Yelp_Business_Stars’_Rating_Prediction.ipynb

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      "name": "Yelp Business Stars’ Rating Prediction.ipynb",
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        "<a href=\"https://colab.research.google.com/github/mansipatel2508/Yelp-Review-Stars-Prediction-with-Machine-Learning/blob/master/Yelp_Business_Stars%E2%80%99_Rating_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Yelp Business Stars’ Rating Prediction\n",
        "\n",
        "**Big Data | Data Cleaning | Data Preporocessing | Text Processing | TF-IDF vectorization |  Regression | Classification | Model Evaluation | Model Performance Comparison**\n",
        "\n",
        "\n",
        " Tradition AI Models : KNN | SVM | Logistic Regression | Multinomial Naive Bayes | Linear Regression \n",
        " \n",
        " Deep Learning Models : Neural Network ( Regression & Classification )\n",
        " \n",
        "\n",
        "## Problem statement\n",
        "Predicting the review stars from 1-5 star ratings based on the review given by the user.\n",
        "\n",
        "Machine Learning project aims\n",
        "\n",
        "* learn text vectorization (IF-IDF)\n",
        "* big data handling & preprocess the data\n",
        "* merging two big datasets\n",
        "* treat problem as rgression and classification, observe it\n",
        "*Apply and compare tradition AI models with Deep Learning Nueral Network\n",
        "\n",
        "Tools and Libraries used\n",
        "\n",
        "* sklearn\n",
        "*TensorFlow\n",
        "*Numpy\n",
        "*Pandas\n",
        "*Keras\n",
        "*Keras wrappers\n",
        "*optimizers\n",
        "## Dataset\n",
        "https://www.yelp.com/dataset/download"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9zgNYjpYkoT",
        "colab_type": "text"
      },
      "source": [
        "# Load dataset\n",
        "The data containing json files was converted to a compatible file to load on pandas’ data frame.Used business. json and review.json files to understand the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wL6CdSPId_N3",
        "colab_type": "code",
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      },
      "source": [
        "import os\n",
        "import json\n",
        "import csv\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import collections\n",
        "from scipy.stats import zscore\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "pd.set_option('display.max_colwidth', -1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "scrolled": true,
        "id": "PR0DQrKNd_OE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creating Business Dataframe\n",
        "business_df= pd.read_csv('business.tsv', delimiter =\"\\t\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "d3Vw6Lw9d_OH",
        "colab_type": "code",
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      "source": [
        "# Creating Review Dataframe\n",
        "review_df= pd.read_csv('review_stars.tsv', delimiter =\"\\t\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "77230741-85a7-4432-cf6f-afb3cdad6970",
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      },
      "source": [
        "business_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>business_id</th>\n",
              "      <th>categories</th>\n",
              "      <th>stars</th>\n",
              "      <th>review_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1SWheh84yJXfytovILXOAQ</td>\n",
              "      <td>Golf, Active Life</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>QXAEGFB4oINsVuTFxEYKFQ</td>\n",
              "      <td>Specialty Food, Restaurants, Dim Sum, Imported Food, Food, Chinese, Ethnic Food, Seafood</td>\n",
              "      <td>2.5</td>\n",
              "      <td>128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>gnKjwL_1w79qoiV3IC_xQQ</td>\n",
              "      <td>Sushi Bars, Restaurants, Japanese</td>\n",
              "      <td>4.0</td>\n",
              "      <td>170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>xvX2CttrVhyG2z1dFg_0xw</td>\n",
              "      <td>Insurance, Financial Services</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>HhyxOkGAM07SRYtlQ4wMFQ</td>\n",
              "      <td>Plumbing, Shopping, Local Services, Home Services, Kitchen &amp; Bath, Home &amp; Garden, Water Heater Installation/Repair</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "              business_id  \\\n",
              "0  1SWheh84yJXfytovILXOAQ   \n",
              "1  QXAEGFB4oINsVuTFxEYKFQ   \n",
              "2  gnKjwL_1w79qoiV3IC_xQQ   \n",
              "3  xvX2CttrVhyG2z1dFg_0xw   \n",
              "4  HhyxOkGAM07SRYtlQ4wMFQ   \n",
              "\n",
              "                                                                                                           categories  \\\n",
              "0  Golf, Active Life                                                                                                    \n",
              "1  Specialty Food, Restaurants, Dim Sum, Imported Food, Food, Chinese, Ethnic Food, Seafood                             \n",
              "2  Sushi Bars, Restaurants, Japanese                                                                                    \n",
              "3  Insurance, Financial Services                                                                                        \n",
              "4  Plumbing, Shopping, Local Services, Home Services, Kitchen & Bath, Home & Garden, Water Heater Installation/Repair   \n",
              "\n",
              "   stars  review_count  \n",
              "0  3.0    5             \n",
              "1  2.5    128           \n",
              "2  4.0    170           \n",
              "3  5.0    3             \n",
              "4  4.0    4             "
            ]
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        "outputId": "d303bd20-2d25-4bf2-eec2-67e99c1f5b76",
        "colab": {}
      },
      "source": [
        "review_df.head()"
      ],
      "execution_count": 0,
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        {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>business_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ujmEBvifdJM6h6RLv4wQIg</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'Total bill for this horrible service? Over $8Gs. These crooks actually had the nerve to charge us $69 for 3 pills. I checked online the pills can be had for 19 cents EACH! Avoid Hospital ERs at all costs.'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NZnhc2sEQy3RmzKTZnqtwQ</td>\n",
              "      <td>5.0</td>\n",
              "      <td>b\"I *adore* Travis at the Hard Rock's new Kelly Cardenas Salon!  I'm always a fan of a great blowout and no stranger to the chains that offer this service; however, Travis has taken the flawless blowout to a whole new level!  \\n\\nTravis's greets you with his perfectly green swoosh in his otherwise perfectly styled black hair and a Vegas-worthy rockstar outfit.  Next comes the most relaxing and incredible shampoo -- where you get a full head message that could cure even the very worst migraine in minutes --- and the scented shampoo room.  Travis has freakishly strong fingers (in a good way) and use the perfect amount of pressure.  That was superb!  Then starts the glorious blowout... where not one, not two, but THREE people were involved in doing the best round-brush action my hair has ever seen.  The team of stylists clearly gets along extremely well, as it's evident from the way they talk to and help one another that it's really genuine and not some corporate requirement.  It was so much fun to be there! \\n\\nNext Travis started with the flat iron.  The way he flipped his wrist to get volume all around without over-doing it and making me look like a Texas pagent girl was admirable.  It's also worth noting that he didn't fry my hair -- something that I've had happen before with less skilled stylists.  At the end of the blowout &amp; style my hair was perfectly bouncey and looked terrific.  The only thing better?  That this awesome blowout lasted for days! \\n\\nTravis, I will see you every single time I'm out in Vegas.  You make me feel beauuuutiful!\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>WTqjgwHlXbSFevF32_DJVw</td>\n",
              "      <td>5.0</td>\n",
              "      <td>b\"I have to say that this office really has it together, they are so organized and friendly!  Dr. J. Phillipp is a great dentist, very friendly and professional.  The dental assistants that helped in my procedure were amazing, Jewel and Bailey helped me to feel comfortable!  I don't have dental insurance, but they have this insurance through their office you can purchase for $80 something a year and this gave me 25% off all of my dental work, plus they helped me get signed up for care credit which I knew nothing about before this visit!  I highly recommend this office for the nice synergy the whole office has!\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ikCg8xy5JIg_NGPx-MSIDA</td>\n",
              "      <td>5.0</td>\n",
              "      <td>b\"Went in for a lunch. Steak sandwich was delicious, and the Caesar salad had an absolutely delicious dressing, with a perfect amount of dressing, and distributed perfectly across each leaf. I know I'm going on about the salad ... But it was perfect.\\n\\nDrink prices were pretty good.\\n\\nThe Server, Dawn, was friendly and accommodating. Very happy with her.\\n\\nIn summation, a great pub experience. Would go again!\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b1b1eb3uo-w561D0ZfCEiQ</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'Today was my second out of three sessions I had paid for. Although my first session went well, I could tell Meredith had a particular enjoyment for her male clients over her female. However, I returned because she did my teeth fine and I was pleased with the results. When I went in today, I was in the whitening room with three other gentlemen. My appointment started out well, although, being a person who is in the service industry, I always attend to my female clientele first when a couple arrives. Unbothered by those signs, I waited my turn. She checked on me once after my original 30 minute timer to ask if I was ok. She attended my boyfriend on numerous occasions, as well as the other men, and would exit the room without even asking me or looking to see if I had any irritation. Half way through, another woman had showed up who she was explaining the deals to in the lobby. While she admits timers must be reset half way through the process, she reset my boyfriends, left, rest the gentleman furthest away from me who had time to come in, redeem his deal, get set, and gave his timer done, before me, then left, and at this point my time was at 10 minutes. So, she should have reset it 5 minutes ago, according to her. While I sat there patiently this whole time with major pain in my gums, i watched the time until the lamp shut off. Not only had she reset two others, explained deals to other guest, but she never once checked on my time. When my light turned off, I released the stance of my mouth to a more relaxed state, assuming I was only getting a thirty minute session instead of the usual 45, because she had yet to come in. At this point, the teeth formula was not only burning the gum she neglected for 25 minutes now, but it began to burn my lips. I began squealing and slapping my chair trying to get her attention from the other room in a panic. I was in so much pain, that by the time she entered the room I was already out of my chair. She finally then acknowledged me, and asked if she could put vitamin E on my gum burn (pictured below). At this point, she has treated two other gums burns, while neglecting me, and I was so irritated that I had to suffer, all I wanted was to leave. While I waited for my boyfriend, she kept harassing me about the issue. Saying, \"well burns come with teeth whitening.\" While I totally agree, and under justifiable circumstances would not be as irritate, it could have easily been avoid if she had checked on me even a second time, so I could let her know. Not only did she never check on my physical health, she couldn\\'t even take two seconds to reset the timer, which she even admitted to me. Her accuse was that she was coming in to do it, but I had the light off for a solid two minutes before I couldn\\'t stand the pain. She admitted it should be reset every 15 minutes, which means for 25 minutes she did not bother to help me at all. Her guest in the lobby then proceeded to attack me as well, simply because I wanted to leave after the way I was treated. I also expected a refund for not getting a complete session today, due to the neglect, and the fact I won\\'t be returning for my last, she had failed to do that. She was even screaming from the door, and continued to until my boyfriend and I were down the steps. I have never in my life been more appalled by a grown woman\\'s behavior, who claims to be in the business for \"10 years.\" Admit your wrongs, but don\\'t make your guest feel unwelcome because you can\\'t do you job properly.'</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              business_id  stars  \\\n",
              "0  ujmEBvifdJM6h6RLv4wQIg  1.0     \n",
              "1  NZnhc2sEQy3RmzKTZnqtwQ  5.0     \n",
              "2  WTqjgwHlXbSFevF32_DJVw  5.0     \n",
              "3  ikCg8xy5JIg_NGPx-MSIDA  5.0     \n",
              "4  b1b1eb3uo-w561D0ZfCEiQ  1.0     \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             text  \n",
              "0  b'Total bill for this horrible service? Over $8Gs. These crooks actually had the nerve to charge us $69 for 3 pills. I checked online the pills can be had for 19 cents EACH! Avoid Hospital ERs at all costs.'                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 \n",
              "1  b\"I *adore* Travis at the Hard Rock's new Kelly Cardenas Salon!  I'm always a fan of a great blowout and no stranger to the chains that offer this service; however, Travis has taken the flawless blowout to a whole new level!  \\n\\nTravis's greets you with his perfectly green swoosh in his otherwise perfectly styled black hair and a Vegas-worthy rockstar outfit.  Next comes the most relaxing and incredible shampoo -- where you get a full head message that could cure even the very worst migraine in minutes --- and the scented shampoo room.  Travis has freakishly strong fingers (in a good way) and use the perfect amount of pressure.  That was superb!  Then starts the glorious blowout... where not one, not two, but THREE people were involved in doing the best round-brush action my hair has ever seen.  The team of stylists clearly gets along extremely well, as it's evident from the way they talk to and help one another that it's really genuine and not some corporate requirement.  It was so much fun to be there! \\n\\nNext Travis started with the flat iron.  The way he flipped his wrist to get volume all around without over-doing it and making me look like a Texas pagent girl was admirable.  It's also worth noting that he didn't fry my hair -- something that I've had happen before with less skilled stylists.  At the end of the blowout & style my hair was perfectly bouncey and looked terrific.  The only thing better?  That this awesome blowout lasted for days! \\n\\nTravis, I will see you every single time I'm out in Vegas.  You make me feel beauuuutiful!\"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              \n",
              "2  b\"I have to say that this office really has it together, they are so organized and friendly!  Dr. J. Phillipp is a great dentist, very friendly and professional.  The dental assistants that helped in my procedure were amazing, Jewel and Bailey helped me to feel comfortable!  I don't have dental insurance, but they have this insurance through their office you can purchase for $80 something a year and this gave me 25% off all of my dental work, plus they helped me get signed up for care credit which I knew nothing about before this visit!  I highly recommend this office for the nice synergy the whole office has!\"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      \n",
              "3  b\"Went in for a lunch. Steak sandwich was delicious, and the Caesar salad had an absolutely delicious dressing, with a perfect amount of dressing, and distributed perfectly across each leaf. I know I'm going on about the salad ... But it was perfect.\\n\\nDrink prices were pretty good.\\n\\nThe Server, Dawn, was friendly and accommodating. Very happy with her.\\n\\nIn summation, a great pub experience. Would go again!\"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                \n",
