Lab 1 Customer Clustering & Visualization (solved).ipynb

{"cells":[{"cell_type":"markdown","source":["<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"><img alt=\"Creative Commons Licence\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>"],"metadata":{"id":"aB0XJW3Jhmzd"},"id":"aB0XJW3Jhmzd"},{"cell_type":"markdown","id":"4fd2312e-f344-44f2-ac8a-fbf30b80673b","metadata":{"id":"4fd2312e-f344-44f2-ac8a-fbf30b80673b","tags":[]},"source":["# Lab 1: Customer Clustering & Visualization"]},{"cell_type":"markdown","id":"d777dda9-111b-466f-9001-1da0f12f0ce1","metadata":{"id":"d777dda9-111b-466f-9001-1da0f12f0ce1","tags":[]},"source":["## Objective & Description:\n","This Lab focuses on clustering and visualizing bank customers' data, which includes variables such as date of birth (DOB), account balance, transactions, transaction amount, and transaction place. We will use publicly available data from a bank in India to learn how to preprocess data and to prepare it for clustering and visualization.\n","\n","The objective of the lab is to equip participants with the skills to analyze and gain insights from customer data, which they can use to improve business strategies and decision-making processes. Participants will learn how to use Python programming language libraries such as Pandas, NumPy, and Scikit-learn to preprocess the data, perform clustering using algorithms such as K-Means, Hierarchical Clustering, and DBSCAN, and visualize the results using techniques such as scatter plots, 3D plots, and heatmaps.\n","\n","If you don't know Python don't worry we've got you backed up here are some resources to learn python:\n","- [Introduction to Python - DataCamp](https://www.datacamp.com/courses/intro-to-python-for-data-science)\n","- [Python for Data Science: Fundamentals  - DataQuest](https://www.dataquest.io/course/python-for-data-science-fundamentals/)\n","- [Python for Data Science: Intermediate  - DataQuest](https://www.dataquest.io/course/python-for-data-science-intermediate/)\n","- [Maybe you prefer books](https://pythonbooks.org/free-books/)\n","\n","## Tasks:\n","The workshop consists of multiple tasks which have to be solved sequentially:\n","\n","1. **Data cleaning.** Clean the bank customer data by handling missing values, identifying and handling outliers, and transforming the data as needed. \n","\n","2. **Data exploration.** Use appropriate visualization techniques to identify patterns in the data. Create visualizations that clearly and accurately represent the relationships between different features of the data. This is critical to assess your trained model in the next step.)\n","\n","3. **Train your clustering model.** Apply clustering techniques to the preprocessed data and compare the results of at least two different clustering algorithms. Calculate accuracy of your model predictions and the overall quality of clustering models. \n","\n","4. **Visualize clustering results.** Create visualizations that accurately represent the clustering results. Use appropriate visualization techniques to highlight the differences between different clusters and provide insights into the patterns present in the data.\n","\n","5. **Data interpretation and conclusions.** Interpret and analyze the results of their clustering models and visualizations. Draw meaningful insights from analysis and present findings in a clear and concise manner.\n","\n","Follow the steps on this Jupyter notebook.\n"]},{"cell_type":"markdown","id":"746e10ee-6d95-4fc0-a987-1f073dba108d","metadata":{"id":"746e10ee-6d95-4fc0-a987-1f073dba108d"},"source":["### Data Cleaning"]},{"cell_type":"markdown","id":"64c8a193-e660-43a6-a087-92a22e7db908","metadata":{"id":"64c8a193-e660-43a6-a087-92a22e7db908","tags":[]},"source":["#### Task 1.1\n","We have preloaded the dataset into this environment. You are expected to do the following:\n","\n","1. Load the dataset to the notebook as a pandas DataFrame.\n","<details>\n","    <summary>hint:</summary>\n","Pandas has the method `read_csv`.\n","Look for the csv file in the side panel copy its path and load it using the previous method.\n","</details>\n","\n","2. Inspect the data.\n","<details>\n","    <summary>hint:</summary>\n","Pandas Dataframe has the method `head`\n","</details>\n","3. You are provided with the function `data_status`. Call it on the DataFrame. What do you inspect ?\n","<details>\n","    <summary>hint:</summary>\n","Maybe wrong types?\n","</details>\n","4. Look at the info of your data. What do you realize about the numbers ? \n","<details>\n","    <summary>hint:</summary>\n","Pandas DataFrame has the method `info`. invoke it on your data. hint: Any null values? \n","</details>"]},{"cell_type":"code","execution_count":null,"id":"51850ce7-924c-4424-a538-48c8e64d5801","metadata":{"id":"51850ce7-924c-4424-a538-48c8e64d5801","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677834872244,"user_tz":-120,"elapsed":13539,"user":{"displayName":"Mohamed Zaroug","userId":"01425640090858104134"}},"outputId":"c1c6a6e9-0d86-4c38-887c-affc1496ca1e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'Lab-1-Customer-Clustering-Visualization'...\n","remote: Enumerating objects: 7, done.\u001b[K\n","remote: Counting objects: 100% (7/7), done.\u001b[K\n","remote: Compressing objects: 100% (7/7), done.\u001b[K\n","remote: Total 7 (delta 0), reused 5 (delta 0), pack-reused 0\u001b[K\n","Unpacking objects: 100% (7/7), 23.95 MiB 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pd.read_csv('./Lab-1-Customer-Clustering-Visualization/bank_transactions.csv')"]},{"cell_type":"code","execution_count":null,"id":"11ae2f1f-61d2-421c-9498-a94286f650d8","metadata":{"id":"11ae2f1f-61d2-421c-9498-a94286f650d8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677834879013,"user_tz":-120,"elapsed":18,"user":{"displayName":"Mohamed Zaroug","userId":"01425640090858104134"}},"outputId":"da3fa6ae-2efe-4ed9-b464-00a20f349aa4"},"outputs":[{"output_type":"stream","name":"stdout","text":["        TransactionID CustomerID CustomerDOB CustGender   CustLocation  \\\n","0                  T1   C5841053     10/1/94          F     JAMSHEDPUR   \n","1                  T2   C2142763      4/4/57          M        JHAJJAR   \n","2                  T3   C4417068    26/11/96          F         MUMBAI   \n","3                  T4   C5342380     14/9/73          F         MUMBAI   \n","4                  T5   C9031234     24/3/88          F    NAVI MUMBAI   \n","...               ...        ...         ...        ...            ...   \n","1048562      T1048563   C8020229      8/4/90          M      NEW DELHI   \n","1048563      T1048564   C6459278     20/2/92          M         NASHIK   \n","1048564      T1048565   C6412354     18/5/89          M      HYDERABAD   \n","1048565      T1048566   C6420483     30/8/78          M  VISAKHAPATNAM   \n","1048566      T1048567   C8337524      5/3/84          M           PUNE   \n","\n","         CustAccountBalance TransactionDate  TransactionTime  \\\n","0                  17819.05          2/8/16           143207   \n","1                   2270.69          2/8/16           141858   \n","2                  17874.44          2/8/16           142712   \n","3                 866503.21          2/8/16           142714   \n","4                   6714.43          2/8/16           181156   \n","...                     ...             ...              ...   \n","1048562             7635.19         18/9/16           184824   \n","1048563            27311.42         18/9/16           183734   \n","1048564           221757.06         18/9/16           183313   \n","1048565            10117.87         18/9/16           184706   \n","1048566            75734.42         18/9/16           181222   \n","\n","         TransactionAmount (INR)  \n","0                           25.0  \n","1                        27999.0  \n","2                          459.0  \n","3                         2060.0  \n","4                         1762.5  \n","...                          ...  \n","1048562                    799.0  \n","1048563                    460.0  \n","1048564                    770.0  \n","1048565                   1000.0  \n","1048566                   1166.0  \n","\n","[1048567 rows x 9 columns]\n"]}],"source":["print(transactions)\n","def data_status(df):\n","    columns = df.columns\n","    stat = []\n","    for column in columns:\n","        data_type = type(df[column][0])\n","        num_dups = np.sum(df[column].duplicated())\n","        num_null = np.sum(df[column].isna())\n","        stat.append([column, data_type, num_dups, num_null])\n","    status_frame = pd.DataFrame(stat)\n","    status_frame.columns = ['column', 'data type', 'no. duplicates', 'no. null']\n","    return status_frame"]},{"cell_type":"code","execution_count":null,"id":"4ea1bae7-3535-43ac-b62b-76e409d90eda","metadata":{"id":"4ea1bae7-3535-43ac-b62b-76e409d90eda","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1677834884724,"user_tz":-120,"elapsed":702,"user":{"displayName":"Mohamed Zaroug","userId":"01425640090858104134"}},"outputId":"61dff11c-e316-4656-fe79-ba172530c79a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  TransactionID CustomerID CustomerDOB CustGender CustLocation  \\\n","0            T1   C5841053     10/1/94          F   JAMSHEDPUR   \n","1            T2   C2142763      4/4/57          M      JHAJJAR   \n","2            T3   C4417068    26/11/96          F       MUMBAI   \n","3            T4   C5342380     14/9/73          F       MUMBAI   \n","4            T5   C9031234     24/3/88          F  NAVI MUMBAI   \n","\n","   CustAccountBalance TransactionDate  TransactionTime  \\\n","0            17819.05          2/8/16           143207   \n","1             2270.69          2/8/16           141858   \n","2            17874.44          2/8/16           142712   \n","3           866503.21          2/8/16           142714   \n","4             6714.43          2/8/16           181156   \n","\n","   TransactionAmount (INR)  \n","0                     25.0  \n","1                  27999.0  \n","2                    459.0  \n","3                   2060.0  \n","4                   1762.5  "],"text/html":["\n","  <div id=\"df-ff1f2c9c-cefe-4101-b266-baf486fa3e69\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type 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<th>1</th>\n","      <td>T2</td>\n","      <td>C2142763</td>\n","      <td>4/4/57</td>\n","      <td>M</td>\n","      <td>JHAJJAR</td>\n","      <td>2270.69</td>\n","      <td>2/8/16</td>\n","      <td>141858</td>\n","      <td>27999.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>T3</td>\n","      <td>C4417068</td>\n","      <td>26/11/96</td>\n","      <td>F</td>\n","      <td>MUMBAI</td>\n","      <td>17874.44</td>\n","      <td>2/8/16</td>\n","      <td>142712</td>\n","      <td>459.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>T4</td>\n","      <td>C5342380</td>\n","      <td>14/9/73</td>\n","      <td>F</td>\n","      <td>MUMBAI</td>\n","      <td>866503.21</td>\n","      <td>2/8/16</td>\n","      <td>142714</td>\n","      <td>2060.