SentimentAnalysis_NLP.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwEuR3HYErhY"
      },
      "source": [
        "# **Sentiment Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9zv11vG9IMp1"
      },
      "source": [
        "# **Large Movie Review Dataset**\n",
        "This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. Raw text and already processed bag of words formats are provided."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hg5DNGLNIZGu"
      },
      "source": [
        "### **Extract and analyse the contents of the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_HS9Ca-G-ft",
        "outputId": "b6cc9ab2-544e-482d-a218-8f3d6fe5174a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracted /content/aclImdb_v1.tar.gz successfully.\n"
          ]
        }
      ],
      "source": [
        "import tarfile\n",
        "import os\n",
        "\n",
        "# Path to the tar file\n",
        "tar_path = '/content/aclImdb_v1.tar.gz'\n",
        "\n",
        "# Check if the file exists\n",
        "if not os.path.exists(tar_path):\n",
        "    print(f'The file {tar_path} does not exist.')\n",
        "else:\n",
        "    # Open the tar file\n",
        "    with tarfile.open(tar_path, 'r') as tar:\n",
        "        # Extract all contents into the current directory\n",
        "        tar.extractall()\n",
        "        print(f'Extracted {tar_path} successfully.')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQelmxy9Mlgx"
      },
      "source": [
        "#### **Reading Text Files (Reviews):**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gznpghEpG-uB",
        "outputId": "b007eb76-288c-4047-c4df-5bc1d9f957fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Homelessness (or Houselessness as George Carlin stated) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. Most people think of the homeless as just a lost cause while worrying about things such as racism, the war on Iraq, pressuring kids to succeed, technology, the elections, inflation, or worrying if they'll be next to end up on the streets.<br /><br />But what if you were given a bet to live on the streets for a month without the luxuries you once had from a home, the entertainment sets, a bathroom, pictures on the wall, a computer, and everything you once treasure to see what it's like to be homeless? That is Goddard Bolt's lesson.<br /><br />Mel Brooks (who directs) who stars as Bolt plays a rich man who has everything in the world until deciding to make a bet with a sissy rival (Jeffery Tambor) to see if he can live in the streets for thirty days without the luxuries; if Bolt succeeds, he can do what he wants with a future project of making more buildings. The bet's on where Bolt is thrown on the street with a bracelet on his leg to monitor his every move where he can't step off the sidewalk. He's given the nickname Pepto by a vagrant after it's written on his forehead where Bolt meets other characters including a woman by the name of Molly (Lesley Ann Warren) an ex-dancer who got divorce before losing her home, and her pals Sailor (Howard Morris) and Fumes (Teddy Wilson) who are already used to the streets. They're survivors. Bolt isn't. He's not used to reaching mutual agreements like he once did when being rich where it's fight or flight, kill or be killed.<br /><br />While the love connection between Molly and Bolt wasn't necessary to plot, I found \"Life Stinks\" to be one of Mel Brooks' observant films where prior to being a comedy, it shows a tender side compared to his slapstick work such as Blazing Saddles, Young Frankenstein, or Spaceballs for the matter, to show what it's like having something valuable before losing it the next day or on the other hand making a stupid bet like all rich people do when they don't know what to do with their money. Maybe they should give it to the homeless instead of using it like Monopoly money.<br /><br />Or maybe this film will inspire you to help others.\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Reading a positive review from the train set\n",
        "file_path = '/content/aclImdb/train/pos/10000_8.txt'\n",
        "with open(file_path, 'r', encoding='utf-8') as file:\n",
        "    review_text = file.read()\n",
        "\n",
        "print(review_text)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smhJWP0YG-yX",
        "outputId": "adeea3f1-39ca-4065-a1f7-dca3d75b33ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Airport '77 starts as a brand new luxury 747 plane is loaded up with valuable paintings & such belonging to rich businessman Philip Stevens (James Stewart) who is flying them & a bunch of VIP's to his estate in preparation of it being opened to the public as a museum, also on board is Stevens daughter Julie (Kathleen Quinlan) & her son. The luxury jetliner takes off as planned but mid-air the plane is hi-jacked by the co-pilot Chambers (Robert Foxworth) & his two accomplice's Banker (Monte Markham) & Wilson (Michael Pataki) who knock the passengers & crew out with sleeping gas, they plan to steal the valuable cargo & land on a disused plane strip on an isolated island but while making his descent Chambers almost hits an oil rig in the Ocean & loses control of the plane sending it crashing into the sea where it sinks to the bottom right bang in the middle of the Bermuda Triangle. With air in short supply, water leaking in & having flown over 200 miles off course the problems mount for the survivor's as they await help with time fast running out...<br /><br />Also known under the slightly different tile Airport 1977 this second sequel to the smash-hit disaster thriller Airport (1970) was directed by Jerry Jameson & while once again like it's predecessors I can't say Airport '77 is any sort of forgotten classic it is entertaining although not necessarily for the right reasons. Out of the three Airport films I have seen so far I actually liked this one the best, just. It has my favourite plot of the three with a nice mid-air hi-jacking & then the crashing (didn't he see the oil rig?) & sinking of the 747 (maybe the makers were trying to cross the original Airport with another popular disaster flick of the period The Poseidon Adventure (1972)) & submerged is where it stays until the end with a stark dilemma facing those trapped inside, either suffocate when the air runs out or drown as the 747 floods or if any of the doors are opened & it's a decent idea that could have made for a great little disaster flick but bad unsympathetic character's, dull dialogue, lethargic set-pieces & a real lack of danger or suspense or tension means this is a missed opportunity. While the rather sluggish plot keeps one entertained for 108 odd minutes not that much happens after the plane sinks & there's not as much urgency as I thought there should have been. Even when the Navy become involved things don't pick up that much with a few shots of huge ships & helicopters flying about but there's just something lacking here. George Kennedy as the jinxed airline worker Joe Patroni is back but only gets a couple of scenes & barely even says anything preferring to just look worried in the background.<br /><br />The home video & theatrical version of Airport '77 run 108 minutes while the US TV versions add an extra hour of footage including a new opening credits sequence, many more scenes with George Kennedy as Patroni, flashbacks to flesh out character's, longer rescue scenes & the discovery or another couple of dead bodies including the navigator. While I would like to see this extra footage I am not sure I could sit through a near three hour cut of Airport '77. As expected the film has dated badly with horrible fashions & interior design choices, I will say no more other than the toy plane model effects aren't great either. Along with the other two Airport sequels this takes pride of place in the Razzie Award's Hall of Shame although I can think of lots of worse films than this so I reckon that's a little harsh. The action scenes are a little dull unfortunately, the pace is slow & not much excitement or tension is generated which is a shame as I reckon this could have been a pretty good film if made properly.<br /><br />The production values are alright if nothing spectacular. The acting isn't great, two time Oscar winner Jack Lemmon has said since it was a mistake to star in this, one time Oscar winner James Stewart looks old & frail, also one time Oscar winner Lee Grant looks drunk while Sir Christopher Lee is given little to do & there are plenty of other familiar faces to look out for too.<br /><br />Airport '77 is the most disaster orientated of the three Airport films so far & I liked the ideas behind it even if they were a bit silly, the production & bland direction doesn't help though & a film about a sunken plane just shouldn't be this boring or lethargic. Followed by The Concorde ... Airport '79 (1979).\n"
          ]
        }
      ],
      "source": [
        "# Reading a negative review from the train set\n",
        "file_path = '/content/aclImdb/train/neg/10000_4.txt'\n",
        "with open(file_path, 'r', encoding='utf-8') as file:\n",
        "    review_text = file.read()\n",
        "\n",
        "print(review_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDfh4SYmNECW"
      },
      "source": [
        "#### **Reading IMDb URLs (urls_[pos, neg, unsup].txt):**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hU3m8gWbNEUf",
        "outputId": "5e6244f0-09f4-4b3b-b756-975cc2daa9db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                           IMDb_URL\n",
            "0  http://www.imdb.com/title/tt0453418/usercomments\n",
            "1  http://www.imdb.com/title/tt0453418/usercomments\n",
            "2  http://www.imdb.com/title/tt0453418/usercomments\n",
            "3  http://www.imdb.com/title/tt0064354/usercomments\n",
            "4  http://www.imdb.com/title/tt0064354/usercomments\n"
          ]
        }
      ],
      "source": [
        "# Example: Reading IMDb URLs using pandas\n",
        "urls_pos_file = '/content/aclImdb/train/urls_pos.txt'\n",
        "df_urls_pos = pd.read_csv(urls_pos_file, header=None, names=['IMDb_URL'])\n",
        "\n",
        "print(df_urls_pos.head())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qhx97jhbMuz-"
      },
      "source": [
        "#### **Reading Bag of Words (BoW) Features (.feat files):**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ydriDuujKF__",
        "outputId": "bd901d99-97c9-4de3-b77e-49df45eeb188"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   0      1      2      3      4      5      6      7      8      9      ...  \\\n",
            "0    9.0    1.0    4.0    4.0    6.0    4.0    2.0    2.0    4.0    0.0  ...   \n",
            "1    7.0    4.0    2.0    2.0    0.0    4.0    1.0    0.0    2.0    2.0  ...   \n",
            "2    4.0    4.0    4.0    7.0    2.0    1.0    1.0    1.0    0.0    1.0  ...   \n",
            "3   10.0    2.0    2.0    0.0    3.0    2.0    4.0    2.0    0.0    1.0  ...   \n",
            "4   13.0    9.0    6.0    4.0    2.0    5.0   10.0    6.0    0.0    2.0  ...   \n",
            "\n",
            "   89517  89518  89519  89520  89521  89522  89523  89524  89525  89526  \n",
            "0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
            "1    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
            "2    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
            "3    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
            "4    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
            "\n",
            "[5 rows x 89527 columns]\n"
          ]
        }
      ],
      "source": [
        "# Reading a .feat file using pandas (though pandas is not typically used for LIBSVM format)\n",
        "from sklearn.datasets import load_svmlight_file\n",
        "import pandas as pd\n",
        "\n",
        "# Example path to your .feat file\n",
        "feat_file_path = '/content/aclImdb/train/labeledBow.feat'\n",
        "\n",
        "# Load LIBSVM formatted file\n",
        "# This function from scikit-learn correctly loads the LIBSVM formatted file into a sparse matrix X.\n",
        "# The _ variable captures the labels if they are present in the file.\n",
        "\n",
        "X, _ = load_svmlight_file(feat_file_path)\n",
        "\n",
        "# Convert sparse matrix to dense NumPy array\n",
        "# X.toarray(): Converts the sparse matrix X to a dense NumPy array, which can then be converted to a pandas DataFrame\n",
        "dense_matrix = X.toarray()\n",
        "\n",
        "# Create DataFrame if necessary\n",
        "df_feat = pd.DataFrame(dense_matrix)\n",
        "\n",
        "print(df_feat.head())\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fkb7qNKM8-X"
      },
      "source": [
        "#### **Reading imdb.vocab:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acv1nc62Is5Y",
        "outputId": "a004480f-ca82-47c7-f3e3-48205015b149"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  token\n",
            "0   the\n",
            "1   and\n",
            "2     a\n",
            "3    of\n",
            "4    to\n"
          ]
        }
      ],
      "source": [
        "# Reading imdb.vocab using pandas\n",
        "# This file contains text tokens corresponding to feature indices in .feat files.\n",
        "vocab_file = '/content/aclImdb/imdb.vocab'\n",
        "df_vocab = pd.read_csv(vocab_file, sep='\\t', header=None, names=['token'])\n",
        "\n",
        "print(df_vocab.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1gyW2oXMOfjv"
      },
      "outputs": [],
      "source": [
        "# Read the reviews\n",
        "# Define the function to read reviews\n",
        "def read_reviews(directory):\n",
        "    \"\"\"\n",
        "    Read all the reviews from a given directory and label them as positive (1) or negative (0).\n",
        "\n",
        "    Parameters:\n",
        "    directory (str): The path to the directory containing the 'pos' and 'neg' subdirectories with review files.\n",
        "\n",
        "    Returns:\n",
        "    reviews (list): A list of review texts.\n",
        "    labels (list): A list of labels corresponding to the reviews (1 for positive, 0 for negative).\n",
        "    \"\"\"\n",
        "    reviews = []\n",
        "    labels = []\n",
        "\n",
        "    # Loop through both 'pos' and 'neg' subdirectories\n",
        "    for label in ['pos', 'neg']:\n",
        "        label_dir = os.path.join(directory, label)  # Construct the path to the subdirectory\n",
        "\n",
        "        # Loop through all files in the current subdirectory\n",
        "        for filename in os.listdir(label_dir):\n",
        "            if filename.endswith(\".txt\"):  # Check if the file is a text file\n",
        "                # Open and read the content of the text file\n",
        "                with open(os.path.join(label_dir, filename), 'r', encoding='utf-8') as file:\n",