              "4  b'Today was my second out of three sessions I had paid for. Although my first session went well, I could tell Meredith had a particular enjoyment for her male clients over her female. However, I returned because she did my teeth fine and I was pleased with the results. When I went in today, I was in the whitening room with three other gentlemen. My appointment started out well, although, being a person who is in the service industry, I always attend to my female clientele first when a couple arrives. Unbothered by those signs, I waited my turn. She checked on me once after my original 30 minute timer to ask if I was ok. She attended my boyfriend on numerous occasions, as well as the other men, and would exit the room without even asking me or looking to see if I had any irritation. Half way through, another woman had showed up who she was explaining the deals to in the lobby. While she admits timers must be reset half way through the process, she reset my boyfriends, left, rest the gentleman furthest away from me who had time to come in, redeem his deal, get set, and gave his timer done, before me, then left, and at this point my time was at 10 minutes. So, she should have reset it 5 minutes ago, according to her. While I sat there patiently this whole time with major pain in my gums, i watched the time until the lamp shut off. Not only had she reset two others, explained deals to other guest, but she never once checked on my time. When my light turned off, I released the stance of my mouth to a more relaxed state, assuming I was only getting a thirty minute session instead of the usual 45, because she had yet to come in. At this point, the teeth formula was not only burning the gum she neglected for 25 minutes now, but it began to burn my lips. I began squealing and slapping my chair trying to get her attention from the other room in a panic. I was in so much pain, that by the time she entered the room I was already out of my chair. She finally then acknowledged me, and asked if she could put vitamin E on my gum burn (pictured below). At this point, she has treated two other gums burns, while neglecting me, and I was so irritated that I had to suffer, all I wanted was to leave. While I waited for my boyfriend, she kept harassing me about the issue. Saying, \"well burns come with teeth whitening.\" While I totally agree, and under justifiable circumstances would not be as irritate, it could have easily been avoid if she had checked on me even a second time, so I could let her know. Not only did she never check on my physical health, she couldn\\'t even take two seconds to reset the timer, which she even admitted to me. Her accuse was that she was coming in to do it, but I had the light off for a solid two minutes before I couldn\\'t stand the pain. She admitted it should be reset every 15 minutes, which means for 25 minutes she did not bother to help me at all. Her guest in the lobby then proceeded to attack me as well, simply because I wanted to leave after the way I was treated. I also expected a refund for not getting a complete session today, due to the neglect, and the fact I won\\'t be returning for my last, she had failed to do that. She was even screaming from the door, and continued to until my boyfriend and I were down the steps. I have never in my life been more appalled by a grown woman\\'s behavior, who claims to be in the business for \"10 years.\" Admit your wrongs, but don\\'t make your guest feel unwelcome because you can\\'t do you job properly.'  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zC78_E2Kd_OS",
        "colab_type": "code",
        "outputId": "c377e5ce-5386-4bdd-8875-bf35c0bf27ce",
        "colab": {}
      },
      "source": [
        "# Printing Shapes and some information for both the dataframes \n",
        "print(review_df.shape)    \n",
        "print(business_df.shape)\n",
        "print(review_df.stars.value_counts())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(6685900, 3)\n",
            "(192609, 4)\n",
            "5.0    2933082\n",
            "4.0    1468985\n",
            "1.0    1002159\n",
            "3.0    739280 \n",
            "2.0    542394 \n",
            "Name: stars, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnBhWFxAgZnd",
        "colab_type": "text"
      },
      "source": [
        "Grouped the multiple reviews on bussiness_id to get all reviews given by the user into one text."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A5B4ed4yd_OZ",
        "colab_type": "code",
        "outputId": "b206a5c6-84f1-40d7-8dfc-39bc51ba8376",
        "colab": {}
      },
      "source": [
        "df_ready_for_sklearn.shape  # Total - 192606"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(192606, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjvnoVlJgirt",
        "colab_type": "text"
      },
      "source": [
        "Merged the datasets with on BusinessID and got the final dataset shape as below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMJVVwUCd_Od",
        "colab_type": "code",
        "outputId": "e6a14cfb-bbad-4a0a-8e9f-69fbb552bcaa",
        "colab": {}
      },
      "source": [
        "df_merge.shape # (192606, 5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(192606, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Udo-qrxNd_Of",
        "colab_type": "text"
      },
      "source": [
        "##    Data Pre-Processing/ Cleaning\n",
        "\n",
        "* Dropped the rows with categories that have null values\n",
        "*Filtered the data frame more by removing rows with business Ids having review count less than\n",
        "a certain threshold\n",
        "* Cleaned the reviews text data by removing stop words, punctuations and white spaces.\n",
        "* Used TF-IDF vectorization for Feature Extraction and used its parameters \n",
        "* Performed label encoding on the “stars” column (Output Feature)\n",
        "*Normalized the “ Review_count “ Column to make it comparable with min-max normalization\n",
        "\n",
        "\n",
        "![](/content/shape.png \"shape\")\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0Y-4BTbrdtk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "c586d7a2-a9a3-42ec-cc1a-e3318be8adfa"
      },
      "source": [
        "from IPython.display import Image,display\n",
        "display(Image('shape.png',width=1000, height=300))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAOYAAAAoAAAA5gAAAOYAAJA+C8jBVAAAQABJREFUeAHs3Qm4TdX/x/GvO3ENlSHJPPUz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\nEeprsdfb95Mk0Nc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vxUiTh5fLP/vOmuNFi2eUbv3ul9ef3+hyvxx50smwt8pL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\nlaVc5bvkcnSM1Cm9xKOv3fveL627FpKjhy/IoyU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wTmzp1r7dq1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\nYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 1000,
              "height": 300
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEa51inVd_PC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Feature Extraction\n",
        "\n",
        "import sklearn.feature_extraction.text as sk_text\n",
        "Tfidf_vectorizer = sk_text.TfidfVectorizer(max_features=1000, lowercase=True, analyzer='word',\n",
        " stop_words= 'english',ngram_range=(1,1), min_df = .05 , max_df = .85)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTXBtbv5d_PD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "matrix = Tfidf_vectorizer.fit_transform(df_clean1['all_reviews'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WfLYdfASd_PE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tfidf_data = matrix.toarray()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "ggApKSwxd_PE",
        "colab_type": "code",
        "outputId": "6ece51a7-d3b1-4707-95c0-489f09f9e88d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "tfidf_data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.01196282, ..., 0.01173804, 0.        ,\n",
              "        0.00259588],\n",
              "       [0.00541855, 0.00482508, 0.00859305, ..., 0.00790462, 0.        ,\n",
              "        0.00559396],\n",
              "       [0.        , 0.00743404, 0.        , ..., 0.00974297, 0.        ,\n",
              "        0.02585599],\n",
              "       ...,\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.0109784 , 0.01047426, 0.00683971, ..., 0.00183033, 0.00106512,\n",
              "        0.00323823],\n",
              "       [0.02137078, 0.00951506, 0.00847275, ..., 0.        , 0.        ,\n",
              "        0.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "lZUr14b_d_PG",
        "colab_type": "code",
        "outputId": "ee83bfe9-8df7-4980-e1de-a2f1d603dac6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        }
      },
      "source": [
        "print(Tfidf_vectorizer.get_feature_names())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['100', '12', '15', '20', '25', '30', '35', '40', '45', '50', 'able', 'absolutely', 'accommodating', 'actual', 'add', 'added', 'addition', 'additional', 'affordable', 'afternoon', 'ago', 'ahead', 'air', 'airport', 'ambiance', 'american', 'answer', 'apparently', 'appetizer', 'appetizers', 'apple', 'appointment', 'appreciate', 'arent', 'arrived', 'asian', 'asking', 'ate', 'atmosphere', 'attention', 'attentive', 'attitude', 'authentic', 'available', 'average', 'avocado', 'avoid', 'awful', 'baby', 'bacon', 'bag', 'baked', 'bar', 'barely', 'bartender', 'bartenders', 'based', 'basic', 'basically', 'bathroom', 'bbq', 'bean', 'beans', 'beat', 'beautiful', 'bed', 'beef', 'beer', 'beers', 'believe', 'belly', 'bigger', 'birthday', 'bite', 'black', 'bland', 'blue', 'book', 'booked', 'bottle', 'bought', 'bowl', 'box', 'boy', 'boyfriend', 'bread', 'break', 'breakfast', 'bring', 'brisket', 'broth', 'brought', 'brown', 'brunch', 'bucks', 'buffet', 'building', 'bun', 'bunch', 'burger', 'burgers', 'burrito', 'business', 'busy', 'butter', 'buy', 'cafe', 'cake', 'cakes', 'calamari', 'california', 'called', 'car', 'card', 'care', 'case', 'cash', 'cashier', 'casino', 'casual', 'cause', 'center', 'certainly', 'chain', 'chairs', 'chance', 'change', 'changed', 'charge', 'charged', 'charlotte', 'cheap', 'cheaper', 'checked', 'checking', 'cheese', 'cheesecake', 'chef', 'chicken', 'chili', 'chinese', 'chips', 'chocolate', 'choice', 'choices', 'choose', 'chose', 'city', 'class', 'classic', 'cleaning', 'clear', 'clearly', 'close', 'closed', 'club', 'cocktail', 'cocktails', 'coconut', 'coffee', 'cold', 'color', 'combo', 'comes', 'comfortable', 'company', 'compared', 'complain', 'complaint', 'complete', 'completely', 'considering', 'continue', 'convenient', 'conversation', 'cook', 'cooked', 'cookies', 'cool', 'corn', 'corner', 'cost', 'counter', 'course', 'covered', 'cozy', 'crab', 'craving', 'crazy', 'cream', 'creamy', 'credit', 'crispy', 'crowd', 'crowded', 'crust', 'cup', 'curry', 'customers', 'cut', 'cute', 'dance', 'dark', 'date', 'daughter', 'days', 'deal', 'decent', 'decor', 'deep', 'delicious', 'delivered', 'delivery', 'desert', 'desk', 'despite', 'dessert', 'desserts', 'die', 'difficult', 'dim', 'dining', 'dinner', 'dip', 'dirty', 'disappointed', 'disappointing', 'discount', 'dish', 'dishes', 'dog', 'dogs', 'doing', 'dollars', 'donuts', 'door', 'double', 'downtown', 'dr', 'dressing', 'drink', 'drinks', 'drive', 'dry', 'duck', 'early', 'easily', 'easy', 'eat', 'eaten', 'eating', 'efficient', 'egg', 'eggs', 'employee', 'employees', 'end', 'ended', 'enjoy', 'enjoyed', 'entire', 'entree', 'entrees', 'environment', 'especially', 'establishment', 'evening', 'event', 'exactly', 'excited', 'expect', 'expectations', 'expected', 'expecting', 'expensive', 'experienced', 'experiences', 'explain', 'explained', 'extra', 'extremely', 'eye', 'fabulous', 'face', 'fact', 'fair', 'fairly', 'family', 'fan', 'fancy', 'fantastic', 'fast', 'fat', 'favorite', 'favorites', 'fee', 'feeling', 'felt', 'filet', 'filled', 'filling', 'finally', 'fine', 'finish', 'finished', 'fish', 'fix', 'fixed', 'flat', 'flavor', 'flavorful', 'flavors', 'flavour', 'flight', 'floor', 'food', 'foods', 'forever', 'forget', 'forgot', 'forward', 'free', 'french', 'fresh', 'friday', 'fried', 'friend', 'friends', 'fries', 'frozen', 'fruit', 'fun', 'funny', 'future', 'game', 'games', 'garlic', 'gem', 'general', 'generous', 'gets', 'girl', 'girlfriend', 'girls', 'given', 'giving', 'glad', 'glass', 'glasses', 'goes', 'gone', 'gotten', 'grab', 'gravy', 'greasy', 'green', 'greeted', 'grill', 'grilled', 'group', 'groupon', 'guacamole', 'guess', 'guests', 'guy', 'guys', 'hair', 'half', 'hand', 'hands', 'hang', 'happen', 'happened', 'hash', 'hate', 'havent', 'head', 'healthy', 'hear', 'heard', 'heart', 'heat', 'heavy', 'help', 'helped', 'helpful', 'hes', 'hidden', 'high', 'higher', 'highly', 'hit', 'hold', 'homemade', 'honest', 'honestly', 'honey', 'hope', 'horrible', 'hostess', 'hot', 'hotel', 'hour', 'hours', 'house', 'huge', 'hungry', 'husband', 'ice', 'iced', 'idea', 'immediately', 'impressed', 'included', 'including', 'incredible', 'incredibly', 'indian', 'ingredients', 'inside', 'instead', 'interesting', 'interior', 'isnt', 'issue', 'issues', 'italian', 'item', 'items', 'japanese', 'job', 'joint', 'juice', 'juicy', 'kept', 'kids', 'kinda', 'kitchen', 'knew', 'knowledgeable', 'known', 'knows', 'korean', 'la', 'lack', 'ladies', 'lady', 'lamb', 'large', 'las', 'late', 'later', 'leave', 'leaving', 'lemon', 'lets', 'lettuce', 'level', 'life', 'light', 'liked', 'limited', 'line', 'lines', 'list', 'literally', 'live', 'lived', 'lobster', 'local', 'located', 'locations', 'lol', 'longer', 'looks', 'lost', 'lots', 'loud', 'lounge', 'loved', 'lovely', 'loves', 'low', 'lunch', 'mac', 'main', 'makes', 'making', 'mall', 'man', 'management', 'manager', 'mango', 'market', 'massage', 'matter', 'meal', 'meals', 'mean', 'means', 'meat', 'meats', 'mediocre', 'medium', 'meet', 'mention', 'mentioned', 'menu', 'met', 'mexican', 'middle', 'milk', 'min', 'mind', 'mini', 'mins', 'minute', 'miss', 'missing', 'mistake', 'mix', 'mixed', 'modern', 'mom', 'moment', 'monday', 'money', 'month', 'months', 'morning', 'mouth', 'moved', 'movie', 'moving', 'multiple', 'mushroom', 'mushrooms', 'music', 'nachos', 'nail', 'nails', 'near', 'nearly', 'needed', 'needs', 'negative', 'neighborhood', 'ni', 'nicely', 'night', 'nights', 'nnmy', 'nnoverall', 'nnthey', 'nnthis', 'nnwe', 'noodle', 'noodles', 'normal', 'normally', 'notch', 'note', 'notice', 'noticed', 'nthe', 'number', 'obviously', 'offer', 'offered', 'offers', 'office', 'oh', 'oil', 'ok', 'okay', 'old', 'ones', 'onion', 'onions', 'online', 'open', 'opened', 'opening', 'opinion', 'option', 'options', 'orange', 'order', 'ordered', 'ordering', 'orders', 'original', 'outdoor', 'outside', 'outstanding', 'overall', 'overly', 'overpriced', 'owner', 'owners', 'oysters', 'packed', 'pad', 'paid', 'pancakes', 'paper', 'park', 'parking', 'party', 'pass', 'past', 'pasta', 'patient', 'patio', 'pay', 'paying', 'pepper', 'peppers', 'perfect', 'perfection', 'perfectly', 'person', 'personal', 'personally', 'phoenix', 'phone', 'pick', 'picked', 'picture', 'pictures', 'pie', 'piece', 'pieces', 'pizza', 'pizzas', 'placed', 'plain', 'plan', 'plate', 'plates', 'play', 'playing', 'pleasant', 'pleased', 'plenty', 'plus', 'pm', 'point', 'polite', 'pool', 'poor', 'popular', 'pork', 'portion', 'portions', 'positive', 'possible', 'pot', 'potato', 'potatoes', 'prefer', 'prepared', 'presentation', 'previous', 'priced', 'pricey', 'pricing', 'prime', 'problem', 'process', 'products', 'professional', 'provide', 'provided', 'pulled', 'purchase', 'purchased', 'questions', 'quickly', 'quiet', 'quite', 'ramen', 'rare', 'rate', 'rating', 'raw', 'read', 'reading', 'ready', 'real', 'reason', 'reasonable', 'reasonably', 'received', 'recently', 'recommended', 'red', 'regular', 'relaxing', 'remember', 'request', 'reservation', 'reservations', 'resort', 'rest', 'restaurant', 'restaurants', 'return', 'returned', 'returning', 'reviews', 'rib', 'ribs', 'rice', 'rich', 'ride', 'ridiculous', 'roasted', 'rock', 'roll', 'rolls', 'room', 'rooms', 'rude', 'run', 'running', 'rush', 'sad', 'salad', 'salads', 'sales', 'salmon', 'salon', 'salsa', 'salt', 'salty', 'sandwich', 'sandwiches', 'sashimi', 'sat', 'satisfied', 'saturday', 'sauce', 'sauces', 'sausage', 'save', 'saw', 'saying', 'says', 'school', 'scottsdale', 'seafood', 'seasoned', 'seat', 'seated', 'seating', 'seats', 'second', 'section', 'security', 'seeing', 'seen', 'selection', 'sell', 'sent', 'seriously', 'serve', 'served', 'server', 'servers', 'services', 'serving', 'set', 'share', 'shared', 'shes', 'shop', 'shopping', 'shops', 'short', 'shot', 'showed', 'shows', 'shrimp', 'sides', 'sign', 'similar', 'simple', 'simply', 'single', 'sister', 'sit', 'sitting', 'situation', 'size', 'slice', 'slices', 'slightly', 'slow', 'smaller', 'smell', 'smile', 'smoked', 'soft', 'soggy', 'solid', 'son', 'soon', 'sorry', 'sort', 'soup', 'sour', 'spa', 'space', 'speak', 'special', 'specials', 'spend', 'spent', 'spice', 'spicy', 'spinach', 'split', 'spoke', 'sports', 'spot', 'spots', 'spring', 'stand', 'standard', 'standing', 'star', 'start', 'started', 'station', 'stay', 'stayed', 'staying', 'steak', 'stick', 'stop', 'stopped', 'store', 'stores', 'straight', 'strawberry', 'street', 'strip', 'strong', 'stuff', 'stuffed', 'style', 'sugar', 'suggest', 'summer', 'sunday', 'supposed', 'surprise', 'surprised', 'sushi', 'sweet', 'table', 'tables', 'taco', 'tacos', 'taken', 'takeout', 'takes', 'taking', 'talk', 'talking', 'taste', 'tasted', 'tastes', 'tasting', 'tasty', 'tea', 'team', 'telling', 'tender', 'terrible', 'texture', 'thai', 'thank', 'thanks', 'theres', 'theyre', 'thinking', 'tickets', 'tiny', 'tip', 'toast', 'today', 'tofu', 'tomato', 'tomatoes', 'tonight', 'tons', 'topped', 'toppings', 'toronto', 'total', 'totally', 'touch', 'town', 'traditional', 'treat', 'treated', 'trip', 'truck', 'true', 'truly', 'trust', 'tuesday', 'tuna', 'turkey', 'turn', 'turned', 'tv', 'twice', 'type', 'typical', 'understand', 'unfortunately', 'unique', 'unless', 'using', 'usual', 'usually', 'valley', 'value', 'variety', 'vegan', 'vegas', 'vegetables', 'vegetarian', 'veggie', 'veggies', 'vehicle', 'vibe', 'view', 'visited', 'visiting', 'waffle', 'waffles', 'waited', 'waiter', 'waiting', 'waitress', 'walk', 'walked', 'walking', 'wall', 'warm', 'wash', 'waste', 'watch', 'watching', 'water', 'wedding', 'week', 'weekend', 'weeks', 'weird', 'welcoming', 'werent', 'west', 'weve', 'whats', 'white', 'wife', 'willing', 'window', 'wine', 'wings', 'wish', 'woman', 'wonderful', 'word', 'worked', 'working', 'works', 'world', 'worse', 'worst', 'wouldnt', 'wow', 'wrap', 'write', 'wrong', 'yeah', 'year', 'yelp', 'yes', 'yesterday', 'youll', 'young', 'youve', 'yum', 'yummy']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AATlDyMBd_PK",
        "colab_type": "text"
      },
      "source": [
        "# Splitting the data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqKmUCy-d_PK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.cross_validation import train_test_split\n",
        "x_train1, x_test1, y_train1, y_test1 = train_test_split(final_input_features, df_clean1['stars'], test_size=0.2, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtNCq-n2d_PL",
        "colab_type": "code",
        "outputId": "73f2af78-733b-413e-d519-8d9d850fe8c3",
        "colab": {}
      },
      "source": [
        "#train-test data shapes\n",
        "print(x_train1.shape)\n",
        "print(y_train1.shape)\n",
        "print(x_test1.shape)\n",
        "print(y_test1.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32780, 1001)\n",
            "(32780,)\n",
            "(8196, 1001)\n",
            "(8196,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycXvUteXd_PL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Label Encoding the output/Target feature\n",
        "\n",
        "from sklearn import preprocessing\n",
        "\n",
        "le = preprocessing.LabelEncoder()\n",
        "df_clean1['stars_encoded'] = le.fit_transform(df_clean1['stars'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYGb8rC1d_PN",
        "colab_type": "text"
      },
      "source": [
        "# Regression Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WpH_NZSysuW3",
        "colab_type": "text"
      },
      "source": [
        "## Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3XggGpzd_PP",
        "colab_type": "code",
        "outputId": "9c3e829d-cdc2-48fe-a732-d5cb576dea03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#checkig the errors for regression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "print(\"Mean squared error: %.2f\"\n",
        "      % mean_squared_error(y_test1, y_predict_lin_model1))\n",
        "print('R2 score: %.2f' % r2_score(y_test1, y_predict_lin_model1))\n",
        "print(\"RMSE: %.2f\"\n",
        "      % np.sqrt(mean_squared_error(y_test1, y_predict_lin_model1)))\n",
        "score_reg =  np.sqrt(mean_squared_error(y_test1, y_predict_lin_model1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean squared error: 0.10\n",
            "R2 score: 0.82\n",
            "RMSE: 0.32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "FuSW0isQd_PR",
        "colab_type": "code",
        "outputId": "db84b3f3-43e4-4615-bf2e-31fbbba6538b",
        "colab": {}
      },
      "source": [
        "#ploting the predicting and actual values\n",
        "%pylab inline \n",
        "pylab.rcParams['figure.figsize'] = (15,6)\n",
        "plt.plot(y_predict_lin_model1[:10],label = 'Predicted')\n",
        "plt.plot(y_test1[:10].values, label = 'Actual')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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77EYsP8npyEcETXsVK/O8f5eIEmBJJ0iIgolnnpnR/c/Wiyw/GsG2CS2oX9mW\nt1ed4rcr99g01p+GVe2yP+euD+DYbOi9FBr2NPAbEKLwkpm24qScM3T8EqZegte+gMe3Ye0g+MFT\nK2KSlpin0yqKQptXKrBpbDOWBTTB0daSDzZdoM03B1h1/BZpmVn5+z6EEMJI0jN1vL3qFOH3E1jX\n/A6u23tovaNu7tdmDyafg95LilbCBlrBKr9x2nL6W0dzNTTA34W4J+lsPRtjoOBEsRAdApFHwXfs\nMwlb2P1EVhy7RX+f6rhWKYOiKEzv5UZ5G0smrjlNcnpm9udt94lW1GTLRG2PmxAiWzLTVhRlZcLV\n7XBsLkQd156weg7WNsmXrZ7n06qqyqHrD5i59xqnIx9Rxc6KsW1q08fbCUszeQorhCiaVFXlgzVH\nKH3hJybb7sc69a5Wgtx3rFbsybK0sUN8OX9U9fOBgWv0HqaqKh1nHsbURGH7xObPr54pxM9D4MZ+\nmPr/VUpVVWXI0pOcjnzIgX+0pnzp/9+XdvTGA95cdIL+TarzZU+37M/9KAoWtAA7Jxi+V+vpJkQJ\nIzNtxZmpGbh2g+G7YMQ+rT3A8Xkwy137cI0KztNpFUWhVV1HNr7djBXDfKhkZ8W/N1+g9dcHWCkz\nb0KIoij+JmcWjOKDK734l3kg1pXqQP9AGB8KPiOLfsIGYF5KK1Z17VeIvab3MEVRGOrvzKU7jzkR\nHm/AAEWRFR+uLR/2DnimrcT+q/c5dC2WSW3rPJOwATSr5cDolrUIDI5k54UcmriXrQY9FsDd89p+\nUiHEC8lMW3GREA3BCyF0GaQmQFVv8BsL9bvlqqLYn6mqypGwB8zce53QWw+pbGfF2Na16Nukmsy8\nCSEKL1WFW0FwbC7q1R1kqCacK9cOr34foFR2N3Z0hvHkgTbb1qgfdP1e72Ep6Vn4Tf+Npi72LHgr\nxwe9oqTZ8Z5W6n/yuT8qWKdn6ug48xAAOye3fO4e+PRMHb3mHSXqYTI7J7XMuX/bno+0oms9F0Gj\nPvn+NoQozGSmraSxc4L2n8KUS/D6N5ASD+uHabNvQbMg5VGuT6koCi3qOLJ+jB+rhjelatlSfPjL\nRVp9dYAVxyJIzZCZNyFEIZKZDmfXaOXEl3UmPfwoc7K6847TT7hPWFN8EzYAGwfwGKi9/6T7eg8r\nZWHKAJ/q7Ll0j6j4ZAMGKIqc5Hg4vRLc+jzTcmjFsQhuPnjCh11cX1i0zMLMhFn9PUjL0PHOujPo\ndDlMELz6IVTzha2TcjVbLERJIklbcWNZWlvyMz4UBqzRGlfu+UgrZb3j3Txt9lUUheZ1HFg3xo+f\nRjSlmn0pPvrlIq2+3s/yo5KIrf9SAAAgAElEQVS8CSGMLDkeDn2jtUbZNBoy07jd/Euapn7PDofh\nfDm4PeamJeDrznccZKVD8I+5GvaWbw0URWHFsQiDhCWKqNClkJGsFbp56kFSGrN+u06ruo60qVch\n2+E1HUvzSVdXgsLi+PFwDm0ATM21QkDmVrBuCKTLAwQh/qoEfIuVUCYm8EonGLoNRh8G164QshR+\n8ILAgRBxRFtClAuKouBf24GfR/uxekRTatjb8PEWLXlbFhQuyZsQomDFXoOtk7WHUvs+g4qu8OYG\nogbsp/uJulhbl2ZpQBNKW+ZtiXiR41BbawVzclGubnqrlC1Fp4aVWHMyiidpOVT8EyVDZprWaqjW\nq1Cp4R8//nb3NVLSs/iwS329TtPXuxqdGlbim91XuXA7IfuD7apCj4Vw/zL8+u7LRC9EsSRJW0lQ\nuRH0mA9TLkDLf0DkMVjWWVtCdHaNtqQoFxRFoVltB9aO9mX1yKbUKG/DJ1sv0fKr/SyV5E0IYUiq\nCjf2wareWn+yM6u1PTBjj8Nbm0io2oqA5SGkZmSxNKAJFcuUsGp0zSZoy+PPrs7VsAB/FxJTM9l4\nKtpAgYki5fw6SLqn/X166lLMY9aejOQtvxrUrmCbzeD/pygKX/Z0w6G0JRMD9WgDUKcdtHgHTq+C\nM4Ev8w6EKHakEElJlJEC59ZqLQMeXIXSlcBnBHgNA5vyeTrlsRtxzNx7jRPh8TjaWvJ2q1oMbFpd\nGrYKIfJHRiqc/1mrlHv/EthU0JaCew/T9nMBaZlZDF4czOnIRywf5oNfrbx9nhVpqgqL2mmJ2/gQ\nMNHvM1hVVbrPCSIxLZO9U1phYiLl/0ssVYW5vmBiBmOOgKKgqioDfjzO1buJHPhHG+yszXN1ymM3\n4hi46Dj9m1Tjy56Nsj84KxNWdNN6D47cDxXqvcSbEaLwk0Ik4sXMS4HXUBh3At7cABUbwL7/wneu\nTzcBX831Kf1qlWftaD8CR/pSy9GGT7ddosVX+1l8RGbehBAvIek+7P9Sq4y4ZQIoJtBtrrZyoNV7\nfyRsOp3KP9ad40R4PF/3aVQyEzYARdFmR+JvwtUduRimEODvws3YJxy6HmvAAEWhF7YXYq9of4+e\n9u7beeEux2/GM7XDK7lO2EC7RxjTqhaBwVE5twEwNYPei8HC5un+tid5eRdCFDsy0yY09y/D8blw\ndi1kpUHt9lrLgJpt/vjQzo3jN+OYtfc6x27G0bBqGRa+5U2VsqUMELgQoli6d1FbDXD+Z624Rt2O\nWjNsl5bP/Uz68tfLLDh4k392rMfbrWsZIeBCRJcF3zcG20owfLfew9IzdTT/3z7qVy7D8mE+BgxQ\nFGrL34AHYTDpLJhZkJqRRbsZByltaca2Cc0xy2NRn/RMHb3nHyUyXs82ADcPwIru4N4fus/L072I\nEEWBzLSJ3KlQH7r+AFMvQZsP4M5ZWNkD5jWDUyu0pUm54FuzPIGjfPlxsDe3HiTTdXYQobceGih4\nIUSxoNPBtV2wvKv22XNxI3gO1qrhDlwLNVs998ZtxbEIFhy8ySDf6oxpVbPg4y5sTEzBbzxEnYDI\nE3oPszAzYZBvDQ5eiyXsfpIBAxSFVswZCD8EvmPAzAKAxUfCiX6YwkddXPOcsMHvbQAak56pY+rP\nerQBqNkaWv0TzgZqe9yEKOEkaRPPsnHQlhxNufD0yZaptiTpuwaw/4tc9f8BaO9akU3jmlHa0pQB\nC4+zLiTKQIELIYqs9GQ4uRjm+MDqvvDgOrT9GKZchM7falURX2DPpXt8suUi7epX4JM3GqDI03hN\n4zfBqiwc+yFXwwY2rY6FqQnLjoYbKDBRqB2bDRalwXMIAPcepzJnfxgdXCvSrLbDS5/excGGT95o\nwNEbcSzMqQ0AaPcjLq1gxz/g7oWXvr4QRZkkbeL5zCy1Rq1jDsOQreDkDQf/pyVvm8dpS5f0VLuC\nLZvH+ePjYs+768/x2bZLZGbpDBi8EKJIeBwDe/+j7afdPlXrM9lzEUw+By2mgrV9tsNPRz5kQuAp\n3Kra8f2Axi81C1DsWNhAk+FweVuu+nM6lLakq0cVNoTeJiE5w4ABikInIRoubNQStlJlAfjfzitk\nZql80Fm/Ev/66OPtxOtulfhm11XOR+fQBsDEFHotAis7bX9bWmK+xSFEUSPfcCJ7iqLtIRm4Vlui\n5DlYW7I0r5m2hOnaLm1JUw7KWluwLKAJQ5s5s/hIOMOWh5CQIjcEQpRIMadhw0itGXbQTHBuDgE7\ntUpxjfpojXZzcCvuCSOWh1DB1orFQ5tgbVFCerHlhs8o7c/y+LxcDQvwdyYlI4u1IZEGCkwUSr//\nPfEdA8CZqEdsPHWbYc1dqFHeJt8uoygKX/ZohKOtJZPW6NEGoHQF6LVYK66zdXKue8wKUVzonbQp\nimKqKMppRVG2Pee1oYqixCqKcubprxH5G6YoFBxqa0uVplyEdp9oS5hW99WWNJ1clGOFJzNTEz7p\n2oDpPd04duMBPeYEcSNW9k0IUSLosrRZnyWdYGFrrbJhk5Ew4RT0WwU1/PQuNBD/JJ2hS0+iU1WW\nBTTBobSlYWMvqmwrQaO+2n6g5Hi9hzWoYkdTF3uWH70lqyJKitQECF0ODXpA2eqoqsp/tl7E0daS\n8a++eHlyXtlZm/NdPw/C457w6dZLOQ9waQGt/wUX1kPo0nyPR4iiIDczbZOAy9m8vlZVVY+nvxa9\nZFyiMLO2h+ZTtCVMvRZrS5q2vwMzXLWlTo9jsh3e36c6P43wJSElg+5zgjh4TcpLC1FspSXC8fnw\ngyesfVNbgtXhc63oUafpYO+Sq9OlZmQxYvlJbj9KYdEQb2o6ljZQ4MWE33jITNH2DOZCgL8Ltx+l\nsPfyPQMFJgqV0OWQngjNxgPwy5kYTkc+4t3XXqG0pWFmsX1rluftVrVYczKKX8/fyXlAi3egVlv4\ndZpWLE2IEkavpE1RFCegMyDJmPh/pubg1ltb0hSwU3sSFjRTW/K0YaS2BOoFfFzs+WW8P07lrAlY\nGsyiwzcxVvsJIYQBPIqEXR/AjAaw859QuiL0WQ4TT2s3hlZ2uT5llk5l0prTnI56xKx+HnjVyH7P\nm0CrDFynAwQvyFUV4PauFXEqV4olQRGGi00UDpnp2tJI5xZQpTHJ6ZlM//UKblXt6O3pZNBLT2lf\nF3cnO6ZtPM+dhJTsDzYxgZ4Lwbo8rBsKqY8NGpsQhY2+M20zgfeA7NZJ9FIU5ZyiKOsVRan28qGJ\nIkNRtKVN/VZpN2Q+o+Dqr9oSqCWdIDr0ucOcylmzfowfrzWoxH+3X+bd9edIy5RG3EIUaVEntRuq\nWR7ajWCddjBin9YvrEF3rXFuHqiqymfbLrHr4j3+3dmVTm6V8zfu4qzZBHgSC+fW6j3E1ERhiJ8z\nweHxXLidQ7EIUbRd3ASJMdBsIgDzD9zg7uNUPn7DFRMTw1ZjNTfV2gBkZOmYuvYsWTm1AbBxgN5L\n4OEtrbK1POwVJUiOSZuiKF2A+6qqPv/OW7MVcFZVtRGwF1j+gnONUhQlRFGUkNhYWRJXLJVzho5f\nwtSL8NoX8DAClr2u7WV5DhtLM+YM9GRyuzqsD41mwMLj3E/MXU84IYSRZWVqVecWtYPF7SBsH/iN\n05rz9l4CTl4vfYnFR8JZdjSCYf4uDG+euyWVJZ5zC6jsrpVz16Nw1O/6NqmGtYUpS2W2rfhSVTj6\nAzi8ArXbEf0wmQWHbtLVvQrezgUzk+3sYMMnXRtw7GYcCw/p0Qaghh+0/RAubdb20wtRQugz0+YP\ndFUUJQJYA7yqKMozXQ5VVY1TVTXt6b/+CDz3G1pV1YWqqnqrqurt6Oj4EmGLQs/KTrtpG3MYKjaA\ntYMg+MfnHmpiojC5XV3mvunJ5TuJdJsdJE92hSgKUh5B0PfwvQesD4DkOOj0tbZfrcNnUDZ/Fl1s\nP3eH/26/TKeGlfh3PpYeLzEUBfwmwINrcH233sPsSpnTy9OJrWdjiE1My3mAKHrCD8K989qSZRMT\nvvz1CooC0zrVK9Aw+ng50dmtMt/uvsq56Ec5D2g2SVv2u+tf2W7FEKI4yTFpU1X1fVVVnVRVdQb6\nA/tUVR3052MURfnzOpWuZF+wRJQkNg4wZBvU7ag1x9zz8Quf9L7uVpn1b/thoij0nn+UrWezL2gi\nhDCS+Juw4z2tb+OeD7UZ9v6BMD4Emo7SihPlk5MR8Uz5+QxeNcrxXT8Pgy/XKrYadIcyTtpsWy4M\n9XcmPUvH6hNS/r9YOvoD2FQAt74Eh8ez/dwdRresRZWypQo0DEVR+KKHGxVsLZm05gxP0nJoA2Bi\nAj0WaLH/PER7gCREMZfnPm2KonyqKErXp/86UVGUi4qinAUmAkPzIzhRTFhYa/vdvIdphUo2jYLM\n5z+1bVDFjl/G+9Owih0TAk/z7e6r6HJa4y6EMDxVhYggCBwI33tCyBKo1wVGH4Kh26De61oj3Hx0\nIzaJkStCcCpbikWDvbEyz9/zlyim5uD7NkQchtun9B5Wy7E0rV9xZNWJW6RnSvn/YuXeJQjbC01H\nkWVqyX+2XqSynRVjWtUySjh21ubM6OdBhL5tAKztoc9SeHwbfhkn+9tEsZerpE1V1QOqqnZ5+vuP\nVFXd8vT376uq2kBVVXdVVduoqnrFEMGKIszUDDrPgLYfw/l1sKrXC5+MOZS2ZPVIX/p5V+OHfWGM\nWRWa81M3IYRhZKbD2bWwsJW2PzXymFZ6e8oF6LlA2ytlALGJaQxdGoyZicKyAB/K2VgY5Doliudg\nsCyT69m2AH8XYhPT2H5eVj8UK8dmg7k1eA9nfWgUF2MeM61TPUpZGO/hiG/N8oxtXYu1IXq2Aajm\no/WNvbIt103khShq8jzTJkSuKQq0mKotaYg8Bks7aT2bnsPCzITpvdz45A1Xfrtyn17zjhIVn1zA\nAQtRgiXHw6FvYFYjbXY8IxW6zIQpF7UiALaVDHfp9EyGLz/Jg8R0Fg9pQvXy1ga7VoliVQa8hsLF\nzVr1PT21rONALUcblgZFSGuW4uLxHTj3MzQeRKKJLV/vuop3jXJ0da9i7MiY3K4u7tXKMm3jeWIe\n5dAGALRehK+8ri3Vjg4xfIBCGIkkbaLgufeHN9fDoyhY1B7uXXzuYYqiMNTfheUBPsQ8SqHr7CMc\nvxlXwMEKUcLEXoOtk2GGK+z7TOvz9eYGGHscvAO05c4GlJmlY/zq01y4ncDsgY1xr1bWoNcrcZqO\n0R6gnZiv95DfP4vPRSdwKvKhAYMTBSZ4AahZ4Ps2s/eF8SApnY/ecEVRjL9n1NzUhFn9PMjM0jH1\n5zM5twFQFOg+F8pU0dqNJMcXSJxCFDRJ2oRx1GoDw34FVFjSEW4efOGhzes48Mv45tjbWDBo0Ql+\nOqH/E2IhhB5UFW7sh5/6wJwmcGY1uPWGt4/BW5u0Xmsmhv+6UFWVj7ZcZN+V+3zarSFt61c0+DVL\nHLuq0LA3hC6HFP0TsF6eVSljZSbNtouDtKQ/9qSG6yqyJCicPl5ONHIqPA9Ifm8DcPxmPAsO3ch5\nQKly0GcZJN6FzWNlf5soliRpE8ZTyQ2G79Gejq3qBefWvfBQFwcbNo3zp0UdBz7YdIEPN18gI0s2\nxQvxUjJS4dRKmOcPK7trpbNb/0tbAtltNlR0LdBw5h64weoTkbzduhaDfGsU6LVLlGbjIeMJhC7T\ne4i1hRn9faqz88Jd/ZasicLr9CpITYBmE/l8+2UsTE14t+Mrxo7qb3p7OdG5UWVm7L7G2Sg9qkNW\n9YIO/4Vrv2pVMYUoZiRpE8ZVthoM2wnVmsLGEXDkuxc+IStjZc6iIU0Y3aomK4/fYvDiYB4+SS/g\ngIUoBpLuw/4vYWZD2DJeW17Uba6WrLX+J5Qu+D6am0/f5utdV+nmUYV3OxS+G8hipZIb1GwNJxZo\nhWb0NNivBqqqsuKYrHYosrIy4fgcqObL4VRn9l6+x7hXa1PB1srYkf2Noih80V1rAzB5rR5tAACa\njob6XWHvJxB5wuAxClGQJGkTxleqHLy1ERr20j5od/wDdFnPPdTUROH9TvWZ0ded0MiHdJ1zhKt3\nEws2XiGKqnsXtdLY3zWAg9O1J9ODt8CYI9D4TTCzNEpYR8Me8O76s/jWtOer3o2kF1tBaDYBEu/A\nhQ16D3EqZ00H10oEBkeSkv78z2hRyF3eAo8iyfIdz2fbLlHd3pph/i7GjuqF7KzN+a6fB7finvCf\nrc/f//4MRdFWCZStBusD4InsgxfFhyRtonAws4Sei7QbiZOLYO1bkP7iapE9PZ1YO8qXtAwdPecG\nsefSvQIMVogiRKeDa7thRTeY1wzOb4DGb2mNsAeuhZqttBsdI7l6N5HRK0NxLm/Dgre8sTSTXmwF\nolZbqOCqLSPLxf6fYc1dSEjJYNPp2wYMThiEqmr/ve1rsTrBlWv3kvjX6/ULff/DpjXLM7Z1bX4O\niWaHPm0ArOygz3J4EgubRmufgUIUA5K0icLDxERbj97xf3B1B6zomu1TssbVy7FlfHNqVSjNqJUh\nzNkfJuWohfhdejKcXAxzfGB1H60qZNuPYeol6DIDHOoYO0LuJqQydGkwpSxMWTbMB7tS5sYOqeRQ\nFO0h2f2LcGOf3sOaOJejQZUyLDsaLp+3Rc2toxBzimSvMXy79wbNapXntQZFo9jPpHZ18KhWlmkb\nzum3p7KKB7z2BYTtgaCZhg9QiAIgSZsofHzHQN8VcPc8LG4P8TdfeGglOyt+Hu1HV/cqfL3rKpPW\nnCE1Q5btiBLs8R3Y+x/4zhW2TwXL0tos9uRzWp9Ea3tjRwhAYmoGQ5cG8zglg6UBTahatpSxQyp5\nGvYG28q5KtqgKAoB/i5cu5dEUJgsPStSjv4A1uX5LtaLxykZhabEvz7MTU2Y1d+DLJ3KlLV6tAEA\naDICGvSEff+FiCDDBymEgUnSJgon164w+BdIiYfFHeB26AsPtTI3ZWY/D97r+Apbz8XQZ/4x7iRI\ndTNRwsScgY2jYKabVtCnhj8E7ISR+6FRHzAtPLNYGVk6xv50iuv3k5g7yIsGVeyMHVLJZGahFW64\nuV97SKanN9wr41DagqVB4QYMTuSr2Gtw7VfiXAezJPgeA5tWp16lMsaOKldqlLfhP90aciI8nvkH\n9WgDoCjwxiwo5wzrh0FSrMFjFMKQJGkThVd1Xxi2G8xLwbIucG3XCw9VFIWxrWvz41ve3IxNouvs\nIGkCK4o/XRZc3gZLX4eFreDKdu3p8sTT0P8nqOFn1P1qz6OqKtM2nOfw9Qd82dONVnULvlKl+BOv\nALAoDUdn6z3E0syUgU1rsO/qfcIfPDFgcCLfHJ+DamrJRzG+2FiYMrV90azQ2suzKl0aVea7PXq2\nAbAqA32f9iTcOPKFRc6EKAokaROFm2NdGL5X238TOCDHvkLtXCuyaZw/pcxN6b/wOBtCowsmTiEK\nUloiHJ8PP3jB2jfhURR0+Fzbr9ZpOtgX3mpw3+29zoZT0UxqW4e+3tWMHY4oVVYrTHNhPSToX1xk\nkG91zEwUlh+NMFxsIn8kxcKZQGJqdGf7zSwmt6uLvY2FsaPKE0VR+LyHGxXLWDFpzWn92gBUcoPX\nv9JmlA9/a/gghTAQSdpE4WdbEYbugFptYOsk2Pd5ttXO6la05Zdx/nhVL8c7687yxY7L+q1/F6Kw\nexQJuz6AGQ1g5z+hdAWtStrE01rDZKvCvczw55NRfP/bdfp4OTG5nfELoYinfN8GVQfBC/QeUsHW\nii6NqrAuJIrHqRkGDE68tJM/QlYa799tSS1HG97yK9qN6+1KaW0AIuOT+WSLHm0AADyHgFtfOPAl\nhB8ybIBCGIgkbaJosCwNA9ZA40Fw6CvYPBayXnyjUM7GghXDfRjsV4OFh24yfPlJubEQRVfUSVg3\nFGZ5wPF5UKcdjNgHw3dDg+5gambsCHN08Fos7286T4s6DnzR063IFEAoEcrVANfuELIUUh/rPSzA\n35kn6VmsC5EVDYVWejIE/0iEQysOxZfj311cMTct+rd+Pi72jGtTm3Wh0Ww/p0cbAEWBLt9B+dqw\nfjgkSpsgUfQU/f9zRclhag5dZ0Pr9+HsaljdV1sm9gLmpiZ82q0hn/doyJHrD+g+J0j2X4iiIysT\nLmyERe1gcTsI2wd+42DSWei9BJy8jB2h3i7cTmDsqlDqVrRl7puexeKmsdhpNgHSHsPplXoPaeRU\nFu8a5Vh+NEJWMxRWZ1dDSjwfx75Km1ccafNKBWNHlG8mttXaALy/8Ry39WkDYFlaW5mQlggbhsv+\nNlHkyDenKFoUBVpP05K3mwdhaSdIvJvtkDeb1mDViKY8fJJOt9lHOHxdKkiJQizlEQR9D997wPoA\nSI6DTl9r+9U6fAZli9Y+sNuPUhi27CR2pcxZFtAEW6vCU8VS/ElVT6jRXJvJzWYVw18F+LsQGZ/M\nviv3DRicyBNdFhybQ1Sp+gRl1OHfXVyNHVG+ylMbgIqu0PkbiDgMB/9n+CCFyEeStImiyfMtGLgW\n4m5qMxGxV7M93LdmebaMb06VsqUYsiSYJUekMawoZOJvwq//hO8awJ4PtTLV/QNhfAg0HaU9JS5i\nEpIzGLokmJSMLJYN86FiGStjhySy02wCJETBpV/0HvJag4pUsbOS8v+F0dUdEH+T6Y/bM6SZC7Uc\ni95nSE5qlLfh024NCda3DQBo2yzcB8LBr3LVWF4IY5OkTRRdddpDwHbITNN6ud06mu3h1eyt2fB2\nM9rVr8in2y4xbcN50jJleYQwIlXVmr6ueRO+94STi6FeFxh9CIZug3qvg4mpsaPMk7TMLEatDCEi\n7gkL3vKibkVbY4ckclKnAzjUhaPfZ1vs6c/MTE14y8+ZozfiuHJX//1wwvDUo7O5b1qRYEt/JrYt\nvoV/enpW5Q33Kny35xpn9GkDANpsm+MrsGEkPNZjT5wQhYAkbaJoq9IYRuwBG0dY0R0ubsr2cBtL\nM+YP8mLiq7VZGxLFmz+eIDYxrYCCFeKpzHQ4u1brrbbsde2BQ4t3YPJ56LkAKrsbO8KXotOpvLvu\nHCfC4/m6tzvNajkYOyShDxMTbd/knbPa8jE9DfCphpW5CcuCIgwXm8idqGCUqOPMTX2Nya/Vx65U\n8V2WrCgK/+3e8I82AEn6tAGwsNH2t2Uka/vbsvQYI4SRSdImir5yzloVvSoesC4Ajs3N9nATE4Wp\nHV5h9sDGXIhJoNvsI1yMSSiYWEXJlhwPh76BWY1g0yjISIUuM2HKRWj7IZSpbOwI88VXu66y5WwM\n7772Ct0bVzV2OCI3GvXXHoLlotl2WWsLejR2YtPp28Q/STdgcEJfWUHf8xgbzjh0oX+T6sYOx+Ds\nSpkzs78HUblpA1Chnvb5eysI9n9u2ACFyAeStIniwdoeBv8C9TrDrvdh579Ap8t2SJdGVVg/phkq\n0HveMXaclyUSwkBir8HWyTDDFfZ9Bo714M0NMPY4eAeAhbWxI8wzVVWJik9mfWg07647S6uv9zP/\n4A0GNq3O2Na1jB2eyC1zK/AZBdd3wf0reg8L8HcmLVNHYHCkAYMTeom/icmVbazMbMt7Xb0wNSkZ\n7TWaONszvk1t1odGs+1cjH6D3PuB52A4MgOu7zFsgEK8JMVYxRi8vb3VkJAQo1xbFGO6LNj5vtYk\n1rU79Fig3YRkIzYxjdErQzgV+YiJbeswuW0dTErIl5wwIFWFmwfg+Fy4vhtMLaFRX/Adq1UwK6JU\nVSX8wRNOhMdz4mYcweHxxCSkAlDW2pwmzva0qOPAQJ/qmElp/6LpSZxWEMetN3TTf8Zt0KIThN1P\n4vA/20hbByN6smkK5meW82/nNXwV0MHY4RSozCwdfRYcI+x+Ejsnt6Rq2VI5D8pI0QqaPY6BMYfB\nzsnwgQrxJ4qihKqq6p3jcZK0iWJHVeHYbNj9b6jeDPr/pM3EZSMtM4sPNl1gfWg0HRtU4tu+7thY\nFv6GxaIQykiF8+u00un3L2pLzZqMBO9hUNrR2NHlmk6ncv1+EifC4zgRHk9wePwf+0AdSlvQ1KU8\nTWva4+NiT90KtvLAo7jY/g6cWgGTL4BtRb2G7Ltyj2HLQvh+QGO6ulcxcIDiuZLjSf+6Hluy/PCZ\nFEj18kV3Fj+vIuOSef37w7hWLkPgKF/9ZhofhGl7jCs2gKHbtb6wQhQQfZM2uSsVxY+iaKWrbSvD\n5rdhSUcYtB7Kvnhdv6WZKV/3bkS9SrZ8seMyveY94cfB3lSzL3lfeCKPku5r1R9DFsOTWKjYELrN\n1WYrzCyNHZ3esnQql+885vjTWbSTEfE8TNb6dlW2s8K/Vnl8niZqNR1sUBRJ0ool37Ha3+fghdp+\nSz20rlsBFwcblgaFS9JmJDF7Z1NFTSPBY3SJTNgAqpe35tNuDZj681nmHQhj/Kt6VM50qA1vzNKK\nkvz2qdYTU4hCRmbaRPEWflgrp25uBW+u06sq38FrsYxffQpzUxPmD/LCxyX7WTpRwt27qC2BPPcz\nZKVDndfAbyy4tNIeIBRyGVk6zt9OIPjpcseQiIckPq2+Vt3emqYu2iyab83yOJUrJUlaSbJ2kPYZ\nOvWSVm1PD8uCwvlk6yU2j/PHo1pZAwco/kyXnkLCl/W5jDONpu2ldAleLaKqKpPWnGH7+TusH+NH\n4+rl9Bu4bQqELIEBa+GVjoYNUoinZHmkEL+7fxlW9YbUR9B3BdRum+OQm7FJjFgRQlR8Mp92a8gA\nn+JffUvkgk4HYXvh+Bxt35pZKfAYCL5vg0Ph7oeUmpHF2ahHWpIWHk/orYekZGj9Cms52uDjUh7f\np8sdK9vpsR9EFF+RJ2BJB+j0tdbgXQ9JaZn4ffEbr9avwKz+jQ0coPizU5tn4XnmIw75LqZlx97G\nDsfoHqdm0GnmYUxNFHZMaqFfEpuRCovbw6NIbX9bNit0hMgvkrQJ8WePY+CnPhB7Bbr+oN1g5yAh\nJYOJgac5eC2WIX41+H+EzE8AACAASURBVLCLqxRWKOnSk+FsIJyYDw+uaUtwfUaB19Ac900aS3J6\nJqcjH3HiZhzHw+M5E/WI9Eytsmq9SrY0dbGnac3yNHG2x9G26CzjFAVkUXt4ch8mnNK70funWy+x\n4lgEQdNepWKZ7AtBifzxJDWd+9Mbo5pa4PyvUEzkuwqAkIh4/o+9+w6Pqtr6OP7d6YUUQkIPJPQa\nehcpooigiNIUGxbEXu699oINy+tVsWPvioJdFKnKpffQe0kggUAghZB+3j9ORIGQnEAmk/L7PE8e\nkzl7zyzuDcOss/dea+TkRQzrUJ//jnTY//LQdpjcx26+PfZX8PJxbZBS5elMm8g/BdeFsdNhytX2\nObeUvXDuv4vcvhbi780H13XhuV838u78nWxLSueNKzsSGqA38ConNcE+27PiQzh22G7qftl70PrS\ncndgPS0zh+W7D7NkRzJLdx4iNj6F3HwLDwNt6oVwTfeGBUladf0uS/F63gFfXw2bfoZWQx1Nua5n\nFB8u3Mmni3bz74HNXRygAMz4/lMuI56dvV5RwvYPnaPCuL1/U16dvZW+zSO42MlZyxqN7aqp31wL\nsybAhRNdHqeIE1ppk6olNxt+vB1ip0DHa2HwS+BZ/L2LqSvieejbtdQJ9eO9azrTtFZQGQQrbrdv\ntX1ebd23kJ9r9wHscTs06F5uzqsdychmaUFVxyU7k1m/L4V8C7w8DDH1Q+jWqAZdo8Po3LA6QX7l\nK8GUCiA/D17rBAE14MZZjn/vb/x4OSv3HGbhA/3x83a2QidnJi45g72vnEdznySqP7ix3N1Icrfc\nvHxGTl7E1gPp/HpXb+pXd1igZfp/7Jt1oz6Dlhe7Nkip0kp9pc0Y4wksB/ZaljXkpGu+wCdAJ+AQ\nMMqyrF0lilikLHj52L3bguvZzTTTEmHEh8Uesh/eqT7R4YHc/OkKhr25kEmj23NeS2dlsKWCyc+D\nzb/aydruBeBTDbrcCN1uhrBod0dHUlpWQZJml+DflJgGgI+XBx0iQ7m9f1O6RYfRoUEoAT7aTCFn\nycMTetwG0/8NcUvsGxYOXN8rilkb9/Pj6n2M7BLp4iCrti+++4H7PTaQ0uNxJWyF8PL0YNLoDgya\nNJ97p6xx3gbggqchfhl8f5tdDbgcvP9L1eZ4pc0Ycy/QGQguJGm7FYixLGu8MWY0MMyyrFFFPZ9W\n2sTtlr1n30mr0w6u/Bqq1Sx2SkLKMW76ZDnr96Vy38AWjO/TSNX0KousNFj1uX1e7fBOCGlgJ2od\nrwa/ELeFlZByjCU77FW0JTsPsSPpKAD+3p50jqpO1yj7TFpM/RCtaIhrZGfYzbYbFvS9dMCyLAZN\nmg/Ar3f11vukiyzecYgDH45hoE8svv/ZBH7B7g6p3PpuVTz3TFnDv85vxh3nOSwYdXgXTD4XqkfD\nDb9XqPYtUnGU6kqbMaY+MBh4Bri3kCFDgQkF308FXjfGGMtdey9FnOhyIwTVhanX29Wixkyze7UU\noU6IP9/c3JP/TF3D879tYnNiKs9dHqMPyxXZkThYOhlWfAJZKVC/KwyYAC2GONo6W5osyyIu+dgJ\njaz3JGcAEOTrRZfoMEZ2jqRbdBht6oXgrbMrUhZ8Auz3yz//z25CXMz7JIAxhrG9orh/2loW70im\nR+MaZRBo1ZKXb/H29/N4z3MJVudblLAVY1iH+szbnMQrs7fSq2k4HZ20AageZffbnDIGfn8ELvo/\nl8cpcjqOVtqMMVOBZ4Eg4N+FrLStAy60LCu+4OftQDfLsg6e7jm10iblRvxy+GIkWJa94hbZpdgp\nlmXxxtxtvPj7FtrVD+GdazqrSlpFE7fMLtm/4Uf751ZD7W1g9Yu92VVqLMtix8GjBStpdjPrhJRM\nAEIDvI+vonWLDqNlnWBnW3pEXCH9ALzcBjqMgSEvO5qSmZNHj2dn0yUqjHeuKbu/V1XFV0v3kP7j\nfVzvPROPu9dASH13h1TupWbmcNGk+XgYwy93nuP8nO9vD9n/Xoz4CFoPc2mMUvWU2kqbMWYIcMCy\nrBXGmL6nG1bIY6dkg8aYccA4gAYN1PtCyon6neGGmfDZ5fDxEBj+gV1wogjGGG7v35RmtYK4Z8pq\nLn7tf7xzTWc1ky3v8nJh44/2ebX4ZeAbYidqXcdBqOvP3eTnW2w5kFZQ2dHe8ngwPQuA8Gq+dGsU\nRvfoMLpG16BpzWp4KEmT8qJaTWg3ClZ/Af0ehsDwYqf4eXtyZbcGvDlvO3HJGUSGOSwAIcVKzczh\nrd9W8pv3PEyby5SwORTs580ro9ozcvIiHv9xPS+NbO9s4oAJ9pnOH+6A2jF2hUmRMlbsSpsx5lng\naiAX8AOCgW8ty7rqH2NmABMsy1pkjPECEoGIorZHaqVNyp30JPhyFOxbBYNegK43OZq2KTGVGz9e\nzoG0LF64PIZLO9RzcaBSYpkpsPITWDIZUuIgrBF0u8Xu1+dbzWUvm5uXz8aEtOPbHZftSuZIRg4A\ndUP8jq+idY0OIzo8UOd+pHxL2gxvdIW+D0Hf+x1NSUzJ5Jzn53BdzygeGdLKxQFWHROnb8Rj4SQe\n8PoSbv7TPpstjr08cwuTZm9l0uj2DG3v8N/sI3vg7d72Db4bZoG3dtdI6XBJc+2ClbbCtkfeBrT9\nRyGSyyzLGlnUcylpk3Ip+6h9xm3Lb9DrbjjvcfAo/txQ8tFsbvlsBUt2JjO+T2P+M7C5trKVB8k7\n7ERt1WeQnQ4Nz4Eet0KzCx03Ci6JnLx8YuNTClbRDrF812HSs3IBaFgjoCBBsxM1rTpIhfTFKHtL\n+T3rwNvf0ZQ7vlzFvE0HWPTQeVTzVUXTs7Xz4FEGvzybRf73EBLZGq790d0hVTi5efmMemcxWxLT\nmH5Xb+fvx5t/s2/udr7e8TZhkeK4vLm2MeZJYLllWT8C7wOfGmO2AcnA6DN9XhG38gmEUZ/b5a0X\nvAKp+2DoG3argCKEBfrw2Y3dmPDjet7+Yztb9qcxaXR79cVyB8uC3QvtLZCbfgEPL2hzOXS/Beo6\n3ArjUGZOHqvjjhxP0lbuPsKxnDwAmtSsxtD2dekaHUa36BrUDtFdWakEet4BHw2GNV9B57GOpozt\nFcVPa/YxbUU81/aMcm18VcAzv2zgUq/FhOQehJ53ujucCsnL04NXRrXnoknzuWfKar4a1x0vJ4Wd\nml9o/2++8FVo2AvaDnd9sCIF1FxbpDCWZfdxm/0kRJ9rN9d0WPb908W7eeLH9USFB/LeNZ2JCi+6\nB5yUktxsWP+dfVg8YQ34h9l3Q7vcCMF1SuUlMrJzWbn7yPHtjqvjjpCdm48x0KJ2MN2iw+gWHUaX\n6DDCq6k0tFRClgXv9rNbZNy2zNFOBIChbywg9VgOs+/to7OaZ+HPLUlc88ESVtR4nBqBPnDLQscN\nz+VU36/ay91TVnPPgGbcNcBhG4C8HPvGxf71MG4ehDucJ3IaLtkeWZqUtEmFsOYr+OE2CG8OY76B\nEGd73xduP8itn6/EsuDNMR3p1aT4Q/tyhjKSYfkHdt+9tAQIbwbdb4WYUXap8rOQmpnDil2HWVxQ\n2XFtfAq5+RaeHoY2dYOPr6J1iQojJECrqlJFrJtmbyMf/SW0uMjRlB9W7+Wur1bz4XVd6Nei+J6Y\ncqqcvHwumjSfmOyV/Ddzgr0LpMNVxc6Tot391Sp+ik3g65t70KmhgzYAACl74e1zIKgO3DTb8VZh\nkcIoaRMpLdvnwJRr7B44Y6ZCLWeH6fccyuCmT5azLSmdx4a04poeDVVoojQd3GpvgVz9JeQeg0b9\n7EqQjc9zfPf/ZIePZrN0V/Lx7Y4b9qWSb4G3pyGmfujxoiGdo8J0NkeqrrxceLWDXbHw+l8dTcnJ\ny+ec5+fQrFYQn97QzcUBVk4fLdjJhJ82sLzhm4Qf3Qp3r1Wz51LwVxsAY2D6nb2dH2vYOgs+vxw6\nXA1DX3dtkFKpKWkTKU2Ja+HzEZCdAaM/s7dMOpCelcvdX61m1sb9XNE1kicuaYOPlxoinzHLgh3z\n7GRt6+/g6QsxI+2VNYfJ9F+yc/PZcTCdzYlprNh9mCU7ktm8Pw0AXy8POjQIpVtB0ZAODarj76MG\n6iLHLXoTZjwIN8523Nvw9TlbefH3Lcy691ya1AxycYCVy+Gj2fR9cR6DIg7y3IFb4LzHoPe/3B1W\npbFidzIjJy/mknZ1eXlUCc4+z34S5v8Xhk2GdirnIGdGSZtIaTsSB58Ph0Pb4dK3IGaEo2n5+RYv\nzdzC63O30TUqjLeu6kgNnXcqmZxMWPsNLH4LDqyHwAjocpN9Zq1aRJFT8/Mt4g5nsDkxzf7an8aW\n/WnsSDpKbr79/hfg40mnhtXp3qgGXaPDiKkfgq+XkjSR08pKg5daQ+N+MPJjR1MOpWfR47k5jOhU\nn2eGtXVxgJXL4z+s49PFu1nV9ltCdv5qV+8MCHN3WJXKK7O28MqsErYByMuFT4bCvpVw01yo2cK1\nQUqlpKRNxBWOHYavxsDuBTDgCeh1l+ND4D+u2cd/vllDeDVf3r2mM63qBrs42EogPQmWv2+fVzua\nBDVb2yX72ww/pUeOZVkkpWexJTGdTYmpbNlvJ2lb9qcfr+gIEBnmT/NaQTSvHUSzWkG0qB1Mo4hA\nvJ1UDhORv8183K6id+cqqB7laMp9U9fw05oEFj3Yn9CAoqvyim3L/jQGTZrP+PZ+/GfTCLu40qDn\n3R1WpZObl8/odxazuaRtAFIT7PNtgeFw0xy7CrVICShpE3GV3Cz47ma7UmHXcXDhc457fsXGH2Hc\nJytIOZbDy6PacWGb0qlqWOnsX29vgYz9BvKyoOlAO1mL7gPGkJaZU5CUpbM5MbVg9Syd5KPZx58i\nvJoPzQqSs7+StKa1gnQWTaS0pO6DV2Kgyw2Ok4iNCakMmjSfBwa1YHyfxi4OsOKzLItrPlhKbHwK\nS7r8id+yN0qUJEvJxCVncNGk+TSrHcQUp20AALbPhU+HQbsrYNhbrg1SKh0lbSKulJ8PMx+FRa9D\niyFw+XuOq0cdSM1k3KcrWB13hHsGNOOO/k1UAhvs/023zbJL9u+YB17+5LW7gp2Nr2FdVk02JaYd\nXz3be+TY8WmBPp40+0di1rxWEM1qB6nkvkhZ+O4W2PBDibbrjX5nEXsOZfDnff2cfyiuomZu2M9N\nnyzn6UENuGrhRdDkPBjxkbvDqtT+qnR694Cm3D2gmfOJcyfCH8+rqqeUmJI2kbKw+C347UGo3wWu\n+AoCazialpmTx0PfreXblXu5qG1tXhzRjgCfKroClJ1B/pqvyF34Bj6Ht5HuE8GsoEv5JKsPa5I9\nySs4d+btaWgcUe0f2xrt/9YL9VfSK+Iu+9fDWz1LVBhjxvpEbv50BW+O6chFbbXb4HSycvMY+PKf\neHl6MKPbWjxnPmxvv6vXyd2hVXr3TFnND6v38s34HnRq6PDsYH4efHopxC2z/38qYXEsqbqUtImU\nlQ0/wLSbIDTSbgkQFu1ommVZvDd/J8/+upEWtYN599rO1Aut3L1eLMsiKS2LTYlpxO/ZTs1Nn9H1\n0PcEW2nE5kfzfu4gfrW6Uycs2N7a+NfqWe0gosN17kykXPr0Mti/znEJ+rx8i74vzqV2sB/fjO9Z\nBgFWTJP/2M6zv27i4+s60ufX8yEk0nGLBTk7aZk5XPTqfCwLpt/Vm2CnbQDS9tvn2/xD7cIkvtVc\nG6hUCkraRMrS7kXw5Wjw9IYrv4Z6HR1Pnbv5AHd+sQofLw8mX92JzlGVoyJYyrEctu5PO2Fb4+b9\nadQ7toUbvH5liMcivEw+K/x6sr7hVQQ0PofmdYJpWqta1V11FKmIts+xz/OUYFvYe/N38PQvG/n5\njnNoUy/ExQFWPElpWfR7cR5do8P4oNNumHZDiZqZy9lbsfswIycv4uKYOrwyuoPziTv/tCtKthkO\nl73juFiZVF1K2kTKWtIW+OxyyDgIIz6GZhc4nrrtQDo3fbKc+MMZPHNpW0Z2iXRhoKUrMyeP7Unp\nx5OyzYlpbElMY19K5vExIb4eXBm6nhG5P9Ho6GryvALJaTcGv163Ol6ZFJFyyrLs1YX8PLh1kaMP\nqamZOfSYOJuBbWrz0sgS9MWqIu6fGsu0lfH8fndvGn03GLKPwm3LwEO7DcrSpFlbeXnWFl4Z1Z5L\nOzhsAwDwx//B3Kfh4knQ6TqXxSeVg9OkTbezRUpLRDO4cZbdy+3L0TDkZeh0raOpTWpW4/tbe3H7\nlyu5b1osGxNTefiiluXqkH5evsXuQ0fZ8o/Vs02Jaew6eJSCY2f4eHrQuGY1ukaH0bx2MK1qQLuD\n0wmJfR9zeKe9veeCZ/DseDWefrq7LlIpGAM977Cr6m6bDU0HFDsl2M+b4Z3q8+XSOB4c1JKIIBUO\n+su6vSl8vSKOG3pF0+joakhYA0NeUcLmBrf1a8z8rUk88v06OjWs7rwNQO9/wZ6FMP0+qNsR6sS4\nNlCpErTSJlLastLg62th+2zocz/0fdDx9ojcvHwmTt/EBwt20rtpOK9f0ZGQAId76UuJZVnsT836\nR6+zdDbvT2Xr/nSycvMB+4/TMCzghGqNLWoH0bBGwbmzI3GwdDKs+ASyUqB+V7tkf4uLwVP3ikQq\nndxsmNQOwpvCtT86mrIjKZ3+//2j5FX6KjHLshg1eTHbk9KZ8+++hHw7BvausKtzOqxQLKXrrzYA\nTWtV4+ubezi/mXr0oL0C7e0P4/4AP/VmlcJppU3EXXyD4Mop8NPddvnflHh7i4Rn8cmXl6cHj13c\niha1g3j4+7UMfeN/vHdtF5rUdM1h5pSMHHtL4/40NiemsiUxnc3700g5lnN8TM0gX5rXDuLq7g2P\nFwVpWjMIf59CetPFLbNL9m8o+NDWaij0uA3qF/teJCIVmZcPdB8PMx+zV4bqtCt2SqOIavRrHsFn\ni3dzS9/G+Ho563dZmf2yNoGlu5KZOKwtIWnbYesM6PuQEjY3igwL4Olhbbjrq9W8Nmcb95zv8AZD\nYDgM/wA+GgI/3QnDP9T5NjkrStpEXMHTG4a+DiH14Y/nIC0RRn5sJ3QOjOwSSaOIQMZ/toJhbyzg\n1Ss70K95zTMOJzMnj20H0k8sCpKYRmLq3+fOgvy8aF4riCExdf5eQasVRPVAn6KfPC8XNv5oN8OO\nXwa+IXai1nWcXVFTRKqGTtfZZ3kWvg6Xv+toythe0VzzwVJ+XpPA5Z3quza+ci4zJ49np2+iZZ1g\nRnWJhJ/uAC8/6HKju0Or8oa2r8cfm5N4bc5WejcNd14wrGFP6P8IzH4CGvaCrje5NlCp1LQ9UsTV\nVn5ir7rVag1jvoGg2o6n7j1yjJs+Xs7GxFQeHNSCm3o3whRxpy43L5/dyRnHk7LNBUnarkP/OHfm\n5UHTmtWOb2v8K0GrE+JX5HOfIjPF/rMtmQwpcVA9GrrfAu3HqMyxSFU142G7f+XdsfZNq2JYlsX5\nL/+Jn7cHP91+TsnegyqZV2dv5aWZW/hqXHe6R+TCK23sapxDXnZ3aILdBmDwq/8jL9/i17tL0AYg\nPx++HAU75sENv0PdElSilCpB1SNFypOtM+1zbgE14KqpENHc8dSM7Fz+800sv6xN4LKO9Zg4rC2+\nXh4kpGSeUK1x8/40th5IJ7vg3JmHgagagXa/s9p/fzUMCzi7AifJO+xEbdVnkJ0ODc+xz6s1uxA8\ntL1JpEo7Emefbet+Cwx8xtGUz5fs5uHv1vHN+B50qSQtT0oqIeUY/V/8g77NI3jrqk4w52n480W4\nfTmEN3F3eFJg5Z7DjHh7EUNi6jCpJG0Ajh6Cyb3tXTg3/wkqxCX/oKRNpLzZuxK+GAl5OXDFV9Cw\nh+OplmXx2pxtvDRzC3VD/EjLyiUtM/f49drBfn8nZgVJWpOa1fDzLqUkyrJg90J7C+SmX8DDC9pc\nbn8wq6ty3SLyD1NvgC0z4N71jj6cHsvOo/uzs+nVpAZvjulUBgGWP3d/tYrp6xKZfW8fIqtZ8HJr\nezvd6M/dHZqc5K8V0ZdHtWNYhxJs6d2zBD66CJoPgpGf6nybHKdCJCLlTb2OcMNMuyXAJ0Ptpput\nL3U01RjDnec1pXntIL5YsofIMH+a1w62E7RaQa6rMJmbDeu/s4uLJKwB/+rQ+17ochME13HNa4pI\nxdbzdlg3FVZ8DL3uLHa4v48no7tG8t78new9cox6oVWr6MbKPYf5fvU+buvX2C4pv/RdOHbYbqMg\n5c5t/Zowf2sSj36/nk4NwmhQw2EbgAbd4LzHYeaj9m6V7uNdG6hUOlppEylrGcl2H7e4pTBwor21\nsLzJSIYVH9ofHtISILyZvaoWMxp8HP4DJSJV10dD7K3Ud61xVDl375FjnPvCXG7sHc2Dg1qWQYDl\nQ36+xbC3FpJw5Bhz/92XQG8Dr3WEwAj7Jp9WY8ql+MMZDJo0n6Y1S9gGwLLgyytg2yy4fgbUr5or\ny3Iipytt6tQoUtYCwuCaH6DFYJjxoH1wPz/f3VHZDm6Fn++Bl1rB7CchogWMmQq3LoHO1ythExFn\net4JqXvtlXoH6oX6M7B1Lb5aGkdGdm7xEyqJ71btZU3cEe6/sAWBvl6w6Wc4vMteZVPCVm7Vrx7A\nM8PasnLPEV6ds835RGNg2FsQVAe+uc5eURVxSEmbiDt4+8PIT+yy+Iteh2nXQ05m8fNcwbJg+1z4\nfAS83hlWfQ5th8Mti+Ca76Hp+eChtwoRKYEmA+ybPgtftd9jHBjbK5qUYzl8u3Kvi4MrH45m5fL8\nb5toFxnKsA717AcXvgbVo6DFELfGJsW7pF1dLutYj9fnbGXZrmTnE/2rw4iP7F0s39/q+O+HiD6J\nibiLhycMegHOf8q+G/3ZZWV71y0nE1Z+Cm/1gk8vhX2roO+DcM96u8dcrVZlF4uIVC4eHtDjdkhc\nCzv/cDSlc8PqtKkXzEcLd+Guoxtl6c152ziQlsXjF7fCw8PYhSril0H321SJt4J4cmgb6lcP4O6v\nVpNyLMf5xPqd4IKnYPN0+8atiANK2kTcyRj7oP7l79tn3N4fCEf2uPY105Ng3nN2D6Afb7cfG/oG\n3L0O+j4A1SJc+/oiUjXEjITAmvbqkQPGGMb2jGbbgXTmbz3o4uDcKy45g3fn7+TS9nXp2KC6/eDC\nV8EvFDqMcW9w4lg1Xy8mjW5PYmomj36/rmQ3G7qNh5YXw6wJ9r//IsVQ0iZSHrQdDld/C2mJ8N75\nkBBb+q+xfwP8cJtdSnres1C3o3227pYFdgNXb7/Sf00Rqbq8fKHbOLvowv4NjqYMaVeH8Gq+fLhg\np4uDc6+J0zfiaQz3D2phP3Bou91OpcuN4BPo3uCkRDo0qM7d5zXlxzX7+G5VCbb2GgOXvA7B9eCb\nsXYBMJEiKGkTKS+iz4Xrf7O3xXw4CLbPOfvnzM+HLb/bLQbe6gFrp9l3cW9bBmO+hkZ9ddhdRFyn\n8w3gHeB4C5ivlydXdW/A3M1J7EhKd3Fw7rFo+yF+XZfILX0bUyekoL3BojfsKptdx7k3ODkjt/Zr\nQteoMB77YT17DmU4n+gfCiM/hqMH4Luby09RMimXlLSJlCe1WsGNsyC0oV0YZPWXZ/Y82Rmw/AN4\nsxt8MQKSNsN5j8G9G2DIyxDRrHTjFhEpTECYvZIf+7W9k8CBMd0a4uPpwccLd7k2NjfIy7d48ucN\n1Av1Z9y5jewHjx6E1Z9DzCgIquXeAOWMeHoYXh7dHmPgrimryMkrQfJVt4Pd/mfr77BwkuuClApP\nSZtIeRNcF67/FRr2hO/Hw5//57y6VGqCXar/5VZ26X7vALjsXbgrFnr/y/4AJSJSlrrfAlae3VDY\ngYggX4a0q8PUFfGkZpaguEMFMGVZHBsTUnnwohb4eRcUG1n2PuRm2oVbpMKqF+rPxGFtWbXnCK/N\n3lqyyV1uhFaXwuynYPci1wQoFV6xSZsxxs8Ys9QYs8YYs94Y80QhY64zxiQZY1YXfN3omnBFqgi/\nEBgzDdqOhDlP2wlYXhG9i/athm/HwSttYf5L0LAXjP0Vxs2ziwF4+ZRV5CIiJwprZBdcWP4+ZDnb\n8nh9r2iOZufx9bI4FwdXdlKO5fDf3zfTNSqMwW3r2A/mHIOl70DTgVCzhXsDlLN2cbu6XN6xPq/P\n3cbSnSU4o2YMXPIaVG8IU8faq68iJ3Gy0pYF9Lcsqx3QHrjQGNO9kHFTLMtqX/D1XqlGKVIVefnA\nZe/AOffAig9hyhjIPvr39fw82PgzfHgRvNOn4BD7DXDnShj9ub1Sp/NqIlIe9LwTMlNg1WeOhrep\nF0KXqOp8tHAXefmVo/z/a7O3kpyRzWMXt8L89d4cOwUyDkJPrbJVFk8MbU1kWAD3TClhGwC/YBjx\nsV2Q5NtxOt8mpyg2abNsf90a8y74qhzvoCLlnTEwYAJc9KK93/2jIZC8095m9FonO5E7sgcueNo+\nrzboefuutohIeVK/M0R2h8VvFL1r4B/G9oom/vAxZm3c7+LgXG97UjofLdzFqM6RtKkXYj+Ynw8L\nX4c67SCqt3sDlFJTzdeLV0bZbQAeKWkbgDox9r/j22fD//7ruiClQnJ0ps0Y42mMWQ0cAGZalrWk\nkGGXG2NijTFTjTGRpRqlSFXX9SYY9Rkc2Aivtodf74PACBjxEdy5GnreYW+pFBEpr3reYd9k2vij\no+EXtKpFvVD/SlH+/5lfNuLn7cm/Lmj+94NbZ8ChrfYqpHZFVCodGlTnngFN+WnNPr5dWYI2AACd\nroO2I2DuRLvhukgBR0mbZVl5lmW1B+oDXY0xbU4a8hMQZVlWDDAL+Liw5zHGjDPGLDfGLE9KSjqb\nuEWqnhaD4bqf7RLaN8yCG2dC62Hg6eXuyEREitd8EIQ1tpttO1h98PL04JoeDVm8I5mNCallEKBr\nzNt8gDmbDnBH/yZEBPn+fWHhaxASCa2Gui84cZlb+jaha3QYj/2wjt2HjhY/4S/GwJBXIKiOfYNW\n2ySlQImqR1qWoj3FCQAAIABJREFUdQSYB1x40uOHLMvKKvjxXaDTaea/Y1lWZ8uyOkdERJxBuCJV\nXP3OMOQliOzi7khERErGwxN63Ab7VsIeZxXyRndpgL+3Z4VdbcvJy+epnzcQVSOA63pF/X0hfgXs\nXmBX1vT0dlt84jqeHoaXR7XH08Nw11erS9YGwLcanPc4JKy2zz2K4Kx6ZIQxJrTge39gALDppDF1\n/vHjJcDG0gxSREREKoF2V0BADXuVyYGQAG8u61iP71fv41B6VvETypnPFu9me9JRHh7cCl8vz78v\nLHoNfEOg4zXuC05crl6oPxMva8vquCO8WtI2AG1HQN2OMPuJE4uQSZXlZKWtDjDXGBMLLMM+0/az\nMeZJY8wlBWPuLGgHsAa4E7jONeGKiIhIheUTAF1ugs3T4aCzD7Fje0WRnZvPl0v3uDi40pV8NJuX\nZ26hd9NwBrSs+feFw7tgww/Q+TrwDXJXeFJGhsTUZXin+rxR0jYAHh5w4bOQlgALXnVdgFJhOKke\nGWtZVgfLsmIsy2pjWdaTBY8/ZlnWjwXfP2hZVmvLstpZltXPsqxNRT+riIiIVEldbgQvP1j0uqPh\nTWoG0btpOJ8u3l2yLWZu9vLMLRzNzuPRIf8o8Q+w+G0wHtBtvPuCkzI14ZIzbAPQoLt9dn3BJEgp\nYUETqXRKdKZNRERE5KxUi4B2o2H1l5DurCjZ9b2i2Z+axfS1CS4OrnRsSkzl8yW7uapbA5rV+sdq\n2rHDsPITaDMcguu6L0ApU9V8vZg0ugP7UzN5+Lu1JWsDMOAJsPJh9pOuC1AqBCVtIiIiUrZ63A55\nWbDsXUfD+zSLoFF4IB8s2OXauEqBZVk89fMGgvy8uXtAsxMvLv8Qco6qmXYV1D4ylHvOb8bPsQlM\nK0kbgOoNocetEPsV7F3pugCl3FPSJiIiImUrvCk0vwiWvgvZGcUO9/AwXNszijVxR1i553AZBHjm\nZm7Yz4Jth7j3/GZUD/T5+0JuFiyZDI36Qe227gtQ3GZ8n8Z0iw7j8R/WsetgCYqLnHOv3Zt1xkOO\n2mVI5aSkTURERMpezzvgWDKs+cLR8OGd6hPk58WH5Xi1LSs3j2emb6RpzWqM6dbgxItrp0J6ov3n\nlirphDYAU0rQBsAvGPo9bLfK2PCDa4OUcktJm4iIiJS9Bj2gXidY9Cbk5xU7PNDXi1GdI/l1bQKJ\nKZllEGDJfbhgF7sPZfDokFZ4ef7jI5Zl2W0OaraGxv3dF6C4Xd1Qf569LIY1cUeYNKsEbQA6XmP/\n/sx8DHLK5++/uJaSNhERESl7xtirTsnbYfOvjqZc2zOKfMvi08W7XBvbGTiQlslrs7cyoGVNzm0W\nceLFbbMhaaP95/1nJUmpkgbH1GFEp/q8MW8bS3YccjbJwxMGPgNHdsOSt10boJRLStpERETEPVpc\nDKENHTfbjgwLYEDLWnyxZA+ZOcWvzpWlF2dsJjsvn4cHtzr14qLXIKgOtLm87AOTcmnCJa1p+Fcb\ngAyHbQAa94NmF8KfLzquvCqVh5I2ERERcQ9PL+h+K8QthriljqaM7RXN4YwcflhdfvpWrY1P4ZsV\n8YztFU10eOCJFxNiYcc86HYzePkUOl+qnsCCNgAH0rJ46PsStAE4/ynIPQbzJro2QCl3lLSJiIiI\n+3S4CvxCHK+2dW8URovaQXy4YFfJ+l25iGVZPPHTesICfLi9f5NTByx6HXyqQaexZR+clGvtCtoA\n/BKbwNQV8c4mRTSDzjfAio9g/waXxifli5I2ERERcR/favaH0I0/QfKOYocbY7i+VzSbEtNYtN3h\neSAX+jk2geW7D/Ofgc0J9vM+8WJKPKybZheR8A91T4BSrh1vA/DjeudtAPo+AL7B8PvDagFQhShp\nExEREffqdjN4eNmVJB24pH1dwgJ93N5s+1h2Hs9O30irOsGM6Bx56oAlb9sfqruNL/vgpEL4qw2A\nt6cHd321ylkbgIAw6HM/bJ8DW2e6PkgpF5S0iYiIiHsF1YaYUbD6c8hILna4n7cnY7o1YPam/ew+\nVIImxaXsnT93sC8lk8cvboWnx0lVITNTYPlH0PpSqN7QLfFJxVA31J+Jw9qyJj6FV2ZtcTapy40Q\n1thebctzWMhEKjQlbSIiIuJ+PW+HnAxY/r6j4Vd1b4inMXy8cLeLAyvcviPHeOuPbQxuW4dujWqc\nOmDlJ5CdBj1uL/vgpML5qw3Am/O2s9hJGwAvH7jgaTi4BZZ/6PoAxe2UtImIiIj71WwJTc6HJe84\nah5cK9iPwTF1+GZ5HOlZuWUQ4Ime/20T+RY8MKjFqRfzcmDx2xDVG+p1LPPYpGIqcRuA5oPs37F5\nz8Kxw64PUNxKSZuIiIiUDz3vgKMHYO3XjoaP7RVNWlYuU5fHuTiwE63YncwPq/dx87mNiAwLOHXA\n+u8hNV6rbFIif7UBSErL4qHvHLQBMAYGTrQTtj9fLJsgxW2UtImIiEj5EH0u1G4LC1+H/OILMrSP\nDKVDg1A+XrSb/PyyqaKXn2/xxE8bqBXsy/g+jU8dYFmw8FUIbwZNLyiTmKTyON4GYG0C361y0Iuw\nTozdNmPJZDi03fUBitsoaRMREZHywRjoeScc3AzbnFXFG9srmp0HjzJvywEXB2f7dtVeYuNTeGBQ\nCwJ9vU4dsPNPSIy1V9k89DFLSm58n8Z0alidJ3/eQFJaVvET+j8KXr4w8zHXByduo3cTERERKT9a\nD4Pgeo6bbQ9qU5vawX588L9dro0LSM/K5fnfNtE+MpSh7eoVPmjhaxAYYVfDFDkDnh6G5y9vS0ZW\nHhN+Wl/8hKBacM49sOln+6aBVEpK2kRERKT88PSG7rfArvmwb1Wxw709Pbi6R0P+t+0gW/anuTS0\nN+duIykti8cvboXHySX+AfZvsFcIu94M3n4ujUUqtyY1g7jzvCb8EpvAjPWJxU/ocRuERMKMhyA/\nz/UBSplT0iYiIiLlS8drwTfYPtvmwBVdG+Dr5cGHLmy2vedQBu/N38llHerRoUH1wgctegO8/KHL\nDS6LQ6qOm/s0pkXtIB79fh0px4qpJuntDwMmQOJaWPNlWYQnZUxJm4iIiJQvfsHQ6VpY/x0c2VPs\n8LBAH4Z1qMd3q+I5kpHtkpAmTt+Ip4fhvgsLKfEPkJYIsVPsohABYS6JQaoWb08P/m94Ow4dzWbi\nLxuLn9DmcqjfBWY/CVnprg9QypSSNhERESl/uo23C5MsftvR8Ot6RZGZk8+XS0u//P/C7Qf5bX0i\nt/VrTO2Q02x7XPoO5OfaWztFSknb+iHc1LsRU5bHsWDbwaIHGwMDn4X0/bDglbIJUMqMkjYREREp\nf0LqQ+vLYOXHcOxIscNb1A6mZ+MafLpoF7l5xbcLcCov3+LJnzZQL9SfG3s3KnxQVjosex9aDoEa\nhbQBEDkLdw9oSnR4IA98G0tGdjGN5CO7QJvhdkGcI2Xbv1BcS0mbiIiIlE89b4fsdFjxkaPhY3tF\nsy8lkxnr95daCF8t28OmxDQeHtwSP2/Pwget/hwyj9jtCkRKmZ+3J89d1pa45GP89/ctxU8YMMH+\n7+wnXBmWlDElbSIiIlI+1WkH0X1gyduQW/xZtf4tatIgLIAPF+wslZdPOZbDf3/fQtfoMAa1qV34\noLxcuwBJZDeI7Foqrytysm6NanBV9wZ8sGAnK/ccLnpwaKTdJ3DtNxC/vGwCFJdT0iYiIiLlV887\nIS0B1k0rdqinh+HanlEs332Y2Pjit1QW59XZWzmckc3jF7fCmEJK/ANs+gmO7Iaed5z164kU5f4L\nW1A72I/7p8aSlVtMWf9z7oZqtewWAJZVNgGKSylpExERkfKryXlQsxUset3Rh88RnesT6ON51uX/\ntx1I5+OFuxjdJZLWdUMKH2RZsOBVCGsEzS86q9cTKU6QnzcTh7Vl64F03pi7vejBvkHQ/xGIWwLr\nvy2bAMWllLSJiIhI+WWMvdVr/zrYMbfY4cF+3ozoHMnPsfs4kJp5xi/7zC8b8Pf25F8XND/9oD2L\nYN9Ku7Gxx2nOu4mUon4tajKsQz3enLuNjQmpRQ9uPwZqt4WZEyDnzP8uSPlQbNJmjPEzxiw1xqwx\nxqw3xpxyqtEY42uMmWKM2WaMWWKMiXJFsCIiIlIFtR0O1WrbFfEcuK5nFLn5Fp8tKb7HW2Hmbj7A\n3M1J3HleU8Kr+Z5+4MLXwT8M2l15Rq8jciYeHdKKEH9v7p8WW3SlVA9PGDgRUvbA4jfKLkBxCScr\nbVlAf8uy2gHtgQuNMd1PGnMDcNiyrCbAy8DzpRumiIiIVFlevtBtHGyfA4nrih0eFR5I/+Y1+WLJ\n7uLP/pwkJy+fp37eQHR4INf2jDr9wINbYfN06HIj+ASU6DVEzkZYoA8TLmlNbHxK8duAo8+F5oNh\n/kuQVnpVVaXsFZu0Wba/2qp7F3ydvKl8KPBxwfdTgfPMaU/sioiIiJRQp7HgHWifbXNgbK9oDqZn\n89OahBK9zCeLdrMj6SiPDG6Jj1cRH5MWvQGePtD1phI9v0hpGBJThwEta/HfmZvZdfBo0YPPfxJy\nM2HuM2UTnLiEozNtxhhPY8xq4AAw07KsJScNqQfEAViWlQukADVKM1ARERGpwgLCoOPVdhnzlL3F\nDu/VpAbNalXjwwU7sRxWz0s+ms2kWVvo3TSc/i1qnn5gehKs+RLajYZqRYwTcRFjDE9f2gZvDw8e\n+Da26N/x8CbQdRys+tTRSrWUT46SNsuy8izLag/UB7oaY9qcNKSwVbVTfnuMMeOMMcuNMcuTkpJK\nHq2IiIhUXd1vASsflk4udqgxhut6RrN+XyrLdhXT16rASzM3czQ7j8eGFFHiH2DZe/bKRY/bnUYu\nUupqh/jx0OCWLN6RzJdL44oe3Oc+8AtRC4AKrETVIy3LOgLMAy486VI8EAlgjPECQoDkQua/Y1lW\nZ8uyOkdERJxRwCIiIlJFVY+CVkNh+UeQlVbs8GEd6hEa4M0H/yu+2fbGhFS+WLKHq7s3pGmtoNMP\nzM6AZe9Cs0EQ0cx57CIuMLpLJD0a1eDZ6RtJSDl2+oH+1aHvg7DzD9jyW9kFKKXGSfXICGNMaMH3\n/sAAYNNJw34Eri34fjgwx3K6F0FERETEqZ53QFYKrPy02KH+Pp6M7tKA3zckEpeccdpxlmXx5E8b\nCPb35u4BTYt+0jVfQsYhNdOWcsEYw3OXtyUnP59HvltX9DbJztdDeDP4/RHIzS67IKVUOFlpqwPM\nNcbEAsuwz7T9bIx50hhzScGY94EaxphtwL3AA64JV0RERKq0ep2gYS9Y/Cbk5RY7/JoeDTHG8Oni\n3acdM2P9fhbtOMS95zcjNMDn9E+Wn28XIKnbERr2PJPoRUpdwxqB/PuC5szedICfYosovOPpDRc8\nDYe2wfL3yy5AKRVOqkfGWpbVwbKsGMuy2liW9WTB449ZlvVjwfeZlmWNsCyriWVZXS3L2uHqwEVE\nRKSK6nE7pMTBhu+LHVo31J8L29Tmq6V7yMg+NcnLzMlj4vSNNKtVjSu7Nij6ybb8CsnboeftdtNv\nkXJibK9o2kWGMuHH9SQfLWIVrekF0KgvzHsOMk45ySTlWInOtImIiIi4XbMLoUYTWPiqo6IK1/eK\nIjUzl2krT606+cGCnexJzuCxIa3x8izmY9HC1yCkAbQceqaRi7iEp4fhhctjSMvM4cmf1p9+oDF2\nw+2sVPjjhbILUM6akjYRERGpWDw87NW2hDWw63/FDu/YoDox9UP4aMFO8vP/TvIOpGbyxpxtDGhZ\ni3Oahhf9JHHLYM8i6HEreHqd7Z9ApNQ1rx3ErX2b8P3qfczZVEQj7VqtoeM1dkGdg1vLLkA5K0ra\nREREpOJpNxoCwu3Vr2IYYxjbK4rtSUeZv+3g8cdfmLGZ7Lx8HhncsvjXW/SaXTK9w1VnE7WIS93W\nrwnNalXjoW/XkZaZc/qB/R4GL3/4/dGyC07OipI2ERERqXi8/e2GwVtnQNLmYocPbluXiCBfPlxg\nl/+PjT/C1BXxXN8rmqjwwKInJ++AjT/Z1fd8i2gHIOJmPl4evDC8HQfSMnnu15OLvf9DtZpw7r/s\nc5o75pVZfHLmlLSJiIhIxdTlRvDyg0WvFzvUx8uDq7o1ZN7mJLYdSOeJnzYQXs2H2/s3Kf51Fr8F\nxhO63lwKQYu4VvvIUK7vFc3nS/aweMeh0w/sdguENoAZD0N+XtkFKGdESZuIiIhUTIE1oP0YWPMV\npBVxhqfAld0a4OPpwfjPVrBi92H+M7A5QX7eRU/KSIZVn0HMSAiuU0qBi7jWvRc0o0FYAA9MiyUz\n5zQJmbcfDHgC9q+zf8elXFPSJiIiIhVXj9sgL8cuqlCMiCBfLmlfl20H0mlTL5jhnSKLf/7lH0BO\nhv06IhVEgI8Xz13Wll2HMnh51pbTD2w9DCK7w5ynISut7AKUElPSJiIiIhVXjcbQYjAsew+yjxY7\nfNy5jagV7MsTl7TG06OYXmu5WbBkMjQ+z664J1KB9GwSzugukbz75w5i448UPsgYuHAiHD0A818q\n2wClRJS0iYiISMXW8w44dhhWf1Hs0Ga1gljy0AA6NQwr/nljv7Y/zPa8oxSCFCl7D17UkoggX+6b\nGkt2bn7hg+p1gphRsOgNOLy7bAMUx5S0iYiISMUW2Q3qd7ELkpRWQYX8fLudQK220Khv6TynSBkL\n8ffm6Uvbsikxjcl/bD/9wPMeB+MBsyaUWWxSMkraREREpGIzxl4NO7wLNv1cOs+5bRYc3Gw/rylm\nG6VIOXZ+q1oMianDa3O2sXX/ac6thdSDXnfC+m8hbmnZBiiOKGkTERGRiq/FEKgeBQuLL//vyMJX\nIagutLmsdJ5PxI0mXNKaAF9P7p8WS16+VfignndCtdrw24P2SrOUK0raREREpOLz8IQet0P8Utiz\n5Oyea99q2DUfut8CnsW0BBCpAMKr+fL4xa1YuecInyzaVfgg32pw3mOwdzmsm1aW4YkDStpERESk\ncmh/JfiF2qtkZ2PR6+ATBJ2uLZ24RMqBS9vXo2/zCF74bTNxyRmFD2p3BdRpZ59tyz7NGHELJW0i\nIiJSOfgEQpcbYdMvcKiIogtFORIH6761Eza/kNKNT8SNjDE8M6wtHgYe/HYtllXINkkPDxj4LKTG\n29UkpdxQ0iYiIiKVR9dx9pbGM/3AueRt+7/dxpdeTCLlRL1Qfx4Y1IL/bTvINyviCx8U1QtaXgz/\nexlSE8o2QDktJW0iIiJSeQTVsntOrf4cjh4s2dxjR2DFR3bxkdBIl4Qn4m5jujWka1QYT/+8gQOp\nmYUPOv9JyMuGuU+XbXByWkraREREpHLpcTvkZsKy90s2b+XHkJ1uzxeppDw8DM9d3pbM3Hwe+2F9\n4YPCGkG3m2HV55CwpmwDlEIpaRMREZHKpWYLaDoQlr4DOceczcnNhsVvQ/S5ULe9a+MTcbNGEdW4\nZ0AzflufyK9rT7MF8tz/QEAYzHgYCjv/JmVKSZuIiIhUPj3vgIyDEDvF2fj130HaPrtXlUgVcFPv\naNrUC+bRH9ZzJCP71AH+odD3Qbv9xaZfyj5AOYGSNhEREal8os6BOu3tZtvFNQq2LFj4GkS0gCYD\nyiY+ETfz8vTg+ctjOJyRzdO/bCx8UKex9t+LmY/aq9HiNkraREREpPIxxl5tO7QVts4oeuyOebB/\nrX2WzZgyCU+kPGhdN4TxfRoxdUU8f2xJOnWApxdc8Awk77C3G4vbKGkTERGRyqnVUAiJtFfRirLw\nNQisCTEjyyYukXLkjv5NaRwRyEPfriU9K/fUAU0HQOPz4I8X4Oihsg9QACVtIiIiUll5ekP3W2D3\nAohfUfiYxHWwfbZdKc/Lt2zjEykH/Lw9ef7yGPalHOPFGZsLHzTwGbuy6h/PlW1wcpySNhEREam8\nOl4DviGw6DSrbYveAO8A6Hx92cYlUo50jgrj2h5RfLxoF8t3JZ86oGZL6HSd3UYj6TSJnbiUkjYR\nERGpvHyDoPN1sOEHOLzrxGupCbD2G+hwtV3aXKQK+8/A5tQN8ef+abFk5uSdOqDfQ+BTDX5/pOyD\nEyVtIiIiUsl1Gw/Gw+7D9k9LJ4OVZ2+hFKniAn29mHhZW7YnHeX1OdsKGRAO5/4btv4O22aXfYBV\nnJI2ERERqdyC60LbEbDyEzh22H4sKw2WfwAtL4GwaPfGJ1JO9GkWweUd6/PWH9tZvy/l1AHdbobq\nUXbD7bxCipaIyyhpExERkcqvx22QcxSWf2j/vOozyEyx2wKIyHGPDmlJ9QAf7psaS27eST0OvXzh\n/CchaSOs+sQ9AVZRxSZtxphIY8xcY8xGY8x6Y8xdhYzpa4xJMcasLvh6zDXhioiIiJyB2m2hUT9Y\nMhmyM2DRm9CgB9Tv7O7IRMqV0AAfnhramvX7Unl3/s5TB7S8BBr2gjnP2Dc+pEw4WWnLBf5lWVZL\noDtwmzGmVSHj5luW1b7g68lSjVJERETkbPW8A9ITYepYSNmjVTaR0xjUtg4Xtq7Ny7O2sCMp/cSL\nxtgtADIOwfz/uifAKqjYpM2yrATLslYWfJ8GbATquTowERERkVLVuD/UbA1bfoOwxtBskLsjEim3\nnhzaGj8vDx6Ytpb8fOvEi3U7QLsrYPFbkFzIapyUuhKdaTPGRAEdgCWFXO5hjFljjPnVGNO6FGIT\nERERKT3G/L261uM28NDRfpHTqRnsxyNDWrF0VzKfL91z6oDzHgMPL5j1eNkHVwU5frcyxlQDpgF3\nW5aVetLllUBDy7LaAa8B35/mOcYZY5YbY5YnJSWdacwiIiIiZyZmFIyZajcKFpEijehUn95Nw3lu\n+kb2Hjl24sXgOtDrbrsH4u5F7gmwCnGUtBljvLETts8ty/r25OuWZaValpVe8P10wNsYE17IuHcs\ny+psWVbniIiIswxdREREpIQ8PKDp+eDh6e5IRMo9YwwTh7XFAh7+bi2WddI2yZ63Q1BdmPEg5OcX\n+hxSOpxUjzTA+8BGy7JeOs2Y2gXjMMZ0LXjeQ6UZqIiIiIiIlK3IsAD+M7A58zYn8f3qvSde9AmE\nAY/DvlWw9mv3BFhFOFlp6wVcDfT/R0n/i4wx440x4wvGDAfWGWPWAK8Co61TUnEREREREalorukR\nRccGoTzx0wYOpmedeLHtSLswyawnIPuoewKsAoy7cqvOnTtby5cvd8tri4iIiIiIc1v3pzH41f9x\nQetavH5lxxMv7l4EH14IfR+Evg+4J8AKyhizwrKsYhtGqmySiIiIiIgUqWmtIO7o34SfYxP4fX3i\niRcb9oBWl8KCSZC6zz0BVnJK2kREREREpFg392lMi9pBPPrDOlKO5Zx48fwnID8XZj/lnuAqOSVt\nIiIiIiJSLB8vD14YHkNSWhbPTt944sXqUdD9FljzhV2YREqVkjYREREREXEkpn4oN53biK+WxbFg\n28ETL/b+NwSEw28PgWoSliolbSIiIiIi4tg9A5oRVSOAB79dS0Z27t8X/IKh/8OwZyFs/NF9AVZC\nStpERERERMQxP29Pnrs8hj3JGbz0+5YTL3a4Bmq2gpmPQW5W4U8gJaakTURERERESqR7oxqM6daA\nDxbsZNWew39f8PSCgc/A4V2w5G23xVfZKGkTEREREZESe2BQC2oF+3H/tFiyc/P/vtC4PzQdCH++\nCEcPnv4JxDElbSIiIiIiUmJBft48M6wNW/an8+a8bSdevOApyD4Kcye6J7hKRkmbiIiIiIickf4t\nanFp+7q8MXcbmxPT/r4Q0Ry63AArPoQDG0//BOKIkjYRERERETljj13cmiA/b+6buoa8/H+U+u/7\nIPgGwYyH3RdcJaGkTUREREREzlhYoA8TLmnNmvgUPlyw8+8LAWHQ537YPhu2znRfgJWAkjYRERER\nETkrF8fUYUDLmrz4+2Z2Hzr694UuN0FYI3u1LS/39E8gRVLSJiIiIiIiZ8UYw1OXtsHbw4MHpq3F\nsgq2SXr5wPlPwcHN9vk2OSNK2kRERERE5KzVCfHnwYtasmjHIaYsi/v7QovBENXbriR57Ij7AqzA\nlLSJiIiIiEipuKJrJD0a1eCZXzaSmJJpP2gMDJwIxw7Dn//n3gArKCVtIiIiIiJSKowxPHtZW3Ly\n83nk+39sk6wTAx3GwJLJcGi7e4OsgJS0iYiIiIhIqYkKD+Rf5zdn1sYD/Byb8PeF/o+Cpw/MfMx9\nwVVQStpERERERKRUje0VRbv6IUz4cT3JR7PtB4NqQ+97YNPPsOt/7g2wglHSJiIiIiIipcrL04Pn\nh8eQciyHp37e8PeFHrdDcH2Y8RDk57svwApGSZuIiIiIiJS6FrWDubVfE75btZe5mw7YD3r7w4AJ\nkLAG1nzpzvAqFCVtIiIiIiLiErf1a0zTmtV4+Lu1pGXm2A+2HQ71OsPsJyEr3b0BVhBK2kRERERE\nxCV8vTx5YXgMCamZPP/bJvtBY+DCZyE9ERZMcm+AFYSSNhERERERcZkODapzfa9oPlu8hyU7DtkP\nRnaFNpfDwtcgJd69AVYAStpERERERMSl/nVBMyLD/Hng27Vk5uTZDw6YAFa+vU1SiqSkTURERERE\nXCrAx4vnLoth58GjvDJrq/1gaAPocRvEToH4Fe4NsJxT0iYiIiIiIi7Xq0k4ozpH8u78HayNT7Ef\n7H0vBNa0WwBYlnsDLMeUtImIiIiISJl4aHBLagT6cN+0WHLy8sE3CPo/AnGLYf137g6v3Co2aTPG\nRBpj5hpjNhpj1htj7ipkjDHGvGqM2WaMiTXGdHRNuCIiIiIiUlGF+Hvz9KVt2JiQyuQ/ttsPdrgK\narWFWY9DTqZ7AyynnKy05QL/siyrJdAduM0Y0+qkMYOApgVf44C3SjVKERERERGpFC5oXZvBMXV4\ndfY2th1IAw9PGPgMHNkDi990d3jlUrFJm2VZCZZlrSz4Pg3YCNQ7adhQ4BPLthgINcbUKfVoRURE\nRESkwptYZ2OuAAALCUlEQVRwcWsCfD25b2osefkWNOoDzS+C+S9B+gF3h1fulOhMmzEmCugALDnp\nUj0g7h8/x3NqYiciIiIiIkJEkC+PDWnFyj1H+HTRLvvB85+C3GMw9xl3hlYuOU7ajDHVgGnA3ZZl\npZ58uZApp5R/McaMM8YsN8YsT0pKKlmkIiIiIiJSaQzrUI8+zSJ4YcZm4pIzILwJdLkJVn4C+9e7\nO7xyxVHSZozxxk7YPrcs69tChsQDkf/4uT6w7+RBlmW9Y1lWZ8uyOkdERJxJvCIiIiIiUgkYY5h4\n2f+3d/9BVpX3HcffX9hFQAQUUAIiiCIEVpQMEfxN0BaJjoxiOjGp6XQcq40mse2EoAVJolZjWm2n\nOnGssW1Gm04LaZKxGDRVLGkziYhWQPwFWl3FsEZdEUFg+faPvQqiCLq7POfS92vmzNwfZ+75/PHM\nvfdzz3OeezQBXPlvy8lMOHUW7NfXvwDYyZ6sHhnA94FVmXnjLnb7KfCl2iqSk4HWzFzbiTklSZIk\n7WOG9u/F7OljWPL0KyxY9iL0PgimXAFrFsNTi0rHq4w9OdN2InABMDUiHq1tn42ISyLikto+C4E1\nwDPA3wFf7pq4kiRJkvYlX5w0nE+POJCr736cdes3wacvhAFHwr1zoG1L6XiVsCerR/4iMyMzx2fm\nsbVtYWbempm31vbJzLw0M4/IzKMzc2nXR5ckSZJU77p1C66fOZ6NW9qY95OV0L0Rfvca+O3TsPSO\n0vEq4SOtHilJkiRJne2IQX24/PRR3LPiZe5ZvhaOOgMOPxUWXwcbXysdrzhLmyRJkqTiLjp5JOOG\n9GXuT1bSunErTPsL2NQKD95QOlpxljZJkiRJxTV278Z3Zo7ntbc2c82/Pw6Dm2DCBfDr2+CVZ0rH\nK8rSJkmSJKkSmob24+JTRvKvDzez5OkWmDoHGnrBfXNLRyvK0iZJkiSpMr562ihGDtqf2QuWs6Hx\nIDj5T+HJhbDmwdLRirG0SZIkSaqMno3duWHmeF5q3ch3Fz0Jk78M/Q6DRX8O29pKxyvC0iZJkiSp\nUiaOOIgvTR7OP/7yOR5+6S34nW/Cb5bDo3eVjlaEpU2SJElS5Xz9jDEM6deLWfMfY9NRM2DYJPiP\nq+Ht9aWj7XWWNkmSJEmV02e/Bq49p4nVLRu4ZfFqmHYdbFgHv7ipdLS9ztImSZIkqZKmjD6Ycz81\nlO8tXs3j3UbB0b8H/30zvP586Wh7laVNkiRJUmVdddZY+vdu5BsLHmPrZ+ZCBPz8W6Vj7VWWNkmS\nJEmV1b93D749o4nlL7Zy+/ItcMJXYMV8eOGh0tH2GkubJEmSpEqb3jSYaeMO4ab7nuLZ0RdBn8Gw\n6ArILB1tr7C0SZIkSaq0iODqGU30aOjGN+5ew7apc6D5IVixoHS0vcLSJkmSJKnyDu7bk7lnjuXX\nz77KP719EgweD/fNgy0bS0frcpY2SZIkSXXhcxMP5aQjB3L9z57ilZO+CW80wy9vLh2ry1naJEmS\nJNWFiOC6c4+mbVsya2lfcsxZsOQmWP+b0tG6lKVNkiRJUt0YdlBvvj5tNPc/sY6fD70U2jbD/VeX\njtWlLG2SJEmS6sofnDCCCYf1Z9YDb7JxwoXwyJ2w9rHSsbqMpU2SJElSXeneLbhh5ng2vN3GvNYz\nodeBsOjKffYvACxtkiRJkurOqEMO4LKpR/IvK9azasxl8NwSeHJh6VhdwtImSZIkqS5dcuoRjBl8\nABeuaKJtwFFw71zYurl0rE5naZMkSZJUl3o0dOM7M8fz8ptb+cEBF8Grq+Gh20vH6nSWNkmSJEl1\n65hh/bno5JF864khvPaJk+HB6+GtV0vH6lSWNkmSJEl17fLTj2LEgP25/LXzyLfXw+LrS0fqVJY2\nSZIkSXWtV4/uXHfueB58fRDLBs5onyLZ8lTpWJ3G0iZJkiSp7h1/xAC+MOkwLm6eRltDb7h3TulI\nncbSJkmSJGmfMHv6GBoOOJg7up8HTy+C1feXjtQpdlvaIuKOiFgXESt28fyUiGiNiEdr21WdH1OS\nJEmSPlzfno1ce04T3319Cq37DYVFc2BbW+lYHbYnZ9r+AThjN/ssycxja9u3Ox5LkiRJkj660z55\nCNOPHc6cDZ+DdSth2Q9KR+qw3Za2zPxPYN9aM1OSJEnSPuuqs8byXz1OZGXjOPKBa2HTG6UjdUhn\nXdN2fET8T0TcExHjOuk1JUmSJOkjG9BnP+adPY7Zb55PbGiBJX9VOlKHdEZpWwYMz8xjgL8Ffryr\nHSPijyJiaUQsbWlp6YRDS5IkSdL7nX3MEA4ePZkfbzuFtmV3wua3Skf62Dpc2jLzjcx8s3Z7IdAY\nEQN3se9tmTkxMycOGjSoo4eWJEmSpA8UEVxzThM3xgVc2u8WsrFX6UgfW4dLW0QMjoio3T6u9pq/\n7ejrSpIkSVJHfKJfLy7+7CR69j+ETVu2lY7zsTXsboeI+CEwBRgYEc3APKARIDNvBc4D/jgitgIb\ngc9nZnZZYkmSJEnaQ1847jC+OGl46RgdstvSlpnn7+b5m4GbOy2RJEmSJHWS2qTAutZZq0dKkiRJ\nkrqApU2SJEmSKszSJkmSJEkVZmmTJEmSpAqztEmSJElShVnaJEmSJKnCLG2SJEmSVGGWNkmSJEmq\nMEubJEmSJFWYpU2SJEmSKszSJkmSJEkVZmmTJEmSpAqLzCxz4IgW4H+LHPzDDQReKR1C+hCOUVWd\nY1T1wHGqqnOM/v8wPDMH7W6nYqWtqiJiaWZOLJ1D2hXHqKrOMap64DhV1TlGtSOnR0qSJElShVna\nJEmSJKnCLG3vd1vpANJuOEZVdY5R1QPHqarOMap3eU2bJEmSJFWYZ9okSZIkqcIsbTuIiDMi4smI\neCYiZpfOI+0oIoZFxAMRsSoiVkbE10pnkj5IRHSPiEci4u7SWaSdRUT/iJgfEU/U3k+PL51J2lFE\n/Entc35FRPwwInqWzqTyLG01EdEduAWYDowFzo+IsWVTSe+xFfizzPwkMBm41DGqivoasKp0CGkX\n/gb4WWaOAY7BsaoKiYihwFeBiZnZBHQHPl82larA0rbdccAzmbkmMzcD/wzMKJxJeldmrs3MZbXb\n62n/ojG0bCrpvSLiUOBM4PbSWaSdRURf4BTg+wCZuTkzXy+bSnqfBqBXRDQAvYGXCudRBVjathsK\nvLDD/Wb8QqyKiogRwATgV2WTSO/z18AsYFvpINIHGAm0AH9fm8J7e0TsXzqU9I7MfBH4S+B5YC3Q\nmpn3lk2lKrC0bRcf8JhLa6pyIqIPsAC4PDPfKJ1HekdEnAWsy8yHS2eRdqEB+BTwvcycAGwAvIZd\nlRERB9I+0+twYAiwf0T8ftlUqgJL23bNwLAd7h+Kp6NVMRHRSHthuyszf1Q6j7STE4GzI+I52qeY\nT42IO8tGkt6jGWjOzHdmKcynvcRJVXE68GxmtmTmFuBHwAmFM6kCLG3bPQSMiojDI6IH7Rd9/rRw\nJuldERG0X4exKjNvLJ1H2llmXpGZh2bmCNrfQ+/PTH8hVmVk5svACxExuvbQacDjBSNJO3semBwR\nvWuf+6fhYjmifZqAgMzcGhGXAYtoX6nnjsxcWTiWtKMTgQuA5RHxaO2xKzNzYcFMklRvvgLcVfuB\ndg3wh4XzSO/KzF9FxHxgGe2rRj8C3FY2laogMr1sS5IkSZKqyumRkiRJklRhljZJkiRJqjBLmyRJ\nkiRVmKVNkiRJkirM0iZJkiRJFWZpkyRJkqQKs7RJkiRJUoVZ2iRJkiSpwv4PDR2lQHZlYHIAAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x20b4d5254a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fHmxiSPshZQ",
        "colab_type": "text"
      },
      "source": [
        "##Neural Network Using Tensorflow\n",
        "\n",
        "Used earlystopping to prevent overfitting the model and used checkpointer to save the best model ran in the loop several time to jump out of the local mininum.\n",
        "\n",
        "Applied paramter tuning by changing following:\n",
        "\n",
        "* Activation function : relu, sigmoid,tanh\n",
        "* Number of Dense Layers\n",
        "*Number of Neurons in each layer\n",
        "* Learning rate for Activatio\n",
        "*Optimizer\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3Q_ZodCd_PV",
        "colab_type": "code",
        "outputId": "c4efd252-1c55-49ae-9702-32829eeda47a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x1 = final_input_features\n",
        "y1 = df_clean1['stars']\n",
        "print (x1.shape,y1.shape)\n",
        "# Split into train/test - 80/20\n",
        "x_train2, x_test2, y_train2, y_test2 = train_test_split (x1, y1, test_size=0.20, random_state=45)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(40976, 1001) (40976,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDhh4bOybVE8",
        "colab_type": "code",
        "outputId": "b063ff6c-86ca-4ea1-9c97-9e9591d15539",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#loading the best saved model\n",
        "from keras.models import load_model\n",
        "model1 = load_model(os.path.join(save_path,\"best_regression.hdf5\"))\n",
        "\n",
        "# Predict and measure RMSE\n",
        "pred4 = model1.predict(x_test2)\n",
        "scoref = np.sqrt(metrics.mean_squared_error(pred4,y_test2))\n",
        "print(\"Score (RMSE): {}\".format(scoref))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Score (RMSE): 0.25554479688021825\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rWbX2DkbdIs",
        "colab_type": "code",
        "outputId": "8eb6882f-ec27-4772-aff3-5d17ffbc3f73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "chart_regression(pred4[0:20].flatten(),np.array(y_test2[0:20]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6eCT+Hn7YDBvljZXk1xa0O9kXUlxfgs2wXbX/IM9Axg8bS7RfFNH+UcT4RzN1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\nKBcAM7OKcgEwM6uoD47VCZonz/rEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Q144HHJmd_PZ",
        "colab_type": "code",
        "outputId": "983f949c-0498-4b78-9cd2-22be8d33d33b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "#ploting the predicting and actual values\n",
        "%pylab inline \n",
        "pylab.rcParams['figure.figsize'] = (15,6)\n",
        "plt.plot(pred_reg[0:20],label = 'Predicted')\n",
        "plt.plot(y_test2[0:20].values, label = 'Actual')\n",
        "#plt.plot(y_predict_lin_model1[:10],label = 'Previous Predicted')\n",
        "#plt.plot(y_test1[:10].values, label = 'Previous Actual')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hgaLzwjEahIzKtAYStO4ODRpFI50cj6DF7SQhKp5oXTRn55zORXnn0D749Qy+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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdNhXoGKpv47",
        "colab_type": "text"
      },
      "source": [
        "##Comparison"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KrdulWuA11AG",
        "colab_type": "code",
        "outputId": "b22d9d93-15fe-47b9-c14c-043a69586059",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "my_plot2 = df_reg1.plot(kind='bar',legend=None,title=\"Regression Models : RMSE Evaluation\",)\n",
        "my_plot2.set_xlabel(\"Regression Models\")\n",
        "my_plot2.set_ylabel(\"RMSE\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'RMSE')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NYEfgsuHqREREvWodU2jT014gaXXgUuBjtv8qadg67Xp7JzB+/LiRiTBGhUmTJjYdQsSQ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\nIDc6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qG-bKqebiGTi",
        "colab_type": "text"
      },
      "source": [
        "#Classification Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3rZXJhrwd_Pb",
        "colab_type": "text"
      },
      "source": [
        "##Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "va5ERzoMd_Pb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# label Encoded star\n",
        "x_train, x_test, y_train, y_test = train_test_split(final_input_features, df_clean1['stars_encoded'], test_size=0.2, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9x1FPtAGd_Pc",
        "colab_type": "code",
        "outputId": "b1b92238-6806-4ee6-d3b6-29829acd17de",
        "colab": {}
      },
      "source": [
        "#checking the train and test data size\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32780, 1001)\n",
            "(32780,)\n",
            "(8196, 1001)\n",
            "(8196,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkBjCVAud_Pd",
        "colab_type": "code",
        "outputId": "11e3772d-30db-4853-89fb-8a30d329a456",
        "colab": {}
      },
      "source": [
        "f1_score(y_test,y_pred_log_reg,average='micro')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5126891166422645"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouFafvhQd_Pe",
        "colab_type": "code",
        "outputId": "5b80f26f-5086-4e41-8a6a-b7a015fe4bf6",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(\"accuracy: %.2f\"\n",
        "      % accuracy_score(y_test, y_pred_log_reg))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy: 0.51\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BidN5BTVd_Pg",
        "colab_type": "code",
        "outputId": "a1e930e2-3c05-4eed-aefe-3472f8fe3119",
        "colab": {}
      },
      "source": [
        "#ploting the predicting and actual values\n",
        "plt.plot(y_pred_log_reg[:10],label = 'Predicted')\n",
        "plt.plot(y_test[:10].values, label = 'Actual')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CzKBwSQhsC/tkuYcwg0txcHZ37pd55MTZRb918Y48MFrk2pw5c+jUqVOWH+vcuTPnz5+n\nffv2hIeHExISwuTJkwHo3bs3AwYMSF/qMXr0aD744AMaNWqEs7Nz+nsMHz6ckSNH0qBBA3Q6nVn/\nnp6WoprgX5rw8HA1MjLS6O9rab9uPc7Xyw/RtmZZpnQLweVx44s5UVU4tl77jNm5SPCoAI0+1P40\nVEYZhRDCNFLuw96Z2vh44nltpXfTkdYVYdkd3wQzOsBLv0JQV0ufRtizFcO1dfdD4qFQ8Wd7r3//\nA+s+hy7ToMZLRjmeMJ1Dhw5RtWpVSx/DbuT066koSpSqqo/fQoLcIcuTtxpV5uMXtDtlH/yVhztl\nBooCfi3grXXQw3DHbBB8F6aNpsgdMyGEMJ7UBxl3xFYMhaIV4fXF0GclVG5ivTEG4N1YG6WUsUVh\nSslJsH+utszjWWMMIGIglAvVPzBaRheFyC0Jsjzq21gfZfufMspA+ybAP3OYlYSlH0iYCSGEMTwq\nxN5YpT2mxJpDzMDJCcJ6wal/4WqCpU8j7NXBxXD/KZd55MTZBTr+BA8SYcWHxnlPIRyABNlT6Nu4\nMqNeCGT5/gsMetoog0xhth56zMsIs+/DIOoP0Fn/mk4hhLAaqQ9gz68ZIebhBa8tsq0QyyykBzi5\nyF0yYTpR07QHn1dqYLz3LBWojQQfXAxxC433vkLYMQmyp9SvsS+jXghk2bNGGejDrGVGmBUsAUvf\n15Z/SJgJIcTjpT6APb/Bf0Nh+YdQpDy8thDeWA2+zWwvxAwKl9KWe8TIcg9hApcOwpldz7bM41Hq\nvw/lamn/Pt69atz3FkZlil0SjuhZfx0lyJ5Bv8a+jHxei7LBf+97tiiDjDDruwFe/SdrmEXPkDAT\nQojMsoTYEChSTguxN9doz4S01RDLLKwP3LsO8cssfRJhb6L/AOd8EPyq8d/bsHXxQaIWZcIqubm5\nce3aNYmyZ6SqKteuXcPNze2p30MeDP2M+jfxBWDcyngA/vNycN63L2anKFCllRZnCWth01hYMhC2\nTILGwyC4Ozi7PuvRhRDCNqUmQ8yfsOX/4PZZ8KoN7f9rPxGWmU8TbT1/1HSo2cXSpxH2IuUe7JsD\nVdsbZ5lHTkpXgyYfwYavtNHF6p2e/DXCrLy8vDh79ixXrsgClmfl5uaGl5fXU3+9BJkR9G/iiwqM\nXxmPAnxjjCiDbGG2BjaN04fZZH2YdZMwE0I4DkOIbf0Gbp2x7xAzcHKC0F6w/gu4ehRK+Fn6RMIe\nGHuZx6M0GASHlmoPjPZuBIVKmPZ6Ik9cXV3x8fGx9DEEMrJoNAOa+DLi+UCW7DvPEGOML2amKFCl\nNfTdCN3/ggLFYMl72lbG6JkyyiiEsG+pyRA5TRvfXjZYe2RIz/nw5lrwa26/MWaQvtxjmqVPIuxF\n5DTw9AXvhqa9jmHr4v1b2qIdIUSOJMiMaEATXz5qo0XZh/8YOcpA+6YjoA3025Q1zL4Ph71/SpgJ\nIexLlhAblBFib63Tnulo7yFm4F4aAl7QlnukPrD0aYStu3wIzuw0zTKPnJSuBk0/0sYW4xaZ/npC\n2CAJMiN7u6kWZYtjTBRlkC3M5oKbByx+F76vDXtngS7V+NcUQghzSU3WPjP1XVhGiPVwwBDLLFy/\n3OPQUkufRNi6KP0yjxATLPN4lAaDoWyIfuviNfNdVwgbIUFmAm839WV4m4D0KNOlmWh7jaJAwPPQ\nb7M+zIrA4nf0d8wkzIQQNiZziC39QHs2oyHE/B00xAx8mkLRSvJMMvFs0pd5vGjez3M5u0DHH2V0\nUYhHkCAzkXea+qVH2ZC/Y0wXZZA1zLrNgfzuGWEWM1vCTAhh3XQp2p/af585xOZpz2Z09BAzcHKC\nsF5wcqu23EOIp3FwCdy/afplHjkpXV3buhi3QFsqIoRIJ0FmQu809WNYa/2dMlNHGWjftAS+AP23\nZITZorclzIQQ1skQYt+Fas9cLFgiU4i1lBDLLqSnttwjerqlTyJsVdR08KysbTy0hIaDoGywjC4K\nkY0EmYm920yLskXmijLIFmazIX9hLcx+qA0xcyTMhBCWpUvRHnafOcRe/Qf6bpAQexz30to0hCz3\nEE/jcjyc3m6+ZR45cXbVHhh97yasHGaZMwhhhSTIzCBzlA015WfKslMUCGwL/bdqYZavECwaIGEm\nhLCMzCG2ZCAULJ4RYlVaSYjlRlgfSLoG8cssfRJha6L/ACdXCDbjMo+clKkBTYZD7HxthFIIIUFm\nLoYoW7j3nHmjDLKG2SuzwNUQZnVg31wJMyGEaelStGcmfheWKcT+1p6tKCGWN5WbQdGKstxD5E3K\nfe3OatV22mc0La3hYCgTBMuHQNJ1S59GCIuTIDOjd5v5MbRVFRbuPccwc0cZaN/0VG2njTK+Mgtc\nC8LC/hJmQgjTyBJi70FBz0wh1lpC7Gk4OUFoLzixBa4ds/RphK04ZFjm0cfSJ9E4u2oPjL53A1bI\n6KIQEmRm9t5z/gxtVYUFlooy0P6Dnh5mf4JrAS3MfqwL+/6SMBNCPBtdivaw+u/DtRArUEx7mL2E\nmHHU6gmKs9wlE7ln6WUeOSlTAxoPh9h58nw94fAkyCzgvef8+bClPsrmWSjKQB9mL+pHGf8EFzdY\n2C8jzNJ0ljmXEMI2ZQ6xxe9qD63vPld7iH1AGwkxY3Evo1/uMUuWe4gnu3IYTm3T7qw6Wdm3fY2G\nQJmasExGF4Vjs7J/Mx3HwOb6KIu2cJRB1jB7eWZGmP1QB/b/LWEmhHg8Xar2MPrva2cLsc1aOEiI\nGV/6co/llj6JsHZR+mUeIT0sfZKHpY8uXoeVwy19GiEs5olBpihKgKIoMZn+77aiKIPMcTh7N7C5\nP0P0UTZ83n7LRhloYVatfUaYOeeHBX3hh7oSZkKIh6WHWLj2MPr87tozECXETM/3OfCQ5R7iCVLu\nw77Z2mIva1jmkZMyNaHxMDjwDxyS7aHCMT0xyFRVPayqaoiqqiFAGJAELDT5yRzE+/oomx991jqi\nDDLCbMC/8PIMcM6XKcz+kTATwtHpUrWNbdlDrP8W7RmIEmKm5+QEYa/Dic2y3EM82qGl2uKMcCtZ\n5vEojT7Ujy4OltFF4ZDyOrLYHDimquopUxzGUb3f3J/BLbQo+2i+lUQZ6MOsQ6Ywc4UFb0mYCeGo\nMofYore1h853my0hZikh+uUe0X9Y+iTCWkVNh2I+4N3Y0id5vPQHRl+HlR9Z+jRCmJ1LHl/fDZhj\nioM4ug9a+APwn3VHUIAJnYNwcrKSb24MYRb4orY6d/MELcx2fAc9F0ChEpY+oRDChFJTktm7/BfK\n7f+O8mkXiMebn5XhbLoUDn8rwFpLH9FmlfUowPQ+tSldxC3vX1ykrDYauncWNPsEXPIZ/4DCdl05\nAqf+hRafP/Uyj90nrvP18oN883IIfqUKG/V4DykbBI2GwubxUL2jNmYphINQVDV3d2MURckHnAeq\nq6p6KYef7wf0A6hYsWLYqVNyE+1pfLvuCN+uS6BrmJd1RVlmaWkQO19bZ+3pC72WSJQJYYcMIVZ2\n33d4qRc45uzD7op9ifdoLHfDjEAF/tpzhoZ+Jfi1VzjK0/yaJqyFWV2g63So3snYRxS2bPXHsOtn\nGHIICpfK85fffZBK62+3cPbGPWpVLMq8AfVxNvX3JKnJ8MtzcPcyvLNTe3ahEDZMUZQoVVXDn/S6\nvNwhex6IzinGAFRVnQpMBQgPD7eSmTvbM6hFFQC+XZcAWNmdMgMnJwjqqn1AePYr8Ed7iTIh7Ehq\nSjJ7V/xKmZjvqK2e55izD3vr/kBw8+74Ojtb+nh2paJnQb5efoiFe8/xUqhX3t8g83IPCTJhkHJf\nGy8ObPtUMQYwfmU8527eo08Db6ZtO8lv/x6nX2NfIx80G5d80PFH+KUZrBoBL0017fWEsBJ5uYfd\nHRlXNItBLarwQXN//onSPlOWZi2fKcuuclN49S+4fkyLsrvXLH0iIcQzSE1JJnLJT1wYG0ztvSNJ\nUfIRHfE9PqOiqNWqJ04SY0bXp4EP4ZWK8fmSOC7fvp/3N3ByhtDX4fgmuH7c6OcTNip+mfZ5rLCn\nW+ax/dhVZu48RZ/6PnzWrhotq5Vm8pojHL18x8gHzUHZIG3Jx/6/IH6F6a8nhBXIVZApilIQaAks\nMO1xhMHglhlRNmKBlUdZ97n6KHtRokwIG6RLTU0PsfDoEekh5v1xNKGtX5MQMyFnJ4WJXYJ4kJrG\nqIUHyO3HCLKo1UNb7hElyz2EXtR0KOYNPk3y/KV3H6Ty0fz9eBcvyLDWASiKwphONSjg6sxwcz03\ntdFQKF0Dlg2SrYvCIeQqyFRVTVJVtbiqqrdMfSCRwRBlf0daeZT5NsuIshlyp0wIW6GF2M+cH1Mz\nPcT2RvxXQszMKpcszLDWAaw7dJlFMefy/gZFykGVNhAzS/sMjnBsVxPg5FYI7fVUyzwmrIrn7I17\nTOwSTIF82u8Bpdzd+KJ9daJP3+T3f08Y+8QPM4wu3r0Kq0eZ/npCWNjTrd0RZjO4ZRXe10fZyAUH\nrDzK5sC1oxJlQlg5Q4idGxtEePRHpGYKsVqte0mIWUCfBj6EVSrG50sOPt3oYlhvuHsFDsuIl8OL\nmg5OLhDSI89fuuPYNWbsOEXv+t7U8cm6UKNDSDlaVC3N5DWHOXbFHKOLwdro4r45cHil6a8nhAVJ\nkNmAwS38eb+5P39FnrHyKHsuU5R1kDEDIayMLjWVyKX/Sw8xHa5E15tCJQkxizOMLt5P0TFqYWze\nRxf9moNHBe2bceG4Uh9oyzwCXgD30nn60qTkVIbP30cl/ahidoqiMLZTDdxcnRk+z0zPTG08DEpV\nh6WDtAdcC2GnJMhsgKIoWpQ958dfkWcYtdDKo6zbbLiWoC36kCgTwuKyhFjUcHS4pIdYaJveEmJW\nwrdkYYa2CmDdoUssjjmfty9OX+6xEa6bYaRMWKdDS7VlHuF5X+YxcdVhzt64x6QuwRTMl/MS7lJF\n3Pi8fTWiTt1g2jZzji5egVUyuijslwSZjVAURRtffM6PuXusPMr8mmtRdvWIRJkQFqRLTSVy2VTO\njg3OCLG631Lp470SYlbqjYY+hFYsyuglcVxOzOPoYq2eoDhBtCz3cFhR06FoJfBpmqcv23n8GtO3\nn6RXxMOjitl1DClPi6qlmLT6MMfNMbpYLgQaDYF9s+HIatNfTwgLkCCzIYYoG6iPso8XWXmUdZ+j\nRdkMiTIhzClLiEUOIw3njBB7vo+EmBVzdlKY1DWYeyk6Ps7r6KJhucfeP2W5hyO6elRb5hGWt2Ue\nScmpDJ+3n0rFCzK8zcOjitlpWxdrkt/Fycyji9Vg6QcyuijskgSZjVEUhSEtq/BeMz/m7LaRKLsi\nUSaEOehSU4lc/gtnx4YQHjkMFSei6kiI2RptdLEKaw9eYsm+PI4uGpZ7HJElCA4nerp+mUfPPH3Z\nxFWHOX09iYmdgx45qphd6SJufN6+OpFmG13Mr40u3rkMqz82/fWEMDMJMhukKAoftsocZbFWHmWz\n9VEmiz6EMIUsIbZnKCoKUXW+peLHMYS9ICFmi95sWJlaTzO66NcCinjJcg9Hk77M4/k8LfPYpR9V\n7F3fm7qVi+fpkp1qlad5oDa6eOLq3byeOO/K1YKGg7XHO8joorAzEmQ2yhBl7zbzZc7u01YeZS20\nz5RdOSxRJoQR6VJTiVr+K2fSQwyi6nwjIWYHnJ0UJnUJJilZxyd5GV00LPc4tgFunDTpGYUViV8G\nSdcgLPfLPLStivup6Jm7UcXsFEVh7Eva6OKwf8z0wOgmwzONLt40/fWEMBMJMhumKApDWwWkR9kn\ni604yvwlyoQwljSdLj3EwvZ8CBhCbB9hL7wpIWYn/EoV5sOWVVhz8BJL91/I/RcalntEyXIPhxE1\nHYpWhMrNcv0lk1Yf5tS1JCZ2yf2oYnali7gx+kVtdPGP7Sef6j3yxCU/dPhBRheF3ZEgs3GGKHun\nqS+zd0mUCWHP0nQ6olb8xukxwYTt+RAFiKr9f1QYFSMhZqfealSZkApFGb04liuJD3L3RR7lwb+1\nttxDl2LaAwrLu3YMTmyB0Nwv89h94rp+q2Il6uVxVDG7l0LL81xgKSaujjfP6GL5UGg4CGL+hCNr\nTH89IcxAgswOKIrCsNYZUfapTURZPMzsKFEmRC5khFgIYbuHpIeY16gYwtq+hbPL0/3ptrB+zk4K\nk7sGcTdZxyeLDuR+dDGsN9xPXCmhAAAgAElEQVS9DIdluYfdi5oOirN2ZzQX7iXrGDZvH17FCjC8\nTeAzX157YHRNXJ2dGD5vn3m+/2jyEZSsKqOLwm5IkNkJQ5S93dSXWbYSZZcPSZQJ8RhaiE3LFGIq\nkbUnS4g5GL9S7gxpWYXVcZdYltvRRf+WUKS8LPewd6kPtCUXAc+De5lcfUn6qGLnYArlN87vIWU8\ntNHFPSdv8MeOk0Z5z8dK37p4CdbI6KKwfRJkdkRRFIZnirLPllhzlLXMGmXyXBEh0mUNsUEopKWH\nWHjbvhJiDuithj4EVyjKZ7kdXZTlHo4hfnmelnnsPnGdadtP8HpEJSJ8n21UMbvOoeVpFlCSCavi\nOWmu0cUGH2ijuQlrTX89IUxIgszOGKJsQBNf/typRVmeHixqTv4t4ZVZWpTN6CBRJhxemk5H9Mpp\nnBpTKyPEwifhNWqfhJiDc3F2YnKXIO4+0PHpolz+vl6rJygKRM8w/QGFZURNB4+K4PvcE196L1nH\ncP2o4kdGGFXMTlEUxr0UpB9d3G+ePxBuOgJKBsKS9+H+LdNfTwgTkSCzQ4qi8FGbjCj7dLEVR1mV\nVpmiTO6UCceUptMRvWo6p8aEErprEE7oMkKsXT8JMQGAf2l3Breswqq4iyw/kIvRRQ8v8G8lyz3s\n1bVjcGIzhL2eq2Uek9cc5uS1JCZ0DjLaqGJ2ZTzc+KxdNXafvM6MHSdNco0s0kcXL8rWRWHTJMjs\nlCHK+jepbCNR9idcPihRJhxKlhDb+QHOpBAZNlFCTDxS30Y+BHt58NniOK7eycXoYlhv7XM2R1aZ\n/GzCzKL/0JZ5hDx5mceek9f5fdsJXqtXifq+JUx6rC5hXjQNKMmEVYc5dc0co4th+tHFmZCwzvTX\nE8IEJMjsmKIojGgTmB5lny2Os+Ioa50RZTM7SZQJu5am07F39R+czBZi5UftJ/zF/hJi4pFcnJ2Y\n3DWYO/dT+Wxx7JO/wK8luJeT5R72JjUZ9uqXeRQp+9iXaqOK+ylftAAjnjf+qGJ22uhiTVycFYaZ\na3SxyQgoEQBLZXRR2CYJMjuXHmWNKzNz5ynrj7KXZ8KlOH2UySpbYV8yh1itHe/jqiYTGTpBQkzk\niX9pdwa19GfFgYssf9LWRWcXbbnH0fVw45R5DihM7/BySLqq3QF9gv9bc5gTV+8y0YSjitmV9SjA\np+2qsfvEdWbuNMM/d65u0PEnSLwAaz4x/fWEMDIJMgegKAojns+IstFLrDjKAtpoUXYxVr99UaJM\n2L40nY7o1TM5MTYsS4iV+/gA4e0HSIiJPOvXqDJBXh58ujj2yaOLhuUee2ea53DC9HK5zCPy5HV+\n23aCnvUqUt/PtKOK2XUN86JJlZKMXxlvntFFrzCo/762xOaojC4K2yJB5iAMUdavcWVm7LCBKHvl\nT4kyYfMyh1jojvfIl/aAyNDxlB21T0JMPJPMo4ujF8c9/sVFK2iji9EzZbmHPbh+HI5v0u58Ojk/\n8mX3U3QMm7efch4FGPF8VfOdT09RFMZ3romLk2LGrYsjoUQV2boobI4EmQNRFIWRmaLsc5uJMhlf\nFLZFTUtj75o/OTE2PIcQexsX13yWPqKwA1VKu/NBC3+WH7jw5NHFsN7aJrojq81yNmFCUfplHrV6\nPPZl6aOKXYIobKZRxewMo4u7Tlznz13mHl381PTXE8JIJMgcjCHK+jby4Q+biLKZcPGARJmwCYYQ\nOz4mjFrb3yVf2n321BonISZMpn/jytQs78Fni2O59rjRRf9WstzDHqQmQ8wsqNIGipR75MuiTl3n\n139P0KNuRRqYeVQxu67hXjSuUpJxK+I5fS3J9Bf0Cof6A7UtlEfXm/56QhiBBJkDUhSFUS9UTY+y\nL5YetOIoez4jyv58SaJMWKXsIZY/7V56iNXu8I6EmDAZw+hi4v1UPlvymNFFZxcIfU37bM3N0+Y7\noDCuwyvg7pXHLvO4n6Jj2D/aqOLIF8w/qpidoiiMf0k/ujh/n5lGF0dlGl28bfrrCfGMJMgclCHK\n3mrow/TtJ60/yl6eARf2a1Emc+HCShhC7NiY8IwQCxlLmVH7JcSE2QSU0Y8u7r/Aisc9MLrWa9pf\no2W5h82Kmg4eFcCv+SNf8s3aIxy/epcJnS03qphduaIF+KRdVXYev84sc40udvgREs/DWhldFNZP\ngsyBKYrCx21tJMoCX8iIspmdJMqERalpacSsnZ0eYm5pSRkh1vFdCTFhdobRxU8XPWZ0sWgF8G+p\nbVvUpZr3gOLZXT8Bxzc+dplH1Kkb/Lr1OK/WrUhDf8uOKmb3cngFGvmXYNzKeM5cN8PoYoXaEPGe\nFrHHNpj+ekI8AwkyB2eIsjf1UfblMmuPsj/0USZ3yoT5ZQ6xkG1v45Z2lz0hYyTEhMW5ODsxqWsQ\nt++nMPpxo4thvbWFBwmy3MPmRP8BipP2GIMcaFsV91HWowAjzfAA6LzSti4G4aQoDJtnptHFZqOg\nuL+MLgqrJ0EmUBSFT/RRNm2btUdZW32U7ZMoE2ajpqURs25ODiF2gNod35MQE1YhsEwRPmjuz7L9\nF1j5qNFF/9bgXlaWe9ia1GTY++djl3n8Z+0Rjl+5y/jONXF3czXzAXOnfNECfNJWP7q42wyfZXQt\nAB1/hNvnYO1npr+eEE9JgkwAGVH2RgMtyr5adsgGoiwG/uwsUSZMJkuI/TuAAml32B38tYSYsFr9\nm/hSo3wRPl0cy/W7yQ+/wNlF+yxZwlq4ecb8BxRP58jKxy7ziD59g1+2Hqd7nYo08i9p3rPl0Su1\n9aOLKw6ZaXSxDkS8C1HT4NhG019PiKcgQSbSKYrCp+20KPt92wkbiLIZcH6vPspkFEEYj5qWRsz6\nuRwdUzs9xPYEf0WpkQeo02mghJiwWq76rYu37j1mdDFUv9xjryz3sBlR06GIF/i1eOintK2K+yhT\nxI1RL1jfqGJ2mUcXzfbA6GYfQ3E/WDIQHiSa/npC5JEEmcjCEGV9Gnjz+7YTfL3cyqOs6x/6KHtJ\nokw8MzUtjX0b5nJ0bB1CtvanUFoiu/UhVrvT+7jmy2/pIwrxRIFlivD+c/4s3XeeVbE5jC4Wrah9\nYx89Q5Z72IIbJ7WlFI9Y5vGfdUc4duUu4zsHWe2oYnblixbg47ZV2XH8GrPNNbrY4Ue4dVZGF4VV\nkiATD1EUhc/aVaNPA29++9fKo6xqO4ky8cwyh1jwlv4U0t1mT9CXlBx5gDoSYsIGDWjqS/VyRfhk\n0SNGF9OXe6wx+9lEHkXPeOQyj72nb/DLluN0r1OBxlWse1Qxu261K9DQz4yjixXraqOLkb/D8U2m\nv54QeSBBJnKUPcrGWH2UTZfxRZFnWoj9TcLYug+FWO2XPpAQEzYr8+ji5zmNLlZpDYXLyHIPa6dL\n0ZZ5+LcGj/JZfup+io6h6aOKln8AdF5po4s1URSFj+bvN8/3GM99oo0uLpbRRWFdJMjEIxmirHd9\nb361+ih7EbpMg/PREmXiibKGWF8K626yu+YXEmLCrlQtW4SBz/mzZN95VsVezPqTzq7aZ8mOynIP\nq3Z4Jdy5lOMyj2/XJXDsyl3G2dCoYnZexQoy6oWqbD9mztHFH+DWGVg72vTXEyKXchVkiqIUVRRl\nnqIo8YqiHFIUJcLUBxPWQVEURr+YEWVjV1hxlFVrnxFls7pIlImHqGlp7Nv4Tw4hFkudzoMkxITd\nebupL9XKaqOLN7KPLtZ6DVRVuwMjrFPUdChSXnugdyYxZ24ydcsxutWuQBMbG1XMrnsdbXRx7HJz\njS7W048u/gbHN5v+ekLkQm7vkE0BVqmqGggEA4dMdyRhbTJH2S9bbSHKfodzUVqUyUiCIFuIbX4L\n99Qb7K75OSVGHJAQE3bNMLp4MymZz5dmG10sVgn8mstyD2v1iGUehlHF0kXcGNXW9kYVs1MUhXEv\n1QRgxAIzjS42+xg8fWHxe/J9grAKTwwyRVGKAI2B3wBUVU1WVfWmqQ8mrEv2KBu3Mt6Ko6yDFmVn\nI7XxRfnN1mGpaWns3ziPI2PrZQmx4iNjqdN5MPnyu1n6iEKYXLVy2uji4pjzrI7LNroY1hsSz2uj\ni8K6RM8ERXlomceU9QkcvXyH8Z2DKGKjo4rZVfAsyMgXqrLt6DXm7DbDCG2+gtoDo2+dgXWfm/56\nQjxBbu6QVQauANMURdmrKMqviqIUMvG5hBUyRFmviEpM3XKczxbHcft+iqWPlbNqHaDrNIkyB5U5\nxII2v0mR1OvsrjFaQkw4rHeaaaOLHy/MNrpYpQ0ULi3LPayNLkV7Tpx/K/DwSv/hfWdu8r/Nx3gl\n3PZHFbPrUbci9X2LM2b5Qc7eMNPoYr13YM+vMrooLC43QeYChAI/qapaC7gLjMj+IkVR+imKEqko\nSuSVK1eMfExhLRRF4fP21XmzoQ8zd56i4fgN/Hd9gnWGWZYok/FFR6CmpbF/03yOjI0gaPObeGQO\nsS5DJMSEw3J1dmJS1yBuJiXzRebRRWdX7Q5MwhrtGU3COhxZ9dAyj8yjih+3s/1RxewURWFC5yBU\nYMT8A+bbuuhZGZa8Bw/umP56QjxCboLsLHBWVdVd+v9/HlqgZaGq6lRVVcNVVQ0vWdK+/tRGZKU9\nPLoaywY2pG7l4nyz9giNJmzku/UJJFpbmKWPL+6RKLNjWUJs0xt4pF5jV/XP8JQQEyJd9XIevPec\nH4tizrMm8+hi6Ouy3MPaGJZ5+GUs8/jv+gQSLt9h7Es17WZUMTvD6OK/R68yd4+ZRhc7/KBtGpXR\nRWFBTwwyVVUvAmcURQnQ/1Bz4KBJTyVsQo3yHvzyejjLBjaktrcn/7f2CA2tMcyqd8yIslldJcrs\niJqWxoHNCzg8tv5DIVa364cSYkJk805TP6qWLcLHi2K5maQfXSzmDb7PyXIPa3HjFBxdr23BdHYB\ntFHFnzcfo2uYF80CSln4gKbVo45hdPEQ527eM/0FK9WHugNgzy9wYovprydEDnK7ZXEgMEtRlP1A\nCDDWdEcStqZGeQ9+7RXO0veyhtn3G6wozKp3hC6/wZndEmV2IHOI1dzYh6KpV9hV/VMJMSGeIJ+L\nE5O7BnHjbjJfLM30Z6thveH2OTi6zmJnE3p7sy7zeJCqY9i8fZRyd+OTdtUsfDjTc3LSRhfTVJUR\n5npgdPPPoJiPfuuijC4K88tVkKmqGqMfRwxSVbWjqqo3TH0wYXtqemUOs2JMXmNlYVa9U7Yok990\nbY2alsaBLQs5PK5BlhArNuIAdbsOlRATIheql/Pg3WZ+LNx7jrUHL2k/GPA8FColyz0sTZeibVf0\nawlFKwDaqOKRS3cY91JNPArY56hidhU8CzLy+UC2JlzlL3ONLnb8EW6ehvVfmP56QmST2ztkQuSa\nFma1s4RZo4kb+WHjUcuHWfVO0PlXfZR1kSizEVlCbENviqZcZle1T9JDLL9bQUsfUQib8m4zPwLL\nuDNq4QFtdDF9ucdquHXO0sdzXEdWw52L6cs89p+9yc+bj9MlzItmgfY9qphdj7qViKhcnK/NOrrY\nH3ZPhZP/mv56QmQiQSZMxhBmS95rQFjFYkxafTg9zO48sODnFGq8lCnK5E6ZNdNCbDHx+hArlnIp\nI8ReHiYhJsRT0kYXg7l+N5kvDaOLYb1ATZPlHpYUNR3cy4F/K21U8Z/9lCicj08dYFQxOycnhYld\ntNHFkQvMtHUxfXTxXUi+a/rrCaEnQSZMLsirKL/1zhpmDSdssGyY1XgJOv8CZ3ZJlFmhrCH2Op4p\nl9hV7WOKjoiVEBPCSGqU9+Ddpr4s2HuOdQcvZV3ukaaz9PEcz83T2mf4QrVlHt+tP8rhS4kONaqY\nXQXPgox4PpAtR67wd6Q5RhcLaVsXb5yEdTK6KMxHgkyYjSHMFr/bgFBrCLManTOibPbLEmVWQE1L\nI3brYuLHNcwhxIZLiAlhZO89558+ungrKUW/3OOsLPewhOiZ2l9rvcaBs7f4afMxOod68Vxgacue\ny8J61q1EvcqefL3sEOfNMbro3QDq9Ifd/5PRRWE2iiluAYeHh6uRkZFGf19hX/aducmU9QlsiL9M\nsYKu9G1cmdcjvCmc38W8B4mdD/PfgooR8OrfkL+wea8vUNPSiNu2FOctE6iaEsdlPDlRdQAhHQZK\nhAlhYrHnbtHhh210CCnHN52rwzfVwCscus+x9NEchy4Vvq0BZWqS/MpfvPjdv9y8l8yaQU3wKOiY\nd8cyO30tidbfbqG2jyd/9KmNoiimvWDyXfipvva/396u3TkT4ikoihKlqmr4k14nd8iExQRXKMrv\nvWuz6N0GhFQoysRVh2k0YQM/bjLzHbManbXPlJ3eod0pk7lxs1HT0oj9dwmHxjWixvrXKZ5ygV1V\nR+ExIo66r3wkMSaEGdQo78E7TX1ZEH2O9UeuQ60ecGQV3D5v6aM5joTVkHgBwnrz3YaEjFFFiTEA\nKhbPGF38J/Ks6S+YeXRx/Zemv55weBJkwuJCKhRlWp86D4XZT5uOcddcYVajM7z0ixZlsyTKTE0L\nsaUcGteYGuteo0TKeXZVHUmRj2IlxISwgIGZRhdvV+shyz3MLWo6uJcltlA9ftx0jJdCyzv8qGJ2\nr9WrRF0fT75adpALt8wxutgQ6vSDXT/DyW2mv55waDKyKKzO3tM3mLI+gU2Hr1CsoCv9GvvyekQl\nCpljlPHAPFjQFyrWhx5/y5iCCcRuW4rT5glUSz6gH03sT3D7gbgVkF9rISzpwNlbdPxxGx1DyvN/\n90fD1QQYtB+cnC19NPt28wx8WxNdw6G0jW3M9bvJrB0so4o5OXXtLm2+3UodH0+mm3V0UYG3t8n3\nBCLPZGRR2KxaFYsxvU8dFr5Tn+AKRZmwKp5GEzfy82Yz3DGr2UV/p2w7zH5F7pQZUdy25cSNbUiN\ntT0pkXyOXYEj9HfERkiMCWEFanp58HYTX+ZHn+VA2U765R7rLX0s+7dXW+bxe1JD4i8mMraTjCo+\nSqXihfioTQCbj1zhnygzjS62/x5unID1X5n+esJhyR0yYfWiT99gyroENh+5gmehfPRvXJnXIipR\nMJ8J75gZ7pRVagCv/iV/KvYM4rYth83jqG64IxbYj+AO70uECWGFHqTqaP/dNu4k3WWr60CcKtSB\n7rMtfSz7pUuFb2uSWDSAkGP96BBcjm9eCbH0qaxaWppKt192cujCbdYMbkxZjwKmv+jyobDnV+iz\nQnuAtBC5JHfIhN0IrViMP96ow4J36lOzvAfjVsbTaMJG/rf5GEnJJrpjVrMLdJoKp7bJnbKnFLd9\nBXFjG1F97auUSj7LzoCPtDti3UZKjAlhpfK7ODOpaxAX76psKthSlnuYWsIaSDzPN9ciKF4oH6Nf\nrG7pE1k9JyeFSV2CSNGlme+B0S0+h6IV9Q+MTjL99YTDkSATNsMQZvPfrk/1TGE2dYuJwiyoa7Yo\nk9+EcyM9xNZ0p1TyGXYGfIT7R3HU6z5KQkwIGxDkVZQBTSrz+bnaoOpg7yxLH8l+RU3njmsJZl4P\nlFHFPNBGFwPZdPgK88wxupi/MHT4Hq4fhw0yuiiMT4JM2JywSsWYkSnMxq4wYZgFdYVO/9NH2csS\nZY9xcMdK4sY2zhRiw3EfHishJoQNer+5P26lfNmtBJEWNR3SdJY+kv25eQb16Fqm32/Ei7Uq0aKa\nbFXMi14R3tTx9uTLZQe5eOu+6S/o0xhqvwU7f4JTO0x/PeFQJMiEzcoIswiqlSvC2BXxNJ64kV+2\nHDdumAW9LFH2GIYQq7a6G6WTT2UKsY9xKygP2RbCFuV3cWZy12BmJDfD6fZZOLbB0keyO7qomaiq\nypr8rRj9YjVLH8fmODkpTEwfXdxvptHFL6BoBVj8jnwvIIxKgkzYvLBKnsx8sy7z346gatkijFlx\nKD3M7iUb6U91g16Gjj9rUTZHxhcBDu5cRey4JhkhVmUYhYfHSYgJYSeCvIri3aArV9UiXNn8P0sf\nx77oUknaNY0tuiAGvtScogXzWfpENsm7RCGGtw5k4+ErzI8+Z/oL5i+sbV28fhw2fG366wmHIUEm\n7IYhzOYNyAizRhM38OtWI4VZ8CtalJ3Y6tBRlh5iq16hzIOTGSH26icSYkLYmYGtqrE+fwuKnV1P\n4pXTlj6O3Ti1exHuyZc5VrELLWVU8Zn0rq+NLn6xNM48o4uVm0D4m7DzRzi90/TXEw5B1t4LuxV5\n8jpT1iewNeEqJQrnY0ATX3rUrUSBfM/4kNN9f8HC/to8efe5kK+gcQ5s5Q7tWo1uw1hqPIjhKkU5\nWuUtQjoOlggTws4diouh6j9NWFXqLdq883+WPo7NS9GlET2+Fb4pCTgPOUixIvIZ22d14updnp+y\nhfq+JfitV7jpHxj9IBF+rA/OrjDgX4f5PkDknay9Fw4v3Fu7Y/bPgAgCyxTh6+WHaDRx47PfMQt+\nBTr9DCe2wJxudn+n7NCu1cSOa0LVlS9rd8T8P6TQsFjqvfqpxJgQDqBq9RBOFqlNjUuL2HTogqWP\nY/NmrNpGeHIkt6t2kxgzEp8ShRjWOpAN8ZdZYJbRRXf91sVjsHGM6a8n7J4EmbB7tb09+fOtuvzd\nP4KAMoWNE2bB3TKibG53u4yy+F1rODCu6cMh1uMzChRyt/TxhBBmVL7FALyUqyxe8Ce376dY+jg2\n6+D529zZMQ1Fgcqt3rb0cexKn/re1PYuxhdL47h021yji2/Ajh/g9C7TX0/YNQky4TDq+Hgy6616\nWcKs8aSN/PbvCe6nPEWYBXeDjj/B8c1alKXcM/6hLcAQYoEru1LuwXF2+g+REBPCwblWa0+KW3Ha\n3F/NmGWHLH0cm5SiS2P439G84rIJnXdTKOZt6SPZFW3rYjAPUtMYZa4HRrf8Ejz0Wxft5HsAYRkS\nZMLhGMLsr3718C9VmK+WHaTRxKcMs5DuGVE2p5tN/4Ycv3stB8Y1yxJiBYfFUa/HaAkxIRydSz5c\nQ3vQ0jmaDZH72XzkiqVPZHN+2nSMUpe3UoZruNZ5w9LHsUva6GIA6+Mvs3CvuUYXv4NrR2Xrongm\nEmTCYdWtXJzZfR8Os9/zGmYh3aHjjzYbZekhtqIL5R4cY6ffYAoMPSAhJoTIKqw3TugY4LGDEfP3\ny+hiHhy6cJvvNiQwuNh2KFwaAp639JHsVp8GPoRXKsbnS+K4bJbRxaYQ1kdGF8UzkSATDs8QZnP7\n1cOvZGG+fJowC3k1U5TZxvhi/J517B//HIErulD+wdGMEOv5OQULe1j6eEIIa1PcF3wa09N1E5dv\nJzF2uYwu5kaKLo2h/+zD3+02NZJ2Qq2e2nY+YRLO+gdGP0hNY9RCc44uesnoonhqEmRC6NWrXJw5\n/bKGWeOJG5m2LZdhFvIqdPgBjm+y6ihLD7HlnfG6n8BOv0G4DY2VEBNCPFlYb/LfOcuYoKvM3XOG\nLTK6+EQ/bzpG3PnbTAmIRVHTIPR1Sx/J7lUuWZhhrQNYd+gyi2LMMLroVgTa60cXZeuieAryHDIh\nHmHHsWtMWX+EncevU8o9P2839aV7nYq4uT7hOWZ7Z8Hid8G3GXSbDa4FzHPgJ4jfs47k9eMIuh/J\ndYpwxO8NgjoOkQgTQuRe6gP4piq6ChG0Ot+Xe8k6Vg9ujLub3PHJyaELt2n//b88X70U/73YC0pW\ngdcWWvpYDkGXpvLy/3Zw9PId1g5uTKkibqa/6NIPIHoGvLEaKtQx/fWE1ZPnkAnxjCJ8izO3XwRz\n+tbDp0Qhvliq3TGb/qQ7ZrV6aM8nObYR5r5q8Ttl8ZHr2T++OYHLO1Ph/hF2+n6gvyP2hcSYECJv\nXPJDyKs4J6ziP23LcvH2fcaukNHFnKTo0hg2bx8eBVwZU+Mi3D4LYb0tfSyHYRhdvJ+iY9TCWDON\nLn4FRcrDIhldFHkjQSbEE0T4Fuev/lqYeZcoxOdLD9Jk0hPCrFbPTFHWA1LM8MHibLQQa0HgspfS\nQyzfhweo99qXEmJCiKcX2hvSUgm6soy+jSozZ7eMLubkf5uPEXvuNl93rIF73CwoVAoCXrD0sRyK\nb8nCDG0VwLpDl1gcc970F3QrAu3/C9cSYONY019P2A0JMiFyKcK3OH/rw6xS8Yww+2P7yZzDrFZP\nbab82Ab9nTLzRNnhyA2ZQuwwOyu/nx5ihdyLmuUMQgg7VsIPvBtB1B8MbuGHb8lCjFxwgETZupgu\n/uJtpqxPoF1QWdpUVOHIKm16QpZ5mN0bDX0IrViU0UviuJxohv8O+z4Hob1gx/dwZo/pryfsggSZ\nEHkU4Vucv/rVY3bfulTyLMToJXGPDrPQ18wWZYejNrJvfAsClnXKGmKvfyUhJoQwrrDecPMUbqe3\nMKlrMBdu3WPsinhLn8oqGLYqFnFz5Yv21WHvn6Cmad+kC7NzdlKY1DWYeyk6PjbX6GKrr8G9nH7r\novknZITtkSAT4ikoikJ93xL81T9rmDWdtIkZO7KFWeYo+8v444tHojexb0JLApZ2pOL9eHZIiAkh\nTK3qi1DAE6KmE1qxGG81qsyc3af5N+GqpU9mcVO3HE8fVSxe0EVb8lC5GXj6WPpoDksbXazC2oOX\nWLLPjKOLV4/AJhldFE8mQSbEM8gSZm/VpYJnAT5brIXZzB0neZCqDzNDlB1dZ7QoM4RYlSUdqHjv\nkD7EYomQEBNCmJp+uQeHV0DiJYa0rELlkoX4aP5+7jxItfTpLObwxUS+XXeEtkFleb5mWTi6Hm6d\nkWUeVuDNhpWpZc7RRb/m2iMOtn8HZ2XzuHg8CTIhjEBRFOr7leDv/hHpYfZp9jDLEmU9nzrKjkRv\nZt+EVhkh5vMerkMOSIgJIcwrrDekpULMLNxcnZnUJZjzt+4xzkG3LqbqRxXd3Vz5sn117QejpkOh\nkrLMwwo4OylM6hJMUrKOT8w6ulgWFr0to4visSTIhDCizGE26626lC+aKcx2nuJBUA99lK3Nc5Rl\nhFh7Kt2LywixXmMoXB0g3CsAACAASURBVKSYCf+uhBAiByX8teUe0X9AWhphlYrxVkMfZu06zbaj\njje6+L8txzlw7hZfdahB8cL54fZ5bZlHSA9wyWfp4wnAr1RhPmxZhTVmG130yDS6OM701xM2S4JM\nCBNQFIUGfiX4Z0CmMFsUq4VZchNS2n6rRdnfrz0xyhL2biFmQutMIfYuLkNiJcSEEJYX1htunIQT\nmwD4sFUAlUsUYvg8xxpdPHIpkSnrEmhbsyxtg8pqP7h3Fqg6CJNlHtbkrUaVCamgjS5eSXxg+gv6\ntYBar8H2/8LZKNNfT9ikXAWZoignFUU5oChKjKIoMggrRC5lDrM/36xLOX2YNVlXkZ3VR0PCGi3K\nUh/+j4IhxPwXv4j3vdhMITZWQkwIYR0C26Uv9wC00cWuQZy/dY/xKx1jdNEwqljYzYUvOuhHFdN0\n+mUeTcGzsiWPJ7JxdlKY3DVIG11cdMA8o4utx8joonisvNwha6aqaoiqquEmO40QdkpRFBr6l2Ce\nPszKFi1At6gAxru8DQlr0M3tkR5lCTFbiZnYJj3Ednq/i/PgAxJiQgjr4+qmLfeIXw53LgMQVsmT\nNxv48OfO02x3gNHFqVuPs//sLb7sUJ0ShfNrP3hsI9w6Lcs8rJRfKXeGtKzC6rhLLN1/wfQXdPOA\nF/8LVw/D5vGmv56wOTKyKIQZZQ6zmW/WYbfni4xIeQvno2s5+2MHLcQWtcMnaX96iNXrPRZ3D09L\nH10IIXIW2it9uYfBh60C8ClRiGF2PrqYcCmRb9cm8ELNMrQLKpfxE1HToGAJCGhrucOJx3qroQ/B\nFYoyenGseUYX/VtArZ6wbQqck9FFkVVug0wF1iiKEqUoSj9THkgIR6AoCo38SzL/7fq07T2Cn9wH\n4nV9Bz5J+9nh/TZOg2MlxIQQtqFkFajUEKK05R4ABfI5M6mLNro4YaV9PjDaMKpYKL8zX3aokfET\nty/A4ZVQS5Z5WDMXZyf+r2sQd5N1fLrIXFsXx0DhMrBsMJjjesJm5DbIGqiqGgo8D7yrKErj7C9Q\nFKWfoiiRiqJEXrlyxaiHFMJeGcJswJCvONF1DU6DY4noPV5CTAhhW8J6w40TcGJz+g+Fe3vyRgMf\nZu48ZZeji79sPcG+s7f4skONjFFFgJg/tWUeobLMw9r5lXJncIsqrIq7yDJzjC4WKArPfQwX9mkb\nOIXQy1WQqap6Xv/Xy8BCoE4Or5mqqmq4qqrhJUuWNO4phbBziqLgU72uhJgQwjZVfREKFEtf7mEw\nVD+6OHz+fu7a0ehiwqVE/rP2CM/XKEM7w1ZF0O4QRs0AnyZQ3NdyBxS51reRNrr42eJYrt4xw+hi\n0CtQtJK2Bl/ukgm9JwaZoiiFFEVxN/xvoBUQa+qDCSGEEMJGuLpB8KsQvyx9uQdoo4sTuwRx7uY9\nJqyyj9HFVF0aQ+ftTx9VVBQl4yePb5BlHjbGxdmJyV2CuPtAx2eLzfDtrbMrNB6mv0u22vTXEzYh\nN3fISgP/KoqyD9gNLFdVVe6zCiGEECJDmGG5x+wsP1zb25M+9X2YseMU24/Z/ujir/+eYN+Zm3zR\noQYl3fNn/clI/TKPwHaWOZx4Kv6l3RnU0p8VBy6ybL8ZHhgd3E3ukoksnhhkqqoeV1U1WP9/1VVV\nHWOOgwkhhBDChpQMgIr1ITpjuYfBsNYBeBcvyEc2Prp49HIi36w9QpvqZXgx86giQOJFbZlHyKuy\nzMMG9WtUmWAvDz5bHGf60cX0u2QxcpdMALL2XgghhBDGEt4Hrh+Hk1uy/LA2uhjM2Rv3mGijo4u6\nNJWh/+ynYD5nvuqYbVQRYK9+mYeMK9okF2cnJnUN5s79VEYvjjP9BQ13yTaPl7tkQoJMCCGEEEZS\ntT24FX1ouQdAHR9Petf35o8dp9hx7Jr5z/aMft16nJgzN/miffWHRxXT0rQ7gz6NZZmHDatS2p0P\nWviz/MAFlpt666KzKzQeCuf3QsIa015LWD0JMiGEEEIYh6ubNrJ3aBncefgROMNaB1CpeEGGz99H\nUrLtjC4evXyH/1t7hNbVS9M+uNzDLzi+EW7KMg970L9xZYK8PPh0cSzXTD26GNwdilaUz5IJCTIh\nhBBCGFFoL0hLgX2zH/qpgvlcmJQ+unjYAofLO12ayrB5+x49qggQNQ0KFpdlHnbAxdmJSV200cXP\nlph4dNHwWTK5S+bwJMiEEEIIYTylAqFihDa2mMOf+tfx8aRXhDfTt59k53HrH1387d/j7D2tjSqW\ncnd7+AVZlnnkf/jnhc0JKKMfXdx/gRUHTDy6mH6XTD5L5sgkyIQQQghhXGH65R4ntuT40//f3p1H\nR1mfbRy/7pCVXSWyBFArIGtYElnFBWlfrNYFxGqrFepWFUVrpWptq7ZVEarWtm9ba5X2ra2VxbVW\nZccFwQTZd0iCoJWwqDVgUfm9fzwzYTEhM8nMPM/MfD/neI5CMnOfnhzL5XU/90wYEVpdnLY80KuL\nG7d/osmvrtfXuteyqihJS5/0zv33G5PQ2RBf15z6FfUqaKEfPxvn1cVGWdLQH0jvLZE2zIzf+yDQ\nCGQAACC2utd+3EPyVhcfGFWoLbv2BHZ18Yv9ThOmLVNeViP9/IJaVhX375dK/ywdP1Rq1SnxQyJu\nMhtlaPLo3vr408/003ivLvIsWdojkAEAgNjKyvP+kLnmBamq5g+DHvCVYzRmsLe6uCiAq4uPv16m\nJUdaVZSksnnShxUc80hRJ7VppvFndtaLy9/Xv+K5upiZTUuW5ghkAAAg9opCxz2Wfvm4R9iEESep\n49GNNWF6sFYXN1V+osmvrtPwbq11Xp9aVhUlqeQJKe9oqds3EjccEuqa005Uz4LmuvPZldpVtS9+\nb9T7EqlFRz6XLE0RyAAAQOwd203qMLDW4x5SaHXxwkJV7NyjSa8EY3XRW1VcrtysRrq3tlVFSfrP\nB9K6lzjmkeKyErW6mJktnXqLtK1U2jgrfu+DQCKQAQCA+CgeK+3aJJW/VuuXDPzKMbp80HGa8ma5\nFpftSuBwNXvijTKVVuzWXed217HNa1lVlA4c8ygam7jh4IuubZrrxmGd9cKy9/TyyjiuLvb+lteS\n8SxZ2iGQAQCA+Oh+npTbotbjHmETRnRV+6PydOu0Zdq774vEzFaDzZWfaNIr6zS827E6v09B7V+4\nf7+0hGMe6eR7pydgdZGWLG0RyAAAQHxEcNxDkprkZOqBUb19XV0MryrmZGbo3gt61b6qKEll86Xd\n5RzzSCNZoQ+M/mjvZ7ornquLvb8ltejA55KlGQIZAACIn36XS1/sk5b9/YhfNujEY/SdQcfpiTfL\nfFldfOKNMpVU7NZd5/Y48qqi5DV+eUdLXc9JyGwIhm5tm+uGYZ31/LL39PLKf8fnTTKzpaG3SNtK\npI2z4/MeCBwCGQAAiJ/W3aUOA4543CPsh6HVxQkJXl0s21GlSa+s05ldj9UFfY+wqihJn2yX1r7o\nHfPIqiO4IeVce/qJ6t62ue58doV2x2t1sc+3Qy0Zz5KlCwIZAACIr6Kx0s6NUvnrR/yyJjmZmjiq\nUOU792jyq4lZXfxiv9OtU5d5q4oj61hVlA4c8+h3eULmQ7CEry5+uOcz3fVCnFYXacnSDoEMAADE\nV4/zIzruIUmDT2ylywYep8ffKNPb5fFfXZzyZrlKKnbrp9/oodZ1rSru3y+V/lk67hQpv0vcZ0Mw\ndW/nrS4+t/Q9vbIqTquL4ZaMzyVLCwQyAAAQX1l5UuHF0prnpaqddX75bWd1VUHLPE2Ytjyuq4ve\nquJaDet6rEb2q2NVUZLKF0i7yzjmAV13hre6+KNnVsZndTEzWxr6fWnr29ImWrJURyADAADxVxTZ\ncQ8pfHWxUGU7qvTLOK0u7t/vNGHaMmU3ytB9kawqSqFjHkdJ3b4Rl5mQPLIaZWjS6EJ9uGef7o7X\n6mKfS6Xm7bm4mAYIZAAAIP5a95Da94/ouIckDe7USpcO7Kg/vVGmkjisLk55s1xvl+/WTyJZVZSk\nTyqlNS96Z8k55gFJPdq10LhhnfTs0vf0ajxWF8OfS0ZLlvIIZAAAIDGKx0o7N0gVb0T05bed1U3t\nWuTp1mnL9elnsVtdLN9RpQdCq4qjIllVlELHPD7zmj4g5LrTO6lb2+b60bMr9eGeOKwuVrdkE2nJ\nUhiBDAAAJEb386WcyI57SFLTnEw9cGFsVxf3hz4AOqtRBB8AfeCbpCV/lo4bIuWfFJM5kBqyMzM0\neXShdlft090vrI79G1Q/S7ZY2jQn9q+PQCCQAQCAxMhuLPX+prT6OWlPZGuIQzq10rcHdNRjr5ep\ntKLhq4t/XliuxeW79JNzuqtNiwhXD8tfk3Zt5pgHatSjXQtdf0YnPfPONs1c/UHs36Avz5KlOgIZ\nAABInKIxER/3CLv966HVxakNW10s31GliS+v1Rkn5evCovaRf2PpFCm3pdTt3Hq/N1Lb9Wd0Utc2\nzXTHMytiv7qYmSMNvdlryTbPje1rIxAIZAAAIHFa95DanxzxcQ/JW12cOKpQm3dU6cGZ6+v1tvv3\nO02Y7q0q3jeyMLJVRSl0zOMFqQ/HPFA7b3Wxt3ZX7dM98Vhd7HuZ1LyAlixFEcgAAEBiFY2RdqyX\nKt6M+FtO6dxK3xrQUY+9tlmlFbujfsu/LCzX4rJd+nE0q4qStOxvoWMeY6J+T6SXngUtdN0ZnTTj\nnW2aFevVxcwc71mydxfRkqUgAhkAAEisHiOjOu4RdvtZXdW2RZ5unbYsqtXFip1VmvjyOp3WJV+j\no1lVdM6bseNgjnkgIuMOWl38aM9nsX1xWrKURSADAACJld1YKrwoquMektQsN0v3j+qlzZVVeijC\n1cXwVcXMDNP9oyK8qhjGMQ9EKby6uLNqn+5+McYfGJ2ZI51yc6glmxfb14avCGQAACDxii6Xvviv\ntOypqL5taOd8XdK/o/742mYt2VL36uJfF1VoUWhVsW2LvOhmDB/z6M4xD0SuZ0ELXX/6iZqxZJtm\nr4nx6mK/70jN2tGSpRgCGQAASLw2vaSC4qiOe4Td8fWuatM8V7dOPfLq4pade3TfS2t1apd8jS6O\nYlVRkqp2SKufl3pfImVFGeSQ9sYN66yubZrp9hkxXl2sfpbsLVqyFEIgAwAA/igaI+1YJ21ZGNW3\neauLhdpUWaWHZtW8uuhdVVzmrSqOjHJVUZKWcswD9ZedmaFJF3qri/e8GOOri7RkKYdABgAA/NFz\npJTTPOrjHpJ0apd8XdK/g/64YLPeqWF18clFFXpr8y7deU43tWsZZcNVfcxjkHRs16hnAySpV/sW\nuu70EzV9yVbNWRvD1cWDW7Ky+bF7XfiGQAYAAPyR3cQ77rHq2aiOe4Td8fVuatM8Vz84bHXx3V17\ndN+/1mpo51a6qLhD9HOVvy7t2kQ7hgYbN6yTTmodh9XFvpfRkqWQiAOZmTUys3fM7MV4DgQAANJI\n0RjvuMfyf0T9rc1ys3RfaHXx4VkbJHmrirdOW6YMM90/KooPgD5Y6RQpt4XU/bzovxc4SE5mI00e\n3Vs7Ptmnn/0zhquLWbleS7ZlIS1ZCoimIRsvaU28BgEAAGmoTS+poKhexz0k6bQu+br45A56dMEm\nLX33Qz25eIu3qnh2NxVEu6ooSVU7pTUc80Ds9GrfQteedqKmlW7V3LXbY/fCtGQpI6JAZmbtJZ0t\n6bH4jgMAANJO0Ripcq205a16ffsdZ3dT6+a5uvkfS3XfS2s0tHMrffPkeqwqStKyv0lf7GNdETF1\nw5md1KV1U902Y7k+2huj1cWsXO9zybYslMoWxOY14YtIG7KHJU2QtD+OswAAgHTUc5SU3axexz0k\nqXno6mLZjqqGrSqGj3l0GCgd261eswA1OXh18Y4ZK/TF/hg1Wv2+IzVrS0uW5OoMZGZ2jqTtzrnS\nOr7uajMrMbOSysrKmA0IAABSXPVxj2fqddxD8lYXf3FBT/3u0n71W1WUpIo3pJ0baccQF4XtW+qH\nI07SP1e8r1unLotNKMvKlU75vrTlTVqyJBZJQzZE0rlmVi7pKUnDzOyvh3+Rc+5R51yxc644Pz8/\nxmMCAICUVn3c4+l6v8S3BxynoZ0b8GeQ8DGPHufX/zWAI7j61BN1y1e7aMY722IXymjJkl6dgcw5\nd7tzrr1z7nhJF0ua45y7NO6TAQCA9NG2UGrXr97HPRpszy5p9XNS4cUc80Bc3XBm5wOhbFoMQln1\ns2RvSuWvxWZIJBSfQwYAAIKhaIxUuUZ6d3Hi33tp+JjH5Yl/b6SdG87srO9/tYtmLIlRKOt3udS0\nDS1ZkooqkDnn5jnnzonXMAAAII1VH/d4IrHvGz7m0b6/1LpHYt8baevGg0LZhGnLGxbKwp9LVvEG\nLVkSoiEDAADBkNNUKhztHffYuztx71vxprRzg1Q8NnHvCcgLZTcP76LpS7Y2PJRVt2QTYzcgEoJA\nBgAAgqNojPT5pw067hG10ilSTgupO8c8kHjjhx8IZT+c3oBQFn6WrOJ1qYyWLJkQyAAAQHC07S21\n65u44x7hYx69vyllN47/+wE1GD+8s24a3lnTShsYyooOepYMSYNABgAAgqVojLR9tbT17fi/17K/\ne+f2+ewx+Oym4V2qQ9lt05drf31CWVYeLVkSIpABAIBg6TlKym4qlcT5uEf1MY+TOeaBQLhpeBeN\nP7OzpoaasnqFsnBLNp9nyZIFgQwAAARLTjOp12hp1Yz4HvfYslDasV4q4pgHguPmrzYwlGXlSafc\n5F1bpCVLCgQyAAAQPNXHPabG7z3Cxzx6XBC/9wDqocGhrGiM1LQ1LVmSIJABAIDgaddHatsnfsc9\n9uySVj0rFV7EMQ8E0s1f7aIbQ6HsthlRhrLws2Tlr0nlr8dvSMQEgQwAAART0Rhp+yppa0nsX3vZ\nU6FjHpfH/rWBGLl5eGfdOKyTni7ZqttnrIgulIVbMi4uBh6BDAAABFOvC73jHqUxPu4RPuZRUCy1\n6RXb1wZiyMy8pmxYJ/2j5N3oQllWnjTkJlqyJEAgAwAAwZTTzAtlK2dIez+M3etueUvasU4q5pgH\ngi8cym4IhbI7nokilBWPpSVLAgQyAAAQXEVjpM/3SitieNyjdIqU05xjHkgaZqbvh0LZU29HEcoO\nacneiP+gqBcCGQAACK52faW2vb3PJIvFcY89u6RVz4SOeTRp+OsBCRIOZePO8ELZj56NMJQVj5Wa\nHCvNpyULKgIZAAAItvBxj22lDX+t5f8IHfMY0/DXAhLMzHTL17xQ9vfFEYay8OeSlS2gJQsoAhkA\nAAi2nhdKWU28lqwhqo95FHHMA0krHMquP+PEUChbWXcoK6IlCzICGQAACLbc5qHjHtOlTz+q/+u8\nu0iqXOv94RRIYmamH3ztpFAo21J3KMtufKAlq3gzcYMiIgQyAAAQfOHjHsufrv9rlE6RsptJPUfG\nairAN+FQdt3pXii787k6Qlm4JePiYuAQyAAAQPC16yu1KfRCVX2Oe+zdzTEPpBwz063/44Wyvy2q\nI5RlN5aGjJfK5tOSBQyBDAAABJ+Z15J9sFLatiT671/+tPT5pxzzQMoJh7JrQ6Hsx0cKZcXflZrk\n05IFDIEMAAAkh16jveMepY9H933OeQdB2vWT2hbGZzbAR2amCaFQ9uSRQll2Y+9zycrmSxULEz8o\nakQgAwAAySG3udRrlLRyRnTHPd5dLFWu8T6PCUhR4VD2vdO8UPaT52sJZeGWjIuLgUEgAwAAyaNo\njPTZHmnF1Mi/J3zMowfHPJDazEw/HOGFsr++5YUyd/gzl+FnyTbPoyULCAIZAABIHu36eZ8hVjIl\nsuMee3dLq2ZIhaOlnKZxHw/wWziUXXPaV/TXt7z1xS+FMlqyQCGQAQCA5FF93GOF9F4Exz2WT+WY\nB9KOmem2EV1rD2XZTQ60ZFve8m1OeAhkAAAgufQaLWU19g51HIlzUukT3sn8tr0TMxsQENWh7FQv\nlP3kuVWHhrLi70qNW3FxMQAIZAAAILnktpB6jpJWTpc+/bj2r9v6trR9Ne0Y0paZ6bazvFD2f29V\nHBrKqluyubRkPiOQAQCA5FM0tu7jHqVTpOymUs8LEzYWEDThUHZ1KJT99PmDQtnJV9CSBQCBDAAA\nJJ+CflLrXt5KYk3HPfZ+6J3H78UxD8DMdHsolP1l4UGh7JCWbJHfY6YtAhkAAEg+ZlLR5dK/V0jv\nvfPl318xVfp8L+uKQEg4lF019AT9ZWGF7gqHsnBLxsVF3xDIAABAciq8SMrM81qygznnHfxo20dq\n18ef2YAAMjPd8fVuumroCfpzOJRlNZaG3ChtmkNL5hMCGQAASE7h4x4rDjvusbVE2r6KdgyoQTiU\nXXmKF8rufmG1XPEVUuNjaMl8QiADAADJq3is9FmVtHLagV8LH/PoxTEPoCZmph+d7YWyKW+W6+5X\nKuQGj/dasncX+z1e2iGQAQCA5FVQJLXu6YUwSfr0I+8cfq8LpZxmvo4GBFk4lF0RCmX3Vg6Ra3wM\nFxd9UGcgM7NcM1tsZsvMbJWZ3Z2IwQAAAOpk5q0mvr/MO+6x/GmOeQARMjPdGQplf1z0gWa1/Ka0\naTYtWYJF0pD9V9Iw51xvSX0kjTCzgfEdCwAAIEK9RnvHPUqe8Jqytr2ldn39ngpICuFQ9t0hJ2j8\n5mJVZbaUoyVLqDoDmfN8EvrHrNBfNXzgBwAAgA/yWko9R0pLn5Q+WEk7BkTJzPTjc7rp4iHd9Mje\ns2SbZsvRkiVMRM+QmVkjM1sqabukmc45bmICAIDgKBor7f9cymriNWYAohIOZe7kK7TTNdOmqT/2\nPqcMcRdRIHPOfeGc6yOpvaT+Ztbz8K8xs6vNrMTMSiorK2M9JwAAQO3aF0snnCr1v5JjHkA9mZlu\nP69Y77S/VJ0+fkuPPzWVUJYAFu3/yGb2U0lVzrnJtX1NcXGxKykpaehsAAAAABLM/fc/2vtADy3e\nd7xeG/B73Xl2N5mZ32MlHTMrdc4V1/V1kVxZzDezlqG/z5M0XNLaho8IAAAAIGgsp5nyTr9Jpzda\nptI3ZuoX/1xDUxZHkawstpU018yWS3pb3jNkL8Z3LAAAAAB+sf5XyeUdrUn5/9Jjr5cRyuIokiuL\ny51zfZ1zhc65ns65exIxGAAAAACf5DSTDb5BnT9eqB/1rtJjr5fp3pcIZfEQ0VEPAAAAAGmm/1VS\n3tG68oundfmg4/TH1whl8UAgAwAAAPBlOc2kweNkG2fqrqJPq0PZff9aSyiLIQIZAAAAgJr1v1rK\nO0o2f6LuOreHvjPoOD26YDOhLIYy/R4AAAAAQEDlNJMG3yDNvke2bYnuPrefJOnRBZtlkm47qysn\n8RuIhgwAAABA7UItmeZPlJnp7nN76LKBx+kPCzbrfpqyBqMhAwAAAFC7nGbSoHHSnJ9J20plBUW6\n57wekqQ/LNgsmXTbCJqy+qIhAwAAAHBk4ZZs3kRJkpnpnvN66NKBHfWH+Zt1/8s0ZfVFQwYAAADg\nyHKbH9KSqaBIZqafnddTkvSH+ZtlMv1wxEk0ZVGiIQMAAABQt+pnyR6o/iUz0z3n9tSlAzvq9/M3\naeLL62jKokRDBgAAAKBuuc2lQddLc34ubVsiFXgXFzMyvFDmnPT7+ZtkJk34H5qySNGQAQAAAIhM\n/2uk3JbS/ImH/HJGhre++O0BHfW7eZv0wCs0ZZGiIQMAAAAQmdzm0uBxX2rJpAOhzEn63bxNMkm3\n0pTViYYMAAAAQOSqW7IHvvRbGRmmn5/XU98a0FH/O2+TJtGU1YmGDAAAAEDkwhcX5/5ceu8dqV3f\nQ347HMqck/53nvdM2Q++RlNWGxoyAAAAANEZcLXXks2bWONvZ2SYfnF+T13Sv6N+O3eTJr9KU1Yb\nGjIAAAAA0cltccSWTDoQyiSn387dJJPplq91oSk7DA0ZAAAAgOiFW7IaniUL80JZL13Sv4N+M3ej\nfvnqepqyw9CQAQAAAIhebgvvc8nm/kJ6b6nUrk+NXxYOZc5Jv5m7UWbS979KUxZGQwYAAACgfgZc\n4wWz+TU/SxaWkWG694JeuvjkDvr1nI16cCZNWRgNGQAAAID6qX6W7MgtmXQglEnSr+dslEm6maaM\nhgwAAABAA1S3ZLU/SxYWDmXfLO6gR+Zs1EM0ZTRkAAAAABogt4U08Hpp3r3S+8uktr2P+OUZGab7\nRnpN2SNzNkpmunl457RtymjIAAAAADRMuCWr5XPJDhcOZRcVt9cjszfooVkb4jxgcNGQAQAAAGiY\nvJZRtWSSF8ruH1koSXpk9obqZ8rSDQ0ZAAAAgIYbcI2UE9mzZGHhUDa6qL1+NXuDHpq5Po4DBhMN\nGQAAAICGy2spDbpOmndfxC2Z5IWyiaO8puxXszfITLppePo0ZTRkAAAAAGJjwPeibsmkA6FsdFF7\nPTxrgx6elT5NGQ0ZAAAAgNg4pCVbLrUtjPhbw6HMSXp41gaZTOOHd47frAFBQwYAAAAgdqpbssgu\nLh4sHMpG9Wuvh2at16/S4PoiDRkAAACA2MlrKQ28Vpp/f9QtmSQ1yjA9cKH3PQ/NWi8z6cYzU7cp\noyEDAAAAEFsDr613SyYdCGUj+xXowZnr9cjs1G3KaMgAAAAAxNbBLdm/V0htekX9Eo0yTJMu9C41\nPjhzvUzSDSnYlNGQAQAAAIi9gd+TcprXuyWTDoSykX0L9MuZ6/XrFGzKaMgAAAAAxF7eUaGWbGK9\nWzIpFMpGe03ZL2d6z5SNG5Y6TVmdDZmZdTCzuWa2xsxWmdn4RAwGAAAAIMkNvLbBLZl0IJSN7Fug\nya+u12/mpE5TFklD9rmkW5xzS8ysmaRSM5vpnFsd59kAAAAAJLNDWrKVUpue9X6pcChzkia/ul5m\npuvP6BS7WX1SZ0PmnHvfObck9Pf/kbRGUkG8BwMAAACQAmLUkkleKJs8urcu6FugSa+s02/nbozB\ngP6K6qiHmR0vgXCzHgAAB4JJREFUqa+kRfEYBgAAAECKyTvK+7DoNc97LVkDhUPZ+X3aadIr6/TS\nivdjMKR/Ig5kZtZU0nRJNznnPq7h9682sxIzK6msrIzljAAAAACSWQxbMskLZb+8qI/u+kZ3De/W\nOiav6ZeIApmZZckLY08652bU9DXOuUedc8XOueL8/PxYzggAAAAgmTU++kBL9sGqmLxkowzTmCEn\nKDszuT/JK5IriybpT5LWOOcejP9IAAAAAFLOwGul7GYxa8lSRSRxcoikyyQNM7Olob++Hue5AAAA\nAKSSxkd7Hxa9+rmYtWSpIJIri68758w5V+ic6xP666VEDAcAAAAghQy8jpbsMMm9cAkAAAAgeRzS\nkvGxxhKBDAAAAEAi0ZIdgkAGAAAAIHEaHy0NuEZa/SwtmQhkAAAAABJt0PVeS7bgAb8n8R2BDAAA\nAEBihVuyVbRkBDIAAAAAiTfoeim7Sdq3ZAQyAAAAAIl3cEu2fY3f0/iGQAYAAADAH4PGeS3Z/PRt\nyQhkAAAAAPzR+Gip/9XSqmfStiUjkAEAAADwT5q3ZAQyAAAAAP5pcsxBLdlav6dJOAIZAAAAAH+F\nW7I0vLhIIAMAAADgrybHSP2vklbOSLuWjEAGAAAAwH+DbpCyGqddS0YgAwAAAOC/JsdIA65Ou5aM\nQAYAAAAgGKpbskl+T5IwBDIAAAAAwVD9LNl0qXKd39MkBIEMAAAAQHAMDrVkafK5ZAQyAAAAAMHR\npFVatWQEMgAAAADBMjh9niUjkAEAAAAIliatpP5XSiumSZXr/Z4mrghkAAAAAIJn8I1SVl7Kfy4Z\ngQwAAABA8ISfJUvxloxABgAAACCYqluy1H2WjEAGAAAAIJiatJJOvlJambotGYEMAAAAQHANvlHK\nzE3ZloxABgAAACC4muYfaMl2bPB7mpgjkAEAAAAIthRuyQhkAAAAAIKtab508hXSiqkp15IRyAAA\nAAAE3+DxUqOclGvJCGQAAAAAgq9pvtT/ylBLttHvaWKGQAYAAAAgOaRgS0YgAwAAAJAcqp8lezpl\nWrI6A5mZPW5m281sZSIGAgAAAIBaDUmtliyShmyKpBFxngMAAAAA6tb02AMt2c5Nfk/TYHUGMufc\nAkm7EjALAAAAANQthVoyniEDAAAAkFzCLdnyfyR9SxazQGZmV5tZiZmVVFZWxuplAQAAAODLBt8o\ntesn7d3t9yQNErNA5px71DlX7Jwrzs/Pj9XLAgAAAMCXNWstXTVbal/s9yQNwsoiAAAAAPgkkrP3\nf5e0UNJJZrbVzK6I/1gAAAAAkPoy6/oC59wliRgEAAAAANINK4sAAAAA4BMCGQAAAAD4hEAGAAAA\nAD4hkAEAAACATwhkAAAAAOATAhkAAAAA+IRABgAAAAA+IZABAAAAgE8IZAAAAADgEwIZAAAAAPjE\nnHOxf1GzSkkVMX/hhmslaYffQwBHwM8ogo6fUQQdP6MIOn5G08dxzrn8ur4oLoEsqMysxDlX7Pcc\nQG34GUXQ8TOKoONnFEHHzygOx8oiAAAAAPiEQAYAAAAAPkm3QPao3wMAdeBnFEHHzyiCjp9RBB0/\nozhEWj1DBgAAAABBkm4NGQAAAAAERloEMjMbYWbrzGyjmd3m9zzAwcysg5nNNbM1ZrbKzMb7PRNQ\nEzNrZGbvmNmLfs8CHM7MWprZNDNbG/r36SC/ZwIOZmY3h/5/fqWZ/d3Mcv2eCcGQ8oHMzBpJ+q2k\nsyR1l3SJmXX3dyrgEJ9LusU5103SQEnX8zOKgBovaY3fQwC1+JWkl51zXSX1Fj+rCBAzK5B0o6Ri\n51xPSY0kXezvVAiKlA9kkvpL2uic2+yc2yfpKUnn+TwTUM05975zbkno7/8j7w8RBf5OBRzKzNpL\nOlvSY37PAhzOzJpLOlXSnyTJObfPOfehv1MBX5IpKc/MMiU1lvSez/MgINIhkBVIevegf94q/rCL\ngDKz4yX1lbTI30mAL3lY0gRJ+/0eBKjBVyRVSnoitFb7mJk18XsoIMw5t03SZElbJL0v6SPn3Kv+\nToWgSIdAZjX8GqclEThm1lTSdEk3Oec+9nseIMzMzpG03TlX6vcsQC0yJfWT9DvnXF9JVZJ4ZhyB\nYWZHydvQOkFSO0lNzOxSf6dCUKRDINsqqcNB/9xeVMQIGDPLkhfGnnTOzfB7HuAwQySda2bl8ta+\nh5nZX/0dCTjEVklbnXPh7YJp8gIaEBTDJZU55yqdc59JmiFpsM8zISDSIZC9LamzmZ1gZtnyHqB8\n3ueZgGpmZvKee1jjnHvQ73mAwznnbnfOtXfOHS/v36FznHP8l10EhnPu35LeNbOTQr90pqTVPo4E\nHG6LpIFm1jj0//tnisMzCMn0e4B4c859bmbjJL0i76LN4865VT6PBRxsiKTLJK0ws6WhX7vDOfeS\njzMBQLK5QdKTof/4ulnSWJ/nAao55xaZ2TRJS+RdV35H0qP+ToWgMOd4nAoAAAAA/JAOK4sAAAAA\nEEgEMgAAAADwCYEMAAAAAHxCIAMAAAAAnxDIAAAAAMAnBDIAAAAA8AmBDAAAAAB8QiADAAAAAJ/8\nP5s25QJ1qibcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x20af71f1048>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PCVWOqOSd_Pr",
        "colab_type": "text"
      },
      "source": [
        "##SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XP2Bx6kd_Pu",
        "colab_type": "code",
        "outputId": "6fa86a98-f1b5-421c-9b8a-e7a28b872c0c",
        "colab": {}
      },
      "source": [
        "f1_score(y_test2,y_pred_svm, average ='micro')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.264"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUFP-ETjd_Pv",
        "colab_type": "code",
        "outputId": "5643baf5-92ba-4d77-edcf-6129a9bddf53",
        "colab": {}
      },
      "source": [
        "#ploting the predicting and actual values\n",
        "plt.plot(y_pred_svm[10:20],label = 'Predicted')\n",
        "plt.plot(y_test2[10:20].values, label = 'Actual')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DXy9t74V2kmJgvqe6cjDsENjn1zqRMAd7JqqFeo9foEbXV77c7CkKnPoZtn+qzlHrOBtq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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x20af972df98>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2gS7dJmd_Pm",
        "colab_type": "text"
      },
      "source": [
        "##KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B54bd7y2d_Pq",
        "colab_type": "code",
        "outputId": "aaef8669-3fa8-4476-d358-6c025ee266ea",
        "colab": {}
      },
      "source": [
        "f1_score(y_test2,y_pred_knn,average='micro')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2515"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47qcZKYHd_Pr",
        "colab_type": "code",
        "outputId": "ed65a6b9-df52-4e99-a263-861e91dabf91",
        "colab": {}
      },
      "source": [
        "#ploting the predicting and actual values\n",
        "plt.plot(y_pred_knn[:10],label = 'Predicted')\n",
        "plt.plot(y_test2[:10].values, label = 'Actual')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PA5lhGL5ANDDdvXEcLlj19yJSdGRl5zB0ShwGMHJAGJ4eBk2qBvKvm+sxd8sRpsYk2R1R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\nERERERGHqCETERERERFxyP8DE01a2sgMdC8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x20aed8f5b00>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZHTFIbtd_Pw",
        "colab_type": "text"
      },
      "source": [
        "##MNB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCZPwhldd_P1",
        "colab_type": "code",
        "outputId": "81ca53ce-9196-4c9a-d0e3-15f4a47247f1",
        "colab": {}
      },
      "source": [
        "f1_score(y_test3,y_pred_mnb, average ='micro')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3616398243045388"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9-bvHuLd_P2",
        "colab_type": "code",
        "outputId": "43586f30-565f-45e1-b3c6-3a1652c9c31d",
        "colab": {}
      },
      "source": [
        "#ploting the predicting and actual values\n",
        "plt.plot(y_pred_mnb[:10],label = 'Predicted')\n",
        "plt.plot(y_test3[:10].values, label = 'Actual')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hNmcAHNukPf8jyz1EQZyNg3GtoOv/oO0oq3/5M1dv8duaQ8RsW88/Lu+xv1wH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\neVGHNDB/AWsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x20ae525f828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CnAg6hFUxRwR",
        "colab_type": "text"
      },
      "source": [
        "#Boost up Performances\n",
        "Output feature - review ratings categorised in 3 category high, low and medium to boost the performance of the above applied model and it significantly boosts the performance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U61NTSGWd_QA",
        "colab_type": "text"
      },
      "source": [
        "##KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_Cmatn9d_QC",
        "colab_type": "code",
        "outputId": "cd70589f-705a-47fc-c5bf-24086317444b",
        "colab": {}
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Activation"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYtAI4wEd_QE",
        "colab_type": "code",
        "outputId": "ac833679-7801-4b04-fc45-8f6e171a2841",
        "colab": {}
      },
      "source": [
        "#predict\n",
        "y_pred_knn2=knn2.predict(x_test4)\n",
        "print(\"New F-score\",f1_score(y_test4, y_pred_knn2,average='micro'))\n",
        "print(\"Previous F-score\",f1_score(y_test2,y_pred_knn,average='micro'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "New F-score 0.58\n",
            "Previous F-score 0.2515\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8pC2D1Ltd_QG",
        "colab_type": "code",
        "outputId": "c24d7f36-7ec8-44e9-d375-60733aad4808",
        "colab": {}
      },
      "source": [
        "#ploting the comparison of the previous and new predicted and actual values\n",
        "plt.plot(y_pred_knn2[40:60],label = 'Predicted')\n",
        "plt.plot(y_test4[40:60].values, label = 'Actual')\n",
        "plt.plot(y_pred_knn[40:60], label = 'Previous Prediction')\n",
        "plt.plot(y_test2[40:60].values, label = 'Previous Actual')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KxhXe83/vgde1t2/PhgEbmPrrVCbumcjSJ5fS2bFzNaWsW+rVE7KK8Pf35+DB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\nK2ISsLHQ8NjUTl4/Jj4DFnaKb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duwfgkYAxARP6ZQFH9WUhfwP2CBC+ReNQJ4\nODm/bwF+ERG/l3QjvJWTdRR2WNwFvAp8MqNYm4KkQRR2IvtM0XPF+fAY6UWSVgHTgeGSOoGvAncC\nayRdD/wDuCrp2wbcGBGfjoh9kr5O4cQTYGlEeLXFCUrJx21Af2BDMndtTHZKPhO4PyLmkjK3ZfAr\nNJSUfEyXNInCEsQOkrmrOB9p52MZ/AoNpVw+ImI5Ze5B9vioLw217b2ZmZmZmVk9abQli2ZmZmZm\nZnXDBZmZmZmZmVlGXJCZmZmZmZllxAWZmZmZmZlZRlyQmZmZmZmZZcQFmZmZmZmZWUZckJmZmZmZ\nmWXEBZmZmZmZmVlG/g96rPcfMAgfogAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x20b5b7516d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Otcmaowfd_QJ",
        "colab_type": "text"
      },
      "source": [
        "##Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXx-V6wwd_QK",
        "colab_type": "code",
        "outputId": "f0443ac9-4e46-46f0-bf3d-136adc91118d",
        "colab": {}
      },
      "source": [
        "print (\"Previous Fscore\",f1_score(y_test,y_pred_log_reg,average='micro'))\n",
        "print (\"New Fscore\",f1_score(y_test5, y_pred_log2,average='micro'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Previous Fscore 0.5126891166422645\n",
            "New Fscore 0.8550512445095169\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4-GPWHEd_QM",
        "colab_type": "code",
        "outputId": "67329811-c015-443b-9089-28c3d34e209c",
        "colab": {}
      },
      "source": [
        "#ploting the comparison of the previous and new predicted and actual values\n",
        "plt.plot(y_pred_log2[40:60],label = 'Predicted')\n",
        "plt.plot(y_test5[40:60].values, label = 'Actual')\n",
        "plt.plot(y_pred_log_reg[40:60], label = 'Previous Prediction')\n",
        "plt.plot(y_test[40:60].values, label = 'Previous Actual')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GeR9/bIcohb0sPLwQF60Lr3Z51e5zeTt783jw42xJ3oLRbLT7fEIIIYSo+SQhEw51/fMv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\n9yp0ybLMwqiFeDh4MLqZ9Tdfr0pcunZFoVKhjdhstDkzNBEoVCpculb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8ARtbO4vl6D7h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\niIiIiCUqyERERERERCz5f2Id9Q66n8mQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x20b598911d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mytg1P-d_QM",
        "colab_type": "text"
      },
      "source": [
        "##SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVphFS4md_QN",
        "colab_type": "code",
        "outputId": "6aef7534-2863-44f1-9887-44a61975a503",
        "colab": {}
      },
      "source": [
        "print(\" New f1 score \", f1_score(y_test4, y_pred_svm2,average='micro'))\n",
        "print (\" Previous f1 score\", f1_score(y_test2,y_pred_svm, average ='micro'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " New f1 score  0.6365\n",
            " Previous f1 score 0.264\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKFyG2snd_QO",
        "colab_type": "code",
        "outputId": "2fa26cf5-a5cb-443d-a1ab-a654efe43181",
        "colab": {}
      },
      "source": [
        "#ploting the comparison of the previous and new predicted and actual values\n",
        "plt.plot(y_pred_svm2[0:20],label = 'Predicted')\n",
        "plt.plot(y_test4[0:20].values, label = 'Actual')\n",
        "plt.plot(y_pred_svm[0:20], label = 'Previous Prediction')\n",
        "plt.plot(y_test2[0:20].values, label = 'Previous Actual')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wGzoEy79NejMVC2trqs0KpsLbbxP39dfcnvYxak6Oya8jSiZbW1vi4uKkKCvn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EFX6HwTrHwP0g7bTiNcRAZkrvbrO7JmhzdodwU06QOLX4Qc35nrsX7+l7TL286+To9QJaKq7TjW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\nCCGEEMISGZAJIYQQQgghhCX/B2BgwMKV8xvYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x20b7e0170b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h70XBaUniZAj",
        "colab_type": "text"
      },
      "source": [
        "#Neural Network Using Tensorflow\n",
        "\n",
        "Used earlystopping to prevent overfitting the model and used checkpointer to save the best model ran in the loop several time to jump out of the local mininum.\n",
        "\n",
        "Applied paramter tuning by changing following:\n",
        "\n",
        "* Activation function : relu, sigmoid,tanh\n",
        "* Number of Dense Layers\n",
        "*Number of Neurons in each layer\n",
        "* Learning rate for Activatio\n",
        "*Optimizer : SGD, Adamax, Adam, Adagrad\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMqpg69fk8XL",
        "colab_type": "code",
        "outputId": "bebf5be9-123b-4bdb-a63e-99d66221a569",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "#the bset classification model for 5 stars\n",
        "#loading the best saved model\n",
        "from keras.models import load_model\n",
        "model2 = load_model(os.path.join(save_path,\"best_claasification5.hdf5\"))\n",
        "predc = model2.predict(x_test01)\n",
        "predc = np.argmax(predc,axis=1)\n",
        "y_true = np.argmax(y_test01,axis=1)\n",
        "score = metrics.f1_score(y_true, predc, average= \"weighted\")\n",
        "print(\"F1 score: {}\".format(score))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 score: 0.6861647117031566\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBuS-ZGErhiV",
        "colab_type": "code",
        "outputId": "86fd97f4-30c0-45e2-f6fa-2dcbb03f0fe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        }
      },
      "source": [
        "#loading the best saved model\n",
        "from keras.models import load_model\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn import svm, datasets\n",
        "model1 = load_model(os.path.join(save_path,\"best_claasification5.hdf5\"))\n",
        "pred2 = model1.predict(x_test01)\n",
        "print(\"Shape: {}\".format(pred2.shape))\n",
        "pred2 = np.argmax(pred2,axis=1) # raw probabilities to choose class (highest probability)\n",
        "print(pred2)\n",
        "y_true = np.argmax(y_test01,axis=1)\n",
        "score = metrics.precision_score(y_true, pred2, average= \"weighted\")\n",
        "print(\"Precision score: {}\".format(score))\n",
        "score = metrics.recall_score(y_true, pred2, average= \"weighted\")\n",
        "print(\"Recall score: {}\".format(score))\n",
        "score = metrics.f1_score(y_true, pred2, average= \"weighted\")\n",
        "print(\"F1 score: {}\".format(score))\n",
        "#score = metrics.log_loss(y_true, pred2) #this was not working was showing y_true & y_pred both contain different classes 3 , 2.\n",
        "#print(\"Log loss score: {}\".format(score))\n",
        "cm = confusion_matrix(y_true,pred2)\n",
        "print(cm)\n",
        "print('Plot of Confusion Matrix')\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cm,['stars-1.0','stars-1.5','stars-2.0','stars-2.5','stars-3.0','stars-3.5','stars-4.0','stars-4.5','stars-5.0'])\n",
        "plt.show()\n",
        "print(classification_report(y_true,pred2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape: (10244, 9)\n",
            "[8 7 5 ... 4 6 5]\n",
            "Precision score: 0.6864267094450662\n",
            "Recall score: 0.6871339320577899\n",
            "F1 score: 0.6861647117031566\n",
            "[[   0   22    0    0    0    0    0    0    0]\n",
            " [   0   82   27    4    0    0    0    0    0]\n",
            " [   0   41  172   79    4    1    0    0    0]\n",
            " [   0    0  106  409  194   12    0    0    0]\n",
            " [   0    0    7  157  960  366   13    0    0]\n",
            " [   0    0    1    8  278 1735  414    7    0]\n",
            " [   0    0    0    1   14  477 1946  372    1]\n",
            " [   0    0    0    0    0    8  313 1355  122]\n",
            " [   0    0    0    0    0    0    0  156  380]]\n",
            "Plot of Confusion Matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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BENSbrhjBBkEQVEOMYIMgCCqizGxag82gLTGTtFcFNsdKejBLtN393HhJN0u6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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00        22\n",
            "           1       0.57      0.73      0.64       113\n",
            "           2       0.55      0.58      0.56       297\n",
            "           3       0.62      0.57      0.59       721\n",
            "           4       0.66      0.64      0.65      1503\n",
            "           5       0.67      0.71      0.69      2443\n",
            "           6       0.72      0.69      0.71      2811\n",
            "           7       0.72      0.75      0.73      1798\n",
            "           8       0.76      0.71      0.73       536\n",
            "\n",
            "   micro avg       0.69      0.69      0.69     10244\n",
            "   macro avg       0.58      0.60      0.59     10244\n",
            "weighted avg       0.69      0.69      0.69     10244\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "nrvYHa5e0zN7"
      },
      "source": [
        "##Boost up Performance\n",
        "Output feature - review ratings categorised in 3 category high, low and medium to boost the performance of the above applied model and it significantly boosts the performance\n",
        "\n",
        "Also applied Grid Search to get the best optimizer using keras wrappers library. This gives the best optimizer from given list for best performing model so far with accuracy, this all boost up the performance and beats the standard AI classification models.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7BLEpzBvov5",
        "colab_type": "code",
        "outputId": "20b61fd0-ce69-4ba4-cec0-38127c34c316",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn import svm, datasets\n",
        "from keras.models import load_model\n",
        "\n",
        "\n",
        "score = metrics.precision_score(y_true, pred3, average= \"weighted\")\n",
        "print(\"Precision score: {}\".format(score))\n",
        "score = metrics.recall_score(y_true, pred3, average= \"weighted\")\n",
        "print(\"Recall score: {}\".format(score))\n",
        "scoref3 = metrics.f1_score(y_true, pred3, average= \"weighted\")\n",
        "print(\"F1 score: {}\".format(scoref3))\n",
        "#score = metrics.log_loss(y_true, pred_l_reg) #this was not working was showing y_true & y_pred both contain different classes 3 , 2.\n",
        "#print(\"Log loss score: {}\".format(score))\n",
        "cm3 = confusion_matrix(y_true,pred3)\n",
        "print(cm3)\n",
        "print('Plot of Confusion Matrix')\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cm3,['stars_label-High','stars_label-Medium','stars_label-Low'])\n",
        "plt.show()\n",
        "print(classification_report(y_true,pred3))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision score: 0.8874015500162081\n",
            "Recall score: 0.8878367825068333\n",
            "F1 score: 0.8870019162678694\n",
            "[[4707  438    0]\n",
            " [ 509 4109   49]\n",
            " [   0  153  279]]\n",
            "Plot of Confusion Matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hhIJFiziEEAoWgTiEEAoWEzpCCKFgzReGIxCHEFpMLPoTQggFq1UczneL/wLQ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\ncQghFCwCcQghFCwCcQghFOz/A4+/UEfqxUd/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.91      0.91      5145\n",
            "           1       0.87      0.88      0.88      4667\n",
            "           2       0.85      0.65      0.73       432\n",
            "\n",
            "   micro avg       0.89      0.89      0.89     10244\n",
            "   macro avg       0.88      0.81      0.84     10244\n",
            "weighted avg       0.89      0.89      0.89     10244\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kYuvhHgY1_H_"
      },
      "source": [
        "##Comparison"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJ8LOu6TSPF_",
        "colab_type": "code",
        "outputId": "61487390-5dfd-4005-b167-c310b8be2166",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "my_plot1 = df_comp1.plot(kind='bar',legend=None,title=\"Star Categories Models : Score Evaluation\",)\n",
        "my_plot1.set_xlabel(\"Classification Models\")\n",
        "my_plot1.set_ylabel(\"F1 score\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'F1 score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+71YLi+qlbbYnp4HW4x+B04F3Ab+0vTmQC3DFkiBtsyUJgHrMsf0ssBfwnWbe\nCi3WE9GRttmSBEA9bpL0O2B527+Q9EHg7raLiiBtszUZA6iIpEm2ZzU/rwfMsP1My2VFpG22JAEQ\nEVGpdAFFRFQqAVAZSeMlrdd8CzNiiSJptbZrqEm6gMY4SV+yfUTz887A14E/AqsDh9j+SZv1Rb0k\n7QZ8EbgHOJJyBtB4YGXgA7klZP/lCGDs676eyr8AO9jeEtgaOK6ViiKKjwNvoLTDi4B9bW8CbEna\n5qhIAIx93Yd4D9u+A8D2H4GcZRFtesr23bavBv5g+2YA2/cDT7ZbWh0SAGPfX0k6W9I5wIaS3gkg\n6UPAn9otLSp3v6QPA9jeBkDSOpJOpHQLRZ9lIHDs6/1K/W+b/+8D9h7lWiK67Qe8tWfe6sBdwD+P\nejUVyiBwRESl0gUUEVGpBEBERKUSANEaSWtKOkPSzZKubv7t3CzbT9Jiu0mIpJMkbdb8fLakm5oB\nx3Oe5/reLOklzc9nSVp7MdQ4IOn4nnkvlTRH0n6LsJ6dJV2xkMecJumg51dpjBUZBI5WSBoHnA98\ny/Y+zby/Bn4qaZvF/Xq2j+ya3BNYxfYTPP/rzv8T5Tr2D9t+zwutrzED2EPSsbbnNPPez7yB+4jF\nKgEQbdkJGLD9X50Ztn8p6VW2Z0narjNf0h7A0ZRzw8cD77N9p6QjgH2Ax5t/+1BuMv4dyv1lVwRO\nsf2NZo/434D3UI58fyzpE8B3ba8jaXXgm8BfAHOAQ23/StKnmloB7m1e4yBgO+A7kvan3NR8Z+D3\nwEnAZpTvX1xm+xOStgeOaZ6/CeX7F2+y/XjPZ/IM8P8oN0i/uJn3HuDHXZ/FbpQv9HXe88G2/yDp\n7cBnmtf4bdfjXwp8BVgJWAX4mO1Lu5avAnwXmES5K9eFtj/T+8uKsSldQNGWTYDpvTM7lwTu8WLg\n3bZ3oGxsD2vmfwp4i+0plA3vWsC7gdtsbw9MoWz4utff6fbYifmvOf854BLb21I2sO9rrpf0OLBd\nc576i4E32v4q5XIa77V9S9c63gW8HNgGeD2wi6QpzbKtKRvfrSkB88YhPpdvAwcANCH4W5rva0ha\nCTgV2LP5LH5ECTWA/wT2sv1Gyk3VO74KnGB7R2B34NSe60C9AVjO9nbA64DHJGW7UIkcAURb5gDL\njvCx9wOnNxumNYBrm/lfp+zJnwucY/s3kp4BPiDpNMpe9CkjfI0tKdelwfaVwJUAkuYAV0l6Fngl\nMNzFyrYELrU9AMyRdBWwOXADcKvtB5rH3QW8ZIh1/AQ4pbko2n7AGcBfNcs2Au63fW8zfQVwiKRV\ngRVt39rMv4x5lwDZAZgg6ZN09BPzAAACFUlEQVTN9DOUc+07rgE+JelsSriears7QGIMS9JHW35J\n2eOcj6S/lrRy1/RywPcoXR1TgC93ltk+Cng78DBwvqRdbd8GbEzZcO5M2UiOxAA9fw/NWMQBwC7N\na181gnV0G9c179lBli2guTXi9yl9/2+gXCNnYesfx/x7/d3B+hTwDtvbN/82tD2j6/UeAF4DfIny\nud0gacXB316MNQmAaEWzlz1b0jGdeZI2AX4IrNP10AmUjdudklYA3ga8SNIkSccB9zRdMv8FbCFp\nb2Dzpp/7A8BLR3jp62mUvnckbSvpdOAvgTtt/7m5S9VWlDEGmpqW61nHdcAbJI1rXnNKM29RfRv4\nGPBT293XxPkNsHrTrw8l4K4DHqIccWzYNb/jakrXFJJWk3RS9wtJ2gXYzfY1to8GHmP+I4QYwxIA\n0abdgFdI+pWkKyldMO+27c4DbD9MGaScTjkS+AKwI2UjNwGYLulS4C3A/wC3AF9s1nc58Plmr3ph\nPgFsL+nnwGeBE4CpwERJV1M2yMcBx0raiNJVc6Gk7qOYc4DfUTa6VwPn275mUT8U2zdQur3O6Jn/\nBHAg8L1mUHsn4ONNl9ORlKOgC4Enup52OOXMoqsoXTyX9b4c8CFJVzXrnGr7rkWtOZZOuRRERESl\ncgQQEVGpBEBERKUSABERlUoARERUKgEQEVGpBEBERKUSABERlUoARERU6v8A1B/7CtMnMlkAAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SqxSkqg_2Xnq",
        "colab_type": "text"
      },
      "source": [
        "##comparing all classification models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIcIsMLuXzHQ",
        "colab_type": "code",
        "outputId": "677bffd6-15bd-4d81-d2fd-3a78b35023cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "my_plot = df_comp.plot(kind='bar',legend=None,title=\"Classification Models : Score Evaluation\",)\n",
        "my_plot.set_xlabel(\"Classification Models\")\n",
        "my_plot.set_ylabel(\"F1 score\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'F1 score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S3gCsUs3psyejrYGIiFhGddMC2B/YHJgOfBFYGXhzP4OKiIj+66YFsJXtt/c9koiImFRd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\nREQ0VBJARERD/X9Rdqs2Be/KHwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZ-KA3FXvtN2",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Mini Project 1 & 2\n",
        "Mansi Patel\n",
        "\n",
        "February 13, 2019\n",
        "\n",
        "Prof : H. Chen\n",
        "\n",
        "Class : CSC 215-01\n"
      ]
    }
  ]
}