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>T5</td>\n","      <td>C9031234</td>\n","      <td>24/3/88</td>\n","      <td>F</td>\n","      <td>NAVI MUMBAI</td>\n","      <td>6714.43</td>\n","      <td>2/8/16</td>\n","      <td>181156</td>\n","      <td>1762.5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff1f2c9c-cefe-4101-b266-baf486fa3e69')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 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here\n","transactions.head()"]},{"cell_type":"code","execution_count":null,"id":"2db30c45-21da-4175-969b-1ed5592a2402","metadata":{"id":"2db30c45-21da-4175-969b-1ed5592a2402","colab":{"base_uri":"https://localhost:8080/","height":332},"executionInfo":{"status":"ok","timestamp":1677834897442,"user_tz":-120,"elapsed":10147,"user":{"displayName":"Mohamed Zaroug","userId":"01425640090858104134"}},"outputId":"51acd96b-c9df-42b0-bb74-7d3fbc9dd8e7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    column                data type  no. duplicates  no. null\n","0            TransactionID            <class 'str'>               0         0\n","1               CustomerID            <class 'str'>          164302         0\n","2              CustomerDOB            <class 'str'>         1031312      3397\n","3               CustGender            <class 'str'>         1048563      1100\n","4             CustLocation            <class 'str'>         1039211       151\n","5       CustAccountBalance  <class 'numpy.float64'>          887238      2369\n","6          TransactionDate            <class 'str'>         1048512         0\n","7          TransactionTime    <class 'numpy.int64'>          966649         0\n","8  TransactionAmount (INR)  <class 'numpy.float64'>          955543         0"],"text/html":["\n","  <div id=\"df-463d41d4-f7a7-4a37-8089-ee3f5f46281d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>column</th>\n","      <th>data type</th>\n","      <th>no. duplicates</th>\n","      <th>no. null</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>TransactionID</td>\n","      <td>&lt;class 'str'&gt;</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>CustomerID</td>\n","      <td>&lt;class 'str'&gt;</td>\n","      <td>164302</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>CustomerDOB</td>\n","      <td>&lt;class 'str'&gt;</td>\n","      <td>1031312</td>\n","      <td>3397</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>CustGender</td>\n","      <td>&lt;class 'str'&gt;</td>\n","      <td>1048563</td>\n","      <td>1100</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>CustLocation</td>\n","      <td>&lt;class 'str'&gt;</td>\n","      <td>1039211</td>\n","      <td>151</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>CustAccountBalance</td>\n","      <td>&lt;class 'numpy.float64'&gt;</td>\n","      <td>887238</td>\n","      <td>2369</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>TransactionDate</td>\n","      <td>&lt;class 'str'&gt;</td>\n","      <td>1048512</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>TransactionTime</td>\n","      <td>&lt;class 'numpy.int64'&gt;</td>\n","      <td>966649</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>TransactionAmount (INR)</td>\n","      <td>&lt;class 'numpy.float64'&gt;</td>\n","      <td>955543</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-463d41d4-f7a7-4a37-8089-ee3f5f46281d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    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Probably some missing values, wrong types and feature we don't need?\n","Drop any null values and columns that we don't need.\n","<details>\n","    <summary>hint:</summary>\n","    For drop null values we have the method `dropna`. For dropping a certain column or row we have the drop method. Remember you can always look in the pandas documentation."]},{"cell_type":"code","execution_count":null,"id":"ad53482f-8bd2-4746-bb45-d7b4f2279b4f","metadata":{"id":"ad53482f-8bd2-4746-bb45-d7b4f2279b4f","tags":[]},"outputs":[],"source":["# Solve Task 1.2 here\n","cleaned_df = transactions.copy() \\\n","                              .dropna() \\\n","                              .drop(columns='TransactionID')\n","del transactions"]},{"cell_type":"markdown","id":"6fc185a0-be8f-49ab-b389-5af1a78008c2","metadata":{"id":"6fc185a0-be8f-49ab-b389-5af1a78008c2"},"source":["#### Task 1.3\n","It is always said that data cleaning makes up 80% of a data scientist work. We have quite some cleaning to do if we want to get the most out of our data.\n","\n","Convert columns with wrong types to a more suitable form. The DOB column was processed for you. You only have to work through the other columns. Feel free to add any other columns that you induce from the data or drop any column that you are finished with (extracted the useful data). \n","\n","**Remember it's always a good practice to make a copy from your DataFrame after each major manipulation** use the method `copy`. \n","\n","**Also, remember to inspect your data after each step using the methods in (Task 1.1).** They are quite handy. They also show you if your process succeeded or not.\n","<details>\n","    <summary>hint:</summary>\n","    1. Columns in Pandas DataFrames are basically Pandas Series. A great feature of Pandas Series is that you can apply functions to them in an element-wise fashion. Suppose we have a data series that has a scale form 0 - 2 and we want to scale it from 0 - 1 you can do something like: `series/2` \n","     <br> <br>\n","    2. Pandas has a function : to_datetime that converts any date or time representation in another form to DateTime object\n","     <br> <br>\n","    3. Rare categories should be dealt with either by putting them in a one \"UNKNOWN\" category or by using any statistical method to eliminate them.\n","</details>"]},{"cell_type":"code","execution_count":null,"id":"d5d7ec99-122a-4d06-bd0d-41c2f5599fa0","metadata":{"id":"d5d7ec99-122a-4d06-bd0d-41c2f5599fa0"},"outputs":[],"source":["# feel free to change the variable names.\n","cleaned_df = cleaned_df[cleaned_df['CustomerDOB']\\\n","              .str \\\n","              .fullmatch(\"\\d{1,2}[\\/]\\d{1,2}[\\/]\\d{2}\")]\n","# We used regex pattern matching here because there was some dates \n","# that had an incorrect string representation. \n","# filtered the dataframe and removed any samples such.\n","# Filtering in pandas is quite intuitive it has the following general syntax\n","# df = df[predicate_func(df)]"]},{"cell_type":"code","execution_count":null,"id":"4889c938-3948-446f-8ded-edaf46f6c513","metadata":{"id":"4889c938-3948-446f-8ded-edaf46f6c513"},"outputs":[],"source":["# We could have used pd.to_datetime \n","# but we have dates that are before 1/1/1969\n","# which aren't supported by unix so we use string methods\n","# See Pandas docs \n","# https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html\n","dates = cleaned_df['CustomerDOB'].copy().str.split(\"/\")"]},{"cell_type":"code","execution_count":null,"id":"f882eaaf-3cb7-40c8-ba7c-abd5448f792b","metadata":{"id":"f882eaaf-3cb7-40c8-ba7c-abd5448f792b","scrolled":true,"tags":[]},"outputs":[],"source":["# This data was collected in 2016 so we can assume \n","# that any date birth between 00 - 09 is in the 21st century. \n","# This might not be true for all cases but it won't be harmful \n","# because the percentage of such cases is negligible.\n","\n","def full_year(i):\n","    if i > 9:  return 1900 + i\n","    else: return 2000 + i\n","\n","year_of_birth = dates.apply(lambda x: full_year(int(x[2])))\n","del dates"]},{"cell_type":"code","execution_count":null,"id":"67a65f72-f3a1-4bc6-a1ae-e66ba25f1c5b","metadata":{"id":"67a65f72-f3a1-4bc6-a1ae-e66ba25f1c5b"},"outputs":[],"source":["# Solve task 1.3 here & below\n","# you can add a new cell by pressing ESC then b on your keyboard\n","transaction_date = pd.to_datetime(cleaned_df['TransactionDate'], format=\"%d/%m/%y\")"]},{"cell_type":"code","execution_count":null,"id":"33965a38-bae7-414b-b7bb-aa5041aeecd4","metadata":{"id":"33965a38-bae7-414b-b7bb-aa5041aeecd4"},"outputs":[],"source":["transaction_time = pd.to_datetime(cleaned_df['TransactionTime'] \\\n","                                  .astype(str) \\\n","                                  .str.zfill(6) \\\n","                                  ,format=\"%H%M%S\")"]},{"cell_type":"code","execution_count":null,"id":"933c5598-a918-43f9-9abc-3ba04f9e8a8b","metadata":{"id":"933c5598-a918-43f9-9abc-3ba04f9e8a8b"},"outputs":[],"source":["customer_age = transaction_date.dt.year - year_of_birth"]},{"cell_type":"code","execution_count":null,"id":"6fcfca6a-0ce0-49be-a24c-ff9fa7ac5394","metadata":{"id":"6fcfca6a-0ce0-49be-a24c-ff9fa7ac5394"},"outputs":[],"source":["day_in_month = transaction_date.dt.day"]},{"cell_type":"code","execution_count":null,"id":"789dff2f-b0f6-420c-bb10-1484bc9662d9","metadata":{"id":"789dff2f-b0f6-420c-bb10-1484bc9662d9"},"outputs":[],"source":["day_of_week = transaction_date.dt.day_of_week"]},{"cell_type":"code","execution_count":null,"id":"647fb776-cb9e-46c6-acf5-93625ab0d760","metadata":{"id":"647fb776-cb9e-46c6-acf5-93625ab0d760"},"outputs":[],"source":["seconds = (transaction_time - transaction_time.dt.floor('d')).dt.total_seconds()"]},{"cell_type":"code","execution_count":null,"id":"0dd32b42-cd7f-43f3-90d0-4c432cb295ae","metadata":{"id":"0dd32b42-cd7f-43f3-90d0-4c432cb295ae"},"outputs":[],"source":["cleaned_df['customer_age'] = customer_age\n","cleaned_df['day_in_month'] = day_in_month\n","cleaned_df['day_of_week'] = day_of_week\n","cleaned_df['seconds'] = seconds"]},{"cell_type":"code","execution_count":null,"id":"ba7b7172-3bd5-4834-889d-4312b61cad3d","metadata":{"id":"ba7b7172-3bd5-4834-889d-4312b61cad3d"},"outputs":[],"source":["locations = cleaned_df['CustLocation']"]},{"cell_type":"code","execution_count":null,"id":"7dac5669-bf1a-4b15-bcc3-dccf492675af","metadata":{"id":"7dac5669-bf1a-4b15-bcc3-dccf492675af"},"outputs":[],"source":["known = locations.value_counts()[locations.value_counts(normalize=True).cumsum() < 0.9]"]},{"cell_type":"code","execution_count":null,"id":"97504a3b-413b-44cc-b954-00f35dae43d8","metadata":{"id":"97504a3b-413b-44cc-b954-00f35dae43d8"},"outputs":[],"source":["locations = locations.apply(lambda x: x if x in known else \"UNKNOWN\")\n","del known"]},{"cell_type":"code","execution_count":null,"id":"3dbd606b-3fbd-4550-b019-c1f600a60378","metadata":{"id":"3dbd606b-3fbd-4550-b019-c1f600a60378"},"outputs":[],"source":["cleaned_df['CustLocation'] =  locations"]},{"cell_type":"code","execution_count":null,"id":"9a9e9b6c-2589-4eaa-b247-53da5e5f7a18","metadata":{"id":"9a9e9b6c-2589-4eaa-b247-53da5e5f7a18"},"outputs":[],"source":["cleaned_df = cleaned_df.drop(columns=['TransactionDate', 'TransactionTime', 'CustomerDOB'])"]},{"cell_type":"code","execution_count":null,"id":"8f8cc556-1589-496a-956d-583bbaff4c05","metadata":{"id":"8f8cc556-1589-496a-956d-583bbaff4c05","colab":{"base_uri":"https://localhost:8080/","height":288},"executionInfo":{"status":"ok","timestamp":1677832343643,"user_tz":-120,"elapsed":70,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"0bde1065-ba4f-4d67-92bf-a5e39fc0a82d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  CustomerID CustGender CustLocation  CustAccountBalance  \\\n","0   C5841053          F   JAMSHEDPUR            17819.05   \n","1   C2142763          M      JHAJJAR             2270.69   \n","2   C4417068          F       MUMBAI            17874.44   \n","3   C5342380          F       MUMBAI           866503.21   \n","4   C9031234          F  NAVI MUMBAI             6714.43   \n","\n","   TransactionAmount (INR)  customer_age  day_in_month  day_of_week  seconds  \n","0                     25.0            22             2            1  52327.0  \n","1                  27999.0            59             2            1  51538.0  \n","2                    459.0            20             2            1  52032.0  \n","3                   2060.0            43             2            1  52034.0  \n","4                   1762.5            28             2            1  65516.0  "],"text/html":["\n","  <div id=\"df-8786c9ec-fa1c-47b5-b8d5-185922d794ed\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead 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    <td>1</td>\n","      <td>51538.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>C4417068</td>\n","      <td>F</td>\n","      <td>MUMBAI</td>\n","      <td>17874.44</td>\n","      <td>459.0</td>\n","      <td>20</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>52032.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>C5342380</td>\n","      <td>F</td>\n","      <td>MUMBAI</td>\n","      <td>866503.21</td>\n","      <td>2060.0</td>\n","      <td>43</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>52034.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>C9031234</td>\n","      <td>F</td>\n","      <td>NAVI MUMBAI</td>\n","      <td>6714.43</td>\n","      <td>1762.5</td>\n","      <td>28</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>65516.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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0\n","5             customer_age    <class 'numpy.int64'>          985223         0\n","6             day_in_month    <class 'numpy.int64'>          985291         0\n","7              day_of_week    <class 'numpy.int64'>          985315         0\n","8                  seconds  <class 'numpy.float64'>          903928         0"],"text/html":["\n","  <div id=\"df-6dcfc720-e73c-44e3-bf0f-bce1e1311f3f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>column</th>\n","      <th>data type</th>\n","      <th>no. duplicates</th>\n","      <th>no. null</th>\n","    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You can get some metrics of your data by invoking the `describe`. A better way to explore data is by visualizations. [Matplotlib](https://matplotlib.org/stable/index.html) & [Seaborn](https://seaborn.pydata.org/) are two famous library when it comes to data visualizations. Here are some ideas:\n","- correlation matrix.\n","- age distribution.\n","- gender distribution.\n","- income distribution.\n","- transactions amount vs income.\n","- number of transactions vs income.\n","- income vs age.\n","- income vs gender\n","- time vs transactions amount\n","- date vs transactions amount\n","- time vs number of transactions\n","- date vs number of transactions\n","\n","Feel free to do other investigations. Don't limit your imagination!"]},{"cell_type":"code","execution_count":null,"id":"465ad55a-695b-4668-b3b4-5c41256476a4","metadata":{"id":"465ad55a-695b-4668-b3b4-5c41256476a4","tags":[]},"outputs":[],"source":["# Now we have a clean dataset that we can work with. \n","# Let's start by importing some visualization libraries.\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","import matplotlib\n","# You might need to plot a sample only\n","font = {'weight' : 'bold',\n","        'size'   : 18}\n","\n","matplotlib.rc('font', **font)"]},{"cell_type":"code","execution_count":null,"id":"8d46aa5c-e8ee-43d4-ba8b-416b31eb3eda","metadata":{"id":"8d46aa5c-e8ee-43d4-ba8b-416b31eb3eda"},"outputs":[],"source":["df_vis = cleaned_df.copy()"]},{"cell_type":"code","execution_count":null,"id":"9109ee20-a136-4eb9-8b51-b9f6f11331b8","metadata":{"id":"9109ee20-a136-4eb9-8b51-b9f6f11331b8","colab":{"base_uri":"https://localhost:8080/","height":505},"executionInfo":{"status":"ok","timestamp":1677832351555,"user_tz":-120,"elapsed":2476,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"1ebc6d96-e5e0-4e16-bd88-102f6056f03d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# DataFrame Correlation\n","# Increase the size of the heatmap.\n","plt.figure(figsize=(20, 8))\n","# Store heatmap object in a variable to easily access it when you want to include more features (such as title).\n","# Set the range of values to be displayed on the colormap from -1 to 1, and set the annotation to True to display the correlation values on the heatmap.\n","heatmap = sns.heatmap(df_vis.corr(), vmin=-1, vmax=1, annot=True, cmap='crest', fmt='.3f', linewidths=0.01, linecolor='black')\n","# Give a title to the heatmap. Pad defines the distance of the title from the top of the heatmap.\n","heatmap.set_title('Correlation Heatmap', pad=12, size=25);"]},{"cell_type":"code","execution_count":null,"id":"9e0e9cfe-420e-4b68-9084-d95ec1ab48e6","metadata":{"id":"9e0e9cfe-420e-4b68-9084-d95ec1ab48e6","colab":{"base_uri":"https://localhost:8080/","height":407},"executionInfo":{"status":"ok","timestamp":1677832355401,"user_tz":-120,"elapsed":3879,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"913764e9-074f-4e56-9f8b-e2e8646a9bfa"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Age Distribution')"]},"metadata":{},"execution_count":30},{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# Age Distribution\n","\n","plt.figure(figsize=(20, 8))\n","age_hist = sns.histplot(df_vis['customer_age'], discrete=True)\n","age_hist.set_title('Age Distribution', pad=12, size=25)"]},{"cell_type":"code","execution_count":null,"id":"67bad646-a4ab-49a7-8292-093b79369cc7","metadata":{"id":"67bad646-a4ab-49a7-8292-093b79369cc7","colab":{"base_uri":"https://localhost:8080/","height":436},"executionInfo":{"status":"ok","timestamp":1677832356461,"user_tz":-120,"elapsed":1098,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"55a762fd-ca5b-496c-e3a1-cf3aeb649da9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Age Box (Outliers)')"]},"metadata":{},"execution_count":31},{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# Age Distribution\n","\n","plt.figure(figsize=(20, 8))\n","age_hist = sns.boxplot(x=df_vis['customer_age'])\n","age_hist.set_title('Age Box (Outliers)', pad=12, size=25)"]},{"cell_type":"code","execution_count":null,"id":"6b133258-32fb-4471-a96f-345164f3a1cb","metadata":{"id":"6b133258-32fb-4471-a96f-345164f3a1cb","colab":{"base_uri":"https://localhost:8080/","height":497},"executionInfo":{"status":"ok","timestamp":1677832357356,"user_tz":-120,"elapsed":931,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"02b8663d-bc85-4f54-f21e-ae84fb4c99c2"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Gender Distribution\n","gender_values = df_vis['CustGender'].value_counts()\n","plt.figure(figsize=(20, 8))\n","plt.pie(gender_values, labels=gender_values.index, autopct='%1.3f%%', )\n","plt.title(\"Gender Distribution\", pad=12, size=25)\n","plt.show()\n","del gender_values"]},{"cell_type":"code","execution_count":null,"id":"585396d1-fd8f-407a-802c-1f092a85862c","metadata":{"id":"585396d1-fd8f-407a-802c-1f092a85862c","colab":{"base_uri":"https://localhost:8080/","height":383},"executionInfo":{"status":"ok","timestamp":1677832357357,"user_tz":-120,"elapsed":65,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"a70adca9-1273-4d44-e860-05871f691515"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# Accummulated wealth over the population\n","income = df_vis['CustAccountBalance'].sort_values()\n","plt.figure(figsize=(20, 8))\n","acc_wealth = sns.lineplot(data=income.cumsum().quantile([0.01*i for i in range(101)]))\n","acc_wealth.set_title('Accumulated Wealth', pad=12, size=25);\n","del income"]},{"cell_type":"code","execution_count":null,"id":"a2f5f502-b8b7-4f5f-9b37-9a54881cd110","metadata":{"id":"a2f5f502-b8b7-4f5f-9b37-9a54881cd110","colab":{"base_uri":"https://localhost:8080/","height":396},"executionInfo":{"status":"ok","timestamp":1677832361894,"user_tz":-120,"elapsed":4599,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"e3456246-afde-437e-e679-e90532ebfa07"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize=(20, 8))\n","balance_transaction = sns.scatterplot(x = df_vis['CustAccountBalance'],\\\n","                                   y = df_vis['TransactionAmount (INR)'])\n","balance_transaction.set_title('Transaction Amount VS Balance', pad=12, size=25);\n"]},{"cell_type":"code","execution_count":null,"id":"5f4919f1-3942-411b-b4bc-ef923f864f7c","metadata":{"id":"5f4919f1-3942-411b-b4bc-ef923f864f7c","colab":{"base_uri":"https://localhost:8080/","height":403},"executionInfo":{"status":"ok","timestamp":1677832369704,"user_tz":-120,"elapsed":7844,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"2ba65bd3-f42e-4895-af96-4acbdb23beac"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["transactions_per_user = df_vis.groupby(by='CustomerID')['TransactionAmount (INR)'].count()\n","mean_balance = df_vis.groupby(by='CustomerID')['CustAccountBalance'].mean()\n","plt.figure(figsize=(20, 8))\n","balance_ntransaction = sns.scatterplot(x=mean_balance, y=transactions_per_user)\n","balance_ntransaction.set_title('No. Transactions VS Balance', pad=12, size=25);\n","del transactions_per_user\n","del mean_balance"]},{"cell_type":"code","execution_count":null,"id":"9ef5bd76-0e2d-40e0-8c11-a2c8f7d8eb95","metadata":{"id":"9ef5bd76-0e2d-40e0-8c11-a2c8f7d8eb95","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677832372577,"user_tz":-120,"elapsed":2910,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"c9396b8a-aa1d-4765-c0b6-00dbd6d136cd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["1    710185\n","2    113138\n","3     14291\n","4      1353\n","5       108\n","6         6\n","Name: TransactionAmount (INR), dtype: int64"]},"metadata":{},"execution_count":36}],"source":["df_vis.groupby(by='CustomerID')['TransactionAmount (INR)'] \\\n","             .count() \\\n","             .value_counts()"]},{"cell_type":"code","execution_count":null,"id":"08c16a44-ce3d-4c94-a16d-79687f5d09bc","metadata":{"id":"08c16a44-ce3d-4c94-a16d-79687f5d09bc","colab":{"base_uri":"https://localhost:8080/","height":399},"executionInfo":{"status":"ok","timestamp":1677832373262,"user_tz":-120,"elapsed":703,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"c8c7a5b5-a7ba-4d14-b7de-db4ed6be746e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAABKcAAAIPCAYAAACmH73EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAABLzklEQVR4nO3deZhkVX038O9vBETZRETQqIyo0WiM28QdBXeNGrcQd1Hf1z3GkLhEfQ2aGLOpSUw0wSUaExdcouIWJYji7uBOMCIILhFBRDYZRee8f9zbTNF091T1VM3t5fN5nnqq7r2nTv2q6MtUf/ucc6u1FgAAAAAYwoahCwAAAABg/RJOAQAAADAY4RQAAAAAgxFOAQAAADAY4RQAAAAAgxFOAQAAADAY4RQAACteVR1VVa2qTtiBPs7o+zhiepUBADtKOAUA7FRVtW9VXdKHBK2qbjR0TWtFVW3uP9P3b6fdD0Y+/41LtHte3+bnVbXn1Auekqp6UB9ePWjoWgCAyQmnAICd7VFJdh/ZfsJQhaxBH+vv71xVV1qoQVX9WpIDR3YdukR/h/X3X2itXbTj5c3Mg5L8SX8PAKwywikAYGd7Yn//qv7+cYsFKUxsLpzaJ8mtFmlzaH9/1rzty6mqXZPcaV6/AABTJ5wCAHaaqrp1klsm+UmS5yT5dpJrJbnfcFWtKScm+UX/+LBF2hza3//VvO35fjPJHv1j4RQAMDPCKQBgZ5obNfX21tqWJP/ab481ta+qfruqjq+qn1TVRVX1lap6TlXtOs6C2VW1sar+tqpO7p//06r6RlX9XVVdb9I3U1Xv7V/z3dtpd4ORNZ4OmXfs3lX17qr6Xr+20wVVdXpVfaSq/qiqrj5uPa21C5Oc1G8eukizu/b3b01yapKDqur6C7Sbe/7Pk3x6Xs0bqupRVfXBqvphX/c5fc2PqKpa6IWr6sCq+r3+czulqs7v1x/7VlW9rqpuNu577fs7tKpaksf1ux438jnP3Q5d5Lm7VdWz+5+hi/tajq+q+0xSAwCw44RTAMBOUVW7J3lkv/mvI/ctyf2r6oDtPP9vkrwn3YigfZJcmuSmSf4yyXFJdt3O8x+V5BtJfr9/3i79oRsneWaSr1fVvSZ6U8mb+/vf2k6I9Oj+/ttJPjlS04uSfDjJg5P8Srr3VEmun+SeSf46yW9MWNOi6071600dkOSbrbWzkny8P3ToAv3M7ftsa+2SkT6u3r/GvyW5b5JrJvlpkmv0Nb8lyXuqarcF+vyLJH+f5IFJbphulNcuSW6QLrg8qaoeOsF7/XmSHybZ0m9v6bdHbz9f4Hl7JvlEutFjv5Zka5K90/1sfbCqrIMGADuRcAoA2FkemuRqSb7VWvt0krTWTk8X1uyS5LGLPbGqHp7kD/vNtyS5Tmtt3yR7JXlSktsmeeoSz79nuiDsSukCiesnuUq6aWs3SfKOvq93TDiC6tgk5yXZLcnhS7SbC6fe3FprfU0HpVvEO0lekeRXWmt7tNb2Svc5HZLk1UkunKCeZFs4tXeS28w7dmh/f0J/PxdOXW4K4GLrTfVh17uT3CXJl5M8IMkerbWrpQt8Hpfk7HTh018uUNu3kjw7yc2TXKW1tl+SKyf59ST/3j9+U1Vde4z3mdbap1trByZ5e7/r7a21A+fdPr3AU1+S5DrpFlCf+8xvkuSz6cLBv6uqfcapAQDYccIpAGBnmZvS96/z9i85ta+fIvan/eZHkzy6tfb9JGmtbWmtvTZdMLXvIs/fkOQf033veXpr7bmttTPaNv/TWjs8yfvSBTpHjvuGWms/S3JMv7lguFZVd0g3SijZNtIqSW7X1/TN1tofttb+d6Tf81trn2ytPb21dlIm86l0I7CSK46Imtv++Lz7+e1um+Sq/ePR9aYemW5a4DeSHNpae39r7ad9zRe31v413fphLcnTquqao5221v6stfY3rbWvt9Z+0e/b2lo7ubX26CQfSBcYznrk0lWT3KO19t7W2qV9Hf+TLlTbki5ou/+MawAAesIpAGDmqurgdAFIy+UDmqQLdy5JcpOquuMCT79ltoU7fz438mieNyX5ziIvf5ckN0ryoySvW6LMuZDs3ku0Wcjc+7lDVd1wgeOP6e8/01r71sj+n/T3e1XVHpmS1trFST7fb85fFP3Q/v6Evu130001vG5V3WCk3dzztqQbTTRnLmB8TWvt/EVe/6QkJ6cbTbbYouyL+UB/f+cJnzepd7bWvjF/Z2vtnCSf6TcnnU4JACyTcAoA2Bken2661ImttTNGD7TWLki3llSyLfwYdev+/tLMW5h7pI+WbaOA5pubnrZPkv+tqrMWuiV5bd/uoDHez+hrfyrJaf3mo0eP9esu/W6/OX/E2OfTBWbXSvK5qnpGVd1kscXEJ3RCf3+nqtqlr+Wm6daH+tboKK0sPHpq7vGn+9Fhc1P6bt/vP2qxz7H/LG/ct7vCZ1lVt6iqV1fVV/vF37fOLV6ebhpj0k25m6XPLXFs7rMZeyF6AGDHCKcAgJnqp9Ud0W/OD2jmvKm/P7yq9px3bP/+/tzW2kKLW8/5/iL759Yv2jXdYuCL3eamBV5liddYzNzoqUfP23+/dCHHz7NtXaQkSWvtJ0kekeScJDdL8qokpyQ5r6reV1WP7td+Wo65qXh7Zdu6U4f29yfMa3u5cKoP1OZGsB0/0u7q6daESrrPaqnPcq7uq448P1X1jCRfTDcN8+bpps+dn22Ll1/QN53aSLJFLLWO1y/6++V+9gDAhIRTAMCs3TvbRsK8bm6UzOgt3RXrki6sWGxh8YWm841j7op1n2ut1Ti3ZbzGXDh1g6q608j+uSl972+tnTf/Sa2149Itzv7YdAHdqelGeD2g7/NLVfUry6jn09l2lbq5qXWH9vfzR5jNHzl1u2wL6EbXmxq98t99x/wsj5p7Qn+lwL9N9/3zHenWtdq9tbbv3OLl2bbe1zRGjwEAq4RwCgCYtYWm6k3S/pz+/hr9qJ7FLBbinNXfTzRdbxL9VQc/1W8+Jkmqat8kv9XvW2zE2NxC4m9urR3RWvvVdEHec9Ot9zQ3omrSei7JtrWiDu3v79rfnzCv7beTfDfJdfo1s+baX5zkCyNNz822UUXL+Swfli7gOiXJw1trX1hgJNyBy+gXAFjlhFMAwMxU1f7proCWdOHEXkvcbtu3u2NV3Xikmy/297tm23Sz+a9T6RY+X8hcaHRgVW1axtsY11wAdXgfoh2ebhrcj5J8cNxOWmvfb639VZKX97vuucx65kY93bmqfiPdelOnt9a+t0DbudFTh2XbSKtPzl3Jrq/r0mxbaP0By6jnuv39V1prWxdpc49l9Jskc/0ZcQUAq5BwahFV9ayqekdVfXve1IMjptD3FaYzLHA7Y8ffBQAM7jHpQqXzkxzbWrtoidsXksxdQW109NSXk8xd5e55iywY/ugsPprnYyPPf+V2Rl+lqpa7EPYxSX6Wbj2mB2TblL63jYY8I69z5fn75rmkv18syNmeuXBqjyTP7h+fsEjbuXDq3tm26PnHFmh3dH9/v6q631IvvsDnOHd1v5sv9N+wqu6byy/KPom5taqutsznAwADEk4t7qh0f+HdONDrX+FLLACsQnMh03u3s5j5nHf094+du8pcfyW+P+n33zvJm6rq2klSVbtX1ROT/HOSK6zp1D//F0mekm5K2p2TfKKq7j662HhVHVxVT6mqLyR52kTvcNvr/CTJsf3mH2fbVQLfvOATkudW1Yeq6jFVddnV6arqylV1eLYFSh9YTj3ppvVt6R8/or9f7IqGc/sflIXXm5rzb0mOSzdC6T+q6oVz/y2SpKr2qKrDquofk5w+77lz64rdLMk/zoVX/XOenOSd6aYOLsfX+/tDquomy+wDABiIcGpxX0vyhnRfUM+ect+HLHIbvcrQMVN+TQDYqarq9klu2m++Y6m2I+baHZBt6zWltfaWdItpJ92IpO9V1Y/TjZh5XZLPJPmn/vhcIJOR5/9Xkt9Jd5W226ULWC6uqh9V1ZYkpyV5TZJNWf7C68m2qX1zV8j7Rmvt84u03ZDkPv1zvltVP62qc9ONmHp7uoXRT8m2RcIn0lr7WbqF0ZNti5mfsEjbU5P8YKTdBUlOWqDdL5M8NMn7k+yW5E+TfL+qzq+q89J9vsen+/60x7zn/leSt/WbT01ybv+c89P9tzsl3R8Hl+Nd6dYm2zfJKVV1TlWd0d9uv53nAgADE04torV2SGvtia2112TbsPpFVdVtq+qtVfXdqvp5VZ1XVcdV1QPnt22tfXL+Ld0XvLmFXH+WZSx+CgArzNyoqfOTfGScJ7TWvpYupBh9/tyxP0jykHQBy4Xp1nM6Jd0Io3tnWxjyk0X6fk+SGyZ5cbq1ky5KNw3sZ0m+ki7kenCSvx6n1kV8KNsWcE8WHzWVdFPknpTkrelG/vw0yd7pRoCdmORZSW7dWjtrsQ7GMDr66YzW2neWaDs6qurEPoi6gtbaBa21ByS5X7oQ7Tvp/ltcNd0f2j6SbuTYjRd4+qPSva+vpvvcr5TuD4JzI80u2v5bWrCm89KtOfa2voZ90k3zPCjJ7svpEwDYeaobKc9S+vWf5taxeHxr7Y3zjj8tXZi0WNj3stba87fzGh9I9yUvSd7QWpv0ykYAsK5V1afSLZj+otbanw5dDwAA4zFyagdV1dwlnjekW7D0pUnuleTJ2bb2xR9X1d2W6OPXkty332zZdnUeAGAMVXXXbLuS34eXagsAwMoinNpxj8+2z/H4dF+IL0ny30n+Y6Td/12ijyOz7dLHH2qt/fe0iwSA1a6q/rGqjqiqA+eu9lZVV+sX035v3+z4/qp/AACsErsMXcAacNORx/fobwv59YV2VtU1s+1S00nyN1OqCwDWmjtl25X0flZVP023ZtTcH3j+O8ljB6gLAIAdIJzaefZcZP8z0i0imiQntdYWumwzAJC8KMmD0l1t74B0i16fl+TkJO9OcnRr7aeDVQcAwLIIp3bcKdm2XtRbW2uPXKhRVe2xwL6rpLuU8hxrTQHAIlpr70vyvqHrAABguoRTi6iqe6W7JHJG7pPk1lX1k/7xJ5O8Md0lkTckeURVXZjk/ekuj3yddNP+Hpjkz/u2ox6X5Br94zOTvGOKbwEAAABgxavW2tA1rEhVdUaSg7bT7LDW2glV9fQkf5+lF5h/fGvtjSP9V5JvJPnVfteRrbVXLr9iAAAAgNXHyKkpaK39Y1V9PsnvJzkkyYFJtiT5QZKvJjk2264iNOcB2RZMnZ/kdTunWgAAAICVw8gpAAAAAAaz1DQ0AAAAAJgp4RQAAAAAg7Hm1DzXuMY12saNG4cuAwAAAGDNOOmkk37UWtt/oWPCqXk2btyYzZs3D10GAAAAwJpRVWcudsy0PgAAAAAGI5wCAAAAYDDCKQAAAAAGI5wCAAAAYDDCKQAAAAAGI5wCAAAAYDDCKQAAAAAGI5wCAAAAYDDCKQAAAAAGI5wCAAAAYDDCKQAAAAAGI5wCAAAAYDDCKQAAAAAGI5wCAAAAYDDCKQAAAAAGs8vQBQAAALA6bd3acsa5F+eHF2zJAXvvno377ZENG2rosoBVRjgFAADAxLZubfnwyWflyGO+nC2Xbs3uu27IKw6/Ze5zswMFVMBETOsDAABgYmece/FlwVSSbLl0a4485ss549yLB64MWG2EUwAAAEzshxdsuSyYmrPl0q05+8ItA1UErFbCKQAAACZ2wN67Z/ddL/8r5e67bsg199p9oIqA1Uo4BQAAwMQ27rdHXnH4LS8LqObWnNq43x4DVwasNhZEBwAAYGIbNlTuc7MDc5NnHpKzL9ySa+7lan3A8ginAAAAWJYNGyoH779nDt5/z6FLAVYx0/oAAAAAGIxwCgAAAIDBCKcAAAAAGIxwCgAAAIDBCKcAAAAAGIxwCgAAAIDBCKcAAAAAGIxwCgAAAIDBCKcAAAAAGIxwCgAAAIDBCKcAAAAAGIxwCgAAAIDBCKcAAAAAGIxwCgAAAIDBCKcAAAAAGIxwCgAAAIDBCKcAAAAAGMyg4VRV3aqq/ryqTqyq71TVJVV1cVV9par+pKr2nLC/g6rq6Ko6s6p+VlVnV9V7q+pOs3oPAAAAACzfLgO//pP723y/0d8Or6o7ttbO315HVXXrJMcl2Xdk9/5JHpjk/lX1hNbam6ZQMwAAAABTshKm9f04yd8leVCS+yd5x8ixmyb5/e11UFW7JHlLtgVTH0wXSr28396Q5DVVdfB0SgYAAABgGoYeOfWWJM9urV04t6OqPpTkxulGTiXJ7cfo5779c5LkgiQPa61dkuTYqrpFknskuUqSpyZ59pRqBwAAAGAHDTpyqrX2idFgqt+3Nck3R3ZdNEZXdxt5/MU+mJrzqUXaAQAAADCwlTCt73Kqar8kdx/Z9b4xnjY6Xe+secdGt2+w3LoAAAAAmL4VFU5V1T5J3ptta0d9ON3Uv+3ZY+Txz+cdG91e8Op/VfWkqtpcVZvPOeecccsFAAAAYAetmHCqqq6T5JNJ7tTvOj7JQ/tpfttz8cjjK887Nrq94BTB1trRrbVNrbVN+++//7glAwAAALCDVkQ4VVU3T/KZJL/e7zomyf1aaz8ds4vTRx4fOO/YtUYen7a8CgEAAACYhcHDqao6LMmJSa7T73p5koe31n42QTfHjzy+dVVddWT7Lou0AwAAAGBgg4ZTVfXgdOtK7dPvemuS9yS5U1Xdub9tGmn/xqpq/e2oka4+lOTU/vFeSd5ZVQ+oqlcmuWu/f0uSf5rduwEAAABgUrsM/Pq/nWS3ke1H9LdRZybZuFQnrbVfVNUjkxyXLui6b3+7rEmSp7fWTOsDAAAAWEEGn9Y3La21zUluleT1Sb6X5NIk5yY5NsldW2tvGLA8AAAAABZQrbWha1hRNm3a1DZv3jx0GQAAAABrRlWd1FrbtNCxNTNyCgAAAIDVRzgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGAmDqeq6kpV9diq+req+mhV3arfv2+//1emXyYAAAAAa9EukzSuqqsm+UiSOya5OMlVk+zbH74gyV8keUOSF06xRgAAAADWqElHTh2VZFOSByc5OEnNHWit/TLJu5Pce1rFAQAAALC2TRpO/U6So1tr702ydYHj30qycUeLAgAAAGB9mDScunaSryxx/KdJ9lp+OQAAAACsJ5OGU+cmWWrB85sl+d/llwMAAADAejJpOPVfSR7fL4x+OVV1/SRPSPLhcTurqmdV1Tuq6ttV1UZuR0xSVFWdMO/582/vnKQ/AAAAAHaOia7Wl+TFSTYn+UKStyZpSe5TVfdM8pQkP0vysgn6OyrJPhPWAAAAAMAaMVE41Vr7VlXdPckbkryk3/1H/f3XkzymtfbdCbr8WpJvpgu8jkpyzUnqWcQhC+z70RT6BQAAAGDKJh05ldbaSUluUVW/nuTXklSSU1trX1pGX5cFSVX13Emfv0ifn5xGPwAAAADM3sTh1JzW2tfTjZZaUarqjHRXFbwoyZeS/FNr7R2DFgUAAADAgiZaEL2q7l5Vi64pVVUvq6rDdrysHXJQkl2T7JvkbkmOqapXDlsSAAAAAAuZ9Gp9z01ywyWOX79vs7OdneToJI9Lcs90Vw08eeT4s6rqtos9uaqeVFWbq2rzOeecM9tKAQAAALjMpNP6bpHkr5Y4/rkkz1l+OcvTWjt8/r6qen+S05Ls1e96QJLPL/L8o9OFW9m0aVObUZkAAAAAzDPpyKl9kly8xPFL0k2nG1xr7Zx0VwKcc8BQtQAAAACwsEnDqe8nuc0Sx2+T5KzllzO5qrp2VV1vgf37J/nVkV0/2HlVAQAAADCOScOpDyR5XFXdY/6Bqrp7ujWfPjhuZ1V1r6p6UFU9KMlVRw7dem5/VV2jb/vGqmr97aiRtr+a5NSqentVPa6q7lZVRyT5WLZN6dua5J3jv00AAAAAdoZJ15x6aZKHJvnPqvpQki/3+2+Z5L7pRk396QT9HZ3u6nrz/V5/S5LDkpywnX52S3J4f5uvJfnj1trXJqgLAAAAgJ1gonCqtfbDqrpjktekC6PuN3coyYeSPKO1trOnz21Od3W+30ryG+nWlrpKkh8m+XSSV7XWPrmTawIAAABgDNXa8i5OV1X7Jrlhv/mt1tp5U6tqQJs2bWqbN28eugwAAACANaOqTmqtbVro2KTT+i7Th1FfWHZVAAAAAKx7ywqnquqqSTYm2S9JzT/eWvvEjpUFAAAAwHowUTjVh1KvSPL4RZ5b6dafutKOlwYAAADAWjfpyKm/S/LEJB9McnySc6deEQAAAADrxqTh1IOTvLW19qhZFAMAAADA+rJhwva7JzlhBnUAAAAAsA5NGk5tTnKjWRQCAAAAwPozaTj1vCSPr6pNsygGAAAAgPVl0jWnnpTke0k+W1WfSXJ6kl/Oa9Naa0+cRnEAAAAArG2ThlNHjDy+U3+br6W7oh8AAAAALGmicKq1Nuk0QAAAAABYlLAJAAAAgMEIpwAAAAAYzKRrTqWq9k23ptTtkuybKwZcrbV29ynUBgAAAMAaN1E4VVUHJflUkmsnOT/J3kl+nG0h1Y+SXDzlGgEAAABYoyad1vdnSa6W5O5JbpSkkvxuupDqZUkuTHLIFOsDAAAAYA2bNJy6e5LXttY+lqT1+6q19tPW2guSfC3JX06zQAAAAADWrknDqf2SfL1/fGl/f5WR4x9Ncs8dLQoAAACA9WHScOqcJFfvH1+YZEuSjSPHd8vlwyoAAAAAWNSk4dTJSW6RdJfkS/L5JE+rqutV1cYkT0ryjalWCAAAAMCaNdHV+pK8N8kfVtVVWmuXJHlJkv9M8u3+eEvykCnWBwAAAMAaNlE41Vp7dZJXj2wfX1V3SPLIJL9M8h+ttU9Pt0QAAAAA1qpJR05dQWttc5LNU6gFAAAAgHVm0jWnAAAAAGBqlhw5VVUvWkafrbX2p8usBwAAAIB1ZHvT+o5aRp8tiXAKAAAAgO3aXjh1/Z1SBQAAAADr0pLhVGvtzJ1VCAAAAADrjwXRAQAAABjM9qb1LaiqNiW5XZJ9c8WAy4LoAAAAAIxlonCqqq6S5N1J7pWk0i1+Xv3hNrJPOAUAAADAdk06re9F6YKplyY5LF0Y9bgk901yYpIvJLnpNAsEAAAAYO2aNJx6WJJ3tNZelOTr/b7vt9b+M8k9kuyW5IjplQcAAADAWjZpOHXdJB/vH/+yv98tSVprv0jy1iQPn05pAAAAAKx1k4ZTF2bbOlUXJtma5Nojx89PcuAU6gIAAABgHZg0nDotya8mSWvtl0lOTjfVL1VVSR6S5LvTLBAAAACAtWvScOq4JA+tqiv12/+c5D5VdVqSU9OtO/X6KdYHAAAAwBq2y/abXM5fJHlzuqv0pbX26qraPcmj061B9dokfzXVCgEAAABYsyYKp1prFyX5n3n7XpHkFdMsCgAAAID1YdJpfQAAAAAwNWOPnKqqWyT5eWvtlH57lyRPSHK3JHsl2ZzkVa21H82iUAAAAADWnu2GU1W1b7qF0G/Zb5+Y5H5J3pXk3iNN75vkcVV1u9baD6dfKgAAAABrzTjT+o5Mcqsk70nymiSbkvxbksOSPK8/dtskL0ty3ST/bxaFAgAAALD2jDOt7yFJ3tVa+50kqaovJnldkle01kavzLe5qq6fblQVAAAAAGzXOCOnrpPkYyPbJ/T3n1qg7YlJfmUHawIAAABgnRgnnNoryU9Gts+fdz/qgkywyDoAAAAA69s44RQAAAAAzMS4o5w2VtWt+8f79Pc3qqqfzGt3/alUBQAAAMC6MG449af9bdSrF2hXSdoOVQQAAADAujFOOPXimVcBAAAAwLq03XCqtSacAgAAAGAmJloQvaoeW1Ublzi+saoeu8NVAQAAALAuTHq1vn9Jcscljt+ubwMAAAAA2zVpOFXbOb5rkq3LrAUAAACAdWbScCpZ5Gp8VXW1JL+V5Ac7UhAAAAAA68d2w6mq+pOq+mVV/TJdMPVvc9ujtyTnJjk8ydtmXDMAAAAAa8R2r9aX5MtJ/jXdlL7HJjkxyenz2rQkFyX5bJK3TrE+AAAAANaw7YZTrbX3JnlvklTVQUn+rLX2X7MuDAAAAIC1b5yRU5dprR02q0IAAAAAWH8mCqfmVNVVk2xMsl8WuIJfa+0TO1YWAAAAAOvBROFUVe2R5OVJHr/Icyvd+lNX2vHSAAAAAFjrJh059bdJnpjkg0mOT3eFPgAAAABYlknDqQcneWtr7VGzKAYAAACA9WXDhO13T3LCDOoAAAAAYB2aNJzanORGsygEAAAAgPVn0nDqeUkeX1WbZlEMAAAAAOvLpGtOPSnJ95J8tqo+k+T0JL+c16a11p44jeIAAAAAWNsmDaeOGHl8p/42X0t3RT8AAAAAWNJE4VRrbdJpgAAAAACwKGETAAAAAIMRTgEAAAAwmImm9VXV8WM0a621uy+zHgAAAADWkUkXRD843YLn8/u4VrpRWD9KcvEU6gIAAABgHZh0QfSNC+2vqisnOTLJ45PcdcfLAgAAAGA9mMqaU621n7XWXpbkc0leMY0+AQAAAFj7pr0g+ieT3HvKfQIAAACwRk07nLp+kt2m3CcAAAAAa9SkV+u73iKHrp7kHkmemeSEHawJAAAAgHVi0qv1nZErXq1vTiX5n3QB1Viq6llJ7pRkU5KNI4ce31p74ySFVdVBSV6QblrhgUnOT/KZJH/VWvvUJH0BAAAAsHNMGk69JFcMp1qSHyf5ZpLjWmtbJ+jvqCT7TFjDFVTVrZMcl2Tfkd37J3lgkvtX1RNaa2/a0dcBAAAAYLomCqdaa0dN+fW/li7U2pwuqLrmpB1U1S5J3pJtwdQHk/xTkrsm+cN062q9pqpObK2dPoWaAQAAAJiSSUdOTVVr7ZC5x1X13GV2c98kN+4fX5DkYa21S5IcW1W3SLcW1lWSPDXJs3egXAAAAACmbOKr9VXVHlX14qr6alVd1N++WlVHVdUesyhyO+428viLfTA151OLtAMAAABgBZgonKqqqyf5fJL/l+SAJF/qbwckeVGSz/dtdqaDRx6fNe/Y6PYNFuugqp5UVZuravM555wz1eIAAAAAWNykI6dekuQmSZ6R5NqttUP6qXnXTvL0dNPrjppqhds3Olrr5/OOjW7vuVgHrbWjW2ubWmub9t9//6kWBwAAAMDiJg2nHpjkda21V7fWfjm3s7X2y9baa5K8IcmDpljfOC4eeXzlecdGty/aCbUAAAAAMIFJw6m5qXyL+WLfZmcavQLfgfOOXWvk8Wk7oRYAAAAAJjBpOPXDJLda4vit+jY70/Ejj29dVVcd2b7LIu0AAAAAWAEmDaeOTfLEqnpyVV323KraUFVPSvKEJO8bt7OquldVPaiqHpRkNFS69dz+qrpG3/aNVdX621EjbT+U5NT+8V5J3llVD6iqVya5a79/S5J/muytAgAAADBru0zY/kVJ7pnk1UleXFX/0++/cZL9k3wryZ9M0N/RSQ5aYP/v9bckOSzJCYt10Fr7RVU9MslxSfZJct/+dlmTJE9vrZnWBwAAALDCTDRyqrV2bpJNSf4iyblJfrO//SjJy5L8Zt9mp2qtbU43pfD1Sb6X5NK+vmOT3LW19oadXRMAAAAA21ettaFrWFE2bdrUNm/ePHQZAAAAAGtGVZ3UWtu00LFJ15wCAAAAgKmZKJyqqhdX1deXOP7VqnrhjpcFAAAAwHow6cipByf56BLHP5rkYcsvBwAAAID1ZNJw6vpJvrHE8f/p2wAAAADAdi1nzamrLXFs3yRXWl4pAAAAAKw3k4ZTJyf57YUOVFUleWCWHlkFAAAAAJeZNJx6fZLbV9Ubq2r/uZ394zckuX3fBgAAAAC2a5dJGrfWXltVd03y2CSPqaof9IeulaSSvL219pop1wgAAADAGjVROJUkrbVHV9X7kjwqyQ373V9I8u+ttXdOszgAAAAA1raJw6kkaa0dk+SYKdcCAAAAwDoz0ZpTVbVLVe29xPG9q2pZgRcAAAAA68+kC6K/PMnmJY5/IclfLr8cAAAAANaTScOpeyd51xLH35XkvssvBwAAAID1ZNJw6rpJTlvi+Ol9GwAAAADYrknDqZ8nudYSxw9MsnX55QAAAACwnkwaTn05yeFVtdv8A1W1a5LfTfLVKdQFAAAAwDowaTj1D0luluQDVbWpqnarql2ralOSDyS5ad8GAAAAALZrl0kat9beVVUvS/LHST6XpPW3DUkqyV+21t4+9SoBAAAAWJMmCqeSpLX2gqp6T5JHJ7lhv/ubSd7SWvvCFGsDAAAAYI2bOJxKkj6EWjCIqqrbt9Y+u0NVAQAAALAuTLrm1IKqav+q+sOqOjnJp6bRJwAAAABr37JGTiVJVW1Icr8kT0jyW0l2TXJ2ktdOpzQAAAAA1rqJw6mqulG6QOqxSQ7sd/9Hkr9PcmJrrU2vPAAAAADWsrGm9VXVVarqcVX1iSTfSHJkuqv1/X66q/S9pbX2CcEUAAAAAJPY7sipqnptksOT7JXkS0melS6MOreqbjDb8gAAAABYy8aZ1vfEJN9Kcs/W2udnXA8AAAAA68g40/q+kOSGST5SVa+tqjvPuCYAAAAA1onthlOttdsluXmSf0ny20k+XlWnVdWLklx/xvUBAAAAsIaNtSB6a+3k1tofJPmVJA9P8s0kL0ryn0lakjtW1X4zqxIAAACANWmscGpOa+3S1to7Wmv3TbIxyVFJzkh39b4fVNV/VdXTpl0kAAAAAGvTROHUqNba91prf9pau0GSeyR5R5I7JHnVtIoDAAAAYG0b52p929VaOz7J8VW1T5JHTaNPAAAAANa+iUZOVdXpVfXAJZockuSPdqwkAAAAANaLSaf1bUyy5xLH90hy0LKrAQAAAGBdWfaaU4s4IMlPp9wnAAAAAGvUdtecqqq7JDl0ZNdDquqGCzS9epKHJ/nyVCoDAAAAYM0bZ0H0w5L8Sf+4JXlIf1vIt5L8wRTqAgAAAGAdGCec+tskb0xSSU5P8qwk753XpiW5qLX24ynWBgAAAMAat91wqrV2fpLzk6SqDktySmvt7FkXBgAAAMDaN87Iqcu01j6+0P6quk26NadObK1tmUZhAAAAAKx9E12tr6r+qKqOnbfvLUk+n+TDSb5WVQdMsT4AAAAA1rCJwql0V+P7ztxGVd2t3/e2JC9Icq0kz5ladQAAAACsaRNN60uyMd3i6HMelOQHSR7dWmtVdY0kD0zyh9MoDgAAAIC1bdKRU3skuWRk+25JjmuttX77v5P8yjQKAwAAAGDtmzSc+n6SmydJVR2U5KZJRhdJ3zfJz6ZTGgAAAABr3aTT+o5N8rSq2iXJ7dIFUR8YOf7rSc6YTmkAAAAArHWThlMvSfIbSZ6WLph6Vmvth0lSVVdJ8uAkr59qhQAAAACsWROFU62185Lcvar2TnJJa+3SeU3umuS70yoOAAAAgLVt0pFTSZLW2gUL7LskyVd2uCIAAAAA1o2Jwqmquss47Vprn1heOQAAAACsJ5OOnDohSRuj3ZUmLwUAAACA9WbScOrxi/RxgyRHpLtS3z/vWEkAAAAArBeTLoj+psWOVdVfJ/niDlcEAAAAwLqxYVod9Vfye12S50yrTwAAAADWtqmFU73zkhw85T4BAAAAWKOmFk5V1e5JHpPkrGn1CQAAAMDaNtGaU1X1hkUOXT3JHZLsn+TZO1oUAAAAAOvDpFfrO2KR/T9O8s0kf9Bae8sOVQQAAADAujHp1fqmvUYVAAAAAOuYsAkAAACAwWw3nKqqK1XVX1TVU7bT7qlV9edVVdMrDwAAAIC1bJyRU49Ot8j5F7bT7vNJnpvkETtaFAAAAADrwzjh1OFJjmutnbRUo/74f0Y4BQAAAMCYxgmnbpPkuDH7+1iSTcsvBwAAAID1ZJxw6upJzh6zv3P69gAAAACwXeOEUxcmucaY/e2X5KLllwMAAADAejJOOHVyknuN2d89+/YAAAAAsF3jhFPvTnKPqvrtpRpV1QPThVPvmkZhAAAAAKx944RT/5zkW0mOqaqXVtXG0YNVtbGq/izJMUm+2bcHAAAAgO3aZXsNWmuXVNVvJXl/kj9O8ryquiDdWlR7Jdk7SSX5nyT3b61tmWG9AAAAAKwh44ycSmvtW0lumeT3k3wyyS+THNjfn9jvv3Vr7bTZlAkAAADAWrTdkVNz+hFRr+pvAAAAALDDxho5BQAAAACzIJwCAAAAYDDCKQAAAAAGI5wCAAAAYDDCKQAAAAAGI5wCAAAAYDArIpyqqgdW1Uer6sdVtaWqTq2ql1fVfhP0cUJVtSVu75zlewAAAABgcoOHU1X14iTvTXKPJPsmuXKSGyY5MsnmqrrugOUBAAAAMEO7DPniVXVIkhf1m1uTvDDJKUmem+T2STYmeV2Se0/Y9SEL7PvR8qoEAAAAYFYGDaeSPGvk8Rtaay9Lkqo6KcmZSSrJvarqZq21k8fttLX2yalWCQAAAMBMDD2t77CRx5cFSq217yb5zsixu03SaVWdUVU/79ew+q+q+p0drBMAAACAGRgsnKqqfdOtMTXnrHlNRrdvMGH3ByXZte//bkmOqapXLlHLk6pqc1VtPueccyZ8KQAAAACWa8iRU3vM2/75Ett7jtHf2UmOTvK4JPdM8oQko1MBn1VVt13oia21o1trm1prm/bff/8xXgoAAACAaRhyzamL521feYnti7bXWWvt8Pn7qur9SU5Lsle/6wFJPj9BjQAAAADM0GAjp1pr5yU5b2TXgfOaXGvk8WnLfI1zknxzZNcBy+kHAAAAgNkYekH0j408PmTuQVVdP8l1R44dv1QnVXXtqrreAvv3T/KrI7t+sMw6AQAAAJiBocOpvx95fERVPb+qHpTk7SP7j2utnZwkVfXGqmr97aiRNr+a5NSqentVPa6q7lZVR6QLv+am9G1N8s5ZvREAAAAAJjfkmlNprX28ql6a5AXpgrKXzmvynST/Z8zudktyeH+7wksl+ePW2teWWysAAAAA0zf0yKm01l6Y5MHppu79JN1V+k5L8sokm1prZ47RzeZ0V+d7V5JTk1yQ5NIk30tyTJK7tNb+aurFAwAAALBDqrU2dA0ryqZNm9rmzZuHLgMAAABgzaiqk1prmxY6NvjIKQAAAADWL+EUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwmF2GLgAAAADWoq1bW8449+L88IItOWDv3bNxvz2yYUMNXRasOMIpAAAAmLKtW1s+fPJZOfKYL2fLpVuz+64b8orDb5n73OxAARXMY1ofAAAATNkZ5158WTCVJFsu3Zojj/lyzjj34oErg5VHOAUAAABT9sMLtlwWTM3ZcunWnH3hloEqgpVLOAUAAABTdsDeu2f3XS//K/fuu27INffafaCKYOUSTgEAAMCUbdxvj7zi8FteFlDNrTm1cb89Bq4MVh4LogMAAMCUbdhQuc/NDsxNnnlIzr5wS665l6v1wWKEUwAAADADGzZUDt5/zxy8/55DlwIrmml9AAAAAAxGOAUAAADAYIRTAAAAAAxGOAUAAADAYIRTAAAAAAxGOAUAAADAYIRTAAAAAAxGOAUAAADAYIRTAAAAAAxGOAUAAADAYIRTAAAAAAxGOAUAAADAYIRTAAAAAAxGOAUAAADAYIRTAAAAAAxGOAUAAADAYIRTAAAAAAxGOAUAAADAYIRTAAAAAAxGOAUAAADAYHYZugAAAFiurVtbzjj34vzwgi05YO/ds3G/PbJhQw1dFgAwAeEUAACr0tatLR8++awcecyXs+XSrdl91w15xeG3zH1udqCACgBWEdP6AABYlc449+LLgqkk2XLp1hx5zJdzxrkXD1wZADAJ4RQAAKvSDy/YclkwNWfLpVtz9oVbBqoIAFgO4RQAAKvSAXvvnt13vfzX2d133ZBr7rX7QBUBAMshnAIAYFXauN8eecXht7wsoJpbc2rjfnsMXBkAMAkLogMAsCpt2FC5z80OzE2eeUjOvnBLrrmXq/UBwGoknAIAYNXasKFy8P575uD99xy6FABgmUzrAwAAAGAwwikAAAAABmNaHwAAAKwxW7e2nHHuxfnhBVtywN7W5GNlE04BAADAGrJ1a8uHTz4rRx7z5Wy5dOtlVzO9z80OFFCxIpnWBwAAAGvIGedefFkwlSRbLt2aI4/5cs449+KBK4OFCacAAABgDfnhBVsuC6bmbLl0a86+cMtAFcHShFMAAACwhhyw9+7ZfdfL/7q/+64bcs29dh+oIliacAoAAGAn2rq15fRzLspnTvtRTj/nomzd2oYuiTVm43575BWH3/KygGpuzamN++0xcGWwMAuiAwAA7CQWqt751uNV6zZsqNznZgfmJs88JGdfuCXX3Gt9vG9WL+EUAAA7xXr8BRHmW2yh6ps885AcvP+eA1e39qznMHDDhsrB++/p54pVwbQ+AABmbu4XxPv9/Yl5xGs/l/v9/Yn58Mlnmc7EumOh6p3LVetgdRBOAQAwc35BZGdZ6es5Wah65xIGwuognAIAYOb8gjg9swhfVnqgM67VMELPQtU716zCwLVyzsBKYc0pAABmbu4XxNGAymiRyc1i/Zy1tCbPaljPyULVO9dcGDj/53tHwsC1dM6sV9ZAXHmqNQnvqE2bNrXNmzcPXQYArBu+IK4P6/mXuWn+jJ9+zkW539+feIWQ74M7EL7Mos+hfOa0H+URr/3cFfa/7Um3y+0PvsYAFbESzJ2D0woD19I5sx6t53+PhlZVJ7XWNi10zMgpAGAwa+0L4noN2sZ53+t1tMi0f8aXmh653F+KZ9HnUIzQYyHTvmrdWjpn1qPVMMJyPbLmFAAwmKEXyZ7mmiGzWutmpa9rMsn7nvsF8fYHXyMH77/nmg+mkun/jM9i/Zy1tEC39ZzYGdbSObMeWQNxZRJOAQCDGfIL4iShyjgB0aQhxDh9TrvGSdqNaxYB40oP5CYxyc/4OO97FuHLWgp05kboffCZh+RtT7pdPvjMQxYdpbaWfs7YudbSObMeCRdXJtP6AGDGJpnqNe1pYeP2N4sax2k3yRScaX824w7rH3da1iTTPMbtc9o1TjrFbJzPfNrTW2Y11XPa58K47cb9GR/3fU8yPXLcGoeccjmLqbDjTOFaa1OK2bnW6zTltWIWi+Sz44RTADBDk/wCNGnb7f1CN4vAYtp9jvsFcchQZdyAaJKgbdw+p13jJOtsjPuZT3uNn1msBTLtn9tJfh7H/Rmf5H3PInwZd02eaYZJszivxzXpuTDUWnLr9bXHNdQfdJLpr2PFziNcXJlWxLS+qnpgVX20qn5cVVuq6tSqenlV7TdhPwdV1dFVdWZV/ayqzq6q91bVnWZVOwCTmfbUo0mmZQzx2pNMeRq37bhTvcbtbxY1jttu3Ck4k9Q47ucz7rD+cadlTTLNY9w+p13jJFPMxv3Mpz29ZRZTPaf9czvJz+O4P+PTft+zmm451FTYaa/pNu7nPenrroZ17IZ67WlPo5x2jUN+3ux863ENxJVu8HCqql6c5L1J7pFk3yRXTnLDJEcm2VxV1x2zn1sn+VKS/5vkekl2S7J/kgcm+URVPW761a9M5s/D2jNUoDPt4GfcL37Tbjfka0/yC+e4bcf9hW4WgcUs+hznC+KQocq4AdEka92M2+e0a5xknY1xP/NJ3vc4ZrEWyLR/bicNksb5GZ/2+x4y5JvF/x+HWlh+yABt0tdeyWvJzSLkm3aNQ1+gA9a7QcOpqjokyYv6za1Jnp/kwUk+2+/bmOR1Y/SzS5K3pAu3kuSD6UKpl/fbG5K8pqoOnkrhK9jQC6cO+YvxUFZDjavBWvp5XCuBziyCnyFHLwz12pP8wjntUTKzCCxm0ec4hgxVJhkZNO5fYsftc9o1TvJeJvnMp/kX6FksNDztn9tZBGjTft9Dhnyz+P/jtMO2cT/vIQO0IUd3DTmSbxbh5jjW8xXc/D6ztLX0u+tKNvSaU88aefyG1trLkqSqTkpyZpJKcq+qullr7eQl+rlvkhv3jy9I8rDW2iVJjq2qW6QblXWVJE9N8uzpvoWVZciFU6fd56SvPcRCvquhxlm0G+pzXA0/j7OocdprycxibZppr58z7XZDvvYki26O23bcNX7G7W8WNU57sdFJ+ptkDaRx1gyZxdoUk/Q5zRoned2hFoydxec97Z/bWXw2037fs6hx3HNrFv9/nPbaZuN+3pO87rQvDjDua6+GteQm+Wxmsc7fOKbd36Rm8X193Ned9u8z065xyNce8nfXWXyOK9nQ4dRhI48/OfegtfbdqvpOkoP6XXdLslQ4dbeRx1/sg6k5n0oXTs1vtyYNuXDqUL8YDxlYrIYaV0Pws5Z+HtdSoDOL4GfcL37Tbjfka08aQozTdtxf6GYRWMyiz3EMHarMYuHbafc5bn+TtBtqwdhZfDbT/Lmd1Wczzfc9ZMg3i/8/DnVeDxmgjfvaswh+pv15zyLkG/KPINM2i+/r4xr6qrBr5fNZDZ/jSjfYtL6q2jfbpuElyVnzmoxu32A73Y1O19uRfla9IRdOHWo9hyGn9ayGGlfD9Ki19PM4ixqHmo4yi6le0556NMk0mCFfe5IpT+O0nWSNn3Ffe9o1TtrnOCZ53WmugbSeTfu/4ZCm/XO7Gj6bWZyDQ02FHeq8nuR1pz01c9zXXg1ryc1iSvG0axzy345ZfF8f17R/n5lFjavh81kNn+NKN+TIqfn/J/r5Etvb+/PRaF8T91NVT0rypCS53vWut52XWtmm/Ret1TAqYchpPauhxtUwPWot/TzOosahpqPMYqrXkKMXVsvIiXHNYiTPWuLzgdkY59xaDSPLZvG6s3jfQ47uGmok3yTvZ6gRqNM2i+/r45r27zOzqHE1fD6r4XNc6YYMp+ZHfldeYvuiCfqauJ/W2tFJjk6STZs2repVy6Y9JWTIdUimHbRN0nbaocF6DVXW0ro46zHQmUXwM9d22lOPxv0iOeRrA6wn6/X/j0O876GnR05S50qfUjyUWXxfH9eQgxvGtRo+n9XwOa501dpwWUxV/TjbpvY9vrX2xpFj30ly3X7zma21Vy3RzyuzbXH1j7fWDh059pIk/6/f/GJr7TZL1bRp06a2efPmCd7F6jW3wNr2/qc/brtZ9DlOuyHXSloNNa6GNaem/TMxqz6HrBEAYBp891h5hl6HaJq/z6y1Nafm+t3Zv7uu1TWnquqk1tqmBY8NHE69K8lD+s03tNae2O+/fpLTR5r++lJX66uqByR5X795YZIDW2s/7Y+dkOSu/bG/aa0tebW+9RROrSVDBharocbVEPwAAMB6tRq+Ww9Z42r4fMa1lt7LpFZyOHXXJCf0m1vTjXD67yTPT/Kb/f7jWmv37Nu/Mcnj+v0vbq0d1e/fpX/ejfpjH0rymnRX53tWv29LupDrtKVqEk4BAAAATNdS4dSQa06ltfbxqnppkheku3LgS+c1+U6S/zNGP7+oqkcmOS7JPknu298ua5Lk6dsLpgAAAADYuTZsv8lstdZemOTBSY5P8pN0V9c7Lckrk2xqrZ05Zj+bk9wqyeuTfC/JpUnOTXJskru21t4w9eIBAAAA2CGDTutbiUzrAwAAAJiupab1DT5yCgAAAID1SzgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCqtTZ0DStKVZ2T5Myh64AZuUaSHw1dBKwizhmYjHMGJuOcgck4Z1a3g1pr+y90QDgF60hVbW6tbRq6DlgtnDMwGecMTMY5A5NxzqxdpvUBAAAAMBjhFAAAAACDEU7B+nL00AXAKuOcgck4Z2AyzhmYjHNmjbLmFAAAAACDMXIKAAAAgMEIpwAAAAAYjHAKVrmqulVV/XlVnVhV36mqS6rq4qr6SlX9SVXtucBzrlFVL6+qU6tqS1X9uKo+WlX3H+I9wJCq6r5V1UZuZyzQxjnDuldVu1fVs6rq01V1Xn8ufKeqPlxVj5jX1jnDulVVB1XVP1TVN/rvZL+oqnOq6mNV9YSqqnnt9+i/s329qn5aVedX1Ser6rHz28Jq1v8b8o6q+va8715HLNJ+4n9LquqBfZsf9885te9jv5m9MabCmlOwylXVPyV58hJN/jvJHVtr5/ftD0ryiSTXW6T9i1prfzrdKmFl6r+ofD3JgSO7z2ytbRxp45xh3auqayX5UJJbLNLkXa21h/VtnTOsW/3P/xeTXH2JZq9prT2tb79vko9l8XPrDa21J063ShhGVf0kyT4LHHp8a+2N89pO/G9JVb04yYsWaX9Gkru01r47QcnsREZOwdrw4yR/l+RBSe6f5B0jx26a5PdHtl+fbf+T/1ySByd5fpKt/b4XV9UdZ1ksrCD/nC6Y2rJEG+cM61o/cuPt2fbL89eSPCXJPZM8JMkLk3xh5CnOGdaz/5ttwdQFSZ6Q5N5Jjh1p86SRke1/nW3n1jeTHJ7kaUku6fc9Yf7IRFjFvpbkDel+xs/eTtuJ/i2pqkOyLZja2rd9cJLP9vs2JnndjpXPLBk5BatcVd0lyZdaaxeO7NuQ5EtJfqPf9aHW2v2q6uZJvtrva0mu11r7Xv+c1yb5P/2xd7bWfmenvAEYSFU9Nsmbkpyf5BVJXtwfumzklHMGkqr6rSTv7zdPSbKptfbTRdo6Z1jXquofkjy93xwdUbgplw9xr5ZklyT/m2S3ft+dW2uf6tu/IMmf9fs3t9Z+c8alw07VL6NwUL95uZFTy/m3pKrele4PJknyutba/+33XzfJmUnmpsj+emvt5Fm8J3aMkVOwyrXWPjEaTPX7tqb769uci/r7u43sO3Puf/K9T408Pmy6VcLKUlXXS/KqfvMZSb6zSFPnDGz7sp9005XeXFU/6NfG2dwHvXOcM6x3Hxl5fM+qenxV3TOXn2p0bL/cwp2zLZj6RbrRIXNGz5fbVNXeM6kWVqbl/Fsy+viTcw/6aXyj3/NG+2YFEU7BGtSvo3P3kV3v6+8PHtl31rynjW7vV1VXm0FpMLh+ZOGbkuyd5JjW2r8t0dw5A9tG4SbJo9KFVQcmuUqS2yR5U1X9RX/cOcO61lp7X5I/SLfkwt7ppjB9JMkDkvw8yZ8n+d2++ej58qPW2i9GtkfPl5rXFta6if4t6ddu23fM59xgCvUxA8IpWGOqap8k7822/0F/OMlb+sd7jDT9+bynzt++wlX+YI04Msmh6aZSPHU7bZ0z0E0/GnV0kvv293OeU1U3jXMGkuR7Sb6/wP7d0q0ptanfdr7AwiY9N/bYTpvRbefSCiWcgjWkqq6Tbhjrnfpdxyd5aD/NL0kuHml+5XlPn799UWCNqapfSbeGR0u3vsGPt/MU5wxc/oIB/5vkqa21D6cLd3/Q768k94lzhnWuX7z8HUlunuTUdIud75Hkcen+7blhkg/1/x45X2Bhk54bF2+nzei2c2mFEk7BGtEvHPiZJL/e7zomyf3mLVp7+sjjA+d1ca2Rx+e21n4y9SJhePun+4JSSf6zqlpVtST/MtLmoH7/e+KcgaRbSHbOd+b+4NHfjx7bJ84ZeNrI41e31r7aWvtpa+1fk3yl379Huqsrj54v+1XVLiPbo+dLm9cW1rqJ/i1prZ2X5Lwxn3PaFOpjBoRTsAZU1WFJTkxynX7Xy5M8vLX2s3lNjx95fL1+Ueg5dxl5/LHpVwmrknMGko+PPL5ev27b3Ppto+fEmXHOwP4jjy9bxLyqanQ7XZj7ySSX9tu7JLnDyPHR8+Wk1toFU64TVrLl/Fsy+viQuQdVdf0k112kb1aQXbbfBFjJqurBSd6WbVd7eWuS9yS5U/c9KEmypbW2ubX2tar6WLqrWVSSd1TVy5LcNMnc1ZZakr/bSeXDzvb9dAvVznfbJI/oH5+X5CVJTnPOQJLuAgIvTPeL9bWT/GM/svBB/XbSTZM4trV2jnOGde4rSW7cP/6Dqjo73SiQh+byizx/obV2blX9W5LH9/teX1UvSBdwPXek7d/OtmTYOarqXkmu2m9edeTQravqJ/3jTy7z+9ffZ9vVZY+oqtOS/HeS54+0Oa61dvK03g/TVa21oWsAdkBVvTHdOgZLObO1trFvf/0kn8i2UVbzvbi1dtS06oPVoKqOyLapfZedL/0x5wzrXlU9NN0fQhb6w+Yvkjymtfa2vq1zhnWrqn4t3aXu912i2btaaw/r2189yQnp1qhayJtaa0dMs0YYSlWdkeSg7TQ7rLV2wnL+LamqP0vygkXafyfJXVprZy5ynIGZ1gfrTGvt2+ku/f236eZc/zzJT9INcf1tvzDA5TlnIGmtvSvJ7ZO8M8nZ6QKps/vtO8wFU31b5wzrVmvtlCS3TPKqdKM2fprkl0l+nG6K7JOT/O5I+x8nuWO6EbunpLsAwYVJPp1uRNXjA+vQcv4taa29MMmD+zY/6Z9zWpJXJtkkmFrZjJwCAAAAYDBGTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEAAAAwGOEUAAAAAIMRTgEALFNVHVFVzxq6DgCA1axaa0PXAACwKlXVCUk2ttY2DlwKAMCqZeQUAABJkqraa+gaAID1RzgFAKx6VbVbVT2nqr5cVT+tqvOranNVPWOkzRurasEh41XVquqN8/Y9tqo+X1U/qaqLq+r0qvr3qtq/P35GkrsmOah//tzt0JE+7lJVH+3ruaSqvlhVT1zg9U+oqjOqamNV/Uf/muf1Ne9ZVRuq6vlV9e2q2tL3c6cF+qmqempVndR/DhdV1ceq6rB57Tb2tR5VVb/bt78kyasm+MyvXVUv7z/z8/q6/ruqnltVV1qg/caqeldVXdDf3ltV1+/f9wkLtL9HVX2k/yy2VNVXq+op49YHAKweuwxdAADAjqiq3ZL8Z5JDk3wkyb8l2ZLk5kkekuQfltHnY5K8KcmJSV6U5JIk101yvyTXTHJOkmcleVmSayT5g5Gnn9L38YAk/5HkrCQvT3JhkocneV1VHdxae8G8l90jyfFJPp7keUl+M8kTkuye5Nwkt0sXHu2a5I+SHFtVB7XWLhzp481JHpHknUn+JcmVkzwqyUer6iGttffNe80HJXlmktck+ackF4z/KeU30n2+/5HktL6u+yT5iyQHJ3nyXMOq2i/dZ3lA/zqnJDkkycf69305VfWkvt1nk7w0ycVJ7pnkNVV1g9basyeoEwBY4aw5BQCsalX1nCR/meRlrbXnzzu2obW2tX/8xiSPa63VAn20JG9qrR3Rb787yT2SXL219oslXvuELLDmVD9y6PQk+yS5aWvtf/v9u6ULZG6f5CattVNH+rlrkue01v56pJ93pwuQvpjkDq21S/v9D0zy3iRPaa39c7/vwUneneTJrbWjR/rYJV3Is1+Sg1trrao2Jvl2kl8k+Y3W2imLvccl3vtVkmxp875MVtWbkzwyyXVaaz/o9/1VkmcneXRr7d9H2s7t/3hr7dB+37X62t7dWnvkvL7/LskzktyotXb6pDUDACuTaX0AwGr3qCTnJXnJ/ANzwdQynJ/kqkl+q6quEGaN4TZJrpfkDXPBVF/Pz5P8VbrvYL897zm/zBWn1Z2YpJL801wwNbI/SW40su/R6UZnvaeqrjF3S3K1JMcm2TivfZJ8YDnBVP9eLpkLpvpplVfvX+8/+/e3aaT5A5L8IMlb53XzNwt0/bB0I75eP/o++r6P7fu+x3JqBgBWJtP6AIDV7kZJvtxa2zLFPv88yV2SvCfJuVX18SQfSvL2edPoFnP9/v7kBY7N7Tt43v4fLPAezuvvvz26s7V2Xp+Z7Tey+9eS7JXkh0vUdUCSb45sf3OxhtvTj8h6XpLHJrlhuhBt1L4jj6+f5PPzw8LW2tlV9ZN5z/u1/v64JV7+gIkLBgBWLOEUALBeLLYY+hW+D7XWTq2qmya5e3+7a5LXJnlxVd2ltXbaDOr75TKO1bzH56SbUreYr8/b/ukYdS3mFUl+L8nb060LdXaSS5PcOt00y+WO0J97T49NN9pqIab0AcAaIpwCAFa7bya5SVVdubX2syXa/ThJqurqrbUfj+yfP4IpSdL39cH+lqq6X5IPJDkyydPnmi3yWnPhyc0WOHbTeW2m5dQkv5rks621i6bc90Iek+QTrbWHj+6sqhsu0PaMJDccXQOsb3vNdNMOR53a3/+otbbU6CkAYI2w5hQAsNr9e7opZC+cf2DeelFzU9jmr1f0hws87xoLvM4X+/urj+y7KMm+C6xL9cUk30ny+Ko6cKTfXdMtAN7SLWg+Tf+a7rvdyxY6WFXTngr3y8ybyldVe+TyVy6cc2ySa6W7kuCoP1qg7TFJfpZulNpV5h+sqn2q6srLqhgAWJGMnAIAVru/S7fg9gur6jeTfCTJlnSjlm6cbWHUW9OtJXV0Vd0k3Uiq+yRZKIj6SL8W0olJvptudM8R6UKlN4+0+2yS+yf5h6r6dLrA5vh+LaVnJPmPJF+oqqPTLVb+u+mu1Pfnc1fqm5bW2jur6l+SPKOqbp3k/Ul+lOQ6Se6Qbl2oBUeJLdM7kzy5qt6ebn2oA5I8Icm5C7T9y3TTDf+lqm6b5BtJDklyx77Gy0agtda+V1VPTfK6JKf0V/87M8n+SW6e7uqFN003GgsAWAOEUwDAqtZa+3lV3SvdCKhHpgugtqSbHvYvI+0u6KfmvSLJ89ONenp3uqvcnTev29ckOTzJk9ONlDo3yZeS/F5r7WMj7V6ZLvB5WJKnpBu5dFiSs1trx1bV3dON6Hp2kt2SnJLk/7TWXj+1D2BEa+0JVfWxJE9K8sf9a56VbiTXH0/55Y5MF7gdnu7Kg99NcnSSL2TeYuattR9V1Z2TvDxdgNWSfCzdZ/WFJJfMa/8vVfXNdCOrnpwuHPxRkv9J8v/69wQArBHVXwEYAAB2qqraL13o9M+ttacMXQ8AMAxrTgEAMHMLrR+V5Hn9/Ud3Zi0AwMpi5BQAAEkuC5D22V671trE0+r66YZnpptiuCHJ3dOt1/XpJHdprf1y0j4BgLVBOAUAQJKkqo7IyDpdi2mtzb864Th9/2GSxybZmOQqSb6Xbs2vF7fWLpy0PwBg7RBOAQCQJKmqa6W7yuGSWmvHba8NAMC4hFMAAAAADMaC6AAAAAAMRjgFAAAAwGCEUwAAAAAMRjgFAAAAwGCEUwAAAAAM5v8DSmOW9jal2IwAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}],"source":["mean_wealth_by_age = df_vis.groupby(by = 'customer_age')['CustAccountBalance'].mean()\n","plt.figure(figsize=(20, 8))\n","age_wealth_hist = sns.scatterplot(data = mean_wealth_by_age)\n","age_wealth_hist.set_title('Age vs Wealth', pad=12, size=25)\n","del mean_wealth_by_age"]},{"cell_type":"code","execution_count":null,"id":"03cdcc05-a5fe-4c47-aae3-6fdb56d48d94","metadata":{"id":"03cdcc05-a5fe-4c47-aae3-6fdb56d48d94","colab":{"base_uri":"https://localhost:8080/","height":497},"executionInfo":{"status":"ok","timestamp":1677832373263,"user_tz":-120,"elapsed":92,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"17b56f52-725b-4b9a-fc05-23eaffc1bf39"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["mean_wealth_by_gender = df_vis.groupby(by = 'CustGender')['CustAccountBalance'].mean()\n","plt.figure(figsize=(20, 8))\n","plt.pie(mean_wealth_by_gender, labels=mean_wealth_by_gender.index, autopct='%1.3f%%', )\n","plt.title(\"Gender vs Wealth Distribution\", pad=12, size=25)\n","plt.show()\n","del mean_wealth_by_gender"]},{"cell_type":"code","execution_count":null,"id":"8d483670-cd4f-406b-86e2-a52838065bc1","metadata":{"id":"8d483670-cd4f-406b-86e2-a52838065bc1"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"f7d516aa-eade-4770-b980-6ebdfecb06a5","metadata":{"id":"f7d516aa-eade-4770-b980-6ebdfecb06a5","colab":{"base_uri":"https://localhost:8080/","height":390},"executionInfo":{"status":"ok","timestamp":1677832373263,"user_tz":-120,"elapsed":88,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"f751136e-7b1f-4f3b-847e-4bd5dd7890ba"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["mean_transaction_by_age = df_vis.groupby(by = 'customer_age')['TransactionAmount (INR)'].mean()\n","plt.figure(figsize=(20, 8))\n","age_transaction_hist = sns.scatterplot(data = mean_transaction_by_age)\n","age_transaction_hist.set_title('Age vs Transaction Amount', pad=12, size=25)\n","del mean_transaction_by_age"]},{"cell_type":"code","execution_count":null,"id":"b29a35cf-00cd-4abb-800c-df5f5569243b","metadata":{"id":"b29a35cf-00cd-4abb-800c-df5f5569243b","colab":{"base_uri":"https://localhost:8080/","height":385},"executionInfo":{"status":"ok","timestamp":1677832373669,"user_tz":-120,"elapsed":492,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"797054df-74a4-49ae-a69e-4074224de213"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 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\n"},"metadata":{"needs_background":"light"}}],"source":["transaction_by_day = df_vis.groupby(by='day_of_week')['TransactionAmount (INR)'].mean()\n","weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n","plt.figure(figsize=(20, 8))\n","age_transaction_hist = sns.barplot(x = weekdays, y= transaction_by_day.values)\n","age_transaction_hist.set_title('Day vs Transaction Amount', pad=12, size=25)\n","del transaction_by_day"]},{"cell_type":"code","execution_count":null,"id":"16c84e5d-fa35-4d59-b7ce-0576ca530c68","metadata":{"id":"16c84e5d-fa35-4d59-b7ce-0576ca530c68","colab":{"base_uri":"https://localhost:8080/","height":386},"executionInfo":{"status":"ok","timestamp":1677832379296,"user_tz":-120,"elapsed":5686,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"081a7efe-fcff-4e86-8a77-00d77b3a6e93"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["mean_transaction_by_daytime = df_vis.groupby(by = 'seconds')['TransactionAmount (INR)'].mean()\n","plt.figure(figsize=(20, 8))\n","age_transaction_hist = sns.lineplot(data = mean_transaction_by_daytime)\n","age_transaction_hist.set_title('Daytime vs Transaction Amount', pad=12, size=25)\n","del mean_transaction_by_daytime"]},{"cell_type":"code","execution_count":null,"id":"15a9c8f1-3e0b-4ce4-90b0-3e66f5ffe54a","metadata":{"id":"15a9c8f1-3e0b-4ce4-90b0-3e66f5ffe54a"},"outputs":[],"source":["del df_vis"]},{"cell_type":"markdown","id":"97e18cac-2fa4-44ce-8798-0f3990c78564","metadata":{"id":"97e18cac-2fa4-44ce-8798-0f3990c78564"},"source":["### Data Preprocessing\n","Most artificial intelligence models take numbers as their inputs, but we have categorical data in our data. We also have time data. To solve this problem we have to convert these categories to numbers. A way to do that is by [one-hot encoding](https://en.wikipedia.org/wiki/One-hot). This method basically makes each category a column itself and assigns 1 to the sample its present in and 0 if it isn't. In a case where we have binary categorical data (like our gender data) we can basically omit one of the categories.\n","\n","The best order of operations for speeding runtime is \n"," 1. Copying the location column.\n"," 2. Applying One-Hot encoding to the gender and locations.\n","    Note that you don't need to use the One-Hot object for gender you can apply the operation in place try using the `apply` method\n"," 3. Dropping the string columns.\n"," 4. Normalizing the columns.\n"," 5. Concatenating the location data in its one-hot format.\n"," 6. Reducing the dimension of the data.\n","I don't say that this order is the correct but it worked best for me."]},{"cell_type":"code","execution_count":null,"id":"d86ad4bd-5451-4086-83c0-ba884ad759b8","metadata":{"id":"d86ad4bd-5451-4086-83c0-ba884ad759b8"},"outputs":[],"source":["from sklearn.preprocessing import OneHotEncoder, minmax_scale\n","from sklearn.decomposition import PCA\n","from sklearn.model_selection import train_test_split"]},{"cell_type":"markdown","id":"91f66c38-01d3-42fb-935f-6c378977091b","metadata":{"id":"91f66c38-01d3-42fb-935f-6c378977091b"},"source":["#### Task 3.1\n","\n","Convert the location data and the gender data to one-hot encoding format"]},{"cell_type":"code","execution_count":null,"id":"4d03a388-718a-4181-972b-daccc187d7c6","metadata":{"id":"4d03a388-718a-4181-972b-daccc187d7c6","colab":{"base_uri":"https://localhost:8080/","height":288},"executionInfo":{"status":"ok","timestamp":1677832379297,"user_tz":-120,"elapsed":91,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"92ea0f5e-c081-4618-8511-b3921847f9b6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  CustomerID CustGender CustLocation  CustAccountBalance  \\\n","0   C5841053          F   JAMSHEDPUR            17819.05   \n","1   C2142763          M      JHAJJAR             2270.69   \n","2   C4417068          F       MUMBAI            17874.44   \n","3   C5342380          F       MUMBAI           866503.21   \n","4   C9031234          F  NAVI MUMBAI             6714.43   \n","\n","   TransactionAmount (INR)  customer_age  day_in_month  day_of_week  seconds  \n","0                     25.0            22             2            1  52327.0  \n","1                  27999.0            59             2            1  51538.0  \n","2                    459.0            20             2            1  52032.0  \n","3                   2060.0            43             2            1  52034.0  \n","4                   1762.5            28             2            1  65516.0  "],"text/html":["\n","  <div id=\"df-ecc2ef44-19d8-4ae1-9374-85964cd51a06\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CustomerID</th>\n","      <th>CustGender</th>\n","      <th>CustLocation</th>\n","      <th>CustAccountBalance</th>\n","      <th>TransactionAmount (INR)</th>\n","      <th>customer_age</th>\n","      <th>day_in_month</th>\n","      <th>day_of_week</th>\n","      <th>seconds</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>C5841053</td>\n","      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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":44}],"source":["# Copying the data and taking a look at its state.\n","preprocessed = cleaned_df.copy()\n","preprocessed.head()"]},{"cell_type":"code","execution_count":null,"id":"71b69359-743f-461a-bd1c-1941465dfbe4","metadata":{"id":"71b69359-743f-461a-bd1c-1941465dfbe4"},"outputs":[],"source":["preprocessed['CustGender'] = preprocessed['CustGender'].apply(lambda x: 1 if x=='M' else 0)"]},{"cell_type":"code","execution_count":null,"id":"c4fbbb74-9e3c-4ea9-9b9d-fba87cdff99e","metadata":{"id":"c4fbbb74-9e3c-4ea9-9b9d-fba87cdff99e"},"outputs":[],"source":["location_encoder = OneHotEncoder()"]},{"cell_type":"code","execution_count":null,"id":"dd34c288-d5ff-43b3-8b0d-bb69681549da","metadata":{"id":"dd34c288-d5ff-43b3-8b0d-bb69681549da"},"outputs":[],"source":["locations_matrix = location_encoder.fit_transform(preprocessed['CustLocation'].to_numpy().reshape(-1,1))"]},{"cell_type":"code","execution_count":null,"id":"77474e97-b90f-4749-a978-2d5be673214c","metadata":{"id":"77474e97-b90f-4749-a978-2d5be673214c"},"outputs":[],"source":["locations = pd.DataFrame.sparse.from_spmatrix( locations_matrix, columns=location_encoder.categories_)"]},{"cell_type":"code","execution_count":null,"id":"9cd4c763-b63a-494c-97cf-f577369fea6d","metadata":{"id":"9cd4c763-b63a-494c-97cf-f577369fea6d"},"outputs":[],"source":["preprocessed = preprocessed.drop(columns=['CustLocation', 'CustomerID'])"]},{"cell_type":"markdown","id":"930904bb-471a-4bd6-8d95-c38410813f54","metadata":{"id":"930904bb-471a-4bd6-8d95-c38410813f54"},"source":["#### Task 3.2\n","\n","Convert the time and date data into a numerical representation\n","<details>\n","    <summary>hint:</summary>\n","Consider values that repeat and effect the data. Data that doesn't change or has a minimal change can be ignored. For example all our data is from 2016 so we can ignore the year.\n","</details>\n"]},{"cell_type":"code","execution_count":null,"id":"5f38a2bb-6d27-4b80-8f4d-1116b165c36e","metadata":{"id":"5f38a2bb-6d27-4b80-8f4d-1116b165c36e"},"outputs":[],"source":["# I have done this earlier"]},{"cell_type":"markdown","id":"c6cacd04-c4f8-4f62-bed0-183886ea9342","metadata":{"id":"c6cacd04-c4f8-4f62-bed0-183886ea9342"},"source":["#### Task 3.3\n","It is usually better to normalize our data in a controlled range for example (0,1). Scikit-learn toolkit has various tools for normalizing data. One of the famous methods is min-max scaling. Apply it on your data per feature. "]},{"cell_type":"code","execution_count":null,"id":"9f7e3ed1-15af-4f34-9e10-04171b1a3d6f","metadata":{"id":"9f7e3ed1-15af-4f34-9e10-04171b1a3d6f"},"outputs":[],"source":["preprocessed = preprocessed.apply(minmax_scale, axis=0)"]},{"cell_type":"code","execution_count":null,"id":"798c8ee1-ea7e-4c29-ac4d-8295f34283b9","metadata":{"id":"798c8ee1-ea7e-4c29-ac4d-8295f34283b9"},"outputs":[],"source":["preprocessed = pd.concat([preprocessed, locations], join='inner', axis=1)"]},{"cell_type":"markdown","id":"77bc658a-0bca-4672-aea5-cfe5bc1da4ba","metadata":{"id":"77bc658a-0bca-4672-aea5-cfe5bc1da4ba"},"source":["#### Task 3.4\n","Inspect your data now. WOW! those are a lot of features. Data with high dimensionality is hard to train and results in poor results. Fortunately there are techniques to reduce the dimensionality of data such as [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis) and [autoencoders](https://en.wikipedia.org/wiki/Autoencoder). Now using PCA reduce the number of features in your data to a sensible number. \n","\n","Note: *Reducing the dimensions of your data isn't a lossless operation. You will lose a lot of data. It's a matter of losing unneeded data. The truth is that the number of features is a hyperparameter and likely you will have to experiment with different numbers until you find a suitable one.*"]},{"cell_type":"code","execution_count":null,"id":"65fd3228-7ffa-48f6-9bae-05b5ce06338b","metadata":{"id":"65fd3228-7ffa-48f6-9bae-05b5ce06338b"},"outputs":[],"source":["data_reducer = PCA(n_components=64)"]},{"cell_type":"code","execution_count":null,"id":"c3182791-3c8c-400f-9d5f-b344370e6fa5","metadata":{"id":"c3182791-3c8c-400f-9d5f-b344370e6fa5"},"outputs":[],"source":["data = preprocessed.to_numpy()"]},{"cell_type":"code","source":["del preprocessed\n","del cleaned_df\n","del locations\n","del locations_matrix"],"metadata":{"id":"qPpExJgsVFH5"},"id":"qPpExJgsVFH5","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"id":"5eb77006-2272-430c-942a-46d623dac912","metadata":{"id":"5eb77006-2272-430c-942a-46d623dac912"},"outputs":[],"source":["reduced_data = data_reducer.fit_transform(data)"]},{"cell_type":"code","source":["reduced_data.shape"],"metadata":{"id":"HoTyMYRISYB7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677832463322,"user_tz":-120,"elapsed":92,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"4a5d9653-76d8-4829-aae0-2106aca8e31b"},"id":"HoTyMYRISYB7","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(925922, 64)"]},"metadata":{},"execution_count":57}]},{"cell_type":"markdown","id":"64427a17-6fff-4a9c-bee5-23776c325394","metadata":{"id":"64427a17-6fff-4a9c-bee5-23776c325394","tags":[]},"source":["### Training your model\n","At last you have reached to the main! That was a long journey wasn't it?! There are many clustering algorithms that are in use in the ML industry: Kmeans, DBSCAN, and Hierarchical Clustering are a few to name. You are required to implement K-means on the data. Choosing the right number of clusters can be tricky but fortunately there are heuristics that help us determine a good number of clusters. Two of the most popular methods are: \n","1. The elbow method\n","2. The Silhouette Method\n","If you want to learn more read [here](https://towardsdatascience.com/how-many-clusters-6b3f220f0ef5)\n","\n","You are provided with a function that calculates the silhouette score for a range of Ks starting from 2 until the number you specify. \n","\n","I will leave the actual interpretation of the silhouette score as an exercise for the reader to search for."]},{"cell_type":"code","execution_count":null,"id":"0270667d-af53-45f3-ac47-441b013bade7","metadata":{"id":"0270667d-af53-45f3-ac47-441b013bade7","tags":[]},"outputs":[],"source":["from sklearn.cluster import KMeans \n","from sklearn.metrics import silhouette_score\n","from sklearn.utils import resample \n","import matplotlib.cm as cm"]},{"cell_type":"code","execution_count":null,"id":"153a169e-665c-4c65-b778-de51c49b8f9e","metadata":{"id":"153a169e-665c-4c65-b778-de51c49b8f9e","tags":[]},"outputs":[],"source":["def silhouette_plot(data, max_clusters=16, n_samples=256, rounds=50):\n","    silhouette_scores = []\n","    for k in range(2,max_clusters):\n","        clusterer = KMeans(n_clusters=k, n_init='auto')\n","        cluster_labels = clusterer.fit_predict(data)\n","        mean_score = 0\n","        for i in range(rounds):\n","             mean_score += silhouette_score(data, cluster_labels, sample_size=n_samples)\n","        mean_score /= rounds\n","        silhouette_scores.append(mean_score)\n","        print(f\"For n_clusters = {k} The average silhouette_score is:\\\n","              {silhouette_scores[k - 2]}\")\n","    plt.figure(figsize=(20, 8))\n","    plt.plot(range(2, max_clusters), silhouette_scores)\n","    plt.title(\"Silhouette Score per K\")\n","    plt.xlabel(\"K\")\n","    plt.ylabel(\"Silhouette Score\")\n","    plt.show()"]},{"cell_type":"code","execution_count":null,"id":"536fc3ff-c745-4069-9f91-d4591e4c7ed7","metadata":{"id":"536fc3ff-c745-4069-9f91-d4591e4c7ed7","colab":{"base_uri":"https://localhost:8080/","height":632},"executionInfo":{"status":"ok","timestamp":1677832625563,"user_tz":-120,"elapsed":162326,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"ca23e5bf-69ec-4a6e-e09f-c22ae5f07160"},"outputs":[{"output_type":"stream","name":"stdout","text":["For n_clusters = 2 The average silhouette_score is:              0.19071540283115984\n","For n_clusters = 3 The average silhouette_score is:              0.10871329656887309\n","For n_clusters = 4 The average silhouette_score is:              0.14360766766086872\n","For n_clusters = 5 The average silhouette_score is:              0.14463736573772074\n","For n_clusters = 6 The average silhouette_score is:              0.2338307758311235\n","For n_clusters = 7 The average silhouette_score is:              0.15514785061831673\n","For n_clusters = 8 The average silhouette_score is:              0.23554994075073268\n","For n_clusters = 9 The average silhouette_score is:              0.2250929497195729\n","For n_clusters = 10 The average silhouette_score is:              0.23250405394082727\n","For n_clusters = 11 The average silhouette_score is:              0.23013519916945782\n","For n_clusters = 12 The average silhouette_score is:              0.2428017757716348\n","For n_clusters = 13 The average silhouette_score is:              0.2628605577953607\n","For n_clusters = 14 The average silhouette_score is:              0.2272261444263205\n","For n_clusters = 15 The average silhouette_score is:              0.2396802299574744\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x576 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAABLMAAAIGCAYAAACvVc2eAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAACdG0lEQVR4nOzdd1yW973/8deHLYgoyAYVR9wbR/YeJprlapukzek8Hacj7elIx0lHTk/3+HWddKVt0tPEmKVm76kRVHDEaBzIFBRBBZn39/fHfZPcIQrcClyM9/PxuB5c9zXfjBj43N/v5zLnHCIiIiIiIiIiIv1BmNcBREREREREREREukrFLBERERERERER6TdUzBIRERERERERkX5DxSwREREREREREek3VMwSEREREREREZF+Q8UsERERERERERHpN1TMEhERkZMys7PN7F4z229mjWZ2zMz2mtnTZvZfZjat3fEXmZkzsxfOZLv0HDObamZ/NLPdZnbCzOoC398Xzey/zWyh1xnl/QL/nTgzG3OK/SvMrMnMfGb2hV6OJyIi0utUzBIREZH3MbP/BF4FPgQ0A08Ca4BS4BzgDuBWj+L1aWY2JlB42N/BMXcHjrm1F3N9ENgMfByIAZ4DHgb2ADOBbwBf6a080j0CP0P/BMKBTzjnfuVtIhERkZ4X4XUAERER6VvMbBbwI6AFuMk5t6rd/iHANUB0u1PfACYD9b0QU0JgZunAn4BI4AvAb51zrUH7I4DLgHHeJJTTYWafBf4f0Arc7Jz7l8eRREREeoWKWSIiItLeMsCAVe0LWQDOuRPAAyfZXg/s7Pl4chquAWKB15xzv26/0znXAjzR66nktJnZV/EXnRuBFc65Rz2OJCIi0ms0zVBERETaSwl8rAzlpDPpgWVm0Wb2XTN7O9Cfq8TMfmlmcac4PszMbjWzl82sxswazOwtM/uJmY08yfG3BrLdfYrr3RHYf8cp9p9rZqvMrCzQm6jCzO4PjGJ7z3WAfYGXo4N6Hb0z7dDMHPCRwDF/bXfMre2ul2xm/2Nm282sPtC3bL2ZfdzM7ORfzZM6re9pUI6UQE+tQjM7Hsix08z+0L53WuD4GYF+a6WBr9dBM3vIzM49xfX3t/WECvR/esXMagPbhgcdNy0wRfNA4OfksJmtM7OLQvx83pkKamYRZnZ74OenIfA9/l8zS+ng/JByBH1/zcw+bWb5ga9jTSi5g673XfyFrHpgsQpZIiIy2KiYJSIiIu0dCHxcFpie1tOi8Pfk+jywDXgGGIZ/OtyD7Q8OFHH+D/grMA94HXgEiMPf82mTmY3vrnBm9jXgZeBGoBh/n6kSYDmwwcyWBB2+BVgdWK8D/ha0tI1m+xv+PlXg70sWfMzbQfedCRQCXwt8bk8FPtcpwB+Be0L4NNq+p5ea2ZQQzsPM5gJb8ffUSsX//Xki8Pl9Av9IvuDjbwQ24u+3VoX/894LXA+8ZGaf7uB2XwXuw/876jogH3CB694MbMJfCKwGHsU/EvBK4Dkz+/dQPq+2uMAq4DuBjI8Etn0SeMPMMt53wpnl+C3wa6AWfw+67SEHNvtpIO9R4Arn3DOhXkNERKTfc85p0aJFixYtWrS8swCjgeP4iwh1wP34C03nAjEdnHdR4JwXQtzugNeAEUH7xgE1gX0XtDvvc4HtB4DxQdujgXsD+za0O+fWwPa7T5H9jsD+O9ptvyawvQiY027fEvzN8WuAxKDtYwLn7O/ga3V34JhbT7E/Fv8ILwd8CQgL2pfJu0Wej3bxexoPlAXOacJfSPkKcAkQ38l5pYHzfgZEtdufDcwNep2Ov8jigE+1O/YG/H3YmoEZ7fbtD8p2xUlyzArsqwEubbdvIXAksH9iF78ebd8jB5QHnwcMwV+gcsCD3ZEj6F7VwOzT+G+y7fxHAh8PBX/dtWjRokWLlsG2aGSWiIiIvIdzrghYhH/0UCz+EUi/Al4Bas3sUTM7uxtv6QM+7pw7EpRhD++OPLq43fG3BT5+wzn3dtA5jcBn8Y96mW9m53VDtv8KfLzVObcpeIdzbg3weyABuLkb7hXsVvwFl787537hnPMF3bcU/4go8Bf2OuWcOwZcjv9phpHAYuAnwLPAETN7zsyuPsmpHwcygOecc192zjW1u26xcy4/aNMn8BfAnnHO/W+7Yx/C/z2NwF8cPZm/OOeeOsn22wO5v+Sce7bdddcD3w/s/9QprtuR7zvn3gq63gn8P0fNwPVmNrobc/zYObf5NDK2uTbw8Svtvu4iIiKDiopZIiIi8j7OuZeBScDVwC/xj5w6gX9K4BLgFTM7ncLByRxwzu04yfa2AsM7U73MLAvIwT/65X1PbnPO1fDu1MQLzyRUoPfWPPyjYF44xWEvBT4uPJN7ncSiwMf3NeAP2Ix/9NxMM4vpygWdc9uBufi/Lj/C/zkdBcLxFwzXmdmd7U67KvDxL13MfUHg499Osb/tOqf63jzcfoOZheGfwtfKSaadBpzJ9+He9hucc8XAi/inHJ7XjTkePo18J7v+L81s3hleS0REpN9SMUtEREROyjnX4px73Dn3JefcuUAi/r5Hb+H/HeLXZpbdDbcqPsX2Y4GP0UHbMgMfDzjnWk9x3t52x56unMDHkYCvXaN2F2jk3lZsSj7De7U3NvBxzSnu6wOG4v8+JHX1os7vJefc151zFwfOvRTYEDjkdjNbEHTKqMDHt+iatq/5vlPs7+x7U3SSbUn4e6iFAzWn+HpsDBwb6vehxjlXe4p9+wMfs7oxx8k+v1B8DH8hLQF40sxmn+H1RERE+qUIrwOIiIhI/+CcawAeMbONwG78UxCvwt+M/Ez4Oj+kx53sDb7wwMdq/D2mOrKze+O8c+9H8fdh6kjj6d7EOdeCv2n5pfgLVpn4p7K1Fbfc6V77NJ04yba2r0UT/sb/HTnUvXG6N0dgCuOZaAE+gL+Ieh3wtJld4pwrPMPrioiI9CsqZomIiEhInHNlZrYTmEP3j0jqTGng4ygzCz/F6Kyx7Y4FfwEC/KOZTuZkI8zaRozVO+duDSnlmSsGJgK/bt+bqSc45+rMbAP+JzYGf08PAJOBs4C8LlyqFP/01LH4n9TY3sm+N505BDQQ6EUV6I3WXYab2TDn3NGT7BsT+NiWtSdzdJlzrtnMVuB/auZi4BkzuzgwjVRERGRQ0DRDEREReQ8zs072h/PuFLySnk/0LudcCf4pbFH4R6i8h5kl4H9qHvh7HrUpC3yceJJzovA/WbH9vUqBbUBWu6l3nWkrnHX0pmFnxzwR+LgshPueUmff04DxgY/B39O2Zuwf7eKt2no6ffgU+/8t8PHFU+x/n8DosWfwj4y6vqvnheBD7TeYWSb+/l8O/4MPeiNHlwUa8S8DHsdffHzWzCZ5mUlERKQ3qZglIiIi7f3AzH5uZpPb7zCzePzTCkfgb0D+eG+HA34R+PhDMxsXlC0K+A0wHHjDOfdK0DkbgTpgmpktbXfOL3l3FE573wl8/D8ze1/TcjOLMrMl7QoJVfiLValmNuIU120b7fO+r3HAXfiLSp8ys6+bWXT7A8xsipndeIrz2/u0mf3ZzHJPcp3oQOP3Gfibm68O2v0noBy41Mx+FPh6BZ+bbWZzgzb9Ef/PxWVm9ol2x16L/6mPLcCvu5i7zfcC5/3OzK4/yecQbmYXm9npNID/jplNCLpWDPBb/AXTNc65/b2UIySBkWE3Ak8Dqfini07o+CwREZGBQdMMRUREpL044AvAl8xsP7AVfzP2NCAXfxPsRuBW51yVB/l+C5wPLAe2mdnz+J/Kdy7+Zt0lwE3BJwSm0f0Q+AFwv5m9jL8XVS7+aWN/5d1RQ8HnPWRmXwN+CLxgZjuAXfinm2UCs/FPXVxEoG9WYBrYOvwjxDab2av4e0Edcs59PXDpR/AXyr5oZtMCmR3wF+fca865Y2a2GFgbuPdtZlYIVOAv1k3H35z9Pk79ZL1gUfhHV33UzMqBLYHPPxmYFfjoA24Lnq7mnDsaKNqsA74KfMTMXsNf9BobOPf7QH7g+HIz+wj+nlJ3mdmngTfxFwvPCXyOnw21x5NzbqOZ3Qr8GXjIzPbg/3ofxV/ImY2/wPppYH0Ilz6A/8mQW83sOfw/5+cD6YF9n+2lHKfFOddgZtfh/zm5BHjezC50zu3p6XuLiIh4ScUsERERae/7+BuAX47/j/P5+J/kdgL/0+ieB37j1R/MzjmfmX0A/6iwjwHn4X/i4QHgZ8CPTlZkc87daWY1+AsUZwO1wJPAN4CPd3C/H5vZM8DngQvxF64a8Y9Yehx/Yerldqd9An/j+CuBFfh/5yoCvh645hYzWwl8BX+Rp62X1yvAa4FjCsxsRiDvdfi/D1HAQfxP2vsDcH8nX642fw6cc3ngOjPxF7Ca8H/dHgJ+75zbcpLP/41Aji/j79G0CGjGP7rsfRmccw+a2Xzga8DFwDT8X+tHgJ+2GzHXZc65e83sDeCLwGX4izc+/N+HV/A36V99yguc4rL4i6LfwD9qbDT+79sfge845yp6Kcdpc86dMLMlwGP4fz6fCxS09vdWBhERkd5mzvX2Q2pERERERLxjZmPw914rcs6N8TaNiIiIhEo9s0REREREREREpN9QMUtERERERERERPoNFbNERERERERERKTfUM8sERERERERERHpNzQyS0RERERERERE+o0IrwMMBCNHjnRjxozxOoaIiIiIiIiIyICRn59/yDmX3H67ilndYMyYMeTl5XkdQ0RERERERERkwDCzopNt1zRDERERERERERHpN1TMEhERERERERGRfkPFLBERERERERER6TdUzBIRERERERERkX5DxSwREREREREREek3VMwSEREREREREZF+Q8UsERERERERERHpN1TMEhERERERERGRfkPFLBERERERERER6TdUzBIRERERERERkX5DxSwREREREREREek3VMwSEREREREREZF+Q8UsERERERERERHpN1TMEhERERERERGRfkPFLBERERERERER6TdUzBIRERERERERkX5DxSwRERERERGRHuLzOZxzXscQGVBUzBIRERERERHpAc++eZBz/uc5PvmPfFpafV7HERkwVMwSERERERER6UZH6pr44r8287G/5REeZjy94yA/WPem17FEBow+Ucwys2vN7GkzqzazBjPbbWY/M7OkLp6fYGafN7MHzWyXmdWYWZOZFZvZvWY2q5Nzv2Nmm8zsqJnVm9k+M3vYzK7otk9SREREREREBrwntpVz+S9eYm1hOZ+/dALPf+UiPn5eDne/tp+7X93ndTyRASHC6wBm9l3gO+02jwduA240swucc8WdXGYy8KuTbM8CPgQsN7PrnHOPt7v3ZOAJYFS788YElreBp7rwaYiIiIiIiMggduh4I//16HbWFZYzNWMYf//ofKZkDAPgG1dPZv/her63dgejkmK5ZFKqx2lF+jdPR2aZ2fm8W8jyAbcDNwDrA9vGAH/q4uV8wFrgo8DlwNeBusC+SOA37e4dCzzCu4WsV4BbgcuA5cAPAI0DFRERERERkVNyzvFoQRlX/OIlnt5+kK9ccRYPf/bcdwpZAOFhxq8/OIvJ6cP4j39uZkfZUQ8Ti/R/5uVTFcxsNXBj4OWfnHOfCGzPBooAC+yb5pzb3sF1soBE51xhu+23AT8L2pTqnKsM7Pss7xa4ngMud86dVke+3Nxcl5eXdzqnioiIiIiISD9VeayBbz20jad2HGRmVgI/WT6Ts1LjT3l8RW0D1//2Vczg4c+eS+qwmF5MK9L/mFm+cy63/Xave2ZdHLT+SttKYFrhgaB9l3R0EedcSftCVsBb7V7XBa3fGLS+A3jczA6b2XEze8nMruk4uoiIiIiIiAxGzjlW55dw+c9f4oVdVXxj0SRWf/qcDgtZAGkJMfz51lxqTzTzsb9tpL6ppZcSiwwsnhWzzGwEMCJoU0W7Q4JfjzvN26wMWn/WORdczJoRtP454AogEYgDzgfWmtmnT/O+IiIiIiIiMgCV157go3dv5MurChifMpTHv3A+n7pwHBHhXfvzempGAr/50Gx2lB3lC//aQqvPu9lSIv2VlyOz4tq9burg9dBQL25mXwFuCbysBf6j3SHD273+AXA18FDQtp+e6omKZvZJM8szs7yqqqpQ44mIiIiIiEg/4pzjvo0HuOLnL/H63sN8Z/EU7v/U2YxLDvnPVS6ZlMq3F0/h6R0H+Z/H1apZJFRePs2wrt3r6A5eH+/qRc3MgJ/ifxoiQA1wtXOu/b8QDbxbJNvgnPt24PxXgcX4m8bHAhcCD7a/j3PuLuAu8PfM6mo+ERERERER6V9KjtTzjQe38vLuQyzISeTHy2YwOqn9+IzQ/Nu5Oew/VMcfX97HmJFx3LRgdDelFRn4PCtmOeeOmNkR3p1qmNbukPSg9T1duaaZRQN/B1YENpXgL2RtPcnhRcDUwPq+oFxHzexwUJ6ErtxbREREREREBhafz3HvGwf4n8fexAHfv24qNy0YTViYdXpuV3x78RSKquv5ziPbyRoRy4VnJXfLdUUGOq8bwD8ftH5+24qZ5QDZQfue6+xCZjYceIp3C1mFwMJTFLIAXgxaHxN0nXggeGphUWf3FhERERERkYGl6HAdH/rTer798DbmjB7Bk1+8gFvOHtNthSyAiPAwfvOhOUxIGcrn7t3EWxXHuu3aIgOZ18WsXwet32pmt5vZ9cB9Qdufcc5tBzCzu83MBZY72g4wsxTgVeCCwKZi4BtAjpmdF7QEj7L6PdD26IiFZvZdM7sK+Cv+KYZt13kFERERERERGRR8PsdfXtnHVb98me2lR/mfG6fz94/OJzsxtkfuNzQ6gr/cOo+YqHA+evdGqo419sh9RAYSL3tm4Zx70czuBL6Jv7B2Z7tDDgAf78KlpgSWNtnAupMcdzHwQuDe2wJN4n8Z2PeddsfWAbc459o3phcREREREZEBaG/Vcb76QCF5RUe4eGIy/33jdNIThvT4fTOGD+HPH8llxf++zsf/nsd9n1xITGR4j99XpL/yemQWzrlvATfgn0pYg/8phnuAXwC5zrkem+bnnPsVcBnwOFCNf6RWKfA3YI5z7sUOThcREREREZEBoNXn+N8X97DoVy+z6+AxfrZ8Jn+5dV6vFLLazMgazq8+MJvCkhpuu38LPp+eMyZyKuac/gM5U7m5uS4vL8/rGCIiIiIiIhKi3QeP8ZUHCikoruHyKancef00UobFeJbnrpf28N+P7eQzF43jq1dN8iyHSF9gZvnOudz22z2dZigiIiIiIiLiheZWH3e9tJdfPbObuOhwfv3B2SyZkY5Z9zV4Px2fOH8s+w7V87sX9jBmZBwrcrM7P0lkkFExS0RERERERAaVHWVH+erqAraVHuWa6el897qpjBwa7XUsAMyM7103lZIj9dz+4Fayhg/hnPEjvY4l0qd43jNLREREREREpDc0tfj4xdO7uPY3r1BR28Dvb5rDb2+a02cKWW0iw8P47U1zyBkZx7/fk8/blce9jiTSp6iYJSIiIiIiIgPe1pJarv3NK/zq2d0snpHO01+6kEXT072OdUrDYiL5y63ziIoI46N3b+Tw8UavI4n0GSpmiYiIiIiIyIDV0NzKj5/YyfW/e5Uj9U386cO5/PIDsxkRF+V1tE5lJ8Zy14dzOXi0gU/9I5+G5lavI4n0CSpmiYiIiIiIyIC0+cARFv+/V/jdC3u4cXYmT33pQi6bkup1rJDMGTWCn6+YRV7REb62uhDnnNeRRDynBvAiIiIiIiIyoDQ0t/Kzp97iz6/sI21YDHf/2zwumpjidazTds2MdPYfnshPnnyL0Ulx3Hb5WV5HEvGUilkiIiIiIiIyYGzcX81XHyhk36E6PrRgFN9YNIn4mEivY52xz1w0jn2H6vj1s7vJGRnLDbOzvI4k4hkVs0RERERERKTfq29q4cdPvMXfXt9P5vAh3PvxBZw7fqTXsbqNmfHfN0yn5Eg9X3tgK5nDY5mfk+h1LBFPqGeWiIiIiIiI9Guv7TnElb98ibtf289Hzh7Dk1+8YEAVstpERYTxh5vnkjViCJ/8Rx77D9V5HUnEEypmiYiIiIiISL90vLGFbz60lQ/9cQPhZtz/qbO549qpxEUP3ElIw2Oj+Ou/zcOAj969kZr6Jq8jifQ6FbNERERERESk33lpVxVX/uIl/vnGAT5xfg6Pf+GCQTPtbnRSHHd9OJeSIyf41D/yaWrxeR1JpFepmCUiIiIiIiL9Ru2JZr76QAEf/ssbxESGsfrT5/DNa6YwJCrc62i9at6YRH68bAYb9lXzjQe34pzzOpJIrxm4Yy9FRERERERkQHn2zYPc/tBWDh1v4tMXjeMLl04gJnJwFbGCXT87k/2H6/jlM/4nHH7ukgleRxLpFSpmiYiIiIiISJ9WU9/Ed9fs4KHNpUxMjeePH85lRtZwr2P1CV+4dAL7D9Xx06d2MTopjiUzM7yOJNLjVMwSERERERGRPuuJbRV86+Ft1NQ38flLJ/C5i8cTFaGOOW3MjB8tm0FpzQm+vKqAjOFDmDt6hNexRHqU/gUQERERERGRPufw8UY++89N/Ps9+aQOi+aRz53LbZefpULWSURHhPO/t+SSnhDDJ/+ex4HD9V5HEulR+ldARERERERE+gznHGsKyrj8Fy/x1PYKvnLFWTz82XOZmpHgdbQ+LTEuir/cOo8Wn+Ojf9tI7YlmryOJ9BgVs0RERERERKRPqDzWwL/fk89//N9mskcMYd3nz+dzl0wgMlx/unbFuOSh/OHmuew/VMdn7s2nudXndSSRHqF/EURERERERMRTzjke3FTC5T9/ieffquIbiyax+tPncFZqvNfR+p2zxyXxwxun8+rbh/n2w9twznkdSaTbqQG8iIiIiIiIeKaitoFvPrSVZ3dWMnf0CH68bAbjkod6HatfW56bzf7Ddfz2+T3kjIzjUxeO8zqSSLdSMUtERERERER6nXOOVXklfH/dDppbfXx78RRuPWcM4WHmdbQB4cuXT2T/4Xr+54mdjE6K5app6V5HEuk2KmaJiIiIiIhIryqtOcHXVxfy8u5DLMhJ5MfLZjA6Kc7rWANKWJjxs+UzKas5wRfv28J9CUOYmT3c61gi3UI9s0RERERERKRX+HyOe9YXccXPXyS/6Ajfv24q//eJhSpk9ZCYyHDuuiWXkUOj+fjf8yitOeF1JJFuoWKWiIiIiIiI9LgDh+u56U8b+NbD25g9agRPfvECbjl7DGGaVtijkuOj+eut82hoauVjd2/kWEOz15FEzpiKWSIiIiIiItJjfD7HX1/dx5W/fIltpbX8z43T+cfH5pOdGOt1tEFjQmo8v7t5Drsrj/O5f26mpdXndSSRM6JiloiIiIiIiPSIvVXHWXnX63x3zQ4WjE3kyS9dwAfmj8JMo7F62/kTkvnB9dN4cVcV312zA+ec15FETpsawIuIiIiIiEi3avU5/vzKXn721C6iI8L42fKZ3DgnU0Usj31w/ij2Harjrpf2kjMyjo+el+N1JJHTomKWiIiIiIiIdJvdB4/xnw8UsqW4hsunpHLn9dNIGRbjdSwJ+PpVk9h/qI7vr9vBqMRYLpuS6nUkkZBpmqGIiIiIiIicsZZWH799/m2u+fUrFB2u41cfmMVdt8xVIauPCQszfvmBWUzLSODz/9rMttJaryOJhEzFLBERERERETkjb5Yf5frfvcpPnnyLy6ek8vRtF3LdLE0r7KtioyL480dyGT4kko/9bSMVtQ1eRxIJiYpZIiIiIiIiclqaWnz88pldXPubV6iobeD3N83htzfNYeTQaK+jSSdShsXw51vncbyhhY/9bSN1jS1eRxLpMhWzREREREREJGTbSmu59jev8MtndnPN9HSe/tKFLJqe7nUsCcHk9GH85kNzeLP8KF/412ZafXrCofQPKmaJiIiIiIhIlzW2tPKTJ3dy3W9fpbquiT9+OJdffmA2I+KivI4mp+HiSSncce1UnnmzkjvXvel1HJEu0dMMRUREREREpEu2FNfwn6sK2F15nOVzs/jWNVNIiI30OpacoQ+fPYa9VXX85dV95IyM5Zazx3gdSaRDKmaJiIiIiIhIh4qr6/nzK/v4++v7SR0Ww93/No+LJqZ4HUu60bcXT6G4up471uwgOzFW31/p01TMEhERERERkffx+Rwv7qri76/v54VdVYSZ8YH5o/jGoknEx2g01kATHmb8+oOzWf6H1/ncPzfzwKfPZlLaMK9jiZyUOacGb2cqNzfX5eXleR1DRERERETkjB2pa+L+vGLu2VBEcfUJkuOj+eD8UXxo/ijSEmK8jic9rLz2BNf/9lXCzXj4c+eSEq/vuXjHzPKdc7ntt/eJBvBmdq2ZPW1m1WbWYGa7zexnZpbUxfMTzOzzZvagme0ysxozazKzYjO718xmdeEanzYzF7S8cKafl4iIiIiISH9RUFzDl+8vYMEPn+WHj+8kPWEIv/nQbF77+iXcdvlZKmQNEukJQ/jzR+ZxpL6ZT/wtjxNNrV5HEnkfz0dmmdl3ge+cYvd+4ALnXHEn11gIvN7BIc3Adc65x09x/gRgCxAbtPlF59xFHd23jUZmiYiIiIhIf9TQ3MqagjL+sb6IwpJa4qLCuWFOJrcsHMPEtHiv44mHntpewafuyefKKWn87qY5hIWZ15FkEDrVyCxPe2aZ2fm8W8jyAd8C3gS+BiwExgB/Aq7swuV8wGPAg0AxMBf4NhAHRAK/AcadJEME8A/8hawGQG83iIiIiIjIgHbgcD33bCji/rxiauqbGZ8ylO9dN5UbZmeqH5YAcMXUNL559WR+sO5NfvTkTr6xaLLXkUTe4XUD+C8Grf/FOfdD8FfegCLAgCvMbKpzbnsH1ykBZjvnCoO2PWNmzcDPAq/HmlmKc66y3bm3AwsC93sQ+NJpfzYiIiIiIiJ9VKvP8eKuSv7+ehEvBhq6Xzk1lVsWjmHh2ETMNPJG3utj5+Ww/3Ad//viXsYkxfHB+aO8jiQCeF/Mujho/ZW2FedcsZkdAEYHNl0CnLKY5ZwrwV/Qau+tdq/rgl+YWS7+0Vs+4MOB+4iIiIiIiAwY1YGG7vcGGrqnxEfz+Usm8EE1dJdOmBl3LJnKgeoTfPvhbWSPiOW8CSO9jiXiXTHLzEYAI4I2VbQ7pIJ3i1nvmx7YRSuD1p91zr1TzDKzIcA9+L8GP3bOvWRmXS5mmdkngU8CjBql6rSIiIiIiPQdzjm2FNfwj/VFrC0sp6nFx4KcRL5+1WSumJpKZHifeBaY9AMR4WH89kOzWfb71/n0vfk8+OlzmJCqfmp9UeWxBu7fWMyQqAg+dl6O13F6lJcjs+LavW7q4PXQUC9uZl8Bbgm8rAX+o90hPwEmAgX4R2eFxDl3F3AX+BvAh3q+iIiIiIhIdzvR9G5D962l/obuK3OzueXs0ZylAoScpviYSP58ay7X//Y1Pvq3jTz0mXMZOTTa61iCv3D9+t7D3Lv+AE9ur6DF57huVobXsXqcl8Wsunav2/+XEPz6eFcvav6J3j8FbgtsqgGuds69GXTMPOAzQCNws3OufSFNRERERESk39h/qI571hexKr+E2hPNTEgZyvevm8oNc7IYGu11dxkZCLJGxPKnj+Tygbte55N/z+Ofn1hITGS417EGrdr6Zh7YVMK9G4rYW1XH8NhI/u3cMXxowWhyRrYfOzTwePavmnPuiJkd4d2phmntDkkPWt/TlWuaWTTwd2BFYFMJ/kLW1naHZuJvLh8NbD1Fo8MLzcwBv3LOfbEr9xcREREREektrT7H8zsr+fv6Il7aVUVEmHHl1DRuOXs0C3LU0F2636zs4fxixSw+fe8mvrKqgF9/YDZhYfo5600FxTXcs76INYVlNDT7mD1qOD9bPpNrZqQPquKi1yX654EbA+vnA3cDmFkOkB103HOdXcjMhgOPABcENhXiL2SVdlNWERERERERzx0+3sh9ecXcu/4ApTUnSB0WzRcv8zd0Tx2mhu7SsxZNT+drV03iR0/sJGdkHF++YqLXkQa8+qYWHt1Sxr0bDrC1tJbYqHBunJPFTQtGMTUjwet4nvC6mPVr3i1m3Wpme4AdwO1BxzzjnNsOYGZ3Ax8JbP+uc+6OwPYU/IWxKYF9xcA3gJxAYazNVudcLbAV+NJJ8lwFXBlY3wP8Bth8up+ciIiIiIhId3DOsbm4hn+8XsS6wnKaWn2cPTaJb14zmcunqKG79K5/v3As+w/V8f+ee5vRSXEsm5vldaQBaffBY9y74QCrN5VwrKGFianxfP+6qVw/O5P4mEiv43nK02KWc+5FM7sT+CYQBtzZ7pADwMe7cKkpvFvIAv+ornUnOe5i4AXn3B7gl+13BkZ3tRWzSpxz7ztGRERERESkt5xoauXRglL+/noR28uOMjQ6gg/Mz+aWhaP1RDnxjJnxgxumUXyknm88WEjWiCEsHJvkdawBoanFxxPbK7hnfRFv7KsmKjyMq6encfPC0cwdPULThwO8HpmFc+5bZpaH/2mDc4BY/COrHgV+6Jyr8jKfiIiIiIhIb9vX1tA9r5ijDS2clTqU718/jRtmZ6qhu/QJkeFh/P6mudz4+1f51D/yeegz5zA2eajXsfqt4up6/u+NA9yfV8yh402MSozl64smsXxuFkl6cuT7mHPO6wz9Xm5ursvLy/M6hoiIiIiI9GOtPsdzOyv5++v7eXn3ISLCjKumpXHLwtHMV0N36aMOHK7n+t+9yrCYCB78zLkkxkV5HanfaPU5XnirknvWF/HCrioMuHRyKjcvHM3540equT5gZvnOudz3bVcx68ypmCUiIiIiIqfr0PFG7ttYzD83+Bu6pw2L4UMLRvGBedmkqKG79AP5RdV88I8bmJU1nH98fD7REYPnqXqno/JYA/dvLOb/3iimtOYEKfHRfGBeNh+YP4qM4UO8jtennKqYpfGpIiIiIiIivcw5x6YDNfzj9f08trWCplYf54xL4lvXTOYyNXSXfmbu6ER+smwGX/jXFr6+eis/XzFTIwnbcc6xfm8192wo4sltFbT4HOeO13/zp0vFLBEREZFuUlPfxFW/fJm0hBiWzMxg8Yx0UjWqQkSC1De18OiWMv7+ehE7yo8SHx3BhxaM4uaFoxifoobu0n9dNyuTosP1/PzpXeSMjOPzl07wOlKfUHuimdX5Jdy7oYg9VXUkDInkI+eM4aYFo9Rj7AyomCUiIiLSTR4tKKPiaAPDhkTw/bU7+MG6Hcwfk8iSmRksmpamBq4ig9jequPcs/4Aq/KLOdbQwqS0eO68YRrXz8okTg3dZYD4j0vGs/9wHT9/ehejk2K5blam15E8U1hSwz3ri3i0oIyGZh+zsofz0+UzWTwjnZhITcM8U/pXU0RERKSbrMorYXL6MB7/wvnsqTrO2oJyHi0o5VsPb+O/Ht3OueNHsmRGOldMTSNhSKTXcUWkh7W0+nhuZyX/WF/0TkP3RdPTuWXhaOaNGaFpWDLgmBk/vHE6JUdO8J+rCskcPoTcMYlex+o1J5paebSglHvWH2BraS1DIsO5YXYmNy0YzbTMBK/jDShqAN8N1ABeREREdlYc5apfvsx3Fk/ho+flvLPdOcfOimOsKShjbWE5B6rriQoP48KJySyZmcFlk1OIjdL7iyIDSdWxRu7PK+be9UWU1TaQnhDDh+aPYuX8bFLiNfVYBr4jdU3c+PvXqD3RzEOfOYfRSXFeR+pRb1ce4571B1i9qYRjDS2clTqUmxeO5vrZmQyL0ZtXZ0JPM+xBKmaJiPRdTS0+fvfC29y8cDQjNcVLetD31+7g76/vZ8Ptl53yseTOOQpKagOFrTIOHm1kSGQ4l05OYcnMDC48K1lTD0T6Kecc+UVH+Mf6Ih7bWk5zq7+58y0Lx3DZ5BQi1NxZBpl9h+q44XevkhgXxUOfPpeE2IFV1Glq8fHk9gruWV/Ehn3VRIWHsWh6Gjct0MjL7qRiVg9SMUtEpO96Yls5/37PJj514Vi+sWiy13FkgGpu9bHwv59l3phE/nDL3C6d4/M5Nu6vZk1hGY9traC6ron46AiumJrGkpnpnDt+pJ5sJNIP1De18PDmMv6xvog3Aw3dl87N4uaFoxmfoubOMrht2HuYm/+8gdzRifzto/OJiuj//18rrq7nXxsPcN/GEg4dbyQ7cQgfmj+a5blZeuO0B5yqmKUx7SIiMqCtKSgHYHV+KV+5YqKKA9IjnttZyeG6JpbNzeryOWFhxoKxSSwYm8QdS6by2p7DrCko44ntFazeVMKI2EgWTU9nyYwM5uckEh6md3hF+pI9Vce5Z30RD+SXvNPQ/b9vmM51szLU0F0kYMHYJP7nxhl8eVUB33p4Kz9aOqNfjlhq9Tle3FXJPesP8PxblRhwyaRUbl44igsmJBOm/0f3Ov0rKyIiA1ZdYwvP7jzI2OQ49lbV8fzOSq6YmuZ1LBmAVuWVMHJoNBdNTD6t8yPCw7jgrGQuOCuZH9wwjZd2HWJtYRkPby7lnxsOkBIfