxlNg1XET6Pew8FjYpPbA0aSJIKUYDM4DOXnOTWtZKsFzJXWIXnszTVprkz8AdEVjroVXHwpI+0zkkd/B1jXbfNdR921fX7UP241Lg410qVHcBrqm71zTbvNX2unNbq9mHTYFVbJ8iaUlgEdsPlLKffRgOOcc3AV8B3g0cY/vYpnwpjaTvAlfY/k1D9rckve+uoJmdO5LWJx0LXz3Ih2Ipmn6mbTvXwYUcRSQQSwXPf4A5VqhKqjX3mneIGwNLDcq/jqbgjknSocB6pLzzKcC8wM8YyH2W4vGmAmtu/TiE1PbzfWCf0sU8kk6hy2mO7U8WcuEG4DxJI0jV2aXzjXsAq5N67atj4ZI7d4BvAc8B85PeB0EPaVVwtT1hqOsWcJWkC5m9QvWqXLn7dM225wUWJv3OdeZfpwM71Gy7kw+T1LFuBbD9qJrRUy2ec5Q0jhRU3wocCXzKduk+54oLO76en/Tv8mhB+0eRUiRN5RvXt920/OmyJU6r2krfHwsPpyrVpskVqtuTpOcgCSeMsb13QR9WyEICjSDpJtsbVEfR+cHi+tL5t7xzG4zr3LllwZA/k3Kvrwmqtvepy/bcyDvIa21vXMjeNcBmthspJsr//t+zfVcT9rMPRwKXN3U03u+0Yeda7UpWA9YnNY4DfAC4qRGPGsK2JU0FNgR2BB4AflHCtqTjbH8BOK6bGlDdRV2SjrB9MHC2pAnAYkrC5Z8kCdgXxfYepW2SCtqG69P0KqSiqlJMJZ3aXEIz+cYNgcmSHsj2q2Ppkg95nwO+JOlFmjka72v6fudakZ9Ut/HsguUX2X5nQR+6VQ0/Q9K8nWD7hZrsrkoqntgFmEbSE/6S7RXqsDcHH6bbHj2n4q6689KdRVNZ6u09pA+TS21fVqftQX582faRko6le86xsd1jSSQ9y0DlsoG/AAfZLvWwd2i39aqqvYD9ru+9Jk91gt7Shp1rxRhgZsf1zLxWkqmktocz8vVHSdKIq5J2T/+vJrt/IE0g2db2fQCSSovV3w+NFneNlLQ46cP8FjrE+yUtkWUHS1AVMU0sZG8Ww6glDNuNzg0tFUSHsP8QgKSlSTnnRsjviVU6fbB9TVP+9BNtCq6nAjdJqnRkPwSUFnfY2Pb6HdcXSLrZ9vqS7qzR7vakQQVXSvo1SYqx9LD4wVXCs1HgOG51UkCtdkoV1XUR6T/blTD6DNvndN5T/UPb/6vm//8/haTtgOrk6CrbFw71/T22vRTwZVJxV2dg2aKQ/e1IldrLkqr2VyA9eL21hP3sw6eB8aQ5rpNJR9XXk0bhBf8irQmutr+V8yvvyEt72J5U2I2FJY2tlHCyYs7C+d7MOf/Yv0YWTfhlLt75IEkgfGlJPwLOK1TQMJL0Zy0d1Cvucp5ZOUw4iIGq7aHWesYwaglD0ndINRCn56XxkjbOefESnE5Kj2wLfJY0vP3JQrYh9ZduSCooWkfS5sBuBe1DCqzrAzfY3lzS6qR5z0EPaE1wzSwITM/iAUtJWrGweMABwLWS7icFmRWBz+egV/su2vbzwM+Bn+fjoB1JIgIlgutjtg8vYGdYo4Gh7W+UdEzHrdGUmWs7XHg/8LaqWlfST4FJlFPLeoPtkySNzw8dV0u6uZBtSPNc/ypphKQRtq+U9IOC9gFesP2CJCTNZ/sPSuMXgx7QmuDaRTxgFIXFA2xfnBv4V89L93QUMRV9Y9l+ijQdptQ82aZ2rBVHN2y/YrgNbW+SxYAq171oYduVaMZjkrYh/bssUdD+05IWJs3uPV1pclbJ8ZMAD0tajNRrfZmkp0izdoMe0KZq4clk8YDqeFDS7Q30N27MaydhnFrShyYoXDQ07JE0qrQq0nBC0i7Ad0g61yLlXr9q+6xC9rclFfktTxrDOBr4Zim933xa9QJ5tjTp4eJ0238tYb+LP+/KPvzadm0pqjbRpuDauHiApNOAlUnFA1UTv9vSfhEMkE8wvg2swewFNbUVVg2namGYNVu3KvC7yfZfStoPgjppzbEww0M8YD1gjYbk1gJA0vx19RP/k5wCHAr8N7A5SWt2RM02q2rh7YFlSGkRSP3Pj3f9iR4j6b2kQQn/a/sxsqiLpB0kPVN3z7Gkbwxx27b/o2b7D9C9Wr2yv3Kd9rMPnT3GFSbFg3lttyku1EZrdq7QrHhAtn8OSST9sZJ2gwGUptE8TjoS/B1Jcu+ZBvy4xfa/S5pie83OtQK2J9peb25rNdm+DviQ7ScHrS8JXGB7o5rtH9BleSGSetUbbC/c5X4v7b9h0NIIYCfgS6SU1UfqtD8HnxYG9gb2InUPdPs7Cv5JWvWEYvsySTeS/9wN5AGXBO6SdBOzS64VPY5rM7bfnFug3gFsA/xQ0tO231bYlReznu69kr4APMJAW1bdLCRpJdtTASStSAowJZhvcGAFsD0tp2pqxfb3q6+zStt40qnBmaS+07rt/zXbHkESjTmQlCbaprTOcC5m2hf4OKmLYP2mcr79SGuCq6S9gG+SighepbB4QOawgraCLkhajlQh/g5gbeBO4NoGXBlPag3bh9TzuAXpQ64E+5F0daeS3gcrkHYtJRgtaR7bs7UdSRoFLFDCAUlLAPuTCol+Cqybq+dL2B5FSkntR/q9+1ClmlaKfEpwAEkh7mRgnSZOb/qd1hwLS7oX2Mj2tKZ9CZpD0qvAzcARtn/VtD8VkkYCO9s+fa7f3Bt78zHQEvYH2y8O9f09tPsdkuzoF3LfdXUseTQwzfZXarb/PVLO+QTgh7afq9NeF/sPk/qZfwD8afB9FxiWLul5kmDGKaQWsME+tGZSWJ20Kbj+Gtje9owGbF9re9OOQoJZt4gpFEWRtDZp5N47gbHAvcDVtk8qZH80Kb/1RlIxz2X5+gDgdtsfLODDgqSd2wq298yVy6uVkB+UNA/wn8CnGeipHAucBHy97vak/HD1IinAFX8vSvoJc67YtgsMi5d02BA+NK673C+0KbiuQ3pSu5HZ853RBtMy8k5pU9LR8G4ALjQhSNKvSHN0rwe2JI1ZEzDe9uRCPpxFErD4uO1xOdj+vmTeWdICwJvz5X22/17KdhCUoE3B9SZSjmMKKecKgO2i4v35+G8Ms4tIvOZ4KKgHSROB+YDfkyuGXXDM16Dq4JHAY8DYku1BVWWwpEkdgiq32V67lA9B0O+0pqAJGGV7jlNZSiDpi6TexscZCPAGiqpEtZytu1WrFmTWsaftVyQ93EDf7cy8czSApJXpOM0JguBfp0071yOAB4ELmP1YuFgrTu6xfHuUuzeHpDGkyR/L2t5a0hqkQrdSOddXGNCQFalCdgYF8++53/trJHWo35Cqp3e3fVXdtoNEFsp/cW5rweuXNgXXbtNvXKfcXBcfrgS2GtyGEJRDaezgKcAhttfOBTaTqqPatpDFDDYkBfUbSlfRSzrc9jc6rkcCp9retaQfTVHJsM5trWYfxjNQMfw/JO31r7rMCMq+pzXHwrZXbMq2BoaETyX1F17E7LvnKH0vx5K2z5Z0EIDtl/Nusu+RNPiDu1IKG6s0Z/jWgu4sL+kg29/ObUFnk0bOFUHS9sB3GSgoK1UtvAypUnyBXGRZSRCOJvU9l+STto/OkpSLk0QtTqPMCMq+pzXBFUDSOF4rlF5iIs0i+b9/yq958wuGKIkPauH5vGur8o0bAm1poK8UiOYn6VzfRvpwX4s0Bq9W6cFBfJI0au0gkrbyxbZLjl08EviA7bsL2gR4L7A7sBzQ+VD9LOVm2VZUgf39wGm275TU9GjIvqFNx8KHApuRguvFwNYkXdkdCvqwo+1z5rYW1EfevR0LjAPuAJYCdrB9e6OOFUTSucChtqfk63HAYSXeC4N2z6OACcB1pD5XSu2eJV1nu9gs5y72P2L7F03Zzz6cQtpFr0hSKxsJXFVC37oNtCm4TiH9Ak3KubYxwM9sb1XQh8bzLMEsIYPVSE/u99QtXDDckHSn7bfOba0m21cOcdu2t6jbh+zH0aTJQL9k9hRN7QpJ2f58wEd47Wznw0vYzz6MAN4GTLX9dJaFXK5ND5p10qZj4b/bflXSy1kl5wnSoOTakbQ16ejljZKO6bg1mqQUE9SMpC1s/zbn2jpZVVKxD9Vhwu2S/oeBkXO7AkU+UG1vXsLOP8BoUpX2ezrWDJT6PfgVKR1xC821QW0ETLb9vKTdgHVJMpRBD2hTcJ2Yp0CcSPqFfo6kklOCR0k5re2y7YpnSQLeQf28C/gt8IEu90p+qA4H9gA+RxoeAHAN8KOSDuTWuCNtP52vFwcOsP21EvZt71HCzhAsZ/t9DfvwI2DtLAl6AKli+FTSeyX4F2nNsXAnkt4EjC59/JEnYghYNS+17kiyaSSNtN2K6uDhTKc6VMdasRRJno50LKnHF5Ja13jbDxeyfwJwbJX3boLq71tpgPwjtk+KNFXvGNG0AyWR9EZJG5OEwheT9M7CLmxMEor/IXA88McGfGg7D0g6QdKWba2MlLSJpMsk/VHS1OpV2I2ROe9Y+bQASZayFKeQBicsm18X5LVSbArcIukeSbdLmiKpdK7z2VytvRtwUc7BjirsQ9/Smp2rpO+S5hfeBVQ7F7vgoHJJtwAfs31Pvl4VOCOq88qRReq3BXYm5ZguBM603cRM10aQ9AdSOuIWBt4LswZ5F/LhK6Qj+iqg7QGcb/vIQvYnDx5U0G2tRvtdB0UU1rleBvgYcLPt30kaC2xWqD2x72lTcL0HWKtJeTFJt9tea25rQRlynu9oYFfbI5v2pxSSbrT99mHgx9akyUAAl9m+tKDtK0iB/Yy8tAuwh+0t5/xTPfdhU2AV26dIWgpY2HY3JbngdUibguslwI4uPBx5kA8nkwT7O6s0R5aY4RgMIOldpFOM95EKzc5quuewJEoDy0eSirg621BKKjQ1St45HkuqmDVpStI+pSZU5b779UhzdFeVtCxwTsne2yygcizwFpKozUjgOduLlvKhn2lTcP0Fqc/1Chqa55pzTHuT8i2QiiiOD7Huckh6kCSzdzbpGPL5oX+i/5hDr2mxHtPsQ7cP9udLDC4YDkiaTNLyvbVj7F/RUyyl8Ys7A+eQAv3HgVVtH1TKh36mTa045+dXY+QgehSzy54FZVnL9vSmnWiSYdJrehxdPtjrNirpy7aPlHQsXaRHCz5sz7RtSZUM50KF7M6G7fs6KuhPkTQJiODaA1oTXF14KHonWR1qjkcEkXMtyjKSzgPG2B4naS1gO9v/2bRjpVDDY/cqGvpgr7SEJ9ZsZ26cLWkCqWthT5LW8omFfZghaV5gsqQjSYMcWtVBUidtOhbeBDgMWIH0UFFNwah95NycKgMrSlYIth1JVwMHAhM6juPusD2uWc/KoWEwdk/SNcC7ScIFfyF9sO9ue+1SPnT4MoJUTFT0RENpru57SJ9Fl9q+rLD9FUhKdaNI1eOLktJU95X0o19pU3BtrP1A0ptJO6XrBq1vAvzF9v11+xAkJN1se/1OEYOSLRjDgeHwd5A/2B8n5VuLf7BL+jnwWdJnwc0kOcSjbX+vhP0OP0Yzu7bw30raD+qjNcfCwDO2L2nI9g/oftw1Pd/rJskX1MM0SSszMHJuBwbmmraFxsfu2X4oH0m+iVS1fI/tmQVdWMP2dEm7ApcAXyU9eBcJrpL2Ar4JvEDqIBDp36PESVqkqQrQpuB6paTv0Uz7wZhuMme2p2QpxqAcewMnAKtLegR4gNQS1Sb2JxX3rSzpOvLYvZIOSNoG+DFwPymwrChpr4IPwKOyHOmHgONsv1QVFxXiS8A429MK2qzYtgGbraNNwbVqml+vY81AifaDxYa4t0AB+0HG9lTg3bk6cwRpMsrOQGvy3rZvzb2+TY7d+z6weXUMnE8TLiLtIkswAXiQNDD+mnxMXTLnej/pd68JRjFEmqoZl/qP1uRcm0TSGcBvbZ84aP3TwFa2P9qMZ+0h57b2Jg2H/hVweb4+ALjd9gcbdK8okkYC2/DaWaLFWsSqvG/HtYCbOtdKI2ke20VGQEpah1RUdiOF++4lXQgcNPg0TdKawBG2I03VA/p+5ypp/0FLBqYB1xaUGtsXOC/nd6qRc+uRijk+XMiHtnMa8BRpzOCewCGkXduHbU9u0rEGuICU65tCyvcVQwPzdCdKupgk5mFgR1JhUd32d7P9sy6fCxWlHjAmkEYgFv83INJURej74Aos0mXtTcAhkg6zfWbdDth+HNhY0uZA1fJxke3f1m07mMVKVauJ0qDwx4Cxtl9o1q1GWK7BopXOXdHjDMwOfRKYv4D9Sqyh2+dCSUbZnlOAr5tIUxWgtcfCkpYALo/Zhe1Ag+ZUDr5uE3lC1BW2f9O0L00haSnbTzZo/whSzvcCZj8Wrr0VJ9JUZWhtcIXuA5uD/kTSK0ClIyzSE/oMBsREWqFpCyDpw6ThESOAlyj4dyDpmKHul5IflPRHUnA7CzjX9lMl7HbY75aSKiVqMwY4D5hJlzSV7Shq6gFtOBbuSj6iLfqGCpqjTSPl/gGOIk2DmeLyT9fVh/kmwBqk4AYp53pXKSfyJJoNSJXih0i6izTX92dz+dFe8ZbBKQlJJY7FI01ViL7fuc6hYXoJ4FHg47b/UN6rIGiOLD24me3ShTSdPtwAbFpV5+ae09/Z3rABX5YkPXAUm+vbLS3R5lRFP9KGnevghmkDf23jqLEgyEwFrsoaw535vpLTmhYnSQ5WOcaF81oRcmvWh0k715VJx6QbFLC7DKkdbIHcjqN8azSwYN32g3L0fXANUfwgeA0P5Ne8+dUE3wEm5dmyAt5JGqxRituAXwKH276+oN33ArsDy5GENKrgOh04uKAfQc30/bFwEATDk7yLq5TTbixZSCNJDeSbK9sjgF1sn96E/aAMEVyDoGVIWo8kolGNXwTKCrZnRaZdSf3Hh0saCyxj+6aa7f7A9r6SLqD7sPTt6rTf4cdE2+vN/TuD1ysRXIOgZUi6hzTTdjZ1oJIpFEk/yra3sP0WSYsDv6lb/lDSv9u+JWsrvwbbV9dpv8OP75CU4s5ioEUsRs71ERFcg6BlSLrW9qYN+3Cr7XUHzZS9reSwdElLATQhJtFkn2tQhr4vaAqC4DUcmiUgr2D2auFzC/rwUh4gUM2UXYpCGruSDgO+QBLRkKSXgWNtH17CPoDtFUvZCpohgmsQtI89gNVJo8eqgGbSrONSHENqfxkj6VukebJfq9toFuzfBFi/GtwhaSXgR5L2s/3fdfvQ4cs4kpDGLPEI26eWsh/USxwLB0HLkHSP7dWGgR+rA1uS2lGusH13AZuTSPq50watL0XK+RaRQ5V0KLAZKbheDGxNmtRVdGh9UB8jmnYgCILi/F7SGk07ASwJzLB9HDBNUomj0lGDAyvMyruOKmC/YgfSg8VfbO8BrA0sWtB+UDNxLBwE7WNDYHIuqnmRAeH+kq04h5LE4lcjDQ0fRRomsEnNpmf+H+/1mr/bflXSy1kt6glg+YL2g5qJ4BoE7eN9TTtAkh5cB7gVwPajkkrMWF1b0vQu66LMPNmKiZIWA04kDTN4DiipFBXUTATXIGgZth+StDbwjrz0O9u3FXZjpm1LqqqFF5rbD/SC4TIdyfbn85c/lvRrYLTt25v0KegtkXMNgpYhaTxwOrB0fv1M0hcLu3G2pAnAYpL2BC4n7eJag6TtJR0FfJE0PCDoI6JaOAhahqTbgY2qyVB513h9yZxrtrsV8B7Skeylti8rab9JJB0PvBk4Iy99FLjf9t7NeRX0kjgWDoL2IeCVjutXGJjOUq9h6Se2d8+Xy9o+sITdYcgWpIHp1bH4T4E7m3Up6CURXIOgfZwC3CjpvHz9IeCkQrY75Q3HAz8tZHe4cR8wFqj0nJfPa0GfEME1CFqG7aMkXQVU+sJ72J5UynwhO8OdRYC7Jd1E+jvZgFRBfD6Um84T1EfkXIOgZUjaELjT9rP5ejTpiPLGArafAM4kHUN/NH89C9v71O3DcGBOU3kqSk3nCeojgmsQtIwsAbhuR75vBDDR9roFbH9iqPu2W3NMLGkFYBXbl0taAJineuAJXv/EsXAQtA+546k6KwUV+SxoU/Acitx+9BlgCVIbznLAj0mSiEEfEH2uQdA+pkraR9Ko/BoPTG3aqZaxN0nqcTqA7XtJPcdBnxDBNQjax2eBjYFHgIeBt5N2UUE5XrQ9S8s4nxxEjq6PiGPhIGgZtp8Adm7aj5ZztaSDgQWymMbngQsa9inoIbFzDYKWIWlVSVdIuiNfryWp9kHlw82Hhvkq8CQwBdiLNNO1TX/+vieqhYOgZUi6GjgQmFANB5d0h+1xbfKhafKA9mqWbNBnxM41CNrHgrZvGrT2cgt9KI4Sh0maBtwD3CPpSUnfaNq3oLdEcA2C9jFN0srkAhpJOwCPtdCHJtiPVCW8vu0lbC9BKijbRNJ+zboW9JI4Fg6CliFpJeAEUsXwU8ADwK62HxryB/vMhybIAh5b2Z42aH0p4DfVEXnw+ieCaxC0lDxqbgQwA9jZ9ukFbY+0/UrlQ1uUiYbKK7ct59zvxLFwELQESaMlHSTpuNz+MQP4BGkay06F3XlA0gnAhsBzhW03ycz/473gdUbsXIOgJUj6FekI9nqSzN7SJAH98bYnF/ZlQWBbUr/tusCFwJm2ry3pR2kkvQI83+0WML/tUYVdCmoigmsQtARJU2yvmb8eSSogGmv7hYb9Whw4mpRzHdmkL0HQK+JYOAjaw0vVF7ZfAR5uMrBKepek44FbgPkpfzQdBLURO9cgaAmDjiQFLEDKuwqw7dEFfXkQmAScDZxvu9tRaRC8bongGgRBcSSNtj29aT+CoC4iuAZBUAxJX7Z9pKRj6TIFxvY+DbgVBD0npuIEQVCSu/N/JzbqRRDUTATXIAiKYbsaqzbD9jmd9yTt2IBLQVALcSwcBEFxJN1qe925rQXB65XYuQZBUAxJWwPvB94o6ZiOW6NpwVScoD1EcA2CoCSPkvKt25H6WyueJU2MCYK+II6FgyAojqR5bMdONehbIrgGQVAMSWfb3knSFGZvxamELNZqyLUg6CkRXIMgKIakf7P9mKQVut3v93muQXsIbeEgCIph+7H85TTgzzmYzgesTcrHBkFfEDvXIAiKI+kW4B3A4sB1wM3ATNu7NupYEPSI2LkGQdAEsj0D2B443vaOwFsb9ikIekYE1yAImkCSNgJ2BS7KazHLNegbIrgGQdAE+wIHAefZvlPSSsCVDfsUBD0jcq5BEDSGpIUBbD/XtC9B0Eti5xoEQXEkrSlpEnAncJekWyRFzjXoGyK4BkHQBBOA/W2vYHsscABwYsM+BUHPiOAaBEETLGR7Vo7V9lXAQs25EwS9JYT7gyBogqmSvg6clq93A6Y26E8Q9JTYuQZB0ASfBJYCzgV+ASyZ14KgL4hq4SAIiiFpfuCzwJuBKcDJtl9q1qsg6D0RXIMgKIaks4CXgN8BWwMP2t63Wa+CoPdEcA2CoBiSptheM389D3CT7XUbdisIek7kXIMgKMmsI+AYlh70M7FzDYKgGJJeAZ6vLoEFgBkMDEsf3ZRvQdBLIrgGQRAEQY+JY+EgCIIg6DERXIMgCIKgx0RwDYIgCIIeE8E1CIIgCHpMBNcgCIIg6DERXIMgCIKgx/x/CQ5JcBS+BboAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(data.isnull()) # The WHITE REGION shows the NULL values in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MqFme-8DCC3q"
   },
   "source": [
    "**Now I will remove the columns that are not of much use for us in our text based analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 220516,
     "status": "ok",
     "timestamp": 1600525752989,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "FgD0wNH8CC3s"
   },
   "outputs": [],
   "source": [
    "data.drop(['Unnamed: 0', \"Clothing ID\",\"Title\", \"Recommended IND\", \"Division Name\", \"Department Name\",\"Positive Feedback Count\"], axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 218306,
     "status": "ok",
     "timestamp": 1600525752991,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "x5OlH_N1CC37",
    "outputId": "3e39e727-42c7-4b16-f392-a95e9545c54a"
   },
   "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>Age</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
       "      <td>4</td>\n",
       "      <td>Intimates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>5</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>3</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age                                        Review Text  Rating Class Name\n",
       "0   33  Absolutely wonderful - silky and sexy and comf...       4  Intimates\n",
       "1   34  Love this dress!  it's sooo pretty.  i happene...       5    Dresses\n",
       "2   60  I had such high hopes for this dress and reall...       3    Dresses\n",
       "3   50  I love, love, love this jumpsuit. it's fun, fl...       5      Pants\n",
       "4   47  This shirt is very flattering to all due to th...       5    Blouses"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 216883,
     "status": "ok",
     "timestamp": 1600525752993,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "osPPSgmgCC4V",
    "outputId": "2258bcd8-540f-47ee-90fa-b3bb2a45e9b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null values in Review Text column. 845\n",
      "Number of null values in Class Name column. 14\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of null values in Review Text column.\",data[data['Review Text'].isnull()==True].shape[0])\n",
    "print(\"Number of null values in Class Name column.\",data[data['Class Name'].isnull()==True].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RP2gJi6OCC4m"
   },
   "source": [
    "**We have enough data for out analysis so we can simply drop the rows with null values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 214720,
     "status": "ok",
     "timestamp": 1600525752996,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "NZZ1rTuzCC4p",
    "outputId": "6d378e48-5cb6-4086-e9b2-19ea98aad48b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22628, 4)"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(inplace=True)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wghlBSkVCC43"
   },
   "source": [
    "Since we do not have a column which shows the sentiment as positive or negative in the dataset, I will define a new **sentiment column**. To do this, I assume the reviews which have **4 or higher** rating as **positive (True in the new dataframe)** and **2 or lower** rating as **negative (False in the new dataframe)**. Also, I did not include the rows that have neutral ratings which are equal to **3**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 213520,
     "status": "ok",
     "timestamp": 1600525752999,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "mkGvwgrmCC46",
    "outputId": "8987202d-eff6-45ab-c509-dcefd4b53849"
   },
   "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>Age</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Class Name</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
       "      <td>4</td>\n",
       "      <td>Intimates</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>5</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>Pants</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>49</td>\n",
       "      <td>I love tracy reese dresses, but this one is no...</td>\n",
       "      <td>2</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age                                        Review Text  ...  Class Name Sentiment\n",
       "0   33  Absolutely wonderful - silky and sexy and comf...  ...   Intimates         1\n",
       "1   34  Love this dress!  it's sooo pretty.  i happene...  ...     Dresses         1\n",
       "3   50  I love, love, love this jumpsuit. it's fun, fl...  ...       Pants         1\n",
       "4   47  This shirt is very flattering to all due to th...  ...     Blouses         1\n",
       "5   49  I love tracy reese dresses, but this one is no...  ...     Dresses         0\n",
       "\n",
       "[5 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data['Rating'] != 3]\n",
    "data['Sentiment'] = data['Rating'] >= 4\n",
    "data['Sentiment'] = data['Sentiment'].map({True:1,False:0})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 212460,
     "status": "ok",
     "timestamp": 1600525753649,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "D9X420hHCC5L",
    "outputId": "61fc1741-204b-49ab-eef8-569ca1a850f7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8742ac7358>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IGkQ2+nul4PhjixW+dMwVeX5DPuY8PzPreGsV5PW1pG/vwa2uA9WT81frxe9Z4NDCBkl9gI2Buax8TbO5oZOA6yLix0WWLY6IZQXvHwU+TxZq+3fgWrLi98N0rLkNI8pGLgMuioixkkYBowuWvd9Mv8zMKu6/9i7tdofvfK12svygxm94AR4Eekk6ClbclPL/yIrSy8AISWtI2ohstNZgqaRuBfs4VNK6aR/9JQ1p5njjgVOAxyNiNrAOsDkwIyLmk11rPCztR5K2Ttv1BRrOCXx9dT+0mZmtnpoufhERwEHAYZJmAS8Ai8nu5pxAdnrxWeBS4OmCTa8Epkm6MSKeJbsZ5X5J04AHgEHNHHIisB7ZCBBgGjA99QOya4XHSnqGLKPvwNQ+mux06GRgzmp9aDMzW2366Pe2VbMhH+sbP/nZjpXuhplZUdU6vZmkyRExsnF7TY/8zMzMVkWt3/CSGwPX2axq/7IyM6s1HvmZmVnuuPiZmVnu+LRnjfj3e7MY/Rfn+ZlZbRj9peq+TOORn5mZ5Y6Ln5mZ5U6nLX6SzpQ0U9I0SVPTRNPtfYxRknZu7/2amVl5dcprfpJ2AvYHto2IJZIGkE0o3d5GAfXA/7Whb10j4sMy9MXMzErUWUd+g4A5EbEEICLmAIMl3QYg6UBJiyR1l9RD0supfZikeyVNljRe0hap/QBJEyVNkfR3SetJGgocB3w/jSx3kzRQ0q2Snkr/dknbj5b0R0kTgD9K+qSkJ9N20yRt1uHfkJlZjnXKkR9wP3C2pBfI0hfGkM312ZC4sNp5fCnj77dAfUT8EkDSTcCvIuIxSRsD9wGfSPtekdsn6TLgkoi4UVJ3smQIM7OaM/2eZSxZ0HSazKPubhpuWy1BttBJi19E1EvajqzI7UFW/M4AypHHV2gvYMuC7fuk/cLKuX2PA2dK2hC4LSJmFdtZYZht3wEOszWz6rNkQbBoXtP2N+ZVd7htpyx+ACmHbxwwTtJ0siihcuTxFVqDbIS4uLAxFcMVuX0RcZOkicB+wF8lfSciHiryGVaE2W4wzGG2ZlZ91uxdPOy2f13TcNtqCbKFTlr8JG0OLC8YUY0AXiPL47seuD4iZktahyyiaEY6pfmKpMMi4mZlFWuriHiG5vP4FgB9Ct7fD3wXuDD1Y0RETC3Sv02AlyPi0nR6dCugSfEzM6t2n9qv+FWb0V+q7nDbznrDSx1wnaRnU0bflmSjtfbO47sLOKjhhhfgZGBkuonlWbIbYor5EjBD0lRgOFlBNjOzDuI8vxqxwbC+8e3znednZrWhWqY3c56fmZlZ0imv+XVGG6y9WdX8JWVmVus88jMzs9xx8TMzs9zxac8aMWvuS3z+zkMq3Q0zszb524G3VroLRXnkZ2ZmuePiZ2ZmuePiZ2ZmuePi14piobiSXk0ZgY3XLZrrJ6mfpBPK31szMyuFb3hpQVtDcSOiSaq7pK5AP+AEsogkMzOrMBe/lhULxW1IaUBST+A2sliiqyTVR0RdSn44B3gP2AJ4GhiW5vJ8gCxOaQzZpNhdgeMjYnxHfjAzs/a09PZ6YsHyJu1H3do01w8qn+3n4teyJqG4EfFIWlYH/JksIaLYxNTbAsMj4pWU+j68IS5J0mnAfRFxrqQuQK9iBy/M8+sxsGf7fSozs3YWC5bD3KbF74251Znr5+LXgmKhuJLOSIvvBC6IiBub2fzJiGgu9PYp4GpJ3YA7isUepeOvyPPru+nanoHczKqWeq9RJNUPBq81qOj6lc72c/FrRTOhuAATgH0k3RTFozHeL9LWsM9HJe1OFmZ7raSLmhk9mpnVhG4H1RVtv/7A6vzV5rs9WyBpc0mbFTQ1hOICnE12Te+KEna1AOhdsN8hwFsRcRXwe7JTpGZm1kFc/FrWXChug+8BPSW1eNU2It4BJkiaIelCYBTwjKQpwOHAJeXovJmZFefTni2IiMlAk8cXgKEFr79RsH5d+jmO7FRp4b6+2mgf17VHH83MrO1c/GrEZv2GVe0EsWZmtcanPc3MLHdc/MzMLHdc/MzMLHd8za9GzJr7H/a9/eeV7oaZWbv460FnVfT4HvmZmVnuuPiZmVnulFT8JA2RtFd63VNS79a2qQWS6ivdBzMz63itFj9J3wJuAX6XmjYE7ihnp8zMzMqplBteTgR2ACYCRMQsSeuWtVcVJGkE8FuymKGXgGMi4j1J44AfRMSkFGo7KSKGSjoa+EJafxhwe0ScnvZ1LPAjYC7wDLAkIk6SNDAdY+N02FMiYkKHfUgzsw7wwZ1TYf7iosuOur2yOX+lFL8lEfFBQYBrVyiaXNFZXA98NyIekfQz4KfAKa1sMwLYBlgCPC/pMmAZ8N9kk1YvAB4iK4CQzeX5q4h4TNLGwH3AJxrvdOU8v76r+7nMzDrW/MXEvEVFF70xr7I5f6UUv0ck/YRsAufPAicAd5W3W5UhqS/QryCw9jrg5hI2fTAi5qV9PAsMAQYAj0TEu6n9ZuDjaf29gC0b/qAA+kiqi4iVrkGunOc3uDP/wWFmnVGfHqiZRRvU9S/a3lE5f6UUvzOAY4HpwHeAv5LF8OTNh3x0jbRHo2VLCl4vo/XvdQ1gx4gofj7AzKwT6H7giGaXXV/tz/lFxPKIuCoiDouIQ9PrTjkKSaO39yTtlpq+BjSMAl8FtkuvDy1hd08B/yVp7XSq+JCCZfcD3214k64zmplZBynlbs/9JU2R9K6k+ZIWSJrfEZ3rAL0kvV7w71SypPYLU37fCOBnad1fAsenDL4Bre04It4AzgOeJEt9fxWYlxafDIyUNC2dJj2uPT+UmZm1TK0N4iS9CBwMTO+sI75yabiOl0Z+twNXR8Ttq7KvvpsOjl0uPL59O2hmViEdNb2ZpMkRMbJxeykPuf8LmOHCt0pGS5oKzABewc9HmplVhVJGftsD55Bd+1pxY0dEXFTerlmhkSNHxqRJkyrdDTOzmtLcyK+Uuz3PBerJ7nDs3t4dMzMz62ilFL8NImJ42XtiZmbWQUopfn+VtHdE3F/23lizZs2dzX63/abS3TAza5N7Dq7OG/VKueHleOBeSYs64aMOZmaWQ62O/CKiU8QXmZmZNSjltCeS1gY2o2Bar4h4tFydKqE/y8imW2vwReCmiNi5Ql0yM7Ma0mrxk/RN4HtkOX5TgR2Bx4E9y9u1Fi2KiMZTgrnwmZlZSUq55vc9YHvgtYjYgyy6Z25Ze7UKGlLZJY2SNE7SLZKek3SjUnyCpO0kPSJpsqT7JA0qsp9hkp6QNF3Szwv2WyfpQUlPp2UHpvahkmYUbP8DSaPT6+3TFGZTJV3YsJ6kLun9U2n5d8r+BZmZ2QqlnPZcHBGLJSFpzYh4TtLmZe9Zy3qmmVMAXomIgxot3wb4JPBvsnk1d5E0EbgMODAiZks6nOwZxmMabXsJcElE/ElS4Zybi4GDImJ+CrN9QtLYVvp5DfCtiHhc0i8K2o8F5kXE9pLWBCZIuj8iXint45uZVZ8Pxj5KzF+4UttRdzzeZL2OCqxtSSnF73VJ/cim5npA0nvAa+XtVquKnfYs9GREvA6QiuRQstHqcLLPANAF+E+RbXciu4YIcBPZhNYAAs6TtDuwHBgMrNdcB9J31jsiGv7L3wTsn17vDWwlqSEdoi/ZNdVXGu3jozDbAcWzr8zMqkXMX0jMWymWlDcava8Wpdzt2TCqGi3pYbJf1PeWtVerr1i+noCZEbHTKu7zCGAgsF1ELJX0KtkNQIU5f9A0668YkaXF39fSSiuH2Q7x3KpmVtXUp1eTtg3q+jZp66jA2paUdLdng4KE81r0PDBQ0k7pNGQ34OMRMbPRek+QZe+NAb5c0N4XeDsVvj3I0toB3gLWlbQO2TRw+wP3RsTc9EzkpyNiYqN93UcWj/RQ2t/HgTci4v32/tBmZh2l+xd2b9J2fZU+5N5s8ZO0AAhYKYU+0jbdI6JNhbPSIuKDdJrxUkl9yT7HxUDj4ncKcIOkM8lGuA0ZfDcCd0maDkwCnkv7XSrpZ2S5fW80tCfHAldJWk42MXjDvn5Pdir26XQzzmw+OtVqZmZl1mwBa/xwu6Q64ETgO2TZdBUTEXXNtUXEOGBcQftJBa+nAk3/NFnZG8COERGSvgxsnradQ3Y9sFh/LgUuLbJoZkRsBSDpDLKiSUQsB36S/pmZWQcr5Tm/fmSjoaPIbtrYPiLeKXfHKmg74PI0IptL07tB22I/ST8m+55fA45e/e6ZmdnqajbPL93OfxpwOHA1cFlEzCu6spWd8/zMzNpuVfL8XiO7FnUNsBA4Nj0iADjM1szMaldLxe9CshtcADy5tZmZdRot3fAyugP7Ya148b132f+WGyvdDTOzNrv70CMq3YUmSpnb08zMrFNx8TMzs9xx8SuBpDMlzSxIaPh0So5ocgeRmZlVv1aLn6TvSeqjzB9SpM/eHdG5aiBpJ7Ipy7ZND6zvBfyrsr0yM7PVUcrI75iImE+WRLA28DXgFy1v0qkMAuZExBLIZnqJiH8XriBpb0mPpz8Mbk6z4TSbH5hGjZekUeQMSTt0+KcyM8uxUubnbHi4b1/gjxExsyEcNifuB86W9ALwd2BM4QTfaTKAs4C9IuJ9ST8CTpV0Pi3nB/aKiBEpIulqsrglM7Oqt+Sue4kFpUcVHTW2xQCblXRU1l8pxW+ypPuBjwE/ltSbLM8uFyKiXtJ2wG7AHsCYNE9ngx2BLckCaQG6A4+TzQnaUn7gn9L+H02nlftFxNzCYxfm+fUcsE4ZPp2ZWdvFgnpi3vyS13+jDet2lFKK37HACODliFgoqT/wjfJ2q7pExDKyybLHpVSHrxcsFvBARHylcBtJn6Ll/MDG88o1mWeuMM+v37BNnOdnZlVBvZtkC7Rog7rS50npqKy/UorfTsDUdErvSGBb4JLydqt6SNocWB4Rs1LTCLKp3xpOUz4BXCFp04h4UdJaZCnvreUHHg48LGlXYJ7nTTWzWrHmAfu0af3ra/Qh998ACyVtTTbR9UvA9WXtVXWpA66T9KykaWSnOEc3LIyI2WRpDX9Kyx8HtoiID4BDgf+V9AwwFdi5YL+LJU0Bfks2ujYzsw5Sysjvw5RtdyBweUT8QVJufllHxGRWLloNRhWs8xCwfZFtW8oPvCEiTmmPPpqZWduUUvwWpEy6I4HdJa0BdCtvt8zMzMqnlOJ3OPBV4NiIeFPSxmSJD7aKImJUW7fZdO3+VTk5rJlZLWq1+EXEm8BFBe//Sb6u+ZmZWSdTyvRmO0p6SlK9pA8kLZPkOxPNzKxmlXK35+XAV4BZQE/gm8Cvy9kpMzOzcirlmh/p+bUu6WHva9It+j8ub9es0IvvzeMLt9xV6W6Yma2SsYceUOkurKSU4rdQUndgqqQLyKbochSSmZnVrFKK2NfI5qU8CXgf2Ag4pJydMjMzK6dWi19EvBYRiyJifkT8T0ScGhEvdkTnOoqk0qcn/2ibayUdWuK6/SSd0PaemZlZOTR72jNN4NzsZMop2NVK0w84Ad8oZGZWFVq65rd/h/WiCqQA2jvJAnu7AWdFxJ1p2VHAD8j+GJgWEV9rtO05ZKeDjwVOBb4ErAncHhE/JQv/HSZpKvAA2XOTY4A+ZP8Njo+I8WX/kGZmZbLortuIBc1HFx019uZml3VUhl+hlopfN2C9iJhQ2ChpF+DNsvaqMhYDB0XE/BRQ+4SksWQTWZ8F7BwRc1Kk0wqSLgR6k8U8fRbYDNiBLOpobAqrPQMYHhEj0janAfdFxLmSugC9inVo5Ty/ge3+gc3M2kssmE/Mm9vs8jdaWFYJLRW/iyn+OMP8tKy67ltdfQLOS8VqOVks0XrAnsDNETEHICLeLdjmv4GJEfFtAEl7A3sDU9LyOrJi+M9Gx3oKuDrFHN2RJsBuYuU8v82c52dmVUu9+7S4fIO6tZpd1lEZfoVaKn7rRcT0xo0RMV3S0LL1qHKOAAYC20XEUkmvAj1a2eYpYDtJ/VNRFHB+RPyucKXG31dKb98d2A+4VtJFEeEp48ysZvU84OAWl19fZc/5tXS3Z78WlvVs745Ugb7A26nw7QEMSe0PAYdJWgeg0WnPe8mu590jqTdwH3BMun6IpMGS1gUWkJ0aJbUPAd6KiKuA3yJjAuAAABjHSURBVJMFBJuZWQdpaeQ3SdK30i/oFSR9E5hc3m51HEldgSXAjcBd6S7XScBzABExU9K5wCOSlpGd0jy6YfuIuDkVvrHAvsBNwOOSAOqBIyPiJUkTJM0A/gbMAH4oaWla56gO+bBmZgaAIopfSpK0HnA78AEfFbuRQHeyG0M6xU0vKaH+qojYodJ9aUm/YZvF7v97UesrmplVoUpNbyZpckSMbNze7MgvIt4Cdk6nAIen5ntSanmnIOk44GTAiepmZjnS7MjPqsvIkSNj0qRJle6GmVlNaW7k5wmqzcwsd1z8zMwsd0rK87PKe+m9eg669bFKd8PMOoHbD9m10l2oOI/8zMwsd1z8zMwsd3zaswVpVpcH09v1gWXA7PR+h4j4oB2OcS1wd0Tcsrr7MjOz0rj4tSAi3gEakhhGA/UR8cuG5ZK6RsSHFeqemZmtIhe/NkojtcXANsAESVcAV5BNir0Q+FZEPJfWm082K876wOkRcYuyec8uI4s/+hfZDDpmZi16f+z1xIL2iQU66s4r22U/UJksvvbg4rdqNiTL91sm6UHguIiYJenTZGnte6b1BgG7AluQzf15C3AQsDlZTuB6wLPA1cUOsnKe33rl+zRmVvViwVyWz3unXfb1xrx22U1Nc/FbNTenwlcH7AzcnCayhizBvcEdEbEceDbNlQqwO/CniFgG/FtSs9PFFeb5rT1sC0/FY5Zj6t2v3e5QHFTXWlpb6SqRxdceXPxWzfvp5xrA3IaE9iKWFLxWM+uYmbVqrS+0X/jL9X7Oz486rI6ImA+8IukwAGW2bmWzR4HDJXWRNAjYo9z9NDOzlbn4rb4jgGMlPQPMBA5sZf3bgVlk1/quBx4vb/fMzKwxn/YsUUSMbqb9FWCfIu1HN3pfl34GcFL799DMzErlkZ+ZmeWOR341YtjadZ6M1sysnXjkZ2ZmuePiZ2ZmuePTnjXi5blLOPy2FyvdDTOzVo05eNNKd6FVHvmZmVnuuPiZmVnuuPiVSNKZkmZKmiZpaprEurVtfiZpr47on5mZlc7X/EogaSdgf2DbiFgiaQDQvbXtIuLssnfOzMzazCO/0gwC5kTEEoCImAMMlnQbgKQDJS2S1F1SD0kvp/ZrJR2aXr8q6fw0apwkaVtJ90l6SdJxlfpgZmZ55JFfae4Hzpb0AvB3YAwwgZTyDuwGzAC2J/tOJzazn39GxAhJvwKuBXYBeqRtf1u23puZtYN5Yy9m2fzWMwWPuqNbq+tUOgTXxa8EEVEvaTuyIrcHWfE7A3hJ0ieAHYCLyLL6ugDjm9nV2PRzOlAXEQuABZKWSOoXESvFNBeG2fYasEE7fyozs7ZZNv8dls97u9X1aiEs18WvRCl8dhwwTtJ04Otk8USfB5aSjQivJSt+P2xmNw35fstZOetvOUX+WxSG2fbf9FMOszWziurSZ52S1htUV9rIr5Jc/EogaXNgeUTMSk0jgNfIRnjXA9dHxGxJ6wDrkZ3GNDPrVPp+4ZSS1ru+Bh5yd/ErTR1wmaR+wIfAi2SnI98nK3aPpvWmAeun2CIzM6tSLn4liIjJwM7NLF6zYL1vN9ru6ILXQwteX0t2irTJMjMzKz8/6mBmZrnjkV+N2KTfmjUxWayZWS3wyM/MzHLHxc/MzHLHxc/MzHLH1/xqxNtzl3LF7W9VuhtmZu3mxIPWq9ixPfIzM7PccfEzM7PccfErkaT6SvfBzMzah4ufmZnljm94aSNJPwS+RDat2e0R8dPUfgewEVk+3yUpkaFhxHgJWRL8IuDAiHhL0mHAT4FlwLyI2L3DP4yZWQd57M7zWTh/9kptE2/v0mS9jsr5c/FrA0l7A5uR5fcJGCtp94h4FDgmIt6V1BN4StKtEfEOsBbwREScKekC4FvAz4Gzgc9FxBtpwuxix1uR57f2wA3L/vnMzMpl4fzZ1M97c6W2+grm/rn4tc3e6d+U9L6OrBg+Cpws6aDUvlFqfwf4ALg7tU8GPpteTwCulfQX4LZiByvM89t4062dFGFmNatXn4FN2vrWFR/5dQQXv7YRcH5E/G6lRmkUsBewU0QslDSO7PQnwNKCiKNlpO88Io6T9GlgP2CypO3SSNHMrNPZ9cAfN2nzc3614z7gGEl1AJIGS1oX6Au8lwrfFsCOre1I0rCImBgRZwOzyUaLZmbWATzyK4GkrsCSiLhf0ieAxyUB1ANHAvcCx0n6B/A88EQJu71Q0mZko8kHgWfK0nkzM2vCxa80nwReAoiIS8ju3mzs88U2jIi6gte3ALek1we3fzfNzKwUPu3ZCknHAX8Czqp0X8zMrH3oo3sxrJqNHDkyJk2aVOlumJnVFEmTI2Jk43aP/MzMLHdc/MzMLHd8w0uNmPfeh/xtzJxKd8PMrE0+f/iASnehKI/8zMwsd1z8zMwsd3JR/JzFZ2ZmhXJR/GqBpKYzvJqZWVnk9oYXScOAK4CBwELgWxHxXGq/kSyK6E7glIiok3QFcF9EjJV0O9lcnsdIOgYYliKLjgROBroDE4ETImKZpN8A2wM9gVsKMgBfBcaQJT1cAPy5w74AM7MS3HzPucyvn936is340z1tH2N1RKZfbosfWVTQcRExK6Ur/BrYk2zqsksi4k9pdpcG44HdgLHAYGBQat8N+HOa8/NwYJeIWCrp18ARwPXAmSnrrwvwoKStImJa2v6diNi2WAcL8/zWHeA8PzPrePPrZ/PevP+s8vbvVTCzryW5LH4plWFn4OY0QTVkyewAOwFfTK9vAn6ZXo8HTpG0JfAssLakQWn9k4GvA9uRBdlCNsp7O237pVTIupIVzS2BhuI3prl+Fub5bTZshKfiMbMO16euaQ5fW/SqW7WRX7nlsviRXeucGxEjSt2gIHF9H7Lw2v7Al4D6iFigrOJdFxErhVZJ+hjwA2D7iHhP0rV8lPUH8P7qfRQzs/I5bL8zV2t7P+dXRSJiPvCKpMMAlNk6LX4COCS9/nKjTZ8ATiErfuPJitr4tOxB4NCU74ek/pKGAH3ICtw8SevRTPqDmZl1nLwUv16SXi/4dyrZ9bhjJT0DzAQOTOueApwqaRqwKVB4xno80DUiXgSeJhv9jQeIiGfJkh/uT9s+AAyKiGeAKcBzZKdRJ5T5s5qZWStycdozIpor8vsUaXsD2DEiQtKXgc0L9vMH4A/p9VKyO0ILjzOGItfwIuLoZvo1tITum5lZO8tF8Wuj7YDL0zW8ucAxFe6PmZm1Mxe/RiJiPLB1qyt2sL5rd63aC8dmZrUmL9f8zMzMVnDxMzOz3PFpzxqxcM6HTPn9262vaGZWpbb55rqV7sIKHvmZmVnuuPiZmVnuuPgVIelMSTMlTZM0VdKnJb0qaZVvt5Q0QtK+7dlPMzNbNb7m14iknYD9gW0jYkkqeN1Xc59dgRHASOCvq99LMzNbHS5+TQ0C5kTEEoCImAOQkhq+K+kAoBtwWMr/6w9cDWxClgv47YiYJmk0MCy1/xPYBegpaVfgfOBNsvgkgAB2j4gFHfMRzczyzcWvqfuBsyW9APwdGBMRj6RlcyJiW0knkE1q/U3gf4ApEfFFSXuS5fc1pEVsCewaEYskHQ2MjIiTACTdBZwYERNSxNLijvqAZmbt7cqHz+Pd91sOvV3z0S4l7cththUQEfWStiMLqd0DGCPpjLT4tvRzMnBwer0rKQUiIh6StI6kPmnZ2IhY1MyhJgAXSboRuC0iXm+8QmGY7fr9HWZrZtXr3fdnM2fBmy2vVEXntlz8ioiIZcA4YJyk6WRBtQBL0s9llPbdNZvVFxG/kHQPsC8wQdLnIuK5RuusCLPdcqjDbM2sevVfq/XQ2zX7lD7yKzcXv0YkbQ4sj4hZqWkE8BrwqWY2GU8Wj3SOpFFkp0bnFyTEN1gA9C44zrCImA5Ml7Q9sAVZ7JGZWc359h4/aXWdanrI3cWvqTrgspTa/iHwItmpx/2bWX80cHXK8FvIR6PExh4GzpA0leyGl10l7QEsJ8sT/Fu7fQIzM2uRi18jETEZ2LnIoqEF60wCRqXX7wJfLLKf0Y3evwtsX9DUJPfPzMw6hh9yNzOz3PHIr0b0GtC1qs6Xm5nVMo/8zMwsd1z8zMwsd1z8zMwsd3zNr0YsfXMp/7ngjUp3w8xstQw6fXCluwB45GdmZjnk4mdmZrnT6YufpPoOPNZISZd21PHMzGzV+JpfO5HUNc38MqnSfTEzs5blsvilQNqzyBLa3yGbmHo28Dywc0TMlrQG8AKwU9rst8DG6fUpKYdvNAWBtZJ+B/wgIvZPyzZOyzYGLo6IS9Px/xs4Mh3zX8DkiPhleT+1mVn7+8WkC5izqOUcv0JdZrSt7JQr2y+XxQ94DNgxIkLSN4HTI+I0STeQFcKLgb2AZ1IhvAn4VUQ8Jmlj4D7gE2lfhYG1oxodZwuyTMDewPOSfkOWEnEIsDVZIvzTZPmATRTm+Q3uVx13SJmZFZqzaDZvLnyr9A0Wlq8vbZHX4rchWUjtILLR3yup/WrgTrLidwxwTWrfC9iyIKaoT0pfh5YDa++JiCXAEklvA+sBuwB3RsRiYHFKdC+qMM9v6w23dp6fmVWdAT1bz/Er1GXtto/8yiGvxe8y4KKIGJtGa6MBIuJfkt6StCewA9koELIbg3ZMBWuFVAybDazlo/BbKD0A18ysZpwx8vQ2re/n/CqrL9DwxHjj/L3fAzcAN6dEd4D7ge82rCBpxGocewJwgKQeafTYXE6gmZmVSR6KXy9Jrxf8O5VspHezpMnAnEbrjyULtL2moO1kYKSkaZKeBY5b1c5ExFPpGNPIAmynA/NWdX9mZtZ2nf40XEQ0V+DvbKZ9a7IbXZ4r2Mcc4PAi+x7d6P04YFwzy4YXvP1lRIyW1At4lGZueDEzs/Lo9MWvLSSdARzPR9f6yuVKSVsCPYDrIuLpMh/PzMwKKMI3EdaCkSNHxqRJfn7ezKwtJE2OiJGN2/Nwzc/MzGwlLn5mZpY7vuZXI5a+tZC3LvZ9MWa2+tY7ZbtKd6HiPPIzM7PccfEzM7PcKVvxk7S+pD9LeknSZEl/lfTxch2v4LijJN1dpP1oSZe3YT/9JJ1Q8H6opK+2Vz/NzKxyylL8lE16eTswLiKGRcR2wI/JJnauFf2AEwreDwVc/MzMOoFyjfz2AJZGxG8bGiLimYgYL6lO0oOSnpY0XdKBAJLWknSPpGckzZB0eGp/VdKA9HqkpHHp9Q6SHpc0RdL/Sdq8hH5tJGmcpFmSftrQKOnUdMwZkk5Jzb8AhkmaKunC9H639P77aW7Oa9JnmCJpj7SvoyXdIemB1PeT0v6nSHpCUv+03smSnk1Tpv15Nb9vMzNrg3Ld7Tmc5qfsWgwcFBHzU1F7QtJYYB/g3xGxH4Ckvq0c4zlgt4j4UNJewHlkOXkt2SH1bSHwlKR7gAC+AXwaEDBR0iPAGcDwiBiR+jOKFFSb3p8GRER8StIWwP0Fp3WHA9uQzeDyIvCjiNhG0q+Ao8gik84APhYRSyT1K9bZwjy/DdcuT6yHmVkeVeKGFwHnSZoG/B0YTHY6dDrwWUn/K2m3iGhtsue+ZJNTzwB+BXyyhGM/EBHvpPy924Bd07/bI+L9iKhP7buVsK9dydIfSPOAvgY0FL+HI2JBRMwmm7S6IbNvOtnpU8gmtr5R0pHAh8UOEBFXRsTIiBjZf621S+iSmZmVolzFbybQ3IMkRwADge3SqOotoEdEvABsS1Ygfi7p7LT+hwX97FGwn3PIisxw4IBGy5rTeC63cs3tVpjjt7zg/XI+Gm3vB1xB9pmfkuRnLs3MOki5it9DwJrptB0AkraStBvZiO3tiFiarpMNScs3ABZGxA3AhWRFAeBVPiqkhac1CzP5ji6xX5+V1F9ST+CLZNl644EvSuolaS3goNS2AOhdsG3j9+NJE2Cn050bA8+X0glJawAbRcTDwI/SZ6lreSszM2svZSl+kc2WfRCwV3rUYSZwPvAmcCNZNt50sutfDdFBnwKelDQV+Cnw89T+P8AlkiaRpaE3uAA4X9IUSr92+SRwK9kpx1sjYlJKVLg2LZsI/D4ipkTEO8CEdBPMhWmbZemGnO8DvwbWSJ9jDHB0RCwpcsxiugA3pG2nAJdGxNwStzUzs9XkVIcasfVGW8b9p/2x0t0ws04gT9ObOdXBzMws8U0WNaLber1y9deamVk5eeRnZma54+JnZma549OeNeLDt+fz9uX3V7obZtYJrHvS3pXuQsV55GdmZrnj4mdmZrnT6YpfMzmC3y6W8ddOx/u/cuzXzMzKp1MVv0rkCEbEzuXat5mZlUenKn40kyNINg9nnaRbJD0n6cZUKJG0naRH0ijxPkmDUvs4Sb+SNEnSPyRtL+m2lAXYMPUakurTz0GSHk15fzPSPKZI2jvlDj4t6WZJdan9FwV5fr/ssG/IzMw63d2eLeUIbkMWe/Rvsgmtd5E0EbgMODAiZqcA3XOBY9I2H0TESEnfA+4km2D7XeAlSb9K8382+CpwX0ScK6kL0CvlFZ4F7BUR70v6EXCqpCvI5j7dIiKiuTw/MzMrj85W/FryZES8DpAmzx4KzCUrmA+kgWAX4D8F24xNP6cDMyPiP2n7l4GNgMLi9xRwtaRuwB0RMVXSfwFbkk2QDdAdeJws428x8Id0LbLo9ciVw2zXXZ3PbmZmBTpb8ZsJHNrMssLEhWVkn11kRW2nVrYpzORreL/SdxcRj0ranSyn71pJFwHvkQXofqXxjiXtAHwm9fckYM/G60TElcCVACM2/rhnIDczayed7Zpf0RxBmk9mfx4YKGmntG43SaUkwjchaQjwVkRcBfyeLI/wCbLTq5umddaS9PF03a9vRPwV+D6w9aoc08zMVk2nGvml62cHARen62uLycJw72hm/Q8kHQpcKqkv2fdxMdkIsq1GAT+UtBSoB45K1xGPBv4kac203llkwbh3SupBNvo8dRWOZ2Zmq8h5fjVixMYfj/tPv7zS3TCzTiBP05s5z8/MzCzpVKc9O7Ou6/bJ1V9rZmbl5JGfmZnljq/51QhJC8juTrWPDADmVLoTVcbfSVP+TprK03cyJCIGNm70ac/a8Xyxi7Z5JmmSv5OV+Ttpyt9JU/5OfNrTzMxyyMXPzMxyx8WvdlxZ6Q5UIX8nTfk7acrfSVO5/058w4uZmeWOR35mZpY7Ln5VTtI+kp6X9KKkMyrdn2og6WpJb0uaUem+VANJG0l6OIUjz0z5k7knqYekJyU9k76X/6l0n6qBpC6SpqQ4tdxy8atiKRT3CuDzZLmAX5G0ZWV7VRWuBfapdCeqyIfAaRGxJbAjcKL/PwGyGLI9I2JrYASwj6QdK9ynavA94B+V7kSlufhVtx2AFyPi5Yj4APgzcGCF+1RxEfEo8G6l+1EtIuI/EfF0er2A7Bfb4Mr2qvIiU5/edkv/cn2Tg6QNyTJHf1/pvlSai191Gwz8q+D96/iXmrVA0lBgG2BiZXtSHdIpvqnA22TB0nn/Xi4GTicL5M41Fz+zTiKFJN8KnBIR8yvdn2oQEcsiYgSwIbCDpOGV7lOlSNofeDsiJle6L9XAxa+6vQFsVPB+w9RmthJJ3cgK340RcVul+1NtImIu8DD5vla8C/AFSa+SXULZU9INle1S5bj4VbengM0kfUxSd+DLwNgK98mqjCQBfwD+EREXVbo/1ULSQEn90uuewGeB5yrbq8qJiB9HxIYRMZTsd8lDEXFkhbtVMS5+VSwiPgROAu4ju4nhLxExs7K9qjxJfwIeBzaX9LqkYyvdpwrbBfga2V/yU9O/fSvdqSowCHhY0jSyPyQfiIhc395vH/EML2Zmljse+ZmZWe64+JmZWe64+JmZWe64+JmZWe64+JmZWe64+JkZkpalRyRmSLqr4fm4FtYfUfg4haQvOHXEaokfdTAzJNVHRF16fR3wQkSc28L6RwMjI+KkDuqiWbvqWukOmFnVeRzYCkDSDsAlQA9gEfAN4BXgZ0BPSbsC5wM9ScVQ0rXAfGAksD5wekTcImkN4HJgT7IJ25cCV0fELR342cwAn/Y0swIpQ/IzfDSN3nPAbhGxDXA2cF6K1zobGBMRIyJiTJFdDQJ2BfYHfpHaDgaGkmVTfg3YqVyfw6w1HvmZGWSjuKlkkVn/AB5I7X2B6yRtRpaF163E/d0REcuBZyWtl9p2BW5O7W9Kerj9um/WNh75mRnAohT9MwQQcGJqPwd4OCKGAweQnf4sxZKC12q3Xpq1Exc/M1shIhYCJwOnSepKNvJriNE6umDVBUDvNu5+AnCIpDXSaHDU6vXWbNW5+JnZSiJiCjAN+ApwAXC+pCmsfJnkYWDL9HjE4SXu+lbgdeBZ4AbgaWBeu3XcrA38qIOZdRhJdRFRL2kd4Elgl4h4s9L9svzxDS9m1pHuTg/QdwfOceGzSvHIz8zMcsfX/MzMLHdc/MzMLHdc/MzMLHdc/MzMLHdc/MzMLHdc/MzMLHf+P6snULXOtG56AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.barplot(data['Rating'], data['Class Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 211166,
     "status": "ok",
     "timestamp": 1600525754504,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "8BDkK_w0CC5a",
    "outputId": "20b7303f-3809-4ddd-e387-20b391de1458"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f873fca2860>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.barplot(data['Age'], data['Class Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 209356,
     "status": "ok",
     "timestamp": 1600525754506,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "t6E-zHceCC5m",
    "outputId": "75822c5d-9a54-4c3b-85f3-86849d9585b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f873fb93588>"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['Rating'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Mup2QIvECC5x"
   },
   "source": [
    "We can see that Rating of 4 and 5 are in majority as compared to ratings of 1 and 2. Therefore we can say that based on this, our target variable ie, Sentiment column will be unbalanced. Hence our **dataset is unbalanced**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 208172,
     "status": "ok",
     "timestamp": 1600525755237,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "eyx4_DLSCC51",
    "outputId": "86a3f5cc-6616-4266-82bf-69c252094259"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f873fb67390>"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y = data['Class Name'],)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PlQOz8eMCC6E"
   },
   "source": [
    "In the Class Names, Dresses, Knits and Blouses dominate all other classes. On the other hand, some classes namely Chemises, Casual bottoms, Intimates , trend etc, have very few data points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 206212,
     "status": "ok",
     "timestamp": 1600525755240,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "Ta9nJVu9CC6G",
    "outputId": "34088e67-c643-4643-863e-d5f7d4d74de9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8743de9748>"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['Sentiment'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UWCLYVHJCC6a"
   },
   "source": [
    "**We can see that out data is imbalanced.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4ZaUmzDlCC6c"
   },
   "source": [
    "**Now I will preprocess the Review Text Column and create feature vectors from the Reviews to build the Classifier.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 203782,
     "status": "ok",
     "timestamp": 1600525755242,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "Qpb4OFWiCC6f"
   },
   "outputs": [],
   "source": [
    "def decontracted(phrase):\n",
    "    # specific\n",
    "    phrase = re.sub(r\"won't\", \"will not\", phrase)\n",
    "    phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n",
    "\n",
    "    # general\n",
    "    phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'re\", \" are\", phrase)\n",
    "    phrase = re.sub(r\"\\'s\", \" is\", phrase)\n",
    "    phrase = re.sub(r\"\\'d\", \" would\", phrase)\n",
    "    phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n",
    "    phrase = re.sub(r\"\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n",
    "    phrase = re.sub(r\"\\'m\", \" am\", phrase)\n",
    "    return phrase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4360424,
     "status": "ok",
     "timestamp": 1600529914217,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "hG8ScQwfCC6t",
    "outputId": "4dc50ee5-af62-4512-ca0d-b1a829951e35"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 19805/19805 [1:09:19<00:00,  4.76it/s]\n"
     ]
    }
   ],
   "source": [
    "preprocessed_text = []\n",
    "# tqdm is for printing the status bar\n",
    "for sentance in tqdm(data['Review Text'].values):\n",
    "    sentance = re.sub(r\"http\\S+\", \"\", sentance)\n",
    "    sentance = BeautifulSoup(sentance, 'lxml').get_text()\n",
    "    sentance = decontracted(sentance)\n",
    "    sentance = re.sub(\"\\S*\\d\\S*\", \"\", sentance).strip()\n",
    "    sentance = re.sub('[^A-Za-z]+', ' ', sentance)\n",
    "    #preprocessed_tweets.append(' '.join(token.lower() for token in nltk.word_tokenize(sentance) if token.lower() not in stopwords.words('english')))\n",
    "    sentance = ' '.join(e.lower() for e in sentance.split() if e.lower() not in stopwords.words())\n",
    "    preprocessed_text.append(sentance.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4358937,
     "status": "ok",
     "timestamp": 1600529914226,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "sKtaNuKaCC68",
    "outputId": "b49c5eec-b46d-47cf-f773-c1a5bea13100"
   },
   "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>Age</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Class Name</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>processed text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
       "      <td>4</td>\n",
       "      <td>Intimates</td>\n",
       "      <td>1</td>\n",
       "      <td>absolutely wonderful silky sexy comfortable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>5</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>1</td>\n",
       "      <td>love dress sooo pretty happened find store gla...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>Pants</td>\n",
       "      <td>1</td>\n",
       "      <td>love love love jumpsuit fun flirty fabulous ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>1</td>\n",
       "      <td>shirt flattering due adjustable front tie perf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>49</td>\n",
       "      <td>I love tracy reese dresses, but this one is no...</td>\n",
       "      <td>2</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>0</td>\n",
       "      <td>love tracy reese dresses petite feet tall usua...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  ...                                     processed text\n",
       "0   33  ...        absolutely wonderful silky sexy comfortable\n",
       "1   34  ...  love dress sooo pretty happened find store gla...\n",
       "3   50  ...  love love love jumpsuit fun flirty fabulous ev...\n",
       "4   47  ...  shirt flattering due adjustable front tie perf...\n",
       "5   49  ...  love tracy reese dresses petite feet tall usua...\n",
       "\n",
       "[5 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['processed text'] = preprocessed_text\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4356533,
     "status": "ok",
     "timestamp": 1600529914230,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "JnWoIFjUF2RM"
   },
   "outputs": [],
   "source": [
    "data.to_csv(\"processed_wcr.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2572,
     "status": "error",
     "timestamp": 1600525452860,
     "user": {
      "displayName": "Aryan Rastogi",
      "photoUrl": "",
      "userId": "03692411020617163036"
     },
     "user_tz": -330
    },
    "id": "ISTruGnlvufV",
    "outputId": "1edebad1-7e5d-4466-cc55-db38971d1902"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"processed_wcr.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NqD0jWgNv4nq"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 19805/19805 [00:15<00:00, 1251.47it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['absolut wonder silki sexi comfort',\n",
       " 'love dress sooo pretti happen find store glad bc never would order onlin bc petit bought petit love length hit littl knee would definit true midi someon truli petit',\n",
       " 'love love love jumpsuit fun flirti fabul everi time wear get noth great compliment',\n",
       " 'shirt flatter due adjust front tie perfect length wear leg sleeveless pair well cardigan love shirt',\n",
       " 'love traci rees dress petit feet tall usual wear brand dress pretti packag lot dress skirt long full overwhelm small frame stranger alter shorten narrow skirt would away embellish garment love color idea style work return dress']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# STEMMING THE DATA AND ADDING IT TO DATAFRAME.\n",
    "new_list = []\n",
    "for text in tqdm(data['processed text'].values):\n",
    "    text = text.split()\n",
    "    stemmer = SnowballStemmer(\"english\")\n",
    "    stemmed_text = []\n",
    "    for word in text:\n",
    "        word = stemmer.stem(word)\n",
    "        stemmed_text.append(word)\n",
    "    new_list.append(\" \".join(stemmed_text))\n",
    "new_list[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Unnamed: 0</th>\n",
       "      <th>Age</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Class Name</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>processed text</th>\n",
       "      <th>Stemmed text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
       "      <td>4</td>\n",
       "      <td>Intimates</td>\n",
       "      <td>1</td>\n",
       "      <td>absolutely wonderful silky sexy comfortable</td>\n",
       "      <td>absolut wonder silki sexi comfort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>5</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>1</td>\n",
       "      <td>love dress sooo pretty happened find store gla...</td>\n",
       "      <td>love dress sooo pretti happen find store glad ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>Pants</td>\n",
       "      <td>1</td>\n",
       "      <td>love love love jumpsuit fun flirty fabulous ev...</td>\n",
       "      <td>love love love jumpsuit fun flirti fabul everi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>47</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>1</td>\n",
       "      <td>shirt flattering due adjustable front tie perf...</td>\n",
       "      <td>shirt flatter due adjust front tie perfect len...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>49</td>\n",
       "      <td>I love tracy reese dresses, but this one is no...</td>\n",
       "      <td>2</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>0</td>\n",
       "      <td>love tracy reese dresses petite feet tall usua...</td>\n",
       "      <td>love traci rees dress petit feet tall usual we...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Age                                        Review Text  Rating  \\\n",
       "0           0   33  Absolutely wonderful - silky and sexy and comf...       4   \n",
       "1           1   34  Love this dress!  it's sooo pretty.  i happene...       5   \n",
       "2           3   50  I love, love, love this jumpsuit. it's fun, fl...       5   \n",
       "3           4   47  This shirt is very flattering to all due to th...       5   \n",
       "4           5   49  I love tracy reese dresses, but this one is no...       2   \n",
       "\n",
       "  Class Name  Sentiment                                     processed text  \\\n",
       "0  Intimates          1        absolutely wonderful silky sexy comfortable   \n",
       "1    Dresses          1  love dress sooo pretty happened find store gla...   \n",
       "2      Pants          1  love love love jumpsuit fun flirty fabulous ev...   \n",
       "3    Blouses          1  shirt flattering due adjustable front tie perf...   \n",
       "4    Dresses          0  love tracy reese dresses petite feet tall usua...   \n",
       "\n",
       "                                        Stemmed text  \n",
       "0                  absolut wonder silki sexi comfort  \n",
       "1  love dress sooo pretti happen find store glad ...  \n",
       "2  love love love jumpsuit fun flirti fabul everi...  \n",
       "3  shirt flatter due adjust front tie perfect len...  \n",
       "4  love traci rees dress petit feet tall usual we...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Stemmed text'] = new_list\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now, only the stemmed text and sentiment column will be useful for our furthur analysis, therefore I will put them in a seaprate dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Sentiment</th>\n",
       "      <th>Stemmed text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>absolut wonder silki sexi comfort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>love dress sooo pretti happen find store glad ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>love love love jumpsuit fun flirti fabul everi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>shirt flatter due adjust front tie perfect len...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>love traci rees dress petit feet tall usual we...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sentiment                                       Stemmed text\n",
       "0          1                  absolut wonder silki sexi comfort\n",
       "1          1  love dress sooo pretti happen find store glad ...\n",
       "2          1  love love love jumpsuit fun flirti fabul everi...\n",
       "3          1  shirt flatter due adjust front tie perfect len...\n",
       "4          0  love traci rees dress petit feet tall usual we..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data[['Sentiment',\"Stemmed text\"]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now we need to split the data and form feature vectors from the Stemmed text, using Bag of Words.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df['Stemmed text']\n",
    "y=df['Sentiment']\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of rows in training data:  13269\n",
      "No. of rows in testing data:  6536\n"
     ]
    }
   ],
   "source": [
    "print(\"No. of rows in training data: \", x_train.shape[0])\n",
    "print(\"No. of rows in testing data: \", x_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a74683320>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi41LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvSM8oowAAE3hJREFUeJzt3X+wpmV93/H3x13xR9UAslqyu2Zp3ZouJDFwBlEnrYUUFqsutZDixLCSndlOB40m/RFpOm6rMtWJDUGjZnbCyuJYfgyxZW1JyA5CbRtBFqXgQg0btHDCBg5dQB0rdsm3fzzXkYfl2d1nD9c5zx72/Zp55rnv731d933dzGE/c/98UlVIktTDCyY9AEnS84ehIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1M3SSQ9goR133HG1atWqSQ9DkhaVO+6449GqWnawdvMWKkm2AG8DHqmqk1rtt4G3Az8C/hy4sKoeb8suBjYATwG/VlU3tvpa4DJgCfAHVfWxVj8BuBo4Fvg68CtV9aODjWvVqlXs2LGj565K0vNekv89Trv5PP11BbB2n9p24KSq+lngz4CLAZKsAc4HTmx9PpNkSZIlwKeBs4E1wLtaW4CPA5dW1WrgMQaBJEmaoHkLlar6CrBnn9qfVNXeNnsrsKJNrwOurqonq+rbwC7g1PbZVVX3t6OQq4F1SQKcDlzX+m8FzpmvfZEkjWeSF+p/FfijNr0ceHBo2XSr7a/+SuDxoYCarUuSJmgioZLkt4C9wBdmSyOa1Rzq+9vexiQ7kuyYmZk51OFKksa04KGSZD2DC/i/XE//mMs0sHKo2QrgoQPUHwWOTrJ0n/pIVbW5qqaqamrZsoPevCBJmqMFDZV2J9dvAu+oqh8MLdoGnJ/kRe2urtXA14DbgdVJTkhyFIOL+dtaGN0MnNv6rweuX6j9kCSNNm+hkuQq4KvA65JMJ9kA/B7wcmB7kjuT/D5AVe0ErgXuAf4YuKiqnmrXTN4L3AjcC1zb2sIgnH4jyS4G11gun699kSSNJ0fazwlPTU2Vz6lI0qFJckdVTR2sna9pkSR1c8S9pkV6Pnvgwz8z6SHoMPSaD929YNvySEWS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKmbeQuVJFuSPJLkm0O1Y5NsT3Jf+z6m1ZPkk0l2JbkryclDfda39vclWT9UPyXJ3a3PJ5NkvvZFkjSe+TxSuQJYu0/tg8BNVbUauKnNA5wNrG6fjcBnYRBCwCbgDcCpwKbZIGptNg7123dbkqQFNm+hUlVfAfbsU14HbG3TW4FzhupX1sCtwNFJjgfOArZX1Z6qegzYDqxty15RVV+tqgKuHFqXJGlCFvqayqurajdA+35Vqy8HHhxqN91qB6pPj6hLkibocLlQP+p6SM2hPnrlycYkO5LsmJmZmeMQJUkHs9Ch8nA7dUX7fqTVp4GVQ+1WAA8dpL5iRH2kqtpcVVNVNbVs2bLnvBOSpNEWOlS2AbN3cK0Hrh+qX9DuAjsNeKKdHrsRODPJMe0C/ZnAjW3Z95Kc1u76umBoXZKkCVk6XytOchXwFuC4JNMM7uL6GHBtkg3AA8B5rfkNwFuBXcAPgAsBqmpPko8At7d2H66q2Yv//5TBHWYvAf6ofSRJEzRvoVJV79rPojNGtC3gov2sZwuwZUR9B3DScxmjJKmvw+VCvSTpecBQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkrqZSKgk+fUkO5N8M8lVSV6c5IQktyW5L8k1SY5qbV/U5ne15auG1nNxq38ryVmT2BdJ0tMWPFSSLAd+DZiqqpOAJcD5wMeBS6tqNfAYsKF12QA8VlWvBS5t7UiypvU7EVgLfCbJkoXcF0nSM03q9NdS4CVJlgIvBXYDpwPXteVbgXPa9Lo2T1t+RpK0+tVV9WRVfRvYBZy6QOOXJI2w4KFSVX8BfAJ4gEGYPAHcATxeVXtbs2lgeZteDjzY+u5t7V85XB/RR5I0AZM4/XUMg6OME4CfBP4acPaIpjXbZT/L9lcftc2NSXYk2TEzM3Pog5YkjWUSp79+Efh2Vc1U1f8Dvgi8CTi6nQ4DWAE81KangZUAbflPAHuG6yP6PENVba6qqaqaWrZsWe/9kSQ1kwiVB4DTkry0XRs5A7gHuBk4t7VZD1zfpre1edryL1dVtfr57e6wE4DVwNcWaB8kSSMsPXiTvqrqtiTXAV8H9gLfADYD/wW4OslHW+3y1uVy4PNJdjE4Qjm/rWdnkmsZBNJe4KKqempBd0aS9AwLHioAVbUJ2LRP+X5G3L1VVT8EztvPei4BLuk+QEnSnPhEvSSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjdjhUqSm8apSZKObAf85cckLwZeChyX5BggbdErgJ+c57FJkhaZg/2c8D8BPsAgQO7g6VD5LvDpeRyXJGkROmCoVNVlwGVJ3ldVn1qgMUmSFqmDHakAUFWfSvImYNVwn6q6cp7GJUlahMYKlSSfB/4mcCfwVCsXYKhIkn5srFABpoA1VVXzORhJ0uI27nMq3wT++nwORJK0+I17pHIccE+SrwFPzhar6h3zMipJ0qI0bqj8m/kchCTp+WHcu7/+63wPRJK0+I37mpbvJflu+/wwyVNJvjvXjSY5Osl1Sf5XknuTvDHJsUm2J7mvfR/T2ibJJ5PsSnJXkpOH1rO+tb8vyfq5jkeS1MdYoVJVL6+qV7TPi4F/BPzec9juZcAfV9VPAz8H3At8ELipqlYDN7V5gLOB1e2zEfgsQJJjgU3AG4BTgU2zQSRJmow5vaW4qv4TcPpc+iZ5BfB3gMvbun5UVY8D64CtrdlW4Jw2vQ64sgZuBY5OcjxwFrC9qvZU1WPAdmDtXMYkSepj3Icf3zk0+wIGz63M9ZmVvwHMAJ9L8nMM3in2fuDVVbUboKp2J3lVa78ceHCo/3Sr7a8+avwbGRzl8JrXvGaOw5YkHcy4d3+9fWh6L/AdBkcQc93mycD7quq2JJfx9KmuUTKiVgeoP7tYtRnYDDA1NeUDnJI0T8a9++vCjtucBqar6rY2fx2DUHk4yfHtKOV44JGh9iuH+q8AHmr1t+xTv6XjOCVJh2jcu79WJPmPSR5J8nCSP0yyYi4brKq/BB5M8rpWOgO4B9gGzN7BtR64vk1vAy5od4GdBjzRTpPdCJyZ5Jh2gf7MVpMkTci4p78+B/wH4Lw2/+5W+/tz3O77gC8kOQq4H7iQQcBdm2QD8MDQtm4A3grsAn7Q2lJVe5J8BLi9tftwVe2Z43gkSR2MGyrLqupzQ/NXJPnAXDdaVXcyuNi/rzNGtC3gov2sZwuwZa7jkCT1Ne4txY8meXeSJe3zbuD/zOfAJEmLz7ih8qvALwF/CewGzqWdhpIkada4p78+AqxvDxnOPs3+CQZhI0kSMP6Rys/OBgoMLpIDPz8/Q5IkLVbjhsoLht+r1Y5Uxj3KkSQdIcYNhn8P/GmS6xg8tf5LwCXzNipJ0qI07hP1VybZweAlkgHeWVX3zOvIJEmLztinsFqIGCSSpP2a06vvJUkaxVCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHUzsVBJsiTJN5L85zZ/QpLbktyX5JokR7X6i9r8rrZ81dA6Lm71byU5azJ7IkmaNckjlfcD9w7Nfxy4tKpWA48BG1p9A/BYVb0WuLS1I8ka4HzgRGAt8JkkSxZo7JKkESYSKklWAP8A+IM2H+B04LrWZCtwTpte1+Zpy89o7dcBV1fVk1X1bWAXcOrC7IEkaZRJHan8LvAvgb9q868EHq+qvW1+GljeppcDDwK05U+09j+uj+gjSZqABQ+VJG8DHqmqO4bLI5rWQZYdqM++29yYZEeSHTMzM4c0XknS+CZxpPJm4B1JvgNczeC01+8CRydZ2tqsAB5q09PASoC2/CeAPcP1EX2eoao2V9VUVU0tW7as795Ikn5swUOlqi6uqhVVtYrBhfYvV9UvAzcD57Zm64Hr2/S2Nk9b/uWqqlY/v90ddgKwGvjaAu2GJGmEpQdvsmB+E7g6yUeBbwCXt/rlwOeT7GJwhHI+QFXtTHItcA+wF7ioqp5a+GFLkmZNNFSq6hbgljZ9PyPu3qqqHwLn7af/JcAl8zdCSdKh8Il6SVI3hookqRtDRZLUjaEiSerGUJEkdWOoSJK6MVQkSd0YKpKkbgwVSVI3hookqRtDRZLUjaEiSerGUJEkdWOoSJK6MVQkSd0YKpKkbgwVSVI3hookqRtDRZLUjaEiSerGUJEkdWOoSJK6MVQkSd0YKpKkbgwVSVI3hookqRtDRZLUzYKHSpKVSW5Ocm+SnUne3+rHJtme5L72fUyrJ8knk+xKcleSk4fWtb61vy/J+oXeF0nSM03iSGUv8M+q6m8DpwEXJVkDfBC4qapWAze1eYCzgdXtsxH4LAxCCNgEvAE4Fdg0G0SSpMlY8FCpqt1V9fU2/T3gXmA5sA7Y2pptBc5p0+uAK2vgVuDoJMcDZwHbq2pPVT0GbAfWLuCuSJL2MdFrKklWAT8P3Aa8uqp2wyB4gFe1ZsuBB4e6Tbfa/uqjtrMxyY4kO2ZmZnrugiRpyMRCJcnLgD8EPlBV3z1Q0xG1OkD92cWqzVU1VVVTy5YtO/TBSpLGMpFQSfJCBoHyhar6Yis/3E5r0b4fafVpYOVQ9xXAQweoS5ImZBJ3fwW4HLi3qn5naNE2YPYOrvXA9UP1C9pdYKcBT7TTYzcCZyY5pl2gP7PVJEkTsnQC23wz8CvA3UnubLV/BXwMuDbJBuAB4Ly27AbgrcAu4AfAhQBVtSfJR4DbW7sPV9WehdkFSdIoCx4qVfXfGX09BOCMEe0LuGg/69oCbOk3OknSc+ET9ZKkbiZx+mtRO+VfXDnpIegwdMdvXzDpIUiHBY9UJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSuln0oZJkbZJvJdmV5IOTHo8kHckWdagkWQJ8GjgbWAO8K8mayY5Kko5cizpUgFOBXVV1f1X9CLgaWDfhMUnSEWuxh8py4MGh+elWkyRNwNJJD+A5yohaPatRshHY2Ga/n+Rb8zqqI8dxwKOTHsThIJ9YP+kh6Nn8+5y1adQ/lYfsp8ZptNhDZRpYOTS/Anho30ZVtRnYvFCDOlIk2VFVU5MehzSKf5+TsdhPf90OrE5yQpKjgPOBbRMekyQdsRb1kUpV7U3yXuBGYAmwpap2TnhYknTEWtShAlBVNwA3THocRyhPKepw5t/nBKTqWde1JUmak8V+TUWSdBgxVDQnvh5Hh6skW5I8kuSbkx7LkchQ0SHz9Tg6zF0BrJ30II5Uhormwtfj6LBVVV8B9kx6HEcqQ0Vz4etxJI1kqGguxno9jqQjj6GiuRjr9TiSjjyGiubC1+NIGslQ0SGrqr3A7Otx7gWu9fU4OlwkuQr4KvC6JNNJNkx6TEcSn6iXJHXjkYokqRtDRZLUjaEiSerGUJEkdWOoSJK6MVSkMSX5rSQ7k9yV5M4kb5jDOl6f5K1D8++Y77c8J3lLkjfN5zakWYv+lx+lhZDkjcDbgJOr6skkxwFHzWFVrwemaL9WWlXbmP8HR98CfB/403nejuRzKtI4krwTuLCq3r5P/RTgd4CXAY8C76mq3UluAW4D/h5wNLChze8CXgL8BfDv2vRUVb03yRXA/wV+Gvgp4EJgPfBG4Laqek/b5pnAvwVeBPx5G9f3k3wH2Aq8HXghcB7wQ+BW4ClgBnhfVf23vv91pKd5+ksaz58AK5P8WZLPJPm7SV4IfAo4t6pOAbYAlwz1WVpVpwIfADa1nwn4EHBNVb2+qq4ZsZ1jgNOBXwe+BFwKnAj8TDt1dhzwr4FfrKqTgR3Abwz1f7TVPwv886r6DvD7wKVtmwaK5pWnv6QxtCOBU4BfYHD0cQ3wUeAkYHsSgCXA7qFuX2zfdwCrxtzUl6qqktwNPFxVdwMk2dnWsYLBD6P9j7bNoxi8kmTUNt85/h5KfRgq0piq6ingFuCW9o/+RcDOqnrjfro82b6fYvz/12b7/NXQ9Oz80rau7VX1ro7blLrx9Jc0hiSvS7J6qPR6Bi/TXNYu4pPkhUlOPMiqvge8/DkM5VbgzUle27b50iR/a563KY3NUJHG8zJga5J7ktzF4BTUh4BzgY8n+Z/AncDBbt29GVjTbkn+x4c6iKqaAd4DXNXGcSuDC/sH8iXgH7Zt/sKhblM6FN79JUnqxiMVSVI3hookqRtDRZLUjaEiSerGUJEkdWOoSJK6MVQkSd0YKpKkbv4/3NJCSYAJuVEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the distribution of target variable in both training data and test data\n",