        "                    reviews.append(file.read())  # Append the review text to the reviews list\n",
        "\n",
        "                # Append the corresponding label (1 for positive, 0 for negative)\n",
        "                labels.append(1 if label == 'pos' else 0)\n",
        "\n",
        "    return reviews, labels  # Return the lists of reviews and labels\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H7TmECK8O8Yf"
      },
      "outputs": [],
      "source": [
        "# Read reviews from train and test directories\n",
        "train_dir = '/content/aclImdb/train'  # Path to the training dataset\n",
        "test_dir = '/content/aclImdb/test'    # Path to the test dataset\n",
        "\n",
        "# Read the reviews and labels from the training dataset\n",
        "train_reviews, train_labels = read_reviews(train_dir)\n",
        "\n",
        "# Read the reviews and labels from the test dataset\n",
        "test_reviews, test_labels = read_reviews(test_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fc1z840LO8gx",
        "outputId": "00655837-bac6-4573-e52b-2875316a3416"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                              review  label\n",
            "0  \"Mechenosets\" is one of the most beautiful rom...      1\n",
            "1  The film begins with Vincent Price about to be...      1\n",
            "2  I agree with \"Jerry.\" It's a very underrated s...      1\n",
            "3  \"Dô desu ka den\" is the first colored movie of...      1\n",
            "4  Anyone who finds this film boring is a hopeles...      1\n",
            "                                              review  label\n",
            "0  This indie film is worth a look because of the...      1\n",
            "1  Fast Times it ain't. But check this movie out,...      1\n",
            "2  Great period piece that shows how attitudes ha...      1\n",
            "3  I'm giving this film 9 out of 10 only because ...      1\n",
            "4  This has one of the more unusual plots I've se...      1\n"
          ]
        }
      ],
      "source": [
        "# Convert the reviews and labels into DataFrames\n",
        "# Create a DataFrame for the training dataset\n",
        "train_df = pd.DataFrame({'review': train_reviews, 'label': train_labels})\n",
        "\n",
        "# Create a DataFrame for the test dataset\n",
        "test_df = pd.DataFrame({'review': test_reviews, 'label': test_labels})\n",
        "\n",
        "# Display the first few rows of the DataFrames\n",
        "print(train_df.head())\n",
        "print(test_df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "XSKQ-prRRCq3",
        "outputId": "cb1d76a3-d018-4da5-b9a2-1061c25fad26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.10/dist-packages (1.9.3)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.10/dist-packages (from wordcloud) (1.25.2)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from wordcloud) (9.4.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from wordcloud) (3.7.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.4.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2024.5.15)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.4)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (4.53.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (24.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.16.0)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "!pip install wordcloud nltk\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "from nltk.corpus import stopwords\n",
        "import nltk\n",
        "\n",
        "# Read reviews from the training directory\n",
        "train_dir = '/content/aclImdb/train'\n",
        "train_reviews, train_labels = read_reviews(train_dir)\n",
        "\n",
        "# Preprocess the text data\n",
        "# Concatenate all reviews into a single text string\n",
        "all_train_reviews = ' '.join(train_reviews)\n",
        "\n",
        "# Download stopwords if not already downloaded\n",
        "nltk.download('stopwords')\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "# Generate the word cloud\n",
        "wordcloud = WordCloud(stopwords=stop_words, background_color='white', max_words=100, width=800, height=400).generate(all_train_reviews)\n",
        "\n",
        "# Plot the word cloud\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "E5Il2J5FSVNR",
        "outputId": "70e76ed1-e678-4498-8e22-17d31f5089ae"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Count the labels\n",
        "positive_count = train_labels.count(1)\n",
        "negative_count = train_labels.count(0)\n",
        "\n",
        "# Plot the bar chart\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.bar(['Positive', 'Negative'], [positive_count, negative_count], color=['blue', 'red'])\n",
        "plt.xlabel('Review Sentiment')\n",
        "plt.ylabel('Number of Reviews')\n",
        "plt.title('Number of Positive and Negative Reviews in the Training Set')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O76hsMqMEWQP"
      },
      "source": [
        "\n",
        "**Preprocessing**\n",
        "\n",
        "1.   Lower Case: Convert all text to lower case to ensure uniformity.\n",
        "2.   Non-Alpha Numeric Removal: Remove characters that are not letters or numbers to clean the text.\n",
        "3.  Tokenisation: Split text into individual words or tokens.\n",
        "Stopword Removal: Remove common words that do not add significant meaning to the text (e.g., \"and\", \"the\").\n",
        "4.  Lemmatisation: Reduce words to their base or root form (e.g., \"running\" to \"run\") to standardize words.\n",
        "5.  Stemming: Reduce words to their root form by removing suffixes (e.g., \"running\" to \"run\") to simplify text."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJcIdMDuEL8S"
      },
      "source": [
        "### **1. Removing punctuations like., ! $( ) * % @**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwowcUECEMkA",
        "outputId": "e7d4e0c3-e41c-4b36-9f23-87144b561c61"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                              Review  Label\n",
            "0  Mechenosets is one of the most beautiful roman...      1\n",
            "1  The film begins with Vincent Price about to be...      1\n",
            "2  I agree with Jerry Its a very underrated space...      1\n",
            "3  D desu ka den is the first colored movie of Ma...      1\n",
            "4  Anyone who finds this film boring is a hopeles...      1\n"
          ]
        }
      ],
      "source": [
        "# Define the cleaning function\n",
        "import re\n",
        "\n",
        "def clean_text(text):\n",
        "    \"\"\"\n",
        "    Clean the text by removing URLs, punctuation, and non-alphanumeric characters.\n",
        "\n",
        "    Parameters:\n",
        "    text (str): The original text.\n",
        "\n",
        "    Returns:\n",
        "    str: The cleaned text.\n",
        "    \"\"\"\n",
        "    # Remove URLs\n",
        "    text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text, flags=re.MULTILINE)\n",
        "    # Remove punctuation\n",
        "    text = re.sub(r'[^\\w\\s]', '', text)\n",
        "    # Remove non-alphanumeric characters\n",
        "    text = re.sub(r'[^a-zA-Z0-9\\s]', '', text)\n",
        "    # Remove extra spaces\n",
        "    text = re.sub(r'\\s+', ' ', text).strip()\n",
        "    return text\n",
        "\n",
        "# Apply the cleaning function to the train reviews\n",
        "cleaned_train_reviews = [clean_text(review) for review in train_reviews]\n",
        "\n",
        "# Create a DataFrame with the cleaned reviews and labels\n",
        "df_train = pd.DataFrame({'Review': cleaned_train_reviews, 'Label': train_labels})\n",
        "\n",
        "# Display the first few rows of the DataFrame\n",
        "print(df_train.head())\n",
        "\n",
        "# Optionally save the cleaned data to a CSV file\n",
        "# df.to_csv('/content/df_train.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srxsXZvfEMso",
        "outputId": "ebc18bd3-24a1-46fc-aa0e-32acd41ee1d8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                              Review  Label\n",
            "0  This indie film is worth a look because of the...      1\n",
            "1  Fast Times it aint But check this movie out it...      1\n",
            "2  Great period piece that shows how attitudes ha...      1\n",
            "3  Im giving this film 9 out of 10 only because t...      1\n",
            "4  This has one of the more unusual plots Ive see...      1\n"
          ]
        }
      ],
      "source": [
        "# Apply the cleaning function to the test reviews\n",
        "cleaned_test_reviews = [clean_text(review) for review in test_reviews]\n",
        "\n",
        "# Create a DataFrame with the cleaned reviews and labels\n",
        "df_test = pd.DataFrame({'Review': cleaned_test_reviews, 'Label': test_labels})\n",
        "\n",
        "# Display the first few rows of the DataFrame\n",
        "print(df_test.head())\n",
        "\n",
        "# Optionally save the cleaned data to a CSV file\n",
        "# df.to_csv('/content/cleaned_test_reviews.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yLUcM9_LU4cP"
      },
      "source": [
        "#### **Remove Stop Words**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RaNL2jbmU41X",
        "outputId": "bd89116c-05bd-4992-c2d6-453ab37d93a5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "# prompt: Using dataframe df: we need to remove stops words\n",
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "df_train['Review'] = df_train['Review'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)]))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OZH3_kndU45B"
      },
      "outputs": [],
      "source": [
        "df_test['Review'] = df_test['Review'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlfRk3JhXcpV",
        "outputId": "a0118db6-f9cb-4a70-a85f-2e4c1a206393"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 25000,\n  \"fields\": [\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24903,\n        \"samples\": [\n          \"This worst show Buntch grown acting like kids humor nothing Even Sesame Street better humor adult friends Friends may worst thing Ive ever seen television Ive sitting front tube observing Friends simply stack well contemporary series It lacks smartness Seinfeld wonderful selfridicule pomposity hallmark Frasier The characters Friends seem designed make repellant dullards This incestuous group neighbors makes flesh crawlbr br The unintelligent show completely without edge sort The characters caricatures caricatures writing sophomoric though intentionally It might interesting observe writing session since writers may slave aim lower capabilities confuse loyal friends Friends\",\n          \"I lost faith Sooraj R Barjatya movie Main Prem Ki Deewani hoon year back I saw promos Vivah looked good But I didnt want waste hard earned money watching cinema When film first came DVD I rented watched I loved movie took back words Sooraj I finished watching yesterday time I thought I review movie Sooraj R BarjatyaGot right time okay I huge fan Hum App Ke Hai Kaun But I always loved Manie Pyar kiya Manie Pyar kiya I think Vivah Barjatyas best work I hardly ever cry movie movie made feel like crying If ever love many moments touch movie movie sweet falling love view much underrated moviebr br The story movie might call desi old times seemed modern two couples getting arrange marriage aware old tradition Its done present times lots people dont believe arrange marriage I The journey engagement wedding always special movie shows clearly When Prem meets Poonam first time show thats reality parents saying thats got married showed way real yes people way Prem Poonam meet movie marriages happen It sweet feel nervous yet excited song Do Ajnabe shows well Getting back story yes journey soon get glued Prem Poonam Shahid Kapoor Amrita Rao families A twist occurs movie really good last 30mins reaching tissue boxbr br What makes film amazing chemistry Prem Poonam fall sweet Simple boy Simple Girl first meet song Do Anjane Ajnabe Its sweet watch She hardly says anything Prem talking honest past girl liked smoking Then leads family trip thats really fall It makes want watch couple watch sweet moments Another factor Poonam chichi really mean feel sorry Poonam treated bad makes want see happy finally finds happiness start feeling happy movie basically makes fall love Poonam Prem When finds happiness Prem want stay happy also hope nothing goes wrong character shown sweet simple girl Which brings performances Amrita Roa Poonam amazing movie best work till date fall love innocent character root find happiness Shahid Kapoor Prem amazing Poonam support film happiness movie together share amazing chemistry I never seen cuter couple since SRK Kajol If Ishq Vishk didnt touch telling cute together surely Mujhe Haq hai song amazing chemistry show Scenes touched Prem takes Poonam room shows thats staying opens moment sweet Again ever love someone much scenes defiantly connect The film sweetest thing see everbr br The direction spot good movie basically something pull stop believe hours seen fake camera filing To film pulled three hours I felt really connected movie The songs truly like seen movie songs placed situation I saw movie I playing songs non stop The music amazing story simply amazing I ask forbr br What I finally say rarely get movie makes us feel good movie seen make feel really good make want better person Its basically sweetest journey ever basically showing journey engagement marriage many people say bestest part life Well movie actually shows way people actually say Why people actually say journey amazing Watch movie find journey amazing\",\n          \"This movie one worst comedy movies ever seen I hate Napoleon Dynamite ripoffs Just face people dumb humor mastered already Make something new All new comedies horrible And coming SNL Andy Samberg ready lead role yet I hope bounce back awful movie And Will Arnetts character plain bad Hey Will read script The plot truly worst ever written Now tell weird movie Rod Kimbles step dad Frank dying family needs 50000 pay heart surgery Rod planning huge jump raise money Frank Only Rod beat Frank fight prove manliness Yes thats movie tell would u spend 700 see piece crapbr br 310 horriblebr br adam\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_train"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-070f979a-2004-483a-81b8-f578dee03d5f\" class=\"colab-df-container\">\n",
              "    <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>Review</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mechenosets one beautiful romantic movies Ive ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The film begins Vincent Price begin performanc...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I agree Jerry Its underrated space movie cours...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>D desu ka den first colored movie Master Akira...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Anyone finds film boring hopeless bonehead sti...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-070f979a-2004-483a-81b8-f578dee03d5f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-070f979a-2004-483a-81b8-f578dee03d5f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-070f979a-2004-483a-81b8-f578dee03d5f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-030f936b-0476-4306-ab61-2c05c3f7d48e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-030f936b-0476-4306-ab61-2c05c3f7d48e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-030f936b-0476-4306-ab61-2c05c3f7d48e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                              Review  Label\n",
              "0  Mechenosets one beautiful romantic movies Ive ...      1\n",
              "1  The film begins Vincent Price begin performanc...      1\n",
              "2  I agree Jerry Its underrated space movie cours...      1\n",
              "3  D desu ka den first colored movie Master Akira...      1\n",
              "4  Anyone finds film boring hopeless bonehead sti...      1"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Mczhx_CXbHb"
      },
      "source": [
        "#### **Lammatization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "VfPTkY3XU49F",
        "outputId": "4dc88423-66bc-44af-e052-dd6a1824ad48"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 25000,\n  \"fields\": [\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24903,\n        \"samples\": [\n          \"This worst show Buntch grown acting like kid humor nothing Even Sesame Street better humor adult friend Friends may worst thing Ive ever seen television Ive sitting front tube observing Friends simply stack well contemporary series It lack smartness Seinfeld wonderful selfridicule pomposity hallmark Frasier The character Friends seem designed make repellant dullard This incestuous group neighbor make flesh crawlbr br The unintelligent show completely without edge sort The character caricature caricature writing sophomoric though intentionally It might interesting observe writing session since writer may slave aim lower capability confuse loyal friend Friends\",\n          \"I lost faith Sooraj R Barjatya movie Main Prem Ki Deewani hoon year back I saw promos Vivah looked good But I didnt want waste hard earned money watching cinema When film first came DVD I rented watched I loved movie took back word Sooraj I finished watching yesterday time I thought I review movie Sooraj R BarjatyaGot right time okay I huge fan Hum App Ke Hai Kaun But I always loved Manie Pyar kiya Manie Pyar kiya I think Vivah Barjatyas best work I hardly ever cry movie movie made feel like cry If ever love many moment touch movie movie sweet falling love view much underrated moviebr br The story movie might call desi old time seemed modern two couple getting arrange marriage aware old tradition Its done present time lot people dont believe arrange marriage I The journey engagement wedding always special movie show clearly When Prem meet Poonam first time show thats reality parent saying thats got married showed way real yes people way Prem Poonam meet movie marriage happen It sweet feel nervous yet excited song Do Ajnabe show well Getting back story yes journey soon get glued Prem Poonam Shahid Kapoor Amrita Rao family A twist occurs movie really good last 30mins reaching tissue boxbr br What make film amazing chemistry Prem Poonam fall sweet Simple boy Simple Girl first meet song Do Anjane Ajnabe Its sweet watch She hardly say anything Prem talking honest past girl liked smoking Then lead family trip thats really fall It make want watch couple watch sweet moment Another factor Poonam chichi really mean feel sorry Poonam treated bad make want see happy finally find happiness start feeling happy movie basically make fall love Poonam Prem When find happiness Prem want stay happy also hope nothing go wrong character shown sweet simple girl Which brings performance Amrita Roa Poonam amazing movie best work till date fall love innocent character root find happiness Shahid Kapoor Prem amazing Poonam support film happiness movie together share amazing chemistry I never seen cuter couple since SRK Kajol If Ishq Vishk didnt touch telling cute together surely Mujhe Haq hai song amazing chemistry show Scenes touched Prem take Poonam room show thats staying open moment sweet Again ever love someone much scene defiantly connect The film sweetest thing see everbr br The direction spot good movie basically something pull stop believe hour seen fake camera filing To film pulled three hour I felt really connected movie The song truly like seen movie song placed situation I saw movie I playing song non stop The music amazing story simply amazing I ask forbr br What I finally say rarely get movie make u feel good movie seen make feel really good make want better person Its basically sweetest journey ever basically showing journey engagement marriage many people say bestest part life Well movie actually show way people actually say Why people actually say journey amazing Watch movie find journey amazing\",\n          \"This movie one worst comedy movie ever seen I hate Napoleon Dynamite ripoffs Just face people dumb humor mastered already Make something new All new comedy horrible And coming SNL Andy Samberg ready lead role yet I hope bounce back awful movie And Will Arnetts character plain bad Hey Will read script The plot truly worst ever written Now tell weird movie Rod Kimbles step dad Frank dying family need 50000 pay heart surgery Rod planning huge jump raise money Frank Only Rod beat Frank fight prove manliness Yes thats movie tell would u spend 700 see piece crapbr br 310 horriblebr br adam\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_train"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-59da06c3-ac9f-4551-a419-596a1827d069\" class=\"colab-df-container\">\n",
              "    <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>Review</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mechenosets one beautiful romantic movie Ive e...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The film begin Vincent Price begin performance...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I agree Jerry Its underrated space movie cours...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>D desu ka den first colored movie Master Akira...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Anyone find film boring hopeless bonehead stic...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>The sitcom revolved around girl must learn res...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>This movie supposed made fascinating setting c...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Being fan first Lion King I definitely looking...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>In sendup horror film 50 cold war paranoia Rea...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Home Room came total surprise Not clue paid bi...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59da06c3-ac9f-4551-a419-596a1827d069')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-59da06c3-ac9f-4551-a419-596a1827d069 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-59da06c3-ac9f-4551-a419-596a1827d069');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-20f348b0-c4e3-440b-9c3d-1a986bb05975\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20f348b0-c4e3-440b-9c3d-1a986bb05975')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-20f348b0-c4e3-440b-9c3d-1a986bb05975 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                              Review  Label\n",
              "0  Mechenosets one beautiful romantic movie Ive e...      1\n",
              "1  The film begin Vincent Price begin performance...      1\n",
              "2  I agree Jerry Its underrated space movie cours...      1\n",
              "3  D desu ka den first colored movie Master Akira...      1\n",
              "4  Anyone find film boring hopeless bonehead stic...      1\n",
              "5  The sitcom revolved around girl must learn res...      1\n",
              "6  This movie supposed made fascinating setting c...      1\n",
              "7  Being fan first Lion King I definitely looking...      1\n",
              "8  In sendup horror film 50 cold war paranoia Rea...      1\n",
              "9  Home Room came total surprise Not clue paid bi...      1"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "\n",
        "df_train['Review'] = df_train['Review'].apply(lambda x: \" \".join([lemmatizer.lemmatize(word) for word in x.split()]))\n",
        "\n",
        "df_train.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "qWpVXDw6ZFDD",
        "outputId": "9f93ce56-456e-4a12-a529-683103f00e8a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 25000,\n  \"fields\": [\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24798,\n        \"samples\": [\n          \"Im surprised amount negative review film If dont like movie silly overthetop mob story simply reading much This film classic tale mob wife trying escape life trouble follow Michelle Pfeiffer terrificly 80 jersey uncertain uncomfortable mob wife Matthew Modine anal retentivelike mob tracking cop fall The plot mostly predictable cutesy Mercedes Ruel steal show Queen Mob Wives If arent looking something dynamic complex movie absolutely entertaining 80 cult classic You wont able stop watching start\",\n          \"love show hate go season finale feel like forever season start followed show since pilot way threw rusty start college see Casey sister he dating Dana love show cant wait Monday night Also glad cappie Casey finally got back together driving crazy As Evan rebecca hope get back together feel like finale looked main part cast SHOCKED Casey CartwrightSpencer Grammaris Kelsey Grammars daughter surprise much thing thats huge SHOCKER born year born baby brother birth date year watching whole entire time clue\",\n          \"Tycus one worst film direct video film I see everbr br I amazing product appearing cinemaBad Visual effect The start dreadfulBad actor I dont understand Why appear film great Dennis Hopperand Bad screen Without senseMy alternativeArmaggedon Twister SENTENCE FOR TYCUSDo watch film\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_test"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-27ff5f8e-6220-4055-8597-4a2601108a8d\" class=\"colab-df-container\">\n",
              "    <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>Review</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>This indie film worth look enormous talent cre...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fast Times aint But check movie heart Pour dri...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Great period piece show attitude changed 40 ye...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Im giving film 9 10 arent enough specific scie...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>This one unusual plot Ive seen horror film bas...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Many earlier comment right money well muchbr b...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>I saw FAREWELL TO HARRY Plaza Theatre New York...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10 respected director shot short film operatic...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>The creator Donnie Darko brings twilight zone ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Daisy Movie Review By James Mudge From beyondh...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27ff5f8e-6220-4055-8597-4a2601108a8d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-27ff5f8e-6220-4055-8597-4a2601108a8d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-27ff5f8e-6220-4055-8597-4a2601108a8d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fa4c96f6-2195-4f46-b855-84673a2ef829\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fa4c96f6-2195-4f46-b855-84673a2ef829')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fa4c96f6-2195-4f46-b855-84673a2ef829 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                              Review  Label\n",
              "0  This indie film worth look enormous talent cre...      1\n",
              "1  Fast Times aint But check movie heart Pour dri...      1\n",
              "2  Great period piece show attitude changed 40 ye...      1\n",
              "3  Im giving film 9 10 arent enough specific scie...      1\n",
              "4  This one unusual plot Ive seen horror film bas...      1\n",
              "5  Many earlier comment right money well muchbr b...      1\n",
              "6  I saw FAREWELL TO HARRY Plaza Theatre New York...      1\n",
              "7  10 respected director shot short film operatic...      1\n",
              "8  The creator Donnie Darko brings twilight zone ...      1\n",
              "9  Daisy Movie Review By James Mudge From beyondh...      1"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_test['Review'] = df_test['Review'].apply(lambda x: \" \".join([lemmatizer.lemmatize(word) for word in x.split()]))\n",
        "\n",
        "df_test.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpdF2pCiYBPT"
      },
      "source": [
        "#### **Stemming**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gd_1XotUYBex",
        "outputId": "269ed8d9-ad01-45f5-c740-647b3a6b90af"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 25000,\n  \"fields\": [\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24902,\n        \"samples\": [\n          \"first i want clarifi averag user inabl appreci imagin appal what make show uniqu hyperr creat you dont need know ned bring peopl back dead minut where wonder childhood whimsic tale gone much like a wrinkl timebr br i say refreshingli origin polar opposit mass layitallout televis leaf room imagin wonderbr br it nice add bit escap televis experiencebr br the hyperr favorit aspect show the 1950sesqu set innoc rare charact sceneri physic set meant taken pure realitybr br thi show mask morbid natur death other embrac while entertain televis show taken back shelf seri it truli restor sens curios imagin wonder televisionbr br push daisi quickli made top list\",\n          \"a woman borough boy world alon they alon boy gift curs one packag capabl withdraw sword arm there alway wound wrist caus gift wound deadliest weapon insid bodi first kill constantli drunk stepfath hurt mom everi time then grow decid find real father just simpl time superhero reach justicebut societi decid justic necessari danger inde right caus like hollywood movi charact tri kill anyon sasha main hero act artem tkachenko kill person opinion deserv die get blame author run in runaway author mafia meet girl act chulpan hamatova fall love everyth els watchednot told be awar film feel emot action thi film full pain main charact full vision life\",\n          \"a young man name court love everyon hi pain bloodi death bring everyon closer you find symbol allus throughout movi whether predict irrespect ecclesiast belief move stori full milieu sensualitybr br one thing someon mention fate quick didnt seem plausibl but element set subtli note mom say bring lunch field note hold steer wheel glove he sweati oper danger equip to day tractor pretti danger\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_train"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-b50e34b0-26b4-4d83-a2a0-59acbf07089a\" class=\"colab-df-container\">\n",
              "    <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>Review</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>mechenoset one beauti romant movi ive ever see...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the film begin vincent price begin perform mag...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>i agre jerri it underr space movi cours mani g...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>d desu ka den first color movi master akira ku...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>anyon find film bore hopeless bonehead stick c...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b50e34b0-26b4-4d83-a2a0-59acbf07089a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b50e34b0-26b4-4d83-a2a0-59acbf07089a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b50e34b0-26b4-4d83-a2a0-59acbf07089a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7e75185b-fa12-42aa-a379-ac6995013576\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7e75185b-fa12-42aa-a379-ac6995013576')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7e75185b-fa12-42aa-a379-ac6995013576 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                              Review  Label\n",
              "0  mechenoset one beauti romant movi ive ever see...      1\n",
              "1  the film begin vincent price begin perform mag...      1\n",
              "2  i agre jerri it underr space movi cours mani g...      1\n",
              "3  d desu ka den first color movi master akira ku...      1\n",
              "4  anyon find film bore hopeless bonehead stick c...      1"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from nltk.stem import PorterStemmer\n",
        "stemmer = PorterStemmer()\n",
        "\n",
        "df_train['Review'] = df_train['Review'].apply(lambda x: ' '.join([stemmer.stem(word) for word in x.split()]))\n",
        "df_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wgrjHTqKYrda",
        "outputId": "70198e76-df90-48d9-c09d-341462c897d9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 25000,\n  \"fields\": [\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24798,\n        \"samples\": [\n          \"im surpris amount neg review film if dont like movi silli overthetop mob stori simpli read much thi film classic tale mob wife tri escap life troubl follow michel pfeiffer terrificli 80 jersey uncertain uncomfort mob wife matthew modin anal retentivelik mob track cop fall the plot mostli predict cutesi merced ruel steal show queen mob wive if arent look someth dynam complex movi absolut entertain 80 cult classic you wont abl stop watch start\",\n          \"love show hate go season final feel like forev season start follow show sinc pilot way threw rusti start colleg see casey sister he date dana love show cant wait monday night also glad cappi casey final got back togeth drive crazi as evan rebecca hope get back togeth feel like final look main part cast shock casey cartwrightspenc grammari kelsey grammar daughter surpris much thing that huge shocker born year born babi brother birth date year watch whole entir time clue\",\n          \"tycu one worst film direct video film i see everbr br i amaz product appear cinemabad visual effect the start dreadfulbad actor i dont understand whi appear film great denni hopperand bad screen without sensemi alternativearmaggedon twister sentenc for tycusdo watch film\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_test"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-95174500-9ca5-4b61-9994-a0d59e02c820\" class=\"colab-df-container\">\n",
              "    <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>Review</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>thi indi film worth look enorm talent creator ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>fast time aint but check movi heart pour drink...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>great period piec show attitud chang 40 year g...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>im give film 9 10 arent enough specif scientif...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>thi one unusu plot ive seen horror film base g...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-95174500-9ca5-4b61-9994-a0d59e02c820')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-95174500-9ca5-4b61-9994-a0d59e02c820 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-95174500-9ca5-4b61-9994-a0d59e02c820');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dc949acc-bbd7-4ced-a106-602242b51383\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc949acc-bbd7-4ced-a106-602242b51383')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dc949acc-bbd7-4ced-a106-602242b51383 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                              Review  Label\n",
              "0  thi indi film worth look enorm talent creator ...      1\n",
              "1  fast time aint but check movi heart pour drink...      1\n",
              "2  great period piec show attitud chang 40 year g...      1\n",
              "3  im give film 9 10 arent enough specif scientif...      1\n",
              "4  thi one unusu plot ive seen horror film base g...      1"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_test['Review'] = df_test['Review'].apply(lambda x: ' '.join([stemmer.stem(word) for word in x.split()]))\n",
        "df_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZp7F3zIZUfv"
      },
      "source": [
        "#### **Tokenization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHdkgDX5ZUup",
        "outputId": "f71be3ad-f4f6-4c1e-acfa-e6b08fb6b828"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0    [mechenoset, one, beauti, romant, movi, ive, e...\n",
              "1    [the, film, begin, vincent, price, begin, perf...\n",
              "2    [i, agre, jerri, it, underr, space, movi, cour...\n",
              "3    [d, desu, ka, den, first, color, movi, master,...\n",
              "4    [anyon, find, film, bore, hopeless, bonehead, ...\n",
              "Name: Tokenized_Review, dtype: object"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "nltk.download('punkt')\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "df_train['Tokenized_Review'] = df_train['Review'].apply(word_tokenize)\n",
        "df_train['Tokenized_Review'].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikmDhnxzZ_HL",
        "outputId": "b20e844a-3049-4d72-ea8b-ad32152f773b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    [thi, indi, film, worth, look, enorm, talent, ...\n",
              "1    [fast, time, aint, but, check, movi, heart, po...\n",
              "2    [great, period, piec, show, attitud, chang, 40...\n",
              "3    [im, give, film, 9, 10, arent, enough, specif,...\n",
              "4    [thi, one, unusu, plot, ive, seen, horror, fil...\n",
              "Name: Tokenized_Review, dtype: object"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_test['Tokenized_Review'] = df_test['Review'].apply(word_tokenize)\n",
        "df_test['Tokenized_Review'].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RG49OgPMaJH4"
      },
      "source": [
        "#### **Sentiment Analysis Using ML Models**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "jL0Rs9stkA-1",
        "outputId": "0179badb-3ba8-4f24-e035-fcbd5d6c819b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Logistic Regression...\n",
            "Logistic Regression Metrics:\n",
            "Accuracy: 0.8816\n",
            "Precision: 0.8760265936644506\n",
            "Recall: 0.8906560636182903\n",
            "F1-score: 0.8832807570977919\n",
            "ROC-AUC: 0.9508502306083019\n",
            "\n",
            "Training Multinomial Naive Bayes...\n",
            "Multinomial Naive Bayes Metrics:\n",
            "Accuracy: 0.8472\n",
            "Precision: 0.852881902458686\n",
            "Recall: 0.841351888667992\n",
            "F1-score: 0.8470776621297037\n",
            "ROC-AUC: 0.9254369957318463\n",
            "\n",
            "Training Random Forest...\n",
            "Random Forest Metrics:\n",
            "Accuracy: 0.8364\n",
            "Precision: 0.846751123825092\n",
            "Recall: 0.8238568588469185\n",
            "F1-score: 0.8351471180975415\n",
            "ROC-AUC: 0.9183489805633004\n",
            "\n",
            "Training Support Vector Machine...\n",
            "Support Vector Machine Metrics:\n",
            "Accuracy: 0.8854\n",
            "Precision: 0.8772338772338772\n",
            "Recall: 0.8978131212723658\n",
            "F1-score: 0.8874042051483592\n",
            "ROC-AUC: 0.9543976383149794\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABJkAAAPZCAYAAACyNfvJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC9vUlEQVR4nOzde3zP9f//8ft7s5MdnLaZwxhyTCanJYRMCynJWTkTpQNFFGZU6lNOSVFhq4iE0recEiWJcijK+UzMqQ3DZO/n7w+/vfK2YdvLGLtdL5f3pd6v1/P1fD1f79Me7u/X+/lyGGOMAAAAAAAAABvcbvYAAAAAAAAAcOsjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAgB3nrrbdUunRpubu7q2rVqjd7OMgGXbp0UVhY2M0eRq4yfPhwORyOmz0MAABue4RMAABcRWxsrBwOh3Xz9vZWuXLl1LdvX8XHx1/XfS1evFgDBw5UnTp1NG3aNL3++uvXtf/cpkuXLnI4HAoICNDZs2fTrN++fbv1vL799tuZ7v/MmTMaPny4li9ffh1Gm71SUlI0bdo0NWjQQAULFpSXl5fCwsLUtWtX/fbbbzd7eAAA4DaR52YPAACAW8GIESNUqlQpnTt3Tj/99JPef/99ffvtt9q0aZPy5s17Xfbx/fffy83NTVOmTJGnp+d16TO3y5Mnj86cOaOvv/5abdq0cVk3ffp0eXt769y5c1nq+8yZM4qJiZEkNWjQIMPbffjhh3I6nVnaZ1acPXtWLVu21MKFC3Xffffp5ZdfVsGCBbVnzx59/vnniouL0759+1S8ePEbNqYbbciQIRo0aNDNHgYAALc9QiYAADKgSZMmqlGjhiSpR48eKlSokMaMGaOvvvpK7du3t9X3mTNnlDdvXh05ckQ+Pj7XLWAyxujcuXPy8fG5Lv3diry8vFSnTh199tlnaUKmGTNmqFmzZpozZ84NGUtSUpJ8fX3l4eFxQ/aXasCAAVq4cKHGjh2r559/3mVddHS0xo4de0PHcyOlPuZ58uRRnjyUvQAAZDd+LgcAQBbcf//9kqTdu3dbyz799FNVr15dPj4+KliwoNq1a6f9+/e7bNegQQNVrlxZa9eu1X333ae8efPq5ZdflsPh0LRp05SUlGT9hCs2NlaSdOHCBY0cOVJlypSxfub08ssvKzk52aXvsLAwPfTQQ1q0aJFq1KghHx8fTZ48WcuXL5fD4dDnn3+umJgYFStWTP7+/mrVqpUSExOVnJys559/XsHBwfLz81PXrl3T9D1t2jTdf//9Cg4OlpeXlypVqqT3338/zeOSOoaffvpJtWrVkre3t0qXLq2PP/44TduEhAT169dPYWFh8vLyUvHixdWpUycdO3bMapOcnKzo6Gjdcccd8vLyUmhoqAYOHJhmfFfToUMHLViwQAkJCdayX3/9Vdu3b1eHDh3S3SYhIUHPP/+8QkND5eXlpTvuuENvvvmmdQbSnj17FBQUJEmKiYmxnrPhw4dLuvhTPT8/P+3cuVNNmzaVv7+/OnbsaK27fE4mp9Op8ePH66677pK3t7eCgoL04IMPuvyUbcmSJapbt67y588vPz8/lS9fXi+//PJVj/3AgQOaPHmyGjdunCZgkiR3d3e9+OKLLmcxrV+/Xk2aNFFAQID8/PzUqFEj/fLLLy7bpf6M9KefftKzzz6roKAg5c+fX08++aTOnz+vhIQEderUSQUKFFCBAgU0cOBAGWOs7ffs2WP9THHs2LEqWbKkfHx8VL9+fW3atMllX3/88Ye6dOmi0qVLy9vbWyEhIerWrZuOHz/u0i513qW//vpLHTp0UIECBVS3bl2XdZfKyON55MgRde/eXYULF5a3t7fCw8MVFxfn0ubSY/nggw+s92nNmjX166+/XvX5AQDgdsNXOgAAZMHOnTslSYUKFZIkvfbaaxo6dKjatGmjHj166OjRo5owYYLuu+8+rV+/Xvnz57e2PX78uJo0aaJ27drp8ccfV+HChVWjRg198MEHWrNmjT766CNJ0r333ivp4plTcXFxatWqlV544QWtXr1ao0aN0ubNmzVv3jyXcW3dulXt27fXk08+qZ49e6p8+fLWulGjRsnHx0eDBg3Sjh07NGHCBHl4eMjNzU3//POPhg8frl9++UWxsbEqVaqUhg0bZm37/vvv684779TDDz+sPHny6Ouvv9ZTTz0lp9Opp59+2mUMO3bsUKtWrdS9e3d17txZU6dOVZcuXVS9enXdeeedkqTTp0+rXr162rx5s7p166Zq1arp2LFjmj9/vg4cOKDAwEA5nU49/PDD+umnn9SrVy9VrFhRGzdu1NixY7Vt2zZ9+eWXGXquWrZsqd69e2vu3Lnq1q2bpItnMVWoUEHVqlVL0/7MmTOqX7++Dh48qCeffFIlSpTQzz//rMGDB+vQoUMaN26cgoKC9P7776tPnz569NFH1bJlS0lSlSpVrH4uXLigqKgo1a1bV2+//fZVf1bZvXt3xcbGqkmTJurRo4cuXLigFStW6JdfflGNGjX0559/6qGHHlKVKlU0YsQIeXl5aceOHVq5cuVVj33BggW6cOGCnnjiiQw9Vn/++afq1aungIAADRw4UB4eHpo8ebIaNGigH374QRERES7tn3nmGYWEhCgmJka//PKLPvjgA+XPn18///yzSpQooddff13ffvut3nrrLVWuXFmdOnVy2f7jjz/WqVOn9PTTT+vcuXMaP3687r//fm3cuFGFCxeWdDEM2rVrl7p27aqQkBD9+eef+uCDD/Tnn3/ql19+SRMetW7dWmXLltXrr7/uEmxdfpzXejzPnj2rBg0aaMeOHerbt69KlSql2bNnq0uXLkpISNBzzz3n0ueMGTN06tQpPfnkk3I4HPrf//6nli1bateuXTf87DUAAG4aAwAArmjatGlGkvnuu+/M0aNHzf79+83MmTNNoUKFjI+Pjzlw4IDZs2ePcXd3N6+99prLths3bjR58uRxWV6/fn0jyUyaNCnNvjp37mx8fX1dlm3YsMFIMj169HBZ/uKLLxpJ5vvvv7eWlSxZ0kgyCxcudGm7bNkyI8lUrlzZnD9/3lrevn1743A4TJMmTVza165d25QsWdJl2ZkzZ9KMNyoqypQuXdplWeoYfvzxR2vZkSNHjJeXl3nhhResZcOGDTOSzNy5c9P063Q6jTHGfPLJJ8bNzc2sWLHCZf2kSZOMJLNy5co0217q0sezVatWplGjRsYYY1JSUkxISIiJiYkxu3fvNpLMW2+9ZW03cuRI4+vra7Zt2+bS36BBg4y7u7vZt2+fMcaYo0ePGkkmOjo63X1LMoMGDUp33aWP7/fff28kmWefffaKj8XYsWONJHP06NGrHvPl+vXrZySZ9evXZ6h9ixYtjKenp9m5c6e17O+//zb+/v7mvvvus5alvi+ioqKsMRpz8bXjcDhM7969rWUXLlwwxYsXN/Xr17eWpT7uqe+hVKtXrzaSTL9+/axl6b32PvvsszSvs+joaCPJtG/fPk371HWpMvJ4jhs3zkgyn376qbXs/Pnzpnbt2sbPz8+cPHnS5VgKFSpkTpw4YbX96quvjCTz9ddfX3EfAADcbvi5HAAAGRAZGamgoCCFhoaqXbt28vPz07x581SsWDHNnTtXTqdTbdq00bFjx6xbSEiIypYtq2XLlrn05eXlpa5du2Zov99++60kqX///i7LX3jhBUnSN99847K8VKlSioqKSrevTp06uZxRERERIWOMdXbPpcv379+vCxcuWMsundcpMTFRx44dU/369bVr1y4lJia6bF+pUiXVq1fPuh8UFKTy5ctr165d1rI5c+YoPDxcjz76aJpxpp6ZMnv2bFWsWFEVKlRweVxTf6p4+eN6NR06dNDy5ct1+PBhff/99zp8+PAVfyo3e/Zs1atXTwUKFHDZb2RkpFJSUvTjjz9meL99+vS5Zps5c+bI4XAoOjo6zbrUxyL1TLivvvoqU5OGnzx5UpLk7+9/zbYpKSlavHixWrRoodKlS1vLixQpog4dOuinn36y+kvVvXt3lzOJUl9T3bt3t5a5u7urRo0aLs9/qhYtWqhYsWLW/Vq1aikiIsJ63Uuur71z587p2LFjuueeeyRJ69atS9Nn7969r3msGXk8v/32W4WEhLjMuebh4aFnn31Wp0+f1g8//ODSvm3btipQoIB1P/U9kN5xAwBwu+LncgAAZMDEiRNVrlw55cmTR4ULF1b58uXl5nbxu5rt27fLGKOyZcumu+3lP5UpVqxYhif33rt3r9zc3HTHHXe4LA8JCVH+/Pm1d+9el+WlSpW6Yl8lSpRwuZ8vXz5JUmhoaJrlTqdTiYmJ1s8BV65cqejoaK1atUpnzpxxaZ+YmGj1ld5+JKlAgQL6559/rPs7d+7UY489dsWxShcf182bN1tzH13uyJEjV93+UqnzIs2aNUsbNmxQzZo1dccdd2jPnj3p7vePP/6wvd88efJk6IptO3fuVNGiRVWwYMErtmnbtq0++ugj9ejRQ4MGDVKjRo3UsmVLtWrVynodpicgIECSdOrUqWuO4+jRozpz5ozLTyxTVaxYUU6nU/v377d+8ihl7jV16fOfKr33TLly5fT5559b90+cOKGYmBjNnDkzzWN/ecApXf09kCojj+fevXtVtmzZNI9vxYoVrfWXuvyxSA2c0jtuAABuV4RMAABkQK1atayry13O6XTK4XBowYIFcnd3T7Pez8/P5X5WrvZ2+bwzV3K1vtMb29WWm/8/n83OnTvVqFEjVahQQWPGjFFoaKg8PT317bffauzYsWnOBLlWfxnldDp11113acyYMemuvzzIuBovLy+1bNlScXFx2rVrlzVB95X227hxYw0cODDd9eXKlcvwPq8WAGWGj4+PfvzxRy1btkzffPONFi5cqFmzZun+++/X4sWLr/iYV6hQQZK0ceNGVa1a9bqM5VKZeU1l9vlP1aZNG/38888aMGCAqlatKj8/PzmdTj344IPpnoWUkfdXVh/Pq7ler3sAAG5lhEwAANhUpkwZGWNUqlSpDAcQGVWyZEk5nU5t377dOoNCkuLj45WQkKCSJUte1/2l5+uvv1ZycrLmz5/vcrZGZn6udrkyZcqkuYpYem1+//13NWrUKMMh29V06NBBU6dOlZubm9q1a3fV/Z4+fVqRkZFX7e96jCl1f4sWLdKJEyeuejaTm5ubGjVqpEaNGmnMmDF6/fXX9corr2jZsmVXHGuTJk3k7u6uTz/99JqTfwcFBSlv3rzaunVrmnVbtmyRm5tbpoK9jNi+fXuaZdu2bbOuvvfPP/9o6dKliomJcZmIPr3tMutaj2fJkiX1xx9/yOl0uoSFW7ZskaQb8t4DAOBWw5xMAADY1LJlS7m7uysmJibNWQvGmDSXWs+Mpk2bSpLGjRvnsjz17J5mzZplue+MSj1D49JjS0xM1LRp07Lc52OPPabff/89zdXxLt1PmzZtdPDgQX344Ydp2pw9e1ZJSUmZ2mfDhg01cuRIvfvuuwoJCbliuzZt2mjVqlVatGhRmnUJCQnWXFWpV4tLSEjI1Dgu99hjj8kYo5iYmDTrUh+LEydOpFmXemZScnLyFfsODQ1Vz549tXjxYk2YMCHNeqfTqdGjR+vAgQNyd3fXAw88oK+++srlZ4Tx8fGaMWOG6tata/387nr58ssvdfDgQev+mjVrtHr1ajVp0kRS+q89Ke37IbMy8ng2bdpUhw8f1qxZs6w2Fy5c0IQJE+Tn56f69evbGgMAALcjzmQCAMCmMmXK6NVXX9XgwYO1Z88etWjRQv7+/tq9e7fmzZunXr166cUXX8xS3+Hh4ercubM++OADJSQkqH79+lqzZo3i4uLUokULNWzY8DofTVoPPPCAPD091bx5cz355JM6ffq0PvzwQwUHB+vQoUNZ6nPAgAH64osv1Lp1a3Xr1k3Vq1fXiRMnNH/+fE2aNEnh4eF64okn9Pnnn6t3795atmyZ6tSpo5SUFG3ZskWff/65Fi1adMWfMKbHzc1NQ4YMydDY5s+fr4ceekhdunRR9erVlZSUpI0bN+qLL77Qnj17FBgYKB8fH1WqVEmzZs1SuXLlVLBgQVWuXFmVK1fO1GPRsGFDPfHEE3rnnXe0fft262dgK1asUMOGDdW3b1+NGDFCP/74o5o1a6aSJUvqyJEjeu+991S8eHHVrVv3qv2PHj1aO3fu1LPPPqu5c+fqoYceUoECBbRv3z7Nnj1bW7Zssc7sevXVV7VkyRLVrVtXTz31lPLkyaPJkycrOTlZ//vf/zJ1XBlxxx13qG7duurTp4+Sk5M1btw4FSpUyPqpYkBAgO677z7973//07///qtixYpp8eLF2r17t639ZuTx7NWrlyZPnqwuXbpo7dq1CgsL0xdffKGVK1dq3LhxGZpMHQCA3IaQCQCA62DQoEEqV66cxo4da52REhoaqgceeEAPP/ywrb4/+ugjlS5dWrGxsZo3b55CQkI0ePDgdK9Glh3Kly+vL774QkOGDNGLL76okJAQ9enTR0FBQWmuTJdRfn5+WrFihaKjozVv3jzFxcUpODhYjRo1sibLdnNz05dffqmxY8fq448/1rx585Q3b16VLl1azz333HX/aWKqvHnz6ocfftDrr7+u2bNn6+OPP1ZAQIDKlSunmJgYl0nOP/roIz3zzDPq16+fzp8/r+jo6EyHTJI0bdo0ValSRVOmTNGAAQOUL18+1ahRQ/fee68k6eGHH9aePXs0depUHTt2TIGBgapfv36a8VzpeBYsWKDY2FjFxcVp5MiROnPmjIoWLar7779f06dPt67wduedd2rFihUaPHiwRo0aJafTqYiICH366aeKiIjI9HFdS6dOneTm5qZx48bpyJEjqlWrlt59910VKVLEajNjxgw988wzmjhxoowxeuCBB7RgwQIVLVo0y/vNyOPp4+Oj5cuXa9CgQYqLi9PJkydVvnx5TZs2TV26dLF76AAA3JYchtkIAQAAcAPt2bNHpUqV0ltvvZXls/wAAEDOw5xMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA25mQCAAAAAACAbZzJBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAIBcqEuXLgoLC8vUNsuXL5fD4dDy5cuzZUwAbm2ETABsee+99+RwOBQREXGzhwIAAJDjxcbGyuFwWDdvb2+VK1dOffv2VXx8/M0eHgDY4jDGmJs9CAC3rjp16ujvv//Wnj17tH37dt1xxx03e0gAAAA5VmxsrLp27aoRI0aoVKlSOnfunH766Sd98sknKlmypDZt2qS8efPekLH8+++/cjqd8vLyyvA2TqdT58+fl6enp9zcOGcBgCs+FQBk2e7du/Xzzz9rzJgxCgoK0vTp02/2kNKVlJR0s4cAAADgokmTJnr88cfVo0cPxcbG6vnnn9fu3bv11Vdfpds+O+oZDw+PTAVMkuTm5iZvb28CJgDp4pMBQJZNnz5dBQoUULNmzdSqVat0Q6aEhAT169dPYWFh8vLyUvHixdWpUycdO3bManPu3DkNHz5c5cqVk7e3t4oUKaKWLVtq586dkq782/89e/bI4XAoNjbWWtalSxf5+flp586datq0qfz9/dWxY0dJ0ooVK9S6dWuVKFFCXl5eCg0NVb9+/XT27Nk0496yZYvatGmjoKAg+fj4qHz58nrllVckScuWLZPD4dC8efPSbDdjxgw5HA6tWrUq048nAADIve6//35JF7/Eu1o943Q6NW7cON15553y9vZW4cKF9eSTT+qff/5J0+eCBQtUv359+fv7KyAgQDVr1tSMGTOs9enNyTRz5kxVr17d2uauu+7S+PHjrfVXqstmz56t6tWry8fHR4GBgXr88cd18OBBlzapx3Xw4EG1aNFCfn5+CgoK0osvvqiUlBQ7Dx+AHIKQCUCWTZ8+XS1btpSnp6fat2+v7du369dff7XWnz59WvXq1dOECRP0wAMPaPz48erdu7e2bNmiAwcOSJJSUlL00EMPKSYmRtWrV9fo0aP13HPPKTExUZs2bcrSuC5cuKCoqCgFBwfr7bff1mOPPSbpYvFz5swZ9enTRxMmTFBUVJQmTJigTp06uWz/xx9/KCIiQt9//7169uyp8ePHq0WLFvr6668lSQ0aNFBoaGi6odr06dNVpkwZ1a5dO0tjBwAAuVPql2uFChWSdOV65sknn9SAAQNUp04djR8/Xl27dtX06dMVFRWlf//91+ovNjZWzZo104kTJzR48GC98cYbqlq1qhYuXHjFMSxZskTt27dXgQIF9Oabb+qNN95QgwYNtHLlyquOPTY2Vm3atJG7u7tGjRqlnj17au7cuapbt64SEhJc2qakpCgqKkqFChXS22+/rfr162v06NH64IMPsvKwAchpDABkwW+//WYkmSVLlhhjjHE6naZ48eLmueees9oMGzbMSDJz585Ns73T6TTGGDN16lQjyYwZM+aKbZYtW2YkmWXLlrms3717t5Fkpk2bZi3r3LmzkWQGDRqUpr8zZ86kWTZq1CjjcDjM3r17rWX33Xef8ff3d1l26XiMMWbw4MHGy8vLJCQkWMuOHDli8uTJY6Kjo9PsBwAAwBhjpk2bZiSZ7777zhw9etTs37/fzJw50xQqVMj4+PiYAwcOXLGeWbFihZFkpk+f7rJ84cKFLssTEhKMv7+/iYiIMGfPnnVpe2k907lzZ1OyZEnr/nPPPWcCAgLMhQsXrjj+y+uy8+fPm+DgYFO5cmWXff3f//2fkWSGDRvmsj9JZsSIES593n333aZ69epXedQA3Co4kwlAlkyfPl2FCxdWw4YNJUkOh0Nt27bVzJkzrdOd58yZo/DwcD366KNptnc4HFabwMBAPfPMM1dskxV9+vRJs8zHx8f6/6SkJB07dkz33nuvjDFav369JOno0aP68ccf1a1bN5UoUeKK4+nUqZOSk5P1xRdfWMtmzZqlCxcu6PHHH8/yuAEAQO4QGRmpoKAghYaGql27dvLz89O8efNUrFgxq83l9czs2bOVL18+NW7cWMeOHbNu1atXl5+fn5YtWybp4hlJp06d0qBBg+Tt7e3Sx9Xqq/z58yspKUlLlizJ8HH89ttvOnLkiJ566imXfTVr1kwVKlTQN998k2ab3r17u9yvV6+edu3aleF9Asi5CJkAZFpKSopmzpyphg0bavfu3dqxY4d27NihiIgIxcfHa+nSpZIunvZduXLlq/a1c+dOlS9fXnny5Llu48uTJ4+KFy+eZvm+ffvUpUsXFSxY0JoDoH79+pKkxMRESbIKnGuNu0KFCqpZs6bLT+amT5+ue+65hyvsAQCAa5o4caKWLFmiZcuW6a+//tKuXbsUFRVlrU+vntm+fbsSExMVHBysoKAgl9vp06d15MgRSf/99O5a9czlnnrqKZUrV05NmjRR8eLF1a1bt6v+vE6S9u7dK0kqX758mnUVKlSw1qfy9vZWUFCQy7ICBQqkO6cUgFvP9ftXHYBc4/vvv9ehQ4c0c+ZMzZw5M8366dOn64EHHrhu+7vSN25XmiDSy8srzRVPUlJS1LhxY504cUIvvfSSKlSoIF9fXx08eFBdunSR0+nM9Lg6deqk5557TgcOHFBycrJ++eUXvfvuu5nuBwAA5D61atVSjRo1rrg+vXrG6XQqODj4ilf0vTy8yazg4GBt2LBBixYt0oIFC7RgwQJNmzZNnTp1UlxcnK2+U7m7u1+XfgDkTIRMADJt+vTpCg4O1sSJE9Osmzt3rubNm6dJkyapTJky15y8u0yZMlq9erX+/fdfeXh4pNumQIECkpRm4sjLvxm7mo0bN2rbtm2Ki4tzmej78tPBS5cuLUkZmnS8Xbt26t+/vz777DOdPXtWHh4eatu2bYbHBAAAkBllypTRd999pzp16rhMA5BeO+liPZPZM6w9PT3VvHlzNW/eXE6nU0899ZQmT56soUOHpttXyZIlJUlbt261rpCXauvWrdZ6ALkDP5cDkClnz57V3Llz9dBDD6lVq1Zpbn379tWpU6c0f/58PfbYY/r99981b968NP0YYyRJjz32mI4dO5buGUCpbUqWLCl3d3f9+OOPLuvfe++9DI879Vuz1D5T///SS/JKF78BvO+++zR16lTt27cv3fGkCgwMVJMmTfTpp59q+vTpevDBBxUYGJjhMQEAAGRGmzZtlJKSopEjR6ZZd+HCBesLuQceeED+/v4aNWqUzp0759Lu8nrmUsePH3e57+bmpipVqkiSkpOT092mRo0aCg4O1qRJk1zaLFiwQJs3b1azZs0ydGwAbg+cyQQgU+bPn69Tp07p4YcfTnf9Pffco6CgIE2fPl0zZszQF198odatW6tbt26qXr26Tpw4ofnz52vSpEkKDw9Xp06d9PHHH6t///5as2aN6tWrp6SkJH333Xd66qmn9Mgjjyhfvnxq3bq1JkyYIIfDoTJlyuj//u//rHkHMqJChQoqU6aMXnzxRR08eFABAQGaM2dOur//f+edd1S3bl1Vq1ZNvXr1UqlSpbRnzx5988032rBhg0vbTp06qVWrVpKUbsEHAABwvdSvX19PPvmkRo0apQ0bNuiBBx6Qh4eHtm/frtmzZ2v8+PFq1aqVAgICNHbsWPXo0UM1a9ZUhw4dVKBAAf3+++86c+bMFX/61qNHD504cUL333+/ihcvrr1792rChAmqWrWqKlasmO42Hh4eevPNN9W1a1fVr19f7du3V3x8vMaPH6+wsDD169cvOx8SADkMIROATJk+fbq8vb3VuHHjdNe7ubmpWbNmmj59upKTk7VixQpFR0dr3rx5iouLU3BwsBo1amRNZOnu7q5vv/1Wr732mmbMmKE5c+aoUKFCqlu3ru666y6r3wkTJujff//VpEmT5OXlpTZt2uitt97K8ISWHh4e+vrrr/Xss89q1KhR8vb21qOPPqq+ffsqPDzcpW14eLh++eUXDR06VO+//77OnTunkiVLqk2bNmn6bd68uQoUKCCn03nF4A0AAOB6mTRpkqpXr67Jkyfr5ZdfVp48eRQWFqbHH39cderUsdp1795dwcHBeuONNzRy5Eh5eHioQoUKVw19Hn/8cX3wwQd67733lJCQoJCQELVt21bDhw9PMz/Upbp06aK8efPqjTfe0EsvvSRfX189+uijevPNN5U/f/7refgAcjiHudr5kgCAq7pw4YKKFi2q5s2ba8qUKTd7OAAAAABw0zAnEwDY8OWXX+ro0aMuk4kDAAAAQG7EmUwAkAWrV6/WH3/8oZEjRyowMFDr1q272UMCAAAAgJuKM5kAIAvef/999enTR8HBwfr4449v9nAAAAAA4KbLdMj0448/qnnz5ipatKgcDoe+/PLLa26zfPlyVatWTV5eXrrjjjsUGxubps3EiRMVFhYmb29vRUREaM2aNZkdGgDcMLGxsbpw4YJ+++23DE8+DiD3on4CAAC5QaZDpqSkJIWHh2vixIkZar979241a9ZMDRs21IYNG/T888+rR48eWrRokdVm1qxZ6t+/v6Kjo7Vu3TqFh4crKioqU5cnBwAAyKmonwAAQG5ga04mh8OhefPmqUWLFlds89JLL+mbb77Rpk2brGXt2rVTQkKCFi5cKEmKiIhQzZo19e6770qSnE6nQkND9cwzz2jQoEFZHR4AAECOQ/0EAABuV3myewerVq1SZGSky7KoqCg9//zzkqTz589r7dq1Gjx4sLXezc1NkZGRWrVqVbp9JicnKzk52brvdDp14sQJFSpUSA6H4/ofBAAAyLGMMTp16pSKFi0qN7fbY7rJ7KifJGooAADwn+yoobI9ZDp8+LAKFy7ssqxw4cI6efKkzp49q3/++UcpKSnpttmyZUu6fY4aNUoxMTHZNmYAAHDr2b9/v4oXL36zh3FdZEf9JFFDAQCAtK5nDZXtIVN2GDx4sPr372/dT0xMVIkSJbR//34FBATcxJEBAIAb7eTJkwoNDZW/v//NHkqORw0FAABSZUcNle0hU0hIiOLj412WxcfHKyAgQD4+PnJ3d5e7u3u6bUJCQtLt08vLS15eXmmWBwQEUCABAJBL3U4/98qO+kmihgIAAGldzxoq2ycuqF27tpYuXeqybMmSJapdu7YkydPTU9WrV3dp43Q6tXTpUqsNAABAbkL9BAAAbkWZDplOnz6tDRs2aMOGDZIuXmJ3w4YN2rdvn6SLp2F36tTJat+7d2/t2rVLAwcO1JYtW/Tee+/p888/V79+/aw2/fv314cffqi4uDht3rxZffr0UVJSkrp27Wrz8AAAAG4+6icAAJAbZPrncr/99psaNmxo3U/9XX/nzp0VGxurQ4cOWQWTJJUqVUrffPON+vXrp/Hjx6t48eL66KOPFBUVZbVp27atjh49qmHDhunw4cOqWrWqFi5cmGYySwAAgFsR9RMAAMgNHMYYc7MHYdfJkyeVL18+JSYmMp8AAAC5DHVA1vHYAQCQe2VHHZDtczIBAAAAAADg9kfIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYlqWQaeLEiQoLC5O3t7ciIiK0Zs2aK7b9999/NWLECJUpU0be3t4KDw/XwoULXdoMHz5cDofD5VahQoWsDA0AACBHon4CAAC3u0yHTLNmzVL//v0VHR2tdevWKTw8XFFRUTpy5Ei67YcMGaLJkydrwoQJ+uuvv9S7d289+uijWr9+vUu7O++8U4cOHbJuP/30U9aOCAAAIIehfgIAALlBpkOmMWPGqGfPnuratasqVaqkSZMmKW/evJo6dWq67T/55BO9/PLLatq0qUqXLq0+ffqoadOmGj16tEu7PHnyKCQkxLoFBgZm7YgAAAByGOonAACQG2QqZDp//rzWrl2ryMjI/zpwc1NkZKRWrVqV7jbJycny9vZ2Webj45Pmm7bt27eraNGiKl26tDp27Kh9+/ZdcRzJyck6efKkyw0AACAnyin1U2q/1FAAACC7ZCpkOnbsmFJSUlS4cGGX5YULF9bhw4fT3SYqKkpjxozR9u3b5XQ6tWTJEs2dO1eHDh2y2kRERCg2NlYLFy7U+++/r927d6tevXo6depUun2OGjVK+fLls26hoaGZOQwAAIAbJqfUTxI1FAAAyF7ZfnW58ePHq2zZsqpQoYI8PT3Vt29fde3aVW5u/+26SZMmat26tapUqaKoqCh9++23SkhI0Oeff55un4MHD1ZiYqJ1279/f3YfBgAAwA2THfWTRA0FAACyV6ZCpsDAQLm7uys+Pt5leXx8vEJCQtLdJigoSF9++aWSkpK0d+9ebdmyRX5+fipduvQV95M/f36VK1dOO3bsSHe9l5eXAgICXG4AAAA5UU6pnyRqKAAAkL0yFTJ5enqqevXqWrp0qbXM6XRq6dKlql279lW39fb2VrFixXThwgXNmTNHjzzyyBXbnj59Wjt37lSRIkUyMzwAAIAch/oJAADkFpn+uVz//v314YcfKi4uTps3b1afPn2UlJSkrl27SpI6deqkwYMHW+1Xr16tuXPnateuXVqxYoUefPBBOZ1ODRw40Grz4osv6ocfftCePXv0888/69FHH5W7u7vat29/HQ4RAADg5qJ+AgAAuUGezG7Qtm1bHT16VMOGDdPhw4dVtWpVLVy40JrMct++fS7zBZw7d05DhgzRrl275Ofnp6ZNm+qTTz5R/vz5rTYHDhxQ+/btdfz4cQUFBalu3br65ZdfFBQUZP8IAQAAbjLqJwAAkBs4jDHmZg/CrpMnTypfvnxKTExkbgEAAHIZ6oCs47EDACD3yo46INuvLgcAAAAAAIDbHyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGBblkKmiRMnKiwsTN7e3oqIiNCaNWuu2Pbff//ViBEjVKZMGXl7eys8PFwLFy601ScAAMCthvoJAADc7jIdMs2aNUv9+/dXdHS01q1bp/DwcEVFRenIkSPpth8yZIgmT56sCRMm6K+//lLv3r316KOPav369VnuEwAA4FZC/QQAAHIDhzHGZGaDiIgI1axZU++++64kyel0KjQ0VM8884wGDRqUpn3RokX1yiuv6Omnn7aWPfbYY/Lx8dGnn36apT4vd/LkSeXLl0+JiYkKCAjIzOEAAIBb3K1QB+TE+km6NR47AACQPbKjDsjUmUznz5/X2rVrFRkZ+V8Hbm6KjIzUqlWr0t0mOTlZ3t7eLst8fHz0008/ZblPAACAWwX1EwAAyC0yFTIdO3ZMKSkpKly4sMvywoUL6/Dhw+luExUVpTFjxmj79u1yOp1asmSJ5s6dq0OHDmW5z+TkZJ08edLlBgAAkBPllPpJooYCAADZK9uvLjd+/HiVLVtWFSpUkKenp/r27auuXbvKzS3rux41apTy5ctn3UJDQ6/jiAEAAG6u7KifJGooAACQvTJVqQQGBsrd3V3x8fEuy+Pj4xUSEpLuNkFBQfryyy+VlJSkvXv3asuWLfLz81Pp0qWz3OfgwYOVmJho3fbv35+ZwwAAALhhckr9JFFDAQCA7JWpkMnT01PVq1fX0qVLrWVOp1NLly5V7dq1r7qtt7e3ihUrpgsXLmjOnDl65JFHstynl5eXAgICXG4AAAA5UU6pnyRqKAAAkL3yZHaD/v37q3PnzqpRo4Zq1aqlcePGKSkpSV27dpUkderUScWKFdOoUaMkSatXr9bBgwdVtWpVHTx4UMOHD5fT6dTAgQMz3CcAAMCtjPoJAADkBpkOmdq2baujR49q2LBhOnz4sKpWraqFCxdaE0/u27fPZb6Ac+fOaciQIdq1a5f8/PzUtGlTffLJJ8qfP3+G+wQAALiVUT8BAIDcwGGMMTd7EHadPHlS+fLlU2JiIqd9AwCQy1AHZB2PHQAAuVd21AHZfnU5AAAAAAAA3P4ImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAtuW52QMAACA7vbH+2M0eAv6/QXcH3uwhAAAAIBtxJhMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYx8XcGMXFszsCksQAAAAAA5EycyQQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNq8sBAAAAAAAX/8a8cLOHAEke0aNv9hAyhTOZAAAAAAAAYBtnMgEAACBb8C14znGrfRMOALg1cSYTAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2JYnKxtNnDhRb731lg4fPqzw8HBNmDBBtWrVumL7cePG6f3339e+ffsUGBioVq1aadSoUfL29pYkDR8+XDExMS7blC9fXlu2bMnK8AAAAHIc6ifczv6NeeFmDwH/n0f06Js9BAC5WKZDplmzZql///6aNGmSIiIiNG7cOEVFRWnr1q0KDg5O037GjBkaNGiQpk6dqnvvvVfbtm1Tly5d5HA4NGbMGKvdnXfeqe++++6/geXJUv4FAACQ41A/AQCA3CDTP5cbM2aMevbsqa5du6pSpUqaNGmS8ubNq6lTp6bb/ueff1adOnXUoUMHhYWF6YEHHlD79u21Zs0al3Z58uRRSEiIdQsMDMzaEQEAAOQw1E8AACA3yFTIdP78ea1du1aRkZH/deDmpsjISK1atSrdbe69916tXbvWKop27dqlb7/9Vk2bNnVpt337dhUtWlSlS5dWx44dtW/fviuOIzk5WSdPnnS5AQAA5EQ5pX6SqKEAAED2ytQ51ceOHVNKSooKFy7ssrxw4cJX/P1/hw4ddOzYMdWtW1fGGF24cEG9e/fWyy+/bLWJiIhQbGysypcvr0OHDikmJkb16tXTpk2b5O/vn6bPUaNGpZmDAAAAICfKKfWTRA0FAACyV7ZfXW758uV6/fXX9d5772ndunWaO3euvvnmG40cOdJq06RJE7Vu3VpVqlRRVFSUvv32WyUkJOjzzz9Pt8/BgwcrMTHRuu3fvz+7DwMAAOCGyY76SaKGAgAA2StTZzIFBgbK3d1d8fHxLsvj4+MVEhKS7jZDhw7VE088oR49ekiS7rrrLiUlJalXr1565ZVX5OaWNufKnz+/ypUrpx07dqTbp5eXl7y8vDIzdAAAgJsip9RPEjUUAADIXpk6k8nT01PVq1fX0qVLrWVOp1NLly5V7dq1093mzJkzaQohd3d3SZIxJt1tTp8+rZ07d6pIkSKZGR4AAECOQ/0EAAByi0xf57Z///7q3LmzatSooVq1amncuHFKSkpS165dJUmdOnVSsWLFNGrUKElS8+bNNWbMGN19992KiIjQjh07NHToUDVv3twqll588UU1b95cJUuW1N9//63o6Gi5u7urffv21/FQgYx5Y/2xmz0ESBp0N1dIAnD7oH4CAAC5QaZDprZt2+ro0aMaNmyYDh8+rKpVq2rhwoXWZJb79u1z+eZtyJAhcjgcGjJkiA4ePKigoCA1b95cr732mtXmwIEDat++vY4fP66goCDVrVtXv/zyi4KCgq7DIQIAANxc1E8Abhf/xrxws4cASR7Ro2/2EIB0ZTpkkqS+ffuqb9++6a5bvny56w7y5FF0dLSio6Ov2N/MmTOzMgwAAIBbBvUTAAC43WX71eUAAAAAAABw+yNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALbludkDAICb5Y31x272ECBp0N2BN3sIAAAAAK4DzmQCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADAtiyFTBMnTlRYWJi8vb0VERGhNWvWXLX9uHHjVL58efn4+Cg0NFT9+vXTuXPnbPUJAABwK6F+AgAAt7tMh0yzZs1S//79FR0drXXr1ik8PFxRUVE6cuRIuu1nzJihQYMGKTo6Wps3b9aUKVM0a9Ysvfzyy1nuEwAA4FZC/QQAAHKDTIdMY8aMUc+ePdW1a1dVqlRJkyZNUt68eTV16tR02//888+qU6eOOnTooLCwMD3wwANq3769yzdtme0TAADgVkL9BAAAcoNMhUznz5/X2rVrFRkZ+V8Hbm6KjIzUqlWr0t3m3nvv1dq1a62iaNeuXfr222/VtGnTLPcJAABwq6B+AgAAuUWezDQ+duyYUlJSVLhwYZflhQsX1pYtW9LdpkOHDjp27Jjq1q0rY4wuXLig3r17W6d7Z6XP5ORkJScnW/dPnjyZmcMAAAC4YXJK/SRRQwEAgOyV7VeXW758uV5//XW99957WrdunebOnatvvvlGI0eOzHKfo0aNUr58+axbaGjodRwxAADAzZUd9ZNEDQUAALJXps5kCgwMlLu7u+Lj412Wx8fHKyQkJN1thg4dqieeeEI9evSQJN11111KSkpSr1699Morr2Spz8GDB6t///7W/ZMnT1IkAQCAHCmn1E8SNRQAAMhemTqTydPTU9WrV9fSpUutZU6nU0uXLlXt2rXT3ebMmTNyc3Pdjbu7uyTJGJOlPr28vBQQEOByAwAAyIlySv0kUUMBAIDslakzmSSpf//+6ty5s2rUqKFatWpp3LhxSkpKUteuXSVJnTp1UrFixTRq1ChJUvPmzTVmzBjdfffdioiI0I4dOzR06FA1b97cKpau1ScAAMCtjPoJAADkBpkOmdq2baujR49q2LBhOnz4sKpWraqFCxdaE0/u27fP5Zu3IUOGyOFwaMiQITp48KCCgoLUvHlzvfbaaxnuEwAA4FZG/QQAAHIDhzHG3OxB2HXy5Enly5dPiYmJ2Xba9xvrj2VLv8icQXcHZvs+eK5zBp7r3CO7n2ue55wju57rG1EH3K6y+7H7N+aF694nssYjenS29s9znXPwXOcO2f08SzzXOUV2PtfZUQdk+9XlAAAAAAAAcPsjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG1ZCpkmTpyosLAweXt7KyIiQmvWrLli2wYNGsjhcKS5NWvWzGrTpUuXNOsffPDBrAwNAAAgx6KGAgAAt7M8md1g1qxZ6t+/vyZNmqSIiAiNGzdOUVFR2rp1q4KDg9O0nzt3rs6fP2/dP378uMLDw9W6dWuXdg8++KCmTZtm3ffy8srs0AAAAHIsaigAAHC7y/SZTGPGjFHPnj3VtWtXVapUSZMmTVLevHk1derUdNsXLFhQISEh1m3JkiXKmzdvmgLJy8vLpV2BAgWydkQAAAA5EDUUAAC43WUqZDp//rzWrl2ryMjI/zpwc1NkZKRWrVqVoT6mTJmidu3aydfX12X58uXLFRwcrPLly6tPnz46fvz4FftITk7WyZMnXW4AAAA5FTUUAADIDTIVMh07dkwpKSkqXLiwy/LChQvr8OHD19x+zZo12rRpk3r06OGy/MEHH9THH3+spUuX6s0339QPP/ygJk2aKCUlJd1+Ro0apXz58lm30NDQzBwGAADADUUNBQAAcoNMz8lkx5QpU3TXXXepVq1aLsvbtWtn/f9dd92lKlWqqEyZMlq+fLkaNWqUpp/Bgwerf//+1v2TJ09SJAEAgNsWNRQAALgVZOpMpsDAQLm7uys+Pt5leXx8vEJCQq66bVJSkmbOnKnu3btfcz+lS5dWYGCgduzYke56Ly8vBQQEuNwAAAByKmooAACQG2QqZPL09FT16tW1dOlSa5nT6dTSpUtVu3btq247e/ZsJScn6/HHH7/mfg4cOKDjx4+rSJEimRkeAABAjkQNBQAAcoNMX12uf//++vDDDxUXF6fNmzerT58+SkpKUteuXSVJnTp10uDBg9NsN2XKFLVo0UKFChVyWX769GkNGDBAv/zyi/bs2aOlS5fqkUce0R133KGoqKgsHhYAAEDOQg0FAABud5mek6lt27Y6evSohg0bpsOHD6tq1apauHChNZHlvn375Obmml1t3bpVP/30kxYvXpymP3d3d/3xxx+Ki4tTQkKCihYtqgceeEAjR46Ul5dXFg8LAAAgZ6GGAgAAt7ssTfzdt29f9e3bN911y5cvT7OsfPnyMsak297Hx0eLFi3KyjAAAABuKdRQAADgdpbpn8sBAAAAAAAAlyNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsI2QCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQAAAAAAADbCJkAAAAAAABgGyETAAAAAAAAbCNkAgAAAAAAgG2ETAAAAAAAALCNkAkAAAAAAAC2ETIBAAAAAADANkImAAAAAAAA2EbIBAAAAAAAANsImQAAAAAAAGAbIRMAAAAAAABsy1LINHHiRIWFhcnb21sRERFas2bNFds2aNBADocjza1Zs2ZWG2OMhg0bpiJFisjHx0eRkZHavn17VoYGAACQY1FDAQCA21mmQ6ZZs2apf//+io6O1rp16xQeHq6oqCgdOXIk3fZz587VoUOHrNumTZvk7u6u1q1bW23+97//6Z133tGkSZO0evVq+fr6KioqSufOncv6kQEAAOQg1FAAAOB2l+mQacyYMerZs6e6du2qSpUqadKkScqbN6+mTp2abvuCBQsqJCTEui1ZskR58+a1CiRjjMaNG6chQ4bokUceUZUqVfTxxx/r77//1pdffmnr4AAAAHIKaigAAHC7y5OZxufPn9fatWs1ePBga5mbm5siIyO1atWqDPUxZcoUtWvXTr6+vpKk3bt36/Dhw4qMjLTa5MuXTxEREVq1apXatWuXpo/k5GQlJydb9xMTEyVJJ0+ezMzhZMq506eyrW9k3MmTntm+D57rnIHnOvfI7uea5znnyK7nOvXvvzEmW/q/HnJrDfXvueRrN8IN4ZGNdbLEc52T8FznDtn9PEs81zlFdj7X2VFDZSpkOnbsmFJSUlS4cGGX5YULF9aWLVuuuf2aNWu0adMmTZkyxVp2+PBhq4/L+0xdd7lRo0YpJiYmzfLQ0NBrjgG3trTPOm5XPNe5B8917pHdz/WpU6eUL1++bN5L1lBD4aZ7Y+LNHgFuFJ7r3IHnOfe4Ac/19ayhMhUy2TVlyhTdddddqlWrlq1+Bg8erP79+1v3nU6nTpw4oUKFCsnhcNgd5m3p5MmTCg0N1f79+xUQEHCzh4NsxHOde/Bc5x4811dnjNGpU6dUtGjRmz2UbEMNdXPw3ss9eK5zD57r3IPn+tqyo4bKVMgUGBgod3d3xcfHuyyPj49XSEjIVbdNSkrSzJkzNWLECJflqdvFx8erSJEiLn1WrVo13b68vLzk5eXlsix//vwZPIrcLSAggDdYLsFznXvwXOcePNdXllPPYEpFDXVr472Xe/Bc5x4817kHz/XVXe8aKlMTf3t6eqp69epaunSptczpdGrp0qWqXbv2VbedPXu2kpOT9fjjj7ssL1WqlEJCQlz6PHnypFavXn3NPgEAAG4F1FAAACA3yPTP5fr376/OnTurRo0aqlWrlsaNG6ekpCR17dpVktSpUycVK1ZMo0aNctluypQpatGihQoVKuSy3OFw6Pnnn9err76qsmXLqlSpUho6dKiKFi2qFi1aZP3IAAAAchBqKAAAcLvLdMjUtm1bHT16VMOGDdPhw4dVtWpVLVy40Jp0ct++fXJzcz1BauvWrfrpp5+0ePHidPscOHCgkpKS1KtXLyUkJKhu3bpauHChvL29s3BISI+Xl5eio6PTnCKP2w/Pde7Bc5178FzfHqihbj2893IPnuvcg+c69+C5vjkcJidf7xcAAAAAAAC3hEzNyQQAAAAAAACkh5AJAAAAAAAAthEyAQAAAAAAwDZCpuskLCxM48aNy/L2sbGxyp8//3Ubz+0kK4+tw+HQl19+edU2Xbp0yTFX38nIeC81fPhwVa1aNdvGc6nly5fL4XAoISHhhuzvdpfZ5xq43WXk86xBgwZ6/vnnb8h4cONRQ2Ufaqi0qKFuXdRQwH9ycv2UK0KmG/GH8Ndff1WvXr0y1Da9P/ht27bVtm3bsrz/2NhYORwOORwOubm5qUiRImrbtq327duX5T5vlC5dusjhcKh3795p1j399NPau3evfvvttyz3v2fPHjkcDm3YsMFl+fjx4xUbG5vlfq+nQ4cOqUmTJtetv9RjDg4O1qlTp1zWVa1aVcOHD89wX/fee68OHTqkfPnyXbfxXS51vKk3T09P3XHHHXr11Vd1va9NkPp6czgc8vDwUKlSpTRw4ECdO3fuuu4np7n0uC+97dixI9v3ffToUfXp00clSpSQl5eXQkJCFBUVpaZNm+aYf6RcS0b+oTBnzhy5u7vr4MGD6a4vW7as+vfvb3ssdv9Bfi3X+kx2OBzq0qVLtu0/PXPnztXIkSNv6D5xETVUzkYNRQ1FDZX9blYNdaX6aeXKlTkq6L0a6qeLclv9lCtCphshKChIefPmzfL2Pj4+Cg4OtjWGgIAAHTp0SAcPHtScOXO0detWtW7d2lafGfHvv//a7iM0NFQzZ87U2bNnrWXnzp3TjBkzVKJECeXJk8f2Pi6XL1++HPPNZ0hISLZcWvPUqVN6++23bfXh6empkJAQORyO6zSqK/vuu+906NAhbd++XTExMXrttdc0derU676fBx98UIcOHdKuXbs0duxYTZ48WdHR0dd9PzlN6nFfeitVqlSW+jp//nyG2z722GNav3694uLitG3bNs2fP18NGjS4ZYrSjH7GPfzwwypUqJDi4uLSrPvxxx+1Y8cOde/e/XoPL8uu9hxe6zP5RitYsKD8/f1v+H5xY1BD2UMNRQ0lUUNlt5tRQ12pfjp+/HiW9nujUT9dlBvrJ0ImST/88INq1aolLy8vFSlSRIMGDdKFCxes9adOnVLHjh3l6+urIkWKaOzYsWlOPbs0GTXGaPjw4VbqXLRoUT377LOSLp6ytnfvXvXr189KwaX0T/X++uuvVbNmTXl7eyswMFCPPvroVY/D4XAoJCRERYoU0b333qvu3btrzZo1OnnypNXmq6++UrVq1eTt7a3SpUsrJibG5Vi3bNmiunXrytvbW5UqVdJ3333ncmpq6rcls2bNUv369eXt7a3p06dLkj766CNVrFhR3t7eqlChgt577z2r3/Pnz6tv374qUqSIvL29VbJkSY0aNcpanzdvXp05c0b+/v7W4zV37lyVKFFCR48e1V9//WW1DQ0NVeXKleXn56eAgAC1adNGlStXtr5ZSv3v8uXLFRYWZv0BuPvuu+VwONSgQQNJab+dbdCggZ599lkNHDhQBQsWVEhISJpvq/bt26dHHnnEZd/x8fHW+tTTFqdOnaoSJUrIz89PTz31lFJSUvS///1PISEhCg4O1muvvZbmubv09N+XXnpJ5cqVU968eVW6dGkNHTo0S4XoM888ozFjxujIkSNXbPPJJ5+oRo0a8vf3V0hIiDp06ODS/tJvIE6ePCkfHx8tWLDApY958+bJ399fZ86ckSTt379fbdq0Uf78+VWwYEE98sgj2rNnzzXHW6hQIYWEhKhkyZLq2LGj6tSpo3Xr1lnrf/31VzVu3FiBgYHKly+f6tev77K+W7dueuihh1z6/PfffxUcHKwpU6ZIuvj+3Lt3r2rXrq1y5copOjpaFStW1JIlSyRJ//zzj1q1aiUvLy/rW+3ixYvrs88+c+k3I6+X7du367777rPeT6n7uNTGjRt1//33y8fHR4UKFVKvXr10+vRpa33q6/T1119X4cKFlT9/fo0YMUIXLlzQgAEDVLBgQRUvXlzTpk275uOb+i3YpTd3d3dJ1/4cbNCggfr27avnn39egYGBioqKkiRt2rRJTZo0kZ+fnwoXLqwnnnhCx44ds7aLi4vTihUrtH79erVq1Urdu3fXnXfeqeTkZC1btkxfffWV9Vn40UcfWdslJCTI4XBo+fLlkv57HX7zzTeqUqWKvL29dc8992jTpk3WNqmfo19++aXKli0rb29vRUVFaf/+/S6Pw/vvv68yZcrI09NT5cuX1yeffOKy3uFw6P3339fDDz8sX19f9ezZUw0bNpQkFShQ4IrfRHl4eOiJJ55I99v9qVOnKiIiQnfeeacSEhLUo0cPBQUFKSAgQPfff79+//13l/ZX+vy/0t8Q6eI3gXfeeae8vLwUFham0aNHu/QZFhamkSNHqlOnTgoICLjqmSPVqlVTaGio5s6day1L/Uy+++67XdouXLhQdevWVf78+VWoUCE99NBD2rlzp0ubAwcOqH379ipYsKB8fX1Vo0YNrV692qXNJ598orCwMOXLl0/t2rVzOYMgvb+5r7/+urp16yZ/f3+VKFFCH3zwgUt/Wf0cQuZQQ1FDpT431FDUUNRQ16+GSkhI0IoVK3TkyBE1bdpU1apV08svv6xnn31W69atU1xcnEsNlVovUT9RP+WI+snkAp07dzaPPPJIuusOHDhg8ubNa5566imzefNmM2/ePBMYGGiio6OtNj169DAlS5Y03333ndm4caN59NFHjb+/v3nuueesNiVLljRjx441xhgze/ZsExAQYL799luzd+9es3r1avPBBx8YY4w5fvy4KV68uBkxYoQ5dOiQOXTokDHGmGnTppl8+fJZ/f3f//2fcXd3N8OGDTN//fWX2bBhg3n99deveIyXbx8fH28aNmxo3N3dzenTp40xxvz4448mICDAxMbGmp07d5rFixebsLAwM3z4cGOMMRcuXDDly5c3jRs3Nhs2bDArVqwwtWrVMpLMvHnzjDHG7N6920gyYWFhZs6cOWbXrl3m77//Np9++qkpUqSItWzOnDmmYMGCJjY21hhjzFtvvWVCQ0PNjz/+aPbs2WNWrFhhZsyYYYwxpn79+iZPnjymZ8+epk6dOtbj1ahRIzN27Fjj4+NjatasaYwxJiUlxXh4eJhSpUqZ3377zfzyyy+mevXqxtfX13rOoqOjjSRzzz33mI0bN5rJkycbSaZ9+/bm0KFD5vjx4+m+LurXr28CAgLM8OHDzbZt20xcXJxxOBxm8eLF1r6rVq1q6tat67Lv+vXrW31ER0cbPz8/06pVK/Pnn3+a+fPnG09PTxMVFWWeeeYZs2XLFjN16lQjyfzyyy/Wdpc+xsYYM3LkSLNy5Uqze/duM3/+fFO4cGHz5ptvuuwnPDz8iq+H1Odp3bp1pmrVqubpp5+21oWHh7u8vqdMmWK+/fZbs3PnTrNq1SpTu3Zt06RJE2v9smXLjCTzzz//GGOMadWqlXn88cdd9vfYY49Zy86fP28qVqxounXrZv744w/z119/mQ4dOpjy5cub5OTkq453/fr11rJff/3V5M+f38TFxVnLli5daj755BOzefNm89dff5nu3bubwoULm5MnTxpjjFm5cqVxd3c3f//9t7XN3Llzja+vrzl16pQxxpi7777b+Pn5mYULF5qdO3eakSNHGkmmYsWKxhhjnn76aVOpUiXz7LPPmm+++cZ8/PHHplevXsbd3d2sXr3a6jcjr5fKlSubRo0amQ0bNpgffvjB3H333S7P9enTp02RIkVMy5YtzcaNG83SpUtNqVKlTOfOna39dO7c2fj7+5unn37abNmyxUyZMsVIMlFRUea1114z27ZtMyNHjjQeHh5m//796T6+qf3Y+RysX7++8fPzMwMGDDBbtmwxW7ZsMf/8848JCgoygwcPNps3bzbr1q0zjRs3Ng0bNjTGGPP3338bd3d34+XlZbp162Z+++03M3HiRHPq1Clz6tQp06ZNG/Pggw+aNWvWGElmzZo11v7++ecfI8ksW7bMGPPf67BixYpm8eLF5o8//jAPPfSQCQsLM+fPnzfGXPwc9PDwMDVq1DA///yz+e2330ytWrXMvffe6/J68PDwMBMnTjRbt241o0ePNu7u7ub777+32kgywcHBZurUqWbnzp1mz549Zs6cOUaS2bp1qzl06JBJSEhI97H8888/jSTzww8/WMtOnTplfH19rb8DkZGRpnnz5ubXX38127ZtMy+88IIpVKiQ9dl0tc//K/0N+e2334ybm5sZMWKE2bp1q5k2bZrx8fEx06ZNs8ZRsmRJExAQYN5++22zY8cOs2PHjqu+VsaMGWMaNWpkLU/9TH7kkUdcXqNffPGFmTNnjtm+fbtZv369ad68ubnrrrtMSkqKdfylS5c29erVMytWrDDbt283s2bNMj///LMx5r/PzdT3wY8//mhCQkLMyy+/7PL6u/xvbsGCBc3EiRPN9u3bzahRo4ybm5vZsmWLMSZrn0NIHzUUNRQ1FDUUNdSNr6H27dtnJJn77rvPbNmyxfzxxx9paqj77rvPSDJLliyxXh/UT9RPOaF+yvUh08svv2zKly9vnE6ntWzixInGz8/PpKSkmJMnTxoPDw8ze/Zsa31CQoLJmzfvFQuk0aNHm3Llyllv3Mtd2jbV5QVO7dq1TceOHTN8jNOmTTOSjK+vr8mbN6+RZCSZZ5991mrTqFGjNEXWJ598YooUKWKMMWbBggUmT5481pvOGGOWLFmSboE0btw4l37KlCljFTypRo4caWrXrm2MMeaZZ54x999/v8vjnKpGjRrG19fXHDx40Hh5eZk9e/aYPXv2GG9vb3P06FGXAmnx4sVGkhk2bJi1feqHUo8ePYwx/xVIqeNJHXPlypVd9ptegVS3bl2XNjVr1jQvvfSStW93d3ezb9++NPtO/cdxdHS0yZs3r/UH2xhjoqKiTFhYmPWBYYwx5cuXN6NGjbLuX14gXe6tt94y1atXt+5ntEBav369WbhwofHw8LA+EC8vkC7366+/GklWQXF5gTRv3jzj5+dnkpKSjDHGJCYmGm9vb7NgwQJjzMXX1OXvqeTkZOPj42MWLVp01fH6+PgYX19f4+HhYSSZXr16XXGcxlwsQvz9/c3XX39tLatUqZJLMdm8eXPTpUsXY4wx586dM+7u7sbd3d34+voaLy8vI8k4HA7ruW/evLnp2rVrmn01a9bMvPDCC9b9a71eFi1aZPLkyWMOHjxorV+wYIHLc/3BBx+YAgUKWP+IMcaYb775xri5uZnDhw8bYy6+TkuWLJnm9VOvXj3r/oULF4yvr6/57LPPrvhYde7c2Tru1FurVq2MMdf+HEw93rvvvtulz5EjR5oHHnjAZdn+/futYmLt2rVGknn//fdNgQIFjLe3t7n33nvN4MGDze+//269B9MrkK9UJM2cOdNqc/z4cePj42NmzZpljPnvc/DSf3xs3rzZSLKK23vvvdf07NnTZcytW7c2TZs2te5LMs8//7xLm8vfB1dzzz33uBQRU6ZMsT4XVqxYYQICAsy5c+dctilTpoyZPHmyMeban//p/Q3p0KGDady4scuyAQMGmEqVKrls16JFi2uOP/V5OXLkSLqfyZcXSZc7evSokWQ2btxojDFm8uTJxt/f3yoCL5fe5+aAAQNMRESEdT+9IunSf6g5nU4THBxs3n//fWNM1j6HkD5qqIuooaihjKGGooa68TVUvnz50tRPqWNq3Lgx9RP1U46sn3L9z+U2b96s2rVru5wyV6dOHZ0+fVoHDhzQrl279O+//6pWrVrW+nz58ql8+fJX7LN169Y6e/asSpcurZ49e2revHkup0xmxIYNG9SoUaNMbePv768NGzbot99+0+jRo1WtWjWXU4p///13jRgxQn5+ftatZ8+eOnTokM6cOaOtW7cqNDRUISEh1jaXHvelatSoYf1/UlKSdu7cqe7du7v0/eqrr1qn/HXp0kUbNmxQ+fLl9eyzz2rx4sXW9mFhYXI6nYqIiFCRIkX00ksvacqUKWrWrJkCAwNd9rt582a5u7urQIEC1rJKlSrJzc3N5Sc60sU5Gi514sSJaz6GVapUcblfpEgR67TnzZs3KzQ0VKGhoS77zp8/vzZv3uxyPJf+9rVw4cLWGC9ddrXTr2fNmqU6deooJCREfn5+GjJkSJYnII2KilLdunU1dOjQdNevXbtWzZs3V4kSJeTv76/69etL0hX317RpU3l4eGj+/PmSLp5iGhAQoMjISEkXX2c7duyQv7+/9VooWLCgzp07l+YU0PSOe8OGDfr999/1+eef66uvvtKgQYOs9fHx8erZs6fKli2rfPnyKSAgQKdPn3YZa48ePazTnuPj47VgwQJ169ZNkrRjxw6lpKTIGCOn0ymHw2HNVZH6++o+ffros88+U5EiRazTcf38/LRo0aI0j0lGXi9Fixa11teuXdul/ebNmxUeHi5fX19rWZ06deR0OrV161Zr2Z133pnm9XPXXXdZ993d3VWoUKGrvqYkqWHDhtqwYYN1e+edd6xxXO1zMFX16tVd+vv999+1bNkyl/d9hQoVJEk7d+5UeHi4GjVqpIEDB6pBgwbq3bu36tevr+XLl6tatWpZmjDz0sewYMGCKl++vMv7L0+ePKpZs6Z1v0KFCi7v0c2bN6tOnToufdapU8elD8n1My6zunXrpi+++MI6XXnq1Klq3bq1/P399fvvv+v06dMqVKiQy+O2e/du6/2Rlc//Kx3X9u3blZKSkqXjCgoKUrNmzRQbG6tp06al+5ksXfxJQ/v27VW6dGkFBAQoLCxM0n+fIRs2bNDdd9+tggULXnFfl39uXvpeupJL33+pP3VK3cbO5xAyjhqKGupS1FDUUNRQ/43jetVQKSkpqlmzpgIDA/Xdd9+pWrVqWZp0n/opfdRP2VM/Xf+ZAKHQ0FBt3bpV3333nZYsWaKnnnpKb731ln744Qd5eHhkqI/L/7hnhJubm+644w5JUsWKFbVz50716dPH+r3s6dOnFRMTo5YtW6bZ1tvbO1P7uvQDPfW3zx9++KEiIiJc2qX+VrlatWravXu3FixYoO+++05t2rRRZGSkvvjiC/n6+qpRo0bq1auXPvjgA33xxRfKkyePPv/883T37XA4snS1jIxsc/nz43A45HQ6M7Wf9PrITL+rVq1Sx44dFRMTo6ioKOXLl08zZ85M8/vgzHjjjTdUu3ZtDRgwwGV5UlKSoqKiFBUVpenTpysoKEj79u1TVFTUFSe18/T0VKtWrTRjxgy1a9dOM2bMUNu2ba1C4/Tp06pevbo1z8SlgoKCrjrO0NDQNK/hoUOHavjw4fL29lbnzp11/PhxjR8/XiVLlpSXl5dq167tMtZOnTpp0KBBWrVqlX7++WeVKlVK9erVs8YmXfxDm/oH2ul0qnnz5mrVqpUkqUmTJnrxxRc1btw4ValSRb/99psef/xxxcfHp3lMrsfrJSPsvqZS+fr6Wo9vVlz6vpcuPp7NmzfXm2++maZtkSJF5O7uriVLlujnn3/W4sWLNW/ePB0+fFirV6/Wa6+9ppkzZyoyMtIq/i59j16PyXDtuPxYM6Ndu3bq16+fPv/8c913331auXKlNX/K6dOnVaRIEWuuhEulzimTlc//jMrscXXr1k19+/aVJE2cODHdNs2bN1fJkiX14YcfqmjRonI6napcubL1fsnI8WTl9Xy1bex8DuHmoob6DzUUNdSlqKGy5naroR577DFFR0dbcx1J1E/UT//JKfVTrj+TqWLFilq1apXLm3PlypXy9/dX8eLFVbp0aXl4eOjXX3+11icmJl7zUrk+Pj5q3ry53nnnHS1fvlyrVq3Sxo0bJV38A3NpMpqeKlWqaOnSpTaOTBo0aJBmzZplTepXrVo1bd26VXfccUeam5ubm8qXL6/9+/e7TMJ46XFfSeHChVW0aFHt2rUrTb+XXnUhICBAbdu21YcffqhZs2Zpzpw51jdj7u7uat68ub788ksVKlRIycnJKlKkSJp9VaxYURcuXHD5hmLNmjVyOp1XfOF7enpKyliBdDUVK1bU/v37XSbB++uvv5SQkKBKlSrZ6vtSP//8s0qWLKlXXnlFNWrUUNmyZbV3715bfdaqVUstW7Z0+UZLujhJ6fHjx/XGG2+oXr16qlChwjXTb0nq2LGjFi5cqD///FPff/+9OnbsaK2rVq2atm/fruDg4DSvh8xewtfd3V0XLlywPmxXrlypZ599Vk2bNrUm6Lv829dChQqpRYsWmjZtmmJjY9W1a1drXeq3oWfPnrXGVK5cOQ0fPlxjxoyxrgSxYcMGtWnTRitXrtQ777yjzz//PNOXx059vRw6dMha9ssvv6Rp8/vvvyspKclatnLlSuv9eKNc63PwSqpVq6Y///xTYWFhaZ7r1D/GDodDderUUUxMjNavXy9PT0/NmzdPlSpV0oULF5SSkmK9dy99rC6/XHaqSx/Df/75R9u2bVPFihWtZRcuXHC5XPfWrVuVkJBgtalYsaJWrlzp0ufKlSuv+R5O/Ry51me3dPGMiNatW2vq1KmaNm2aypUrZxXp1apV0+HDh5UnT540j1nqt1zX+vxP72/IlY6rXLly1j9Us+LBBx/U+fPn9e+//1oTlV7q+PHj2rp1q4YMGaJGjRqpYsWK+ueff1zaVKlSRRs2bMjQmRDXy/X8HMKVUUNRQ2UUNdR/qKGujRrqyjXUuXPnlJSUJE9PTyucpH6ifrpertdnUK4JmRITE11OcdywYYP279+vp556Svv379czzzyjLVu26KuvvlJ0dLT69+8vNzc3+fv7q3PnzhowYICWLVumP//8U927d5ebm9sVL0caGxurKVOmaNOmTdq1a5c+/fRT+fj4qGTJkpIuntb2448/6uDBg2k+3FNFR0frs88+U3R0tDZv3qyNGzemm3RfTWhoqB599FENGzZMkjRs2DB9/PHHiomJ0Z9//qnNmzdr5syZGjJkiCSpcePGKlOmjDp37qw//vhDK1eutNZd69KrMTExGjVqlN555x1t27ZNGzdu1LRp0zRmzBhJ0pgxY/TZZ59py5Yt2rZtm2bPnq2QkBDlz59fO3bs0N69e7Vp0ybt3btXnTt3tq7ccrnIyEgFBwdr6tSpmjJlimbMmKGoqCi5u7u7nFJ7qeDgYHl4eOjcuXOKj49XYmJiph7HS/d91113qWPHjlq3bp3WrFmjTp06qX79+rZODb1c2bJltW/fPs2cOVM7d+7UO++8o3nz5tnu97XXXtP333/vUlyWKFFCnp6emjBhgnbt2qX58+dr5MiR1+zrvvvuU0hIiDp27KhSpUq5fPvasWNHBQYG6pFHHtGKFSu0e/duLV++XM8++6zLacPpOX78uA4fPqwDBw5owYIFGj9+vBo2bKiAgABJFx+bTz75RJs3b9bq1avVsWPHdFP+Hj16KC4uTps3b1bnzp2t5f7+/qpcubI2btyouLg47dy5U+vWrVN8fLySk5M1ceJEDRs2TG5ublqwYIE+/fRTffbZZ/Lx8XH5h0NGREZGqly5curcubN+//13rVixQq+88opLm44dO1rfLm7atEnLli3TM888oyeeeEKFCxfO1P7suNbn4JU8/fTTOnHihNq3b69ff/1VO3fu1KJFi9S1a1elpKRo9erVGjJkiGrWrKlx48Zp7NixOnLkiI4dO6b//e9/qly5sv744w/t27dPNWrU0KhRo7R582b98MMP1mfP5UaMGKGlS5dq06ZN6tKliwIDA12ucOTh4aFnnnlGq1ev1tq1a9WlSxfdc8891s9WBgwYoNjYWL3//vvavn27xowZo7lz5+rFF1+86mNUsmRJORwO/d///Z+OHj3qcvWa9HTv3l0///yzJk2aZP3UQLr4uqhdu7ZatGihxYsXa8+ePfr555/1yiuvWMXdtT7/0/sb8sILL2jp0qUaOXKktm3bpri4OL377rvXPK5rcXd31+bNm/XXX3+lW2wVKFBAhQoV0gcffKAdO3bo+++/V//+/V3atG/fXiEhIWrRooVWrlypXbt2ac6cOVq1apWtsV2Nnc8hpEUNRQ1FDUUNRQ2VvuyqoRYvXqwyZcpoxIgRWrRokSZNmqT4+HitWLFCjzzyiMLCwvTnn38qPDxcI0eO1B9//EH9RP1k23WrnzI8e9MtrHPnztYkjpfeunfvbowxZvny5aZmzZrG09PThISEmJdeesn8+++/1vYnT540HTp0MHnz5jUhISFmzJgxplatWmbQoEFWm0snEps3b56JiIgwAQEBxtfX19xzzz3mu+++s9quWrXKVKlSxZowz5i0k1YaY8ycOXNM1apVjaenpwkMDDQtW7a84jGmt33qvnTJpG0LFy409957r/Hx8TEBAQGmVq1a1oz9xlyc5K1OnTrG09PTVKhQwXz99ddGklm4cKExJv0rWKSaPn26Nd4CBQqY++67z8ydO9cYc3FyvqpVqxpfX18TEBBgGjVqZNatW2eMMaZhw4amQIECV3y8Lp200hhjNm3aZIoWLWpNNlizZk1z5513prkyyqWTQLZt29a4u7sbNzc360om6U1aeenEaMaYNBO07d271zz88MPG19fX+Pv7m9atW1uTC6bu+/LJJNObNPXyfV0+3gEDBphChQoZPz8/07ZtWzN27FiX5zczk1ZeqlevXkaSy6SVM2bMMGFhYcbLy8vUrl3bzJ8/32XbK03YN3DgwDQTiKY6dOiQ6dSpkwkMDDReXl6mdOnSpmfPniYxMfGq4029ubu7m+LFi5uePXuaI0eOWO3WrVtnatSoYby9vU3ZsmXN7Nmz053Ez+l0mpIlS7pMRpiqc+fOpnLlyqZ8+fLGw8PDBAUFmaioKNOrVy8TFBRkhg4dasqVK2fc3NyMJOPl5WX69u1rOnXqlOnXy9atW03dunWNp6enKVeunFm4cGGa5/qPP/4wDRs2NN7e3qZgwYKmZ8+e1oShqeO91uvHmPQnM7z8uK80ea8x1/4cTG+fxhizbds28+ijj5r8+fMbHx8fU6FCBfP8888bp9Np/vrrL9O4cWPj4+NjHA6HcXNzM56enqZ8+fJmyJAhZu/evaZx48bGz8/PSDJ33nmn8fHxMVWrVrUmqL184sqvv/7a3HnnncbT09PUqlXLmgDTmP8+B+fMmWNKly5tvLy8TGRkpNm7d6/LmN977z1TunRp4+HhYcqVK2c+/vhjl/WXP0epRowYYUJCQozD4bjqxI2pypcvn+ZKPcZc/JvyzDPPmKJFixoPDw8TGhpqOnbs6DIh7tU+/9P7G2LMxauUVKpUyXh4eJgSJUqYt956y2W/13qNpLrWa+Xy1/mSJUtMxYoVjZeXl6lSpYpZvnx5msdwz5495rHHHjMBAQEmb968pkaNGtbfpfQ+z8aOHWtKlixp3U9v4srLj+XyCXkz+zmE9FFDUUNRQ11EDUUNdSXZUUNt2LDBlCpVyuTJk8d6vwYHB5shQ4aYM2fOmCNHjpjGjRtbFyvw8vKifjLUTzmlfnIYY/P811woKSlJxYoV0+jRo9W9e/ebPZxstXLlStWtW1c7duxQmTJlbvZwgAw5ffq0ihUrpmnTpqU7fwZuPcuXL1fDhg31zz//WL+9v1xsbKyef/55JSQk3NCxAcg4aiggZ6OGur1QP+FmYOLvDFi/fr22bNmiWrVqKTExUSNGjJAkPfLIIzd5ZNffvHnz5Ofnp7Jly2rHjh167rnnVKdOHYoj3BKcTqeOHTum0aNHK3/+/Hr44Ydv9pAAIFejhqKGwq2BGgrA9ULIlEFvv/22tm7dKk9PT1WvXl0rVqxI91KEt7pTp07ppZde0r59+xQYGKjIyEhbV+QAbqR9+/apVKlSKl68uGJjY60JEQEANw81FJDzUUMBuF74uRwAAAAAAABsyzVXlwMAAAAAAED2IWQCAAAAAACAbYRMAAAAAAAAsI2QCQAAAAAAALYRMgEAAAAAAMA2QiYAAAAAAADYRsgEAAAAAAAA2wiZAAAAAAAAYBshEwAAAAAAAGwjZAIAAAAAAIBthEwAAAAAAACwjZAJAAAAAAAAthEyAQAAAAAAwDZCJgAAAAAAANhGyAQA/5/D4dDw4cOt+7GxsXI4HNqzZ89NGxMAAAAA3CoImQDcMKmhTeotT548KlasmLp06aKDBw/e7OEBAADcMJfXRZfeBg0aJElavHixunfvrsqVK8vd3V1hYWE3d9AAcA15bvYAAOQ+I0aMUKlSpXTu3Dn98ssvio2N1U8//aRNmzbJ29v7Zg8PAADghkmtiy5VuXJlSdKMGTM0a9YsVatWTUWLFr0ZwwOATCFkAnDDNWnSRDVq1JAk9ejRQ4GBgXrzzTc1f/58tWnT5iaPDgAA4Ma5tC663Ouvv64PP/xQHh4eeuihh7Rp06YbPDr7kpKS5Ovre7OHAeAG4edyAG66evXqSZJ27txpLduyZYtatWqlggULytvbWzVq1ND8+fPTbJuQkKB+/fopLCxMXl5eKl68uDp16qRjx45Jks6fP69hw4apevXqypcvn3x9fVWvXj0tW7bsxhwcAABAFhUtWlQeHh5Z3v7UqVN6/vnnrTopODhYjRs31rp161zarV69Wk2bNlWBAgXk6+urKlWqaPz48S5tvv/+e9WrV0++vr7Knz+/HnnkEW3evNmlzfDhw+VwOPTXX3+pQ4cOKlCggOrWrWut//TTT1W9enX5+PioYMGCateunfbv35/l4wOQ83AmE4CbLnVi7QIFCkiS/vzzT9WpU0fFihXToEGD5Ovrq88//1wtWrTQnDlz9Oijj0qSTp8+rXr16mnz5s3q1q2bqlWrpmPHjmn+/Pk6cOCAAgMDdfLkSX300Udq3769evbsqVOnTmnKlCmKiorSmjVrVLVq1Zt01AAAAFJiYqL15ViqwMDA69J379699cUXX6hv376qVKmSjh8/rp9++kmbN29WtWrVJElLlizRQw89pCJFiui5555TSEiINm/erP/7v//Tc889J0n67rvv1KRJE5UuXVrDhw/X2bNnNWHCBNWpU0fr1q1LM1dU69atVbZsWb3++usyxkiSXnvtNQ0dOlRt2rRRjx49dPToUU2YMEH33Xef1q9fr/z581+XYwZwcxEyAbjhUoupc+fOafXq1YqJiZGXl5ceeughSdJzzz2nEiVK6Ndff5WXl5ck6amnnlLdunX10ksvWSHTW2+9pU2bNmnu3LnWMkkaMmSIVdAUKFBAe/bskaenp7W+Z8+eqlChgiZMmKApU6bcqMMGAABIIzIyMs2y1DrGrm+++UY9e/bU6NGjrWUDBw60/j8lJUVPPvmkihQpog0bNrgEPZeOYcCAASpYsKBWrVqlggULSpJatGihu+++W9HR0YqLi3PZb3h4uGbMmGHd37t3r6Kjo/Xqq6/q5Zdftpa3bNlSd999t9577z2X5QBuXYRMAG64y4upsLAwffrppypevLhOnDih77//XiNGjNCpU6d06tQpq11UVJSio6N18OBBFStWTHPmzFF4eLhLwJTK4XBIktzd3eXu7i5JcjqdSkhIkNPpVI0aNdKcKg4AAHCjTZw4UeXKlcuWvvPnz6/Vq1fr77//Tnfi8PXr12v37t0aO3ZsmjOJUmupQ4cOacOGDRo4cKAVMElSlSpV1LhxY3377bdp+u3du7fL/blz58rpdKpNmzYuZ22FhISobNmyWrZsGSETcJsgZAJww6UWU4mJiZo6dap+/PFH64ylHTt2yBijoUOHaujQoeluf+TIERUrVkw7d+7UY489ds39xcXFafTo0dqyZYv+/fdfa/nlV3IBAAC40WrVqnXFib8zIiUlRUePHnVZVrBgQXl6eup///ufOnfurNDQUFWvXl1NmzZVp06dVLp0aUn/zYeZejW79Ozdu1eSVL58+TTrKlasqEWLFqWZ3PvyGmv79u0yxqhs2bLp7sPOvFMAchZCJgA33KXFVIsWLVS3bl116NBBW7duldPplCS9+OKLioqKSnf7O+64I8P7+vTTT9WlSxe1aNFCAwYMUHBwsNzd3TVq1CiXicYBAABuRfv3708T6ixbtkwNGjRQmzZtVK9ePc2bN0+LFy/WW2+9pTfffFNz585VkyZNsm1MPj4+LvedTqccDocWLFhgnWF+KT8/v2wbC4Abi5AJwE2VGvg0bNhQ7777rrp16ybp4jda6c1RcKkyZcpc81K+X3zxhUqXLq25c+dap31LUnR0tP3BAwAA3GQhISFasmSJy7Lw8HDr/4sUKaKnnnpKTz31lI4cOaJq1arptddeU5MmTVSmTBlJ0qZNm65Yd5UsWVKStHXr1jTrtmzZosDAQJezmNJTpkwZGWNUqlSpbPtpIICcwe1mDwAAGjRooFq1amncuHEKCAhQgwYNNHnyZB06dChN20tPB3/sscf0+++/a968eWnapU5Wmfpt2aWTV65evVqrVq263ocBAABww3l7eysyMtLlVqBAAaWkpCgxMdGlbXBwsIoWLark5GRJUrVq1VSqVCmNGzdOCQkJLm1Ta6ciRYqoatWqiouLc2mzadMmLV68WE2bNr3mGFu2bCl3d3fFxMSkmdTcGKPjx49n4cgB5EScyQQgRxgwYIBat26t2NhYTZw4UXXr1tVdd92lnj17qnTp0oqPj9eqVat04MAB/f7779Y2X3zxhVq3bq1u3bqpevXqOnHihObPn69JkyYpPDxcDz30kHX1uWbNmmn37t2aNGmSKlWqpNOnT9/kowYAALiyP/74Q/Pnz5d0cd7KxMREvfrqq5Iunq3UvHnzK2576tQpFS9eXK1atVJ4eLj8/Pz03Xff6ddff7WuNufm5qb3339fzZs3V9WqVdW1a1cVKVJEW7Zs0Z9//qlFixZJunhF3yZNmqh27drq3r27zp49qwkTJihfvnwaPnz4NY+jTJkyevXVVzV48GDt2bNHLVq0kL+/v3bv3q158+apV69eevHFF20+WgByAkImADlCy5YtVaZMGb399tvq2bOnfvvtN8XExCg2NlbHjx9XcHCw7r77bg0bNszaxs/PTytWrFB0dLTmzZunuLg4BQcHq1GjRipevLgkqUuXLjp8+LAmT56sRYsWqVKlSvr00081e/ZsLV++/CYdLQAAwLWtW7cuzYVQUu937tz5qiFT3rx59dRTT2nx4sXW1d3uuOMOvffee+rTp4/VLioqSsuWLVNMTIxGjx4tp9OpMmXKqGfPnlabyMhILVy4UNHR0Ro2bJg8PDxUv359vfnmmxm+kMqgQYNUrlw5jR07VjExMZKk0NBQPfDAA3r44Ycz/JgAyNkc5vLzFQEAAAAAAIBMYk4mAAAAAAAA2EbIBAAAAAAAANsyHTL9+OOPat68uYoWLSqHw6Evv/zymtssX75c1apVk5eXl+644w7FxsamaTNx4kSFhYXJ29tbERERWrNmTWaHBgAAkCNRPwEAgNwg0yFTUlKSwsPDNXHixAy13717t5o1a6aGDRtqw4YNev7559WjRw/rSgWSNGvWLPXv31/R0dFat26dwsPDFRUVpSNHjmR2eAAAADkO9RMAAMgNbE387XA4NG/ePLVo0eKKbV566SV988032rRpk7WsXbt2SkhI0MKFCyVJERERqlmzpt59911JktPpVGhoqJ555hkNGjQoq8MDAADIcaifAADA7Srb52RatWqVIiMjXZZFRUVp1apVkqTz589r7dq1Lm3c3NwUGRlptQEAAMhNqJ8AAMCtKE927+Dw4cMqXLiwy7LChQvr5MmTOnv2rP755x+lpKSk22bLli3p9pmcnKzk5GTrvtPp1IkTJ1SoUCE5HI7rfxAAACDHMsbo1KlTKlq0qNzcbo9rmmRH/SRRQwEAgP9kRw2V7SFTdhg1apRiYmJu9jAAAEAOsn//fhUvXvxmDyNHo4YCAACXu541VLaHTCEhIYqPj3dZFh8fr4CAAPn4+Mjd3V3u7u7ptgkJCUm3z8GDB6t///7W/cTERJUoUUL79+9XQEDA9T8IAACQY508eVKhoaHy9/e/2UO5brKjfpKooQAAwH+yo4bK9pCpdu3a+vbbb12WLVmyRLVr15YkeXp6qnr16lq6dKk1AabT6dTSpUvVt2/fdPv08vKSl5dXmuUBAQEUSAAA5FK308+9sqN+kqihAABAWtezhsr0j+5Onz6tDRs2aMOGDZIuXmJ3w4YN2rdvn6SL35B16tTJat+7d2/t2rVLAwcO1JYtW/Tee+/p888/V79+/aw2/fv314cffqi4uDht3rxZffr0UVJSkrp27Wrz8AAAAG4+6icAAJAbZPpMpt9++00NGza07qeect25c2fFxsbq0KFDVsEkSaVKldI333yjfv36afz48SpevLg++uj/tXfvYVaVdf/4P8MAM5wG5DigCGKC4AFUhBA8JTZqkVoaKiWi4qV5yCjNA4JIxvNUItpDaj0gZKlooNk3xQOJGaKoiEdADgpogEgCgg8gzP37wx9bBgZhWI6M8npd17501r7Xve6119prf3jvtdf63ygpKcm16d27dyxbtiwGDRoUS5YsiU6dOsXEiRO3upglAMCXkfoJANgd5KWU0q4eRFarVq2K+vXrx8qVK53qDQC7GXXAzvPaAcDuqzLqgK/GfX4BAAAA2KWETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGS2UyHTyJEjo3Xr1lFYWBhdu3aNadOmbbPtxx9/HDfccEPsu+++UVhYGB07doyJEyeWaXP99ddHXl5emcf++++/M0MDAKiS1E8AwFddhUOmcePGxYABA2Lw4MExffr06NixY5SUlMR7771XbvuBAwfGHXfcEb/97W/jjTfeiAsvvDBOPfXUeOmll8q0O+CAA2Lx4sW5x7/+9a+dWyMAgCpG/QQA7A4qHDINHz48+vfvH/369YsOHTrE7bffHrVr147Ro0eX2/6uu+6Ka665Jk466aRo06ZNXHTRRXHSSSfFTTfdVKZd9erVo7i4OPdo3Ljxzq0RAEAVo34CAHYHFQqZ1q9fHy+++GL07Nnz0w6qVYuePXvG1KlTy51n3bp1UVhYWGZarVq1tvqmbc6cOdGiRYto06ZN9OnTJxYuXLjNcaxbty5WrVpV5gEAUBVVlfppU79qKACgslQoZHr//fdj48aN0axZszLTmzVrFkuWLCl3npKSkhg+fHjMmTMnSktL4/HHH48JEybE4sWLc226du0aY8aMiYkTJ8Ztt90Wb731Vhx55JHx4YcfltvnsGHDon79+rlHy5YtK7IaAABfmKpSP0WooQCAylXpd5e75ZZbYr/99ov9998/atasGZdcckn069cvqlX7dNEnnnhinH766XHwwQdHSUlJPPzww7FixYq47777yu3z6quvjpUrV+YeixYtquzVAAD4wlRG/RShhgIAKleFQqbGjRtHfn5+LF26tMz0pUuXRnFxcbnzNGnSJB588MFYs2ZNLFiwIGbNmhV169aNNm3abHM5DRo0iLZt28bcuXPLfb6goCCKiorKPAAAqqKqUj9FqKEAgMpVoZCpZs2acdhhh8WkSZNy00pLS2PSpEnRrVu3z5y3sLAw9txzz9iwYUOMHz8+Tj755G22Xb16dcybNy+aN29ekeEBAFQ56icAYHdR4Z/LDRgwIP7whz/E2LFjY+bMmXHRRRfFmjVrol+/fhERcfbZZ8fVV1+da//cc8/FhAkTYv78+fH000/HCSecEKWlpXHllVfm2vzsZz+Lp556Kt5+++145pln4tRTT438/Pw488wzP4dVBADYtdRPAMDuoHpFZ+jdu3csW7YsBg0aFEuWLIlOnTrFxIkTcxezXLhwYZnrBaxduzYGDhwY8+fPj7p168ZJJ50Ud911VzRo0CDX5p133okzzzwzli9fHk2aNIkePXrEs88+G02aNMm+hgAAu5j6CQDYHeSllNKuHkRWq1ativr168fKlStdWwAAdjPqgJ3ntQOA3Vdl1AGVfnc5AAAAAL76hEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMx2KmQaOXJktG7dOgoLC6Nr164xbdq0bbb9+OOP44Ybboh99903CgsLo2PHjjFx4sRMfQIAfNmonwCAr7oKh0zjxo2LAQMGxODBg2P69OnRsWPHKCkpiffee6/c9gMHDow77rgjfvvb38Ybb7wRF154YZx66qnx0ksv7XSfAABfJuonAGB3kJdSShWZoWvXrnH44YfH//zP/0RERGlpabRs2TIuvfTSuOqqq7Zq36JFi7j22mvj4osvzk373ve+F7Vq1Yo//elPO9XnllatWhX169ePlStXRlFRUUVWBwD4kvsy1AFVsX6K+HK8dgBA5aiMOqBCZzKtX78+XnzxxejZs+enHVSrFj179oypU6eWO8+6deuisLCwzLRatWrFv/71r53uEwDgy0L9BADsLioUMr3//vuxcePGaNasWZnpzZo1iyVLlpQ7T0lJSQwfPjzmzJkTpaWl8fjjj8eECRNi8eLFO93nunXrYtWqVWUeAABVUVWpnyLUUABA5ar0u8vdcsstsd9++8X+++8fNWvWjEsuuST69esX1art/KKHDRsW9evXzz1atmz5OY4YAGDXqoz6KUINBQBUrgpVKo0bN478/PxYunRpmelLly6N4uLicudp0qRJPPjgg7FmzZpYsGBBzJo1K+rWrRtt2rTZ6T6vvvrqWLlyZe6xaNGiiqwGAMAXpqrUTxFqKACgclUoZKpZs2YcdthhMWnSpNy00tLSmDRpUnTr1u0z5y0sLIw999wzNmzYEOPHj4+TTz55p/ssKCiIoqKiMg8AgKqoqtRPEWooAKByVa/oDAMGDIi+fftG586do0uXLjFixIhYs2ZN9OvXLyIizj777Nhzzz1j2LBhERHx3HPPxbvvvhudOnWKd999N66//vooLS2NK6+8cof7BICddcsHt+zqIfD/+/EeP97VQ9hl1E8AwO6gwiFT7969Y9myZTFo0KBYsmRJdOrUKSZOnJi78OTChQvLXC9g7dq1MXDgwJg/f37UrVs3TjrppLjrrruiQYMGO9wnAMCXmfoJANgd5KWU0q4eRFarVq2K+vXrx8qVK532DUAZzmSqOirrTCZ1wM7z2gHA7qsy6oBKv7scAAAAAF99QiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgs+q7egBfFm6BXTVU1u2vAQAAgGycyQQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMxc+BsAAAAoa1berh4BERH7p109ggpxJhMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmbvLAQBQOdyZqOr4kt2dCIAvJ2cyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADLbqZBp5MiR0bp16ygsLIyuXbvGtGnTPrP9iBEjol27dlGrVq1o2bJl/OQnP4m1a9fmnr/++usjLy+vzGP//fffmaEBAFRJ6icA4KuuekVnGDduXAwYMCBuv/326Nq1a4wYMSJKSkpi9uzZ0bRp063a33333XHVVVfF6NGj44gjjog333wzzjnnnMjLy4vhw4fn2h1wwAHxxBNPfDqw6hUeGgBAlaR+AgB2BxU+k2n48OHRv3//6NevX3To0CFuv/32qF27dowePbrc9s8880x07949zjrrrGjdunV885vfjDPPPHOrb++qV68excXFuUfjxo13bo0AAKoY9RMAsDuoUMi0fv36ePHFF6Nnz56fdlCtWvTs2TOmTp1a7jxHHHFEvPjii7miaP78+fHwww/HSSedVKbdnDlzokWLFtGmTZvo06dPLFy4cJvjWLduXaxatarMAwCgKqoq9VOEGgoAqFwVOqf6/fffj40bN0azZs3KTG/WrFnMmjWr3HnOOuuseP/996NHjx6RUooNGzbEhRdeGNdcc02uTdeuXWPMmDHRrl27WLx4cQwZMiSOPPLIeO2116JevXpb9Tls2LAYMmRIRYYOALBLVJX6KUINBQBUrkq/u9zkyZPjl7/8Zfzud7+L6dOnx4QJE+Lvf/97DB06NNfmxBNPjNNPPz0OPvjgKCkpiYcffjhWrFgR9913X7l9Xn311bFy5crcY9GiRZW9GgAAX5jKqJ8i1FAAQOWq0JlMjRs3jvz8/Fi6dGmZ6UuXLo3i4uJy57nuuuvihz/8YZx//vkREXHQQQfFmjVr4oILLohrr702qlXbOudq0KBBtG3bNubOnVtunwUFBVFQUFCRoQMA7BJVpX6KUEMBAJWrQmcy1axZMw477LCYNGlSblppaWlMmjQpunXrVu48H3300VaFUH5+fkREpJTKnWf16tUxb968aN68eUWGBwBQ5aifAIDdRYXvcztgwIDo27dvdO7cObp06RIjRoyINWvWRL9+/SIi4uyzz44999wzhg0bFhERvXr1iuHDh8chhxwSXbt2jblz58Z1110XvXr1yhVLP/vZz6JXr17RqlWr+Pe//x2DBw+O/Pz8OPPMMz/HVQUA2DXUT3zlzcrb1SNgk/3LD6IBvggVDpl69+4dy5Yti0GDBsWSJUuiU6dOMXHixNzFLBcuXFjmm7eBAwdGXl5eDBw4MN59991o0qRJ9OrVK2688cZcm3feeSfOPPPMWL58eTRp0iR69OgRzz77bDRp0uRzWEUAgF1L/QQA7A7y0rbOuf4SWbVqVdSvXz9WrlwZRUVFlbKMWz64pVL6pWJ+vMePd/UQgC8Zx++qo7KO4V9EHfBVVemvnbNbqo7KPrvFtq46nMnE58X7umqoxPd0ZdQBlX53OQAAAAC++oRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADIrPquHgBUNe5EVTW4kyAAAMCXizOZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAILPqu3oAAAAAfEnMytvVIyAiYv+0q0cA5XImEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZFZ9Vw8AYFe55YNbdvUQiIgf7/HjXT0EAADgc+BMJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMdipkGjlyZLRu3ToKCwuja9euMW3atM9sP2LEiGjXrl3UqlUrWrZsGT/5yU9i7dq1mfoEAPgyUT8BAF91FQ6Zxo0bFwMGDIjBgwfH9OnTo2PHjlFSUhLvvfdeue3vvvvuuOqqq2Lw4MExc+bMGDVqVIwbNy6uueaane4TAODLRP0EAOwOKhwyDR8+PPr37x/9+vWLDh06xO233x61a9eO0aNHl9v+mWeeie7du8dZZ50VrVu3jm9+85tx5plnlvmmraJ9AgB8maifAIDdQYVCpvXr18eLL74YPXv2/LSDatWiZ8+eMXXq1HLnOeKII+LFF1/MFUXz58+Phx9+OE466aSd7nPdunWxatWqMg8AgKqoqtRPEWooAKByVa9I4/fffz82btwYzZo1KzO9WbNmMWvWrHLnOeuss+L999+PHj16REopNmzYEBdeeGHudO+d6XPYsGExZMiQigwdAGCXqCr1U4QaCgCoXJV+d7nJkyfHL3/5y/jd734X06dPjwkTJsTf//73GDp06E73efXVV8fKlStzj0WLFn2OIwYA2LUqo36KUEMBAJWrQmcyNW7cOPLz82Pp0qVlpi9dujSKi4vLnee6666LH/7wh3H++edHRMRBBx0Ua9asiQsuuCCuvfbaneqzoKAgCgoKKjJ0AIBdoqrUTxFqKACgclXoTKaaNWvGYYcdFpMmTcpNKy0tjUmTJkW3bt3Kneejjz6KatXKLiY/Pz8iIlJKO9UnAMCXhfoJANhdVOhMpoiIAQMGRN++faNz587RpUuXGDFiRKxZsyb69esXERFnn3127LnnnjFs2LCIiOjVq1cMHz48DjnkkOjatWvMnTs3rrvuuujVq1euWNpenwAAX2bqJwBgd1DhkKl3796xbNmyGDRoUCxZsiQ6deoUEydOzF14cuHChWW+eRs4cGDk5eXFwIED4913340mTZpEr1694sYbb9zhPgEAvszUTwDA7iAvpZR29SCyWrVqVdSvXz9WrlwZRUVFlbKMWz64pVL6pWJ+vMePK30ZtnXVYFvvPip7W9vOVUdlbesvog74qqr0125W3uffJztn/0ou+W3rqsO23j1U9naOsK2rikrc1pVRB1T63eUAAAAA+OoTMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMtupkGnkyJHRunXrKCwsjK5du8a0adO22faYY46JvLy8rR7f+ta3cm3OOeecrZ4/4YQTdmZoAABVlhoKAPgqq17RGcaNGxcDBgyI22+/Pbp27RojRoyIkpKSmD17djRt2nSr9hMmTIj169fn/l6+fHl07NgxTj/99DLtTjjhhLjzzjtzfxcUFFR0aAAAVZYaCgD4qqvwmUzDhw+P/v37R79+/aJDhw5x++23R+3atWP06NHltm/YsGEUFxfnHo8//njUrl17qwKpoKCgTLs99thj59YIAKAKUkMBAF91FQqZ1q9fHy+++GL07Nnz0w6qVYuePXvG1KlTd6iPUaNGxRlnnBF16tQpM33y5MnRtGnTaNeuXVx00UWxfPnyigwNAKDKUkMBALuDCv1c7v3334+NGzdGs2bNykxv1qxZzJo1a7vzT5s2LV577bUYNWpUmeknnHBCfPe734199tkn5s2bF9dcc02ceOKJMXXq1MjPz9+qn3Xr1sW6detyf69ataoiqwEA8IVSQwEAu4MKX5Mpi1GjRsVBBx0UXbp0KTP9jDPOyP3/QQcdFAcffHDsu+++MXny5DjuuOO26mfYsGExZMiQSh8vAEBVoIYCAL4MKvRzucaNG0d+fn4sXbq0zPSlS5dGcXHxZ867Zs2auPfee+O8887b7nLatGkTjRs3jrlz55b7/NVXXx0rV67MPRYtWrTjKwEA8AVTQwEAu4MKhUw1a9aMww47LCZNmpSbVlpaGpMmTYpu3bp95rz3339/rFu3Ln7wgx9sdznvvPNOLF++PJo3b17u8wUFBVFUVFTmAQBQVamhAIDdQYXvLjdgwID4wx/+EGPHjo2ZM2fGRRddFGvWrIl+/fpFRMTZZ58dV1999VbzjRo1Kk455ZRo1KhRmemrV6+OK664Ip599tl4++23Y9KkSXHyySfH1772tSgpKdnJ1QIAqFrUUADAV12Fr8nUu3fvWLZsWQwaNCiWLFkSnTp1iokTJ+YuZLlw4cKoVq1sdjV79uz417/+FY899thW/eXn58crr7wSY8eOjRUrVkSLFi3im9/8ZgwdOjQKCgp2crUAAKoWNRQA8FW3Uxf+vuSSS+KSSy4p97nJkydvNa1du3aRUiq3fa1ateLRRx/dmWEAAHypqKEAgK+yCv9cDgAAAAC2JGQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkJmQCQAAAIDMhEwAAAAAZCZkAgAAACAzIRMAAAAAmQmZAAAAAMhMyAQAAABAZkImAAAAADITMgEAAACQmZAJAAAAgMyETAAAAABkJmQCAAAAIDMhEwAAAACZCZkAAAAAyEzIBAAAAEBmQiYAAAAAMhMyAQAAAJCZkAkAAACAzIRMAAAAAGQmZAIAAAAgs50KmUaOHBmtW7eOwsLC6Nq1a0ybNm2bbY855pjIy8vb6vGtb30r1yalFIMGDYrmzZtHrVq1omfPnjFnzpydGRoAQJWlhgIAvsoqHDKNGzcuBgwYEIMHD47p06dHx44do6SkJN57771y20+YMCEWL16ce7z22muRn58fp59+eq7Nr371q7j11lvj9ttvj+eeey7q1KkTJSUlsXbt2p1fMwCAKkQNBQB81VU4ZBo+fHj0798/+vXrFx06dIjbb789ateuHaNHjy63fcOGDaO4uDj3ePzxx6N27dq5AimlFCNGjIiBAwfGySefHAcffHD88Y9/jH//+9/x4IMPZlo5AICqQg0FAHzVVa9I4/Xr18eLL74YV199dW5atWrVomfPnjF16tQd6mPUqFFxxhlnRJ06dSIi4q233oolS5ZEz549c23q168fXbt2jalTp8YZZ5yxVR/r1q2LdevW5f5euXJlRESsWrWqIqtTIWtX+UawKliVX3nbeBPbumqwrXcflb2tbeeqo7K29abP/5RSpfT/edhta6jVldMtO6ES6+SIsK2rEtt691DZ2znCtq4qKnFbV0YNVaGQ6f3334+NGzdGs2bNykxv1qxZzJo1a7vzT5s2LV577bUYNWpUbtqSJUtyfWzZ56bntjRs2LAYMmTIVtNbtmy53THw5XZVXLWrh8AXxLbefdjWu4/K3tYffvhh1K9fv1KXsbPUUOx6VfO9QWWwrXcPtvPuo/K39edZQ1UoZMpq1KhRcdBBB0WXLl0y9XP11VfHgAEDcn+XlpbGf/7zn2jUqFHk5eVlHeZX0qpVq6Jly5axaNGiKCoq2tXDoRLZ1rsP23r3YVt/tpRSfPjhh9GiRYtdPZRKo4baNbz3dh+29e7Dtt592NbbVxk1VIVCpsaNG0d+fn4sXbq0zPSlS5dGcXHxZ867Zs2auPfee+OGG24oM33TfEuXLo3mzZuX6bNTp07l9lVQUBAFBQVlpjVo0GAH12L3VlRU5A22m7Ctdx+29e7Dtt62qnoG0yZqqC83773dh229+7Ctdx+29Wf7vGuoCl34u2bNmnHYYYfFpEmTctNKS0tj0qRJ0a1bt8+c9/77749169bFD37wgzLT99lnnyguLi7T56pVq+K5557bbp8AAF8GaigAYHdQ4Z/LDRgwIPr27RudO3eOLl26xIgRI2LNmjXRr1+/iIg4++yzY88994xhw4aVmW/UqFFxyimnRKNGjcpMz8vLi8svvzx+8YtfxH777Rf77LNPXHfdddGiRYs45ZRTdn7NAACqEDUUAPBVV+GQqXfv3rFs2bIYNGhQLFmyJDp16hQTJ07MXXRy4cKFUa1a2ROkZs+eHf/617/iscceK7fPK6+8MtasWRMXXHBBrFixInr06BETJ06MwsLCnVglylNQUBCDBw/e6hR5vnps692Hbb37sK2/GtRQXz7ee7sP23r3YVvvPmzrXSMvVeX7/QIAAADwpVChazIBAAAAQHmETAAAAABkJmQCAAAAIDMh0+ekdevWMWLEiJ2ef8yYMdGgQYPPbTxfJTvz2ubl5cWDDz74mW3OOeecKnP3nR0Z7+auv/766NSpU6WNZ3OTJ0+OvLy8WLFixReyvK+6im5r+KrbkePZMcccE5dffvkXMh6+eGqoyqOG2poa6stLDQWfqsr1024RMn0RH4TPP/98XHDBBTvUtrwP/N69e8ebb76508sfM2ZM5OXlRV5eXlSrVi2aN28evXv3joULF+50n1+Uc845J/Ly8uLCCy/c6rmLL744FixYEC+88MJO9//2229HXl5ezJgxo8z0W265JcaMGbPT/X6eFi9eHCeeeOLn1t+mdW7atGl8+OGHZZ7r1KlTXH/99Tvc1xFHHBGLFy+O+vXrf27j29Km8W561KxZM772ta/FL37xi/i8702waX/Ly8uLGjVqxD777BNXXnllrF279nNdTlWz+Xpv/pg7d26lL3vZsmVx0UUXxd577x0FBQVRXFwcJSUlcdJJJ1WZf6Rsz478Q2H8+PGRn58f7777brnP77fffjFgwIDMY8n6D/Lt2d4xOS8vL84555xKW355JkyYEEOHDv1Cl8kn1FBVmxpKDaWGqny7qobaVv00ZcqUKhX0fhb10yd2t/pptwiZvghNmjSJ2rVr7/T8tWrViqZNm2YaQ1FRUSxevDjefffdGD9+fMyePTtOP/30TH3uiI8//jhzHy1btox77703/u///i83be3atXH33XfH3nvvHdWrV8+8jC3Vr1+/ynzzWVxcXCm31vzwww/jN7/5TaY+atasGcXFxZGXl/c5jWrbnnjiiVi8eHHMmTMnhgwZEjfeeGOMHj36c1/OCSecEIsXL4758+fHzTffHHfccUcMHjz4c19OVbNpvTd/7LPPPjvV1/r163e47fe+97146aWXYuzYsfHmm2/GQw89FMccc8yXpijd0WPcd77znWjUqFGMHTt2q+f++c9/xty5c+O88877vIe30z5rG27vmPxFa9iwYdSrV+8LXy5fDDVUNmooNVSEGqqy7Yoaalv10/Lly3dquV809dMndsf6ScgUEU899VR06dIlCgoKonnz5nHVVVfFhg0bcs9/+OGH0adPn6hTp040b948br755q1OPds8GU0pxfXXX59LnVu0aBGXXXZZRHxyytqCBQviJz/5SS4Fjyj/VO+//e1vcfjhh0dhYWE0btw4Tj311M9cj7y8vCguLo7mzZvHEUccEeedd15MmzYtVq1alWvz17/+NQ499NAoLCyMNm3axJAhQ8qs66xZs6JHjx5RWFgYHTp0iCeeeKLMqambvi0ZN25cHH300VFYWBh//vOfIyLif//3f6N9+/ZRWFgY+++/f/zud7/L9bt+/fq45JJLonnz5lFYWBitWrWKYcOG5Z6vXbt2fPTRR1GvXr3c6zVhwoTYe++9Y9myZfHGG2/k2rZs2TIOPPDAqFu3bhQVFcX3v//9OPDAA3PfLG367+TJk6N169a5D4BDDjkk8vLy4phjjomIrb+dPeaYY+Kyyy6LK6+8Mho2bBjFxcVbfVu1cOHCOPnkk8sse+nSpbnnN522OHr06Nh7772jbt268aMf/Sg2btwYv/rVr6K4uDiaNm0aN95441bbbvPTf3/+859H27Zto3bt2tGmTZu47rrrdqoQvfTSS2P48OHx3nvvbbPNXXfdFZ07d4569epFcXFxnHXWWWXab/4NxKpVq6JWrVrxyCOPlOnjgQceiHr16sVHH30UERGLFi2K73//+9GgQYNo2LBhnHzyyfH2229vd7yNGjWK4uLiaNWqVfTp0ye6d+8e06dPzz3//PPPx/HHHx+NGzeO+vXrx9FHH13m+XPPPTe+/e1vl+nz448/jqZNm8aoUaMi4pP354IFC6Jbt27Rtm3bGDx4cLRv3z4ef/zxiIj44IMP4rTTTouCgoLct9p77bVX3HPPPWX63ZH9Zc6cOXHUUUfl3k+blrG5V199Nb7xjW9ErVq1olGjRnHBBRfE6tWrc89v2k9/+ctfRrNmzaJBgwZxww03xIYNG+KKK66Ihg0bxl577RV33nnndl/fTd+Cbf7Iz8+PiO0fB4855pi45JJL4vLLL4/GjRtHSUlJRES89tprceKJJ0bdunWjWbNm8cMf/jDef//93Hxjx46Np59+Ol566aU47bTT4rzzzosDDjgg1q1bF08++WT89a9/zR0L//d//zc334oVKyIvLy8mT54cEZ/uh3//+9/j4IMPjsLCwvj6178er732Wm6eTcfRBx98MPbbb78oLCyMkpKSWLRoUZnX4bbbbot99903atasGe3atYu77rqrzPN5eXlx2223xXe+852oU6dO9O/fP4499tiIiNhjjz22+U1UjRo14oc//GG53+6PHj06unbtGgcccECsWLEizj///GjSpEkUFRXFN77xjXj55ZfLtN/W8X9bnyERn3wTeMABB0RBQUG0bt06brrppjJ9tm7dOoYOHRpnn312FBUVfeaZI4ceemi0bNkyJkyYkJu26Zh8yCGHlGk7ceLE6NGjRzRo0CAaNWoU3/72t2PevHll2rzzzjtx5plnRsOGDaNOnTrRuXPneO6558q0ueuuu6J169ZRv379OOOMM8qcQVDeZ+4vf/nLOPfcc6NevXqx9957x+9///sy/e3scYiKUUOpoTZtGzWUGkoN9fnVUCtWrIinn3463nvvvTjppJPi0EMPjWuuuSYuu+yymD59eowdO7ZMDbWpXlI/qZ+qRP2UdgN9+/ZNJ598crnPvfPOO6l27drpRz/6UZo5c2Z64IEHUuPGjdPgwYNzbc4///zUqlWr9MQTT6RXX301nXrqqalevXrpxz/+ca5Nq1at0s0335xSSun+++9PRUVF6eGHH04LFixIzz33XPr973+fUkpp+fLlaa+99ko33HBDWrx4cVq8eHFKKaU777wz1a9fP9ff//t//y/l5+enQYMGpTfeeCPNmDEj/fKXv9zmOm45/9KlS9Oxxx6b8vPz0+rVq1NKKf3zn/9MRUVFacyYMWnevHnpscceS61bt07XX399SimlDRs2pHbt2qXjjz8+zZgxIz399NOpS5cuKSLSAw88kFJK6a233koRkVq3bp3Gjx+f5s+fn/7973+nP/3pT6l58+a5aePHj08NGzZMY8aMSSml9Otf/zq1bNky/fOf/0xvv/12evrpp9Pdd9+dUkrp6KOPTtWrV0/9+/dP3bt3z71exx13XLr55ptTrVq10uGHH55SSmnjxo2pRo0aaZ999kkvvPBCevbZZ9Nhhx2W6tSpk9tmgwcPThGRvv71r6dXX3013XHHHSki0plnnpkWL16cli9fXu5+cfTRR6eioqJ0/fXXpzfffDONHTs25eXlpcceeyy37E6dOqUePXqUWfbRRx+d62Pw4MGpbt266bTTTkuvv/56euihh1LNmjVTSUlJuvTSS9OsWbPS6NGjU0SkZ599Njff5q9xSikNHTo0TZkyJb311lvpoYceSs2aNUv//d//XWY5HTt23Ob+sGk7TZ8+PXXq1CldfPHFuec6duxYZv8eNWpUevjhh9O8efPS1KlTU7du3dKJJ56Ye/7JJ59MEZE++OCDlFJKp512WvrBD35QZnnf+973ctPWr1+f2rdvn84999z0yiuvpDfeeCOdddZZqV27dmndunWfOd6XXnopN+35559PDRo0SGPHjs1NmzRpUrrrrrvSzJkz0xtvvJHOO++81KxZs7Rq1aqUUkpTpkxJ+fn56d///ndungkTJqQ6deqkDz/8MKWU0iGHHJLq1q2bJk6cmObNm5eGDh2aIiK1b98+pZTSxRdfnDp06JAuu+yy9Pe//z398Y9/TBdccEHKz89Pzz33XK7fHdlfDjzwwHTcccelGTNmpKeeeiodcsghZbb16tWrU/PmzdN3v/vd9Oqrr6ZJkyalffbZJ/Xt2ze3nL59+6Z69eqliy++OM2aNSuNGjUqRUQqKSlJN954Y3rzzTfT0KFDU40aNdKiRYvKfX039ZPlOHj00UenunXrpiuuuCLNmjUrzZo1K33wwQepSZMm6eqrr04zZ85M06dPT8cff3w69thjU0op/fvf/075+fmpoKAgnXvuuemFF15II0eOTB9++GH68MMP0/e///10wgknpGnTpqWISNOmTcst74MPPkgRkZ588smU0qf7Yfv27dNjjz2WXnnllfTtb387tW7dOq1fvz6l9MlxsEaNGqlz587pmWeeSS+88ELq0qVLOuKII8rsDzVq1EgjR45Ms2fPTjfddFPKz89P//jHP3JtIiI1bdo0jR49Os2bNy+9/fbbafz48Ski0uzZs9PixYvTihUryn0tX3/99RQR6amnnspN+/DDD1OdOnVynwM9e/ZMvXr1Ss8//3x68803009/+tPUqFGj3LHps47/2/oMeeGFF1K1atXSDTfckGbPnp3uvPPOVKtWrXTnnXfmxtGqVatUVFSUfvOb36S5c+emuXPnfua+Mnz48HTcccflpm86Jp988sll9tG//OUvafz48WnOnDnppZdeSr169UoHHXRQ2rhxY27927Rpk4488sj09NNPpzlz5qRx48alZ555JqX06XFz0/vgn//8ZyouLk7XXHNNmf1vy8/chg0bppEjR6Y5c+akYcOGpWrVqqVZs2allHbuOET51FBqKDWUGkoN9cXXUAsXLkwRkY466qg0a9as9Morr2xVQx111FEpItLjjz+e2z/UT+qnqlA/7fYh0zXXXJPatWuXSktLc9NGjhyZ6tatmzZu3JhWrVqVatSoke6///7c8ytWrEi1a9feZoF00003pbZt2+beuFvavO0mWxY43bp1S3369NnhdbzzzjtTRKQ6deqk2rVrp4hIEZEuu+yyXJvjjjtuqyLrrrvuSs2bN08ppfTII4+k6tWr5950KaX0+OOPl1sgjRgxokw/++67b67g2WTo0KGpW7duKaWULr300vSNb3yjzOu8SefOnVOdOnXSu+++mwoKCtLbb7+d3n777VRYWJiWLVtWpkB67LHHUkSkQYMG5ebfdFA6//zzU0qfFkibxrNpzAceeGCZ5ZZXIPXo0aNMm8MPPzz9/Oc/zy07Pz8/LVy4cKtlb/rH8eDBg1Pt2rVzH9gppVRSUpJat26dO2CklFK7du3SsGHDcn9vWSBt6de//nU67LDDcn/vaIH00ksvpYkTJ6YaNWrkDohbFkhbev7551NE5AqKLQukBx54INWtWzetWbMmpZTSypUrU2FhYXrkkUdSSp/sU1u+p9atW5dq1aqVHn300c8cb61atVKdOnVSjRo1UkSkCy64YJvjTOmTIqRevXrpb3/7W25ahw4dyhSTvXr1Suecc05KKaW1a9em/Pz8lJ+fn+rUqZMKCgpSRKS8vLzctu/Vq1fq16/fVsv61re+lX7605/m/t7e/vLoo4+m6tWrp3fffTf3/COPPFJmW//+979Pe+yxR+4fMSml9Pe//z1Vq1YtLVmyJKX0yX7aqlWrrfafI488Mvf3hg0bUp06ddI999yzzdeqb9++ufXe9DjttNNSSts/Dm5a30MOOaRMn0OHDk3f/OY3y0xbtGhRrph48cUXU0Sk2267Le2xxx6psLAwHXHEEenqq69OL7/8cu49WF6BvK0i6d577821Wb58eapVq1YaN25cSunT4+Dm//iYOXNmiohccXvEEUek/v37lxnz6aefnk466aTc3xGRLr/88jJttnwffJavf/3rZYqIUaNG5Y4LTz/9dCoqKkpr164tM8++++6b7rjjjpTS9o//5X2GnHXWWen4448vM+2KK65IHTp0KDPfKaecst3xb9ou7733XrnH5C2LpC0tW7YsRUR69dVXU0op3XHHHalevXq5InBL5R03r7jiitS1a9fc3+UVSZv/Q620tDQ1bdo03XbbbSmlnTsOUT411CfUUGqolNRQaqgvvoaqX7/+VvXTpjEdf/zx6if1U5Wsn3b7n8vNnDkzunXrVuaUue7du8fq1avjnXfeifnz58fHH38cXbp0yT1fv379aNeu3Tb7PP300+P//u//ok2bNtG/f/944IEHypwyuSNmzJgRxx13XIXmqVevXsyYMSNeeOGFuOmmm+LQQw8tc0rxyy+/HDfccEPUrVs39+jfv38sXrw4Pvroo5g9e3a0bNkyiouLc/Nsvt6b69y5c+7/16xZE/PmzYvzzjuvTN+/+MUvcqf8nXPOOTFjxoxo165dXHbZZfHYY4/l5m/dunWUlpZG165do3nz5vHzn/88Ro0aFd/61reicePGZZY7c+bMyM/Pjz322CM3rUOHDlGtWrUyP9GJ+OQaDZv7z3/+s93X8OCDDy7zd/PmzXOnPc+cOTNatmwZLVu2LLPsBg0axMyZM8usz+a/fW3WrFlujJtP+6zTr8eNGxfdu3eP4uLiqFu3bgwcOHCnL0BaUlISPXr0iOuuu67c51988cXo1atX7L333lGvXr04+uijIyK2ubyTTjopatSoEQ899FBEfHKKaVFRUfTs2TMiPtnP5s6dG/Xq1cvtCw0bNoy1a9dudQpoees9Y8aMePnll+O+++6Lv/71r3HVVVflnl+6dGn0798/9ttvv6hfv34UFRXF6tWry4z1/PPPz532vHTp0njkkUfi3HPPjYiIuXPnxsaNGyOlFKWlpZGXl5e7VsWm31dfdNFFcc8990Tz5s1zp+PWrVs3Hn300a1ekx3ZX1q0aJF7vlu3bmXaz5w5Mzp27Bh16tTJTevevXuUlpbG7Nmzc9MOOOCArfafgw46KPd3fn5+NGrU6DP3qYiIY489NmbMmJF73HrrrblxfNZxcJPDDjusTH8vv/xyPPnkk2Xe9/vvv39ERMybNy86duwYxx13XFx55ZVxzDHHxIUXXhhHH310TJ48OQ499NCdumDm5q9hw4YNo127dmXef9WrV4/DDz889/f+++9f5j06c+bM6N69e5k+u3fvXqaPiLLHuIo699xz4y9/+UvudOXRo0fH6aefHvXq1YuXX345Vq9eHY0aNSrzur311lu598fOHP+3tV5z5syJjRs37tR6NWnSJL71rW/FmDFj4s477yz3mBzxyU8azjzzzGjTpk0UFRVF69atI+LTY8iMGTPikEMOiYYNG25zWVseNzd/L23L5u+/TT912jRPluMQO04NpYbanBpKDaWG+nQcn1cNtXHjxjj88MOjcePG8cQTT8Shhx66UxfdVz+VT/1UOfXT538lQKJly5Yxe/bseOKJJ+Lxxx+PH/3oR/HrX/86nnrqqahRo8YO9bHlh/uOqFatWnzta1+LiIj27dvHvHnz4qKLLsr9Xnb16tUxZMiQ+O53v7vVvIWFhRVa1uYH9E2/ff7DH/4QXbt2LdNu02+VDz300HjrrbfikUceiSeeeCK+//3vR8+ePeMvf/lL1KlTJ4477ri44IIL4ve//3385S9/ierVq8d9991X7rLz8vJ26m4ZOzLPltsnLy8vSktLK7Sc8vqoSL9Tp06NPn36xJAhQ6KkpCTq168f995771a/D66I//qv/4pu3brFFVdcUWb6mjVroqSkJEpKSuLPf/5zNGnSJBYuXBglJSXbvKhdzZo147TTTou77747zjjjjLj77rujd+/euUJj9erVcdhhh+WuM7G5Jk2afOY4W7ZsudU+fN1118X1118fhYWF0bdv31i+fHnccsst0apVqygoKIhu3bqVGevZZ58dV111VUydOjWeeeaZ2GeffeLII4/MjS3ikw/aTR/QpaWl0atXrzjttNMiIuLEE0+Mn/3sZzFixIg4+OCD44UXXogf/OAHsXTp0q1ek89jf9kRWfepTerUqZN7fXfG5u/7iE9ez169esV///d/b9W2efPmkZ+fH48//ng888wz8dhjj8UDDzwQS5Ysieeeey5uvPHGuPfee6Nnz5654m/z9+jncTHcLLZc14o444wz4ic/+Uncd999cdRRR8WUKVNy109ZvXp1NG/ePHethM1tuqbMzhz/d1RF1+vcc8+NSy65JCIiRo4cWW6bXr16RatWreIPf/hDtGjRIkpLS+PAAw/MvV92ZH12Zn/+rHmyHIfYtdRQn1JDqaE2p4baOV+1Gup73/teDB48OHetowj1k/rpU1Wlftrtz2Rq3759TJ06tcybc8qUKVGvXr3Ya6+9ok2bNlGjRo14/vnnc8+vXLlyu7fKrVWrVvTq1StuvfXWmDx5ckydOjVeffXViPjkA2bzZLQ8Bx98cEyaNCnDmkVcddVVMW7cuNxF/Q499NCYPXt2fO1rX9vqUa1atWjXrl0sWrSozEUYN1/vbWnWrFm0aNEi5s+fv1W/m991oaioKHr37h1/+MMfYty4cTF+/PjcN2P5+fnRq1evePDBB6NRo0axbt26aN68+VbLat++fWzYsKHMNxTTpk2L0tLSbe74NWvWjIgdK5A+S/v27WPRokVlLoL3xhtvxIoVK6JDhw6Z+t7cM888E61atYprr702OnfuHPvtt18sWLAgU59dunSJ7373u2W+0Yr45CKly5cvj//6r/+KI488Mvbff//tpt8REX369ImJEyfG66+/Hv/4xz+iT58+uecOPfTQmDNnTjRt2nSr/aGit/DNz8+PDRs25A62U6ZMicsuuyxOOumk3AX6tvz2tVGjRnHKKafEnXfeGWPGjIl+/frlntv0bej//d//5cbUtm3buP7662P48OG5O0HMmDEjvv/978eUKVPi1ltvjfvuu6/Ct8fetL8sXrw4N+3ZZ5/dqs3LL78ca9asyU2bMmVK7v34RdnecXBbDj300Hj99dejdevWW23rTR/GeXl50b179xgyZEi89NJLUbNmzXjggQeiQ4cOsWHDhti4cWPuvbv5a7Xl7bI32fw1/OCDD+LNN9+M9u3b56Zt2LChzO26Z8+eHStWrMi1ad++fUyZMqVMn1OmTNnue3jTcWR7x+6IT86IOP3002P06NFx5513Rtu2bXNF+qGHHhpLliyJ6tWrb/WabfqWa3vH//I+Q7a1Xm3bts39Q3VnnHDCCbF+/fr4+OOPcxcq3dzy5ctj9uzZMXDgwDjuuOOiffv28cEHH5Rpc/DBB8eMGTN26EyIz8vneRxi29RQaqgdpYb6lBpq+9RQ266h1q5dG2vWrImaNWvmwkn1k/rp8/J5HYN2m5Bp5cqVZU5xnDFjRixatCh+9KMfxaJFi+LSSy+NWbNmxV//+tcYPHhwDBgwIKpVqxb16tWLvn37xhVXXBFPPvlkvP7663HeeedFtWrVtnk70jFjxsSoUaPitddei/nz58ef/vSnqFWrVrRq1SoiPjmt7Z///Ge8++67Wx3cNxk8eHDcc889MXjw4Jg5c2a8+uqr5Sbdn6Vly5Zx6qmnxqBBgyIiYtCgQfHHP/4xhgwZEq+//nrMnDkz7r333hg4cGBERBx//PGx7777Rt++feOVV16JKVOm5J7b3q1XhwwZEsOGDYtbb7013nzzzXj11VfjzjvvjOHDh0dExPDhw+Oee+6JWbNmxZtvvhn3339/FBcXR4MGDWLu3LmxYMGCeO2112LBggXRt2/f3J1bttSzZ89o2rRpjB49OkaNGhV33313lJSURH5+fplTajfXtGnTqFGjRqxduzaWLl0aK1eurNDruPmyDzrooOjTp09Mnz49pk2bFmeffXYcffTRmU4N3dJ+++0XCxcujHvvvTfmzZsXt956azzwwAOZ+73xxhvjH//4R5nicu+9946aNWvGb3/725g/f3489NBDMXTo0O32ddRRR0VxcXH06dMn9tlnnzLfvvbp0ycaN24cJ598cjz99NPx1ltvxeTJk+Oyyy4rc9pweZYvXx5LliyJd955Jx555JG45ZZb4thjj42ioqKI+OS1ueuuu2LmzJnx3HPPRZ8+fcpN+c8///wYO3ZszJw5M/r27ZubXq9evTjwwAPj1VdfjbFjx8a8efNi+vTpsXTp0li3bl2MHDkyBg0aFNWqVYtHHnkk/vSnP8U999wTtWrVKvMPhx3Rs2fPaNu2bfTt2zdefvnlePrpp+Paa68t06ZPnz65bxdfe+21ePLJJ+PSSy+NH/7wh9GsWbMKLS+L7R0Ht+Xiiy+O//znP3HmmWfG888/H/PmzYtHH300+vXrFxs3boznnnsuBg4cGIcffniMGDEibr755njvvffi/fffj1/96ldx4IEHxiuvvBILFy6Mzp07x7Bhw2LmzJnx1FNP5Y49W7rhhhti0qRJ8dprr8U555wTjRs3LnOHoxo1asSll14azz33XLz44otxzjnnxNe//vXcz1auuOKKGDNmTNx2220xZ86cGD58eEyYMCF+9rOffeZr1KpVq8jLy4v/9//+XyxbtqzM3WvKc95558UzzzwTt99+e+6nBhGf7BfdunWLU045JR577LF4++2345lnnolrr702V9xt7/hf3mfIT3/605g0aVIMHTo03nzzzRg7dmz8z//8z3bXa3vy8/Nj5syZ8cYbb5RbbO2xxx7RqFGj+P3vfx9z586Nf/zjHzFgwIAybc4888woLi6OU045JaZMmRLz58+P8ePHx9SpUzON7bNkOQ6xNTWUGkoNpYZSQ5Wvsmqoxx57LPbdd9+44YYb4tFHH43bb789li5dGk8//XScfPLJ0bp163j99dejY8eOMXTo0HjllVfUT+qnzD63+mmHr970Jda3b9/cRRw3f5x33nkppZQmT56cDj/88FSzZs1UXFycfv7zn6ePP/44N/+qVavSWWedlWrXrp2Ki4vT8OHDU5cuXdJVV12Va7P5hcQeeOCB1LVr11RUVJTq1KmTvv71r6cnnngi13bq1Knp4IMPzl0wL6WtL1qZUkrjx49PnTp1SjVr1kyNGzdO3/3ud7e5juXNv2lZsdlF2yZOnJiOOOKIVKtWrVRUVJS6dOmSu2J/Sp9c5K179+6pZs2aaf/9909/+9vfUkSkiRMnppTKv4PFJn/+859z491jjz3SUUcdlSZMmJBS+uTifJ06dUp16tRJRUVF6bjjjkvTp09PKaV07LHHpj322GObr9fmF61MKaXXXnsttWjRInexwcMPPzwdcMABW90ZZfOLQPbu3Tvl5+enatWq5e5kUt5FKze/MFpKaasLtC1YsCB95zvfSXXq1En16tVLp59+eu7igpuWveXFJMu7aOqWy9pyvFdccUVq1KhRqlu3burdu3e6+eaby2zfily0cnMXXHBBiogyF628++67U+vWrVNBQUHq1q1beuihh8rMu60L9l155ZVbXUB0k8WLF6ezzz47NW7cOBUUFKQ2bdqk/v37p5UrV37meDc98vPz01577ZX69++f3nvvvVy76dOnp86dO6fCwsK03377pfvvv7/ci/iVlpamVq1albkY4SZ9+/ZNBx54YGrXrl2qUaNGatKkSSopKUkXXHBBatKkSbruuutS27ZtU7Vq1VJEpIKCgnTJJZeks88+u8L7y+zZs1OPHj1SzZo1U9u2bdPEiRO32tavvPJKOvbYY1NhYWFq2LBh6t+/f+6CoZvGu739J6XyL2a45Xpv6+K9KW3/OFjeMlNK6c0330ynnnpqatCgQapVq1baf//90+WXX55KS0vTG2+8kY4//vhUq1atlJeXl6pVq5Zq1qyZ2rVrlwYOHJgWLFiQjj/++FS3bt0UEemAAw5ItWrVSp06dcpdoHbLC1f+7W9/SwcccECqWbNm6tKlS+4CmCl9ehwcP358atOmTSooKEg9e/ZMCxYsKDPm3/3ud6lNmzapRo0aqW3btumPf/xjmee33Eab3HDDDam4uDjl5eV95oUbN2nXrt1Wd+pJ6ZPPlEsvvTS1aNEi1ahRI7Vs2TL16dOnzAVxP+v4X95nSEqf3KWkQ4cOqUaNGmnvvfdOv/71r8ssd3v7yCbb21e23M8ff/zx1L59+1RQUJAOPvjgNHny5K1ew7fffjt973vfS0VFRal27dqpc+fOuc+l8o5nN998c2rVqlXu7/IuXLnlumx5Qd6KHoconxpKDaWG+oQaSg21LZVRQ82YMSPts88+qXr16rn3a9OmTdPAgQPTRx99lN577710/PHH525WUFBQoH5K6qeqUj/lpZTx/Nfd0Jo1a2LPPfeMm266Kc4777xdPZxKNWXKlOjRo0fMnTs39t133109HNghq1evjj333DPuvPPOcq+fwZfP5MmT49hjj40PPvgg99v7LY0ZMyYuv/zyWLFixRc6NmDHqaGgalNDfbWon9gVXPh7B7z00ksxa9as6NKlS6xcuTJuuOGGiIg4+eSTd/HIPn8PPPBA1K1bN/bbb7+YO3du/PjHP47u3bsrjvhSKC0tjffffz9uuummaNCgQXznO9/Z1UMC2K2podRQfDmooYDPi5BpB/3mN7+J2bNnR82aNeOwww6Lp59+utxbEX7Zffjhh/Hzn/88Fi5cGI0bN46ePXtmuiMHfJEWLlwY++yzT+y1114xZsyY3AURAdh11FBQ9amhgM+Ln8sBAAAAkNluc3c5AAAAACqPkAkAAACAzIRMAAAAAGQmZAIAAAAgMyETAAAAAJkJmQAAAADITMgEAAAAQGZCJgAAAAAyEzIBAAAAkNn/B6JpDqySoojkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix\n",
        "from sklearn.pipeline import Pipeline\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "# Step 1: Vectorize the text data using TfidfVectorizer\n",
        "vectorizer = TfidfVectorizer(max_features=5000)\n",
        "X_train_vec = vectorizer.fit_transform(df_train['Review'])\n",
        "X_test_vec = vectorizer.transform(df_test['Review'])\n",
        "y_train = df_train['Label']\n",
        "y_test = df_test['Label']\n",
        "\n",
        "# Step 2: Split the data into training and validation sets\n",
        "X_train_split, X_val, y_train_split, y_val = train_test_split(X_train_vec, y_train, test_size=0.2, random_state=42)\n",
        "\n",
        "# Step 3: Build and train the models using Pipeline\n",
        "# Pipeline for Logistic Regression\n",
        "pipeline_lr = Pipeline([\n",
        "    ('clf', LogisticRegression(max_iter=1000))\n",
        "])\n",
        "\n",
        "# Pipeline for Multinomial Naive Bayes\n",
        "pipeline_nb = Pipeline([\n",
        "    ('clf', MultinomialNB())\n",
        "])\n",
        "\n",
        "# Pipeline for Random Forest\n",
        "pipeline_rf = Pipeline([\n",
        "    ('clf', RandomForestClassifier(random_state=42))\n",
        "])\n",
        "\n",
        "# Pipeline for Support Vector Machine (SVM)\n",
        "pipeline_svm = Pipeline([\n",
        "    ('clf', SVC(probability=True))\n",
        "])\n",
        "\n",
        "# List of pipelines for easy iteration\n",
        "pipelines = [pipeline_lr, pipeline_nb, pipeline_rf, pipeline_svm]\n",
        "pipeline_names = ['Logistic Regression', 'Multinomial Naive Bayes', 'Random Forest', 'Support Vector Machine']\n",
        "\n",
        "# Metrics storage\n",
        "metrics_dict = {\n",
        "    'Model': [],\n",
        "    'Accuracy': [],\n",
        "    'Precision': [],\n",
        "    'Recall': [],\n",
        "    'F1-score': [],\n",
        "    'ROC-AUC': [],\n",
        "}\n",
        "\n",
        "# Step 4: Train the models and evaluate on the validation set\n",
        "for pipe, name in zip(pipelines, pipeline_names):\n",
        "    print(f'Training {name}...')\n",
        "    pipe.fit(X_train_split, y_train_split)  # Train the pipeline on the split training data\n",
        "    y_pred = pipe.predict(X_val)  # Predict on the validation set\n",
        "\n",
        "    # Calculate evaluation metrics\n",
        "    accuracy = accuracy_score(y_val, y_pred)\n",
        "    precision = precision_score(y_val, y_pred)\n",
        "    recall = recall_score(y_val, y_pred)\n",
        "    f1 = f1_score(y_val, y_pred)\n",
        "\n",
        "    # For ROC-AUC, predict probabilities if available (SVM has probabilities, others use decision_function or predict_proba)\n",
        "    if hasattr(pipe['clf'], 'predict_proba'):\n",
        "        y_prob = pipe.predict_proba(X_val)[:, 1]\n",
        "    elif hasattr(pipe['clf'], 'decision_function'):\n",
        "        y_prob = pipe.decision_function(X_val)\n",
        "    else:\n",
        "        y_prob = y_pred  # Use predictions as probabilities (e.g., for MultinomialNB)\n",
        "\n",
        "    roc_auc = roc_auc_score(y_val, y_prob)\n",
        "\n",
        "    # Store metrics in dictionary\n",
        "    metrics_dict['Model'].append(name)\n",
        "    metrics_dict['Accuracy'].append(accuracy)\n",
        "    metrics_dict['Precision'].append(precision)\n",
        "    metrics_dict['Recall'].append(recall)\n",
        "    metrics_dict['F1-score'].append(f1)\n",
        "    metrics_dict['ROC-AUC'].append(roc_auc)\n",
        "\n",
        "    # Print metrics\n",
        "    print(f'{name} Metrics:')\n",
        "    print(f'Accuracy: {accuracy}')\n",
        "    print(f'Precision: {precision}')\n",
        "    print(f'Recall: {recall}')\n",
        "    print(f'F1-score: {f1}')\n",
        "    print(f'ROC-AUC: {roc_auc}\\n')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plotting metrics\n",
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
        "fig.suptitle('Performance Metrics Comparison')\n",
        "\n",
        "# Bar chart for Accuracy\n",
        "axes[0, 0].bar(metrics_df['Model'], metrics_df['Accuracy'], color='skyblue')\n",
        "axes[0, 0].set_title('Accuracy')\n",
        "axes[0, 0].set_ylim([0.7, 1.0])\n",
        "axes[0, 0].set_xticklabels(metrics_df['Model'], rotation=45)\n",
        "\n",
        "# Bar chart for Precision\n",
        "axes[0, 1].bar(metrics_df['Model'], metrics_df['Precision'], color='salmon')\n",
        "axes[0, 1].set_title('Precision')\n",
        "axes[0, 1].set_ylim([0.7, 1.0])\n",
        "axes[0, 1].set_xticklabels(metrics_df['Model'], rotation=45)\n",
        "\n",
        "# Bar chart for Recall\n",
        "axes[1, 0].bar(metrics_df['Model'], metrics_df['Recall'], color='lightgreen')\n",
        "axes[1, 0].set_title('Recall')\n",
        "axes[1, 0].set_ylim([0.7, 1.0])\n",
        "axes[1, 0].set_xticklabels(metrics_df['Model'], rotation=45)\n",
        "\n",
        "# Bar chart for F1-score\n",
        "axes[1, 1].bar(metrics_df['Model'], metrics_df['F1-score'], color='gold')\n",
        "axes[1, 1].set_title('F1-score')\n",
        "axes[1, 1].set_ylim([0.7, 1.0])\n",
        "axes[1, 1].set_xticklabels(metrics_df['Model'], rotation=45)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.subplots_adjust(top=0.92)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "vAX5al4c1XHz",
        "outputId": "6388b014-4c97-45ef-9609-4b2e819fd4cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-30-5be5d383d869>:11: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  axes[0, 0].set_xticklabels(metrics_df['Model'], rotation=45)\n",
            "<ipython-input-30-5be5d383d869>:17: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  axes[0, 1].set_xticklabels(metrics_df['Model'], rotation=45)\n",
            "<ipython-input-30-5be5d383d869>:23: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  axes[1, 0].set_xticklabels(metrics_df['Model'], rotation=45)\n",
            "<ipython-input-30-5be5d383d869>:29: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  axes[1, 1].set_xticklabels(metrics_df['Model'], rotation=45)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ROC curves\n",
        "plt.figure(figsize=(8, 6))\n",
        "for pipe, name in zip(pipelines, pipeline_names):\n",
        "    if hasattr(pipe['clf'], 'predict_proba'):\n",
        "        y_prob = pipe.predict_proba(X_val)[:, 1]\n",
        "    elif hasattr(pipe['clf'], 'decision_function'):\n",
        "        y_prob = pipe.decision_function(X_val)\n",
        "    else:\n",
        "        y_prob = y_pred  # Use predictions as probabilities (e.g., for MultinomialNB)\n",
        "\n",
        "    fpr, tpr, _ = roc_curve(y_val, y_prob)\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "    plt.plot(fpr, tpr, label=f'{name} (AUC = {roc_auc:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='grey', linewidth=0.8)\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curves')\n",
        "plt.legend(loc='lower right')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "5prmLcPe0Txa",
        "outputId": "16e03e85-501b-4ed5-d181-f7c3263b2c32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yl2KdQlYeRed"
      },
      "source": [
        "This structured approach using Pipeline simplifies the workflow by encapsulating the vectorization and classification steps into a single object, enhancing code readability and maintainability. Adjust parameters (max_features, max_iter, etc.) and pipeline components as per your specific requirements and dataset characteristics."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}