    "sns.countplot(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a74692b38>"
      ]
     },
     "execution_count": 33,
     "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": [
    "sns.countplot(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering And Modelling\n",
    "**Creating BOW from the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorize_train(n1,n2,x_train=x_train,y_train=y_train,x_test=x_test,y_test=y_test):\n",
    "    vec = CountVectorizer(ngram_range=(n1,n2))  # Unigramns only\n",
    "    x_train = vec.fit_transform(x_train)\n",
    "    x_test = vec.transform(x_test)\n",
    "    model= MultinomialNB()\n",
    "    model.fit(x_train,y_train)\n",
    "    pred = model.predict(x_test)\n",
    "    sns.heatmap(confusion_matrix(y_test,pred),annot=True)\n",
    "    print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.62      0.68       809\n",
      "           1       0.95      0.97      0.96      5727\n",
      "\n",
      "    accuracy                           0.93      6536\n",
      "   macro avg       0.85      0.80      0.82      6536\n",
      "weighted avg       0.92      0.93      0.92      6536\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectorize_train(1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.07      0.13       809\n",
      "           1       0.88      1.00      0.94      5727\n",
      "\n",
      "    accuracy                           0.88      6536\n",
      "   macro avg       0.92      0.53      0.53      6536\n",
      "weighted avg       0.89      0.88      0.84      6536\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Xm9no+r6DArOIxErScl/qUQP8l7ufCAwAxprZScB4YKG7lwILo3WAC4DSaCkH7oVUIAcmAmcA/YGJe4N5NgrMIhIryQYsdXH3Knd/Jfr8IbAWKAGGA9OjbtOBi6PPw4GHPeUloIOZ9QDOBxa4e7W7bwUWAEPrOrdqzCISK81RYzaz44DPA0uBbu5eBangbWZdo24lwLtpu1VGbdnas1LGLCKx4g1YzKzczJanLeX7H8/M2gG/Aa519w/qOHWm4ojX0Z6VMmYRiZWGPCvD3acCU7NtN7NiUkH5UXf/v6h5k5n1iLLlHsDmqL0S6JW2e09gQ9Q+aL/2xXWNSxmziMRKE87KMOBBYK2735G2aQ6wd2bFaGB2WvuoaHbGAGB7VPJ4Bigzs47RRb+yqC0rZcwiEivJpnvw50Dga8AqM1sZtV0P3AbMMrMxwDvAZdG2ucAwoAL4CLgSwN2rzWwSsCzqd7O7V9d1YgVmEYmVprr45+5LyFwfBhicob8DY7McaxowLddzKzCLSKzoQfkiIoGJwy3ZCswiEis11vJzZgVmEYmVlh+WFZhFJGZUyhARCUwTTpfLGwVmEYmVlh+WFZhFJGZUyhARCUwiBjmzArOIxIoyZhGRwLgyZhGRsChjlpz07HksD037Od26dyGZTPLAA49y1y8ezPew5CCVjRhN2yOPpKCggMLCQmZNm7LP9mmP/prfPfs8AIlEgjfffpcXfzeT9kcfddDn3L17NxMmTWbNuvV0aH80P715AiU9urFqzTr+5yep8zvONVddwZAvDjz4LxcDmi4nOampqeG/v3cTK1a+Rrt2bXl56XyeW/h71q5dn++hyUGadtdtdOzQPuO2q664lKuuuBSAxUte4uHHn8o5KP+9ahM33DKZh37xv/u0/9/Tz3L0Ue2YN2sac59bzB33TGPypAl8+vjePP7gFIqKCnnv/WpGjL6GQQMHUFRU2Lgv2IK1/LCsB+UfEhs3bmbFytcA2LFjJ6+/vp6SY7vneVRyKMx97gWGnffF2vXfPrOIkf8+jhGjx3LT/04hkajvce0pi178E8OHDQGgbNDZLP3zStydI9q0qQ3Cu3bvBmvA6ztiqgbPeQnVQQdmM7uyKQdyuOjduyd9P3cKS19eke+hyEEyM8qvu4GvXvVtnpg9N2u/jz/5hCUvLee8QWcB8MZb7zB/4Qs8ct9kfjP9bgoKCng6KnnUZ/N7W+je9RgAiooKadf2SLZtT71+7tXVrzP8im9yyairufG//+OwzpYhVdLJ9b9QNaaUcRPwq0wbohcalgNYYXsKCto24jTx0bbtkcx6/H7+87sT+fDDHfkejhykR+6dTNcundmydRvfuPZ6+vTuRb++/3RAv8VLlvL5z55UW8ZYunwla16vYOSYcQDs2rWLTh07APCdCTfz9w2b2FOzh6pN7zFidOp56//21eFccmEZqWew78ui7PizJ/8jsx/9JW+89Q43/GgyZw84ndatWzXLd28JYn/xz8xezbYJ6JZtv/QXHBa1Kgn3n6VDqKioiCcev58ZM57kqafm5Xs40ghdu3QGoHPHDgw+50xWrVmXMTDPW/gCw4YMql13d75ywRCuu/rAPzan/PhGIHuNuVvXY9i4+X26d+1CTU2CHTs/OqBu/anj/oEj2rRh/ZtvccqJn2ns12yxQs6Ec1VfKaMbMAr4coZlS/MOLV7unzqZta9X8LOfZ30hr7QAH338CTt3flT7+Y8vv0Lp8ccd0O/DHTtZvmIVXzr7C7VtA/r1ZcHiJWzZug2A7R98yIaNm3I675fOGsDsuc8B8OziFznjtM9hZlRu2EhNTapOvWHjJt56p5KSHllzpsNCsgFLqOorZTwNtHP3lftvMLPFzTKiGBp45ul87d8u5dVVa1i+7FkAfvjD25g3f1GeRyYNtaV6K+OunwRAoibBsLJBnDWgH48/+TsA/uWSCwFY+MIfObP/qRx5RJvafT/Vpzff/sYoyq+9gaQnKS4q4ob/vIZju9cfSP/5ovOZMOl2LvjqVbQ/+ihuv2k8AK+8upoHH5lFUVERBQXGD747NutskcNFIkPZp6WxTLWrpqRShmTy8YYX8z0ECVDxMcc3elrJv/a+JOeY89jbTwY5jUXzmEUkVuJQY1ZgFpFYCbl2nCsFZhGJFd2SLSISGJUyREQCE4dZGQrMIhIrKmWIiARGF/9ERAIThxqzHvspIrGSxHNe6mNm08xss5m9ltbWycwWmNn66GfHqN3MbIqZVZjZq2Z2ato+o6P+681sdH3nVWAWkVhx95yXHDwEDN2vbTyw0N1LgYXROsAFQGm0lAP3QiqQAxOBM4D+wMS9wTwbBWYRiZUEnvNSH3f/PVC9X/NwYHr0eTpwcVr7w57yEtDBzHoA5wML3L3a3bcCCzgw2O9DNWYRiZVDMCujm7tXAbh7lZl1jdpLgHfT+lVGbdnas1LGLCKx0pBShpmVm9nytKW8EafO9EAkr6M9K2XMIhIrDcmY01/q0QCbzKxHlC33ADZH7ZVAr7R+PYENUfug/doX13UCZcwiEiuH4J1/c4C9MytGA7PT2kdFszMGANujksczQJmZdYwu+pVFbVkpYxaRWGnKW7LNbAapbPcYM6skNbviNmCWmY0B3gEui7rPBYYBFcBHwJUA7l5tZpOAZVG/m919/wuK+1BgFpFYacqLf+5+eZZNgzP0dWBsluNMA6blel4FZhGJFT0rQ0QkMM39urxDQYFZRGJFGbOISGDi8BAjBWYRiZWEt/wHfyowi0isqMYsIhIY1ZhFRAKjGrOISGCSKmWIiIRFGbOISGA0K0NEJDAqZYiIBEalDBGRwChjFhEJjDJmEZHAJDyR7yE0mgKziMSKbskWEQmMbskWEQmMMmYRkcBoVoaISGA0K0NEJDC6JVtEJDCqMYuIBEY1ZhGRwChjFhEJjOYxi4gERhmziEhgNCtDRCQwuvgnIhIYlTJERAKjO/9ERAKjjFlEJDBxqDFbHP51aSnMrNzdp+Z7HBIW/V7I/gryPYDDTHm+ByBB0u+F7EOBWUQkMArMIiKBUWA+tFRHlEz0eyH70MU/EZHAKGMWEQmMAvMhYmZDzWydmVWY2fh8j0fyz8ymmdlmM3st32ORsCgwHwJmVgjcDVwAnARcbmYn5XdUEoCHgKH5HoSER4H50OgPVLj7m+6+G5gJDM/zmCTP3P33QHW+xyHhUWA+NEqAd9PWK6M2EZEDKDAfGpahTdNhRCQjBeZDoxLolbbeE9iQp7GISOAUmA+NZUCpmfUxs1bASGBOnsckIoFSYD4E3L0G+A/gGWAtMMvdV+d3VJJvZjYD+BNwgplVmtmYfI9JwqA7/0REAqOMWUQkMArMIiKBUWAWEQmMArOISGAUmEVEAqPALCISGAVmEZHAKDCLiATm/wEWYhPv984cqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectorize_train(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.18      0.30       809\n",
      "           1       0.90      1.00      0.94      5727\n",
      "\n",
      "    accuracy                           0.90      6536\n",
      "   macro avg       0.91      0.59      0.62      6536\n",
      "weighted avg       0.90      0.90      0.86      6536\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectorize_train(2,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results:\n",
    "\n",
    "**From the above results we can see that the model trained using Unigrams gives the best results. The other two models overfit on the majority class due to the large number of features.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Women Clothing Sentiment Analysis.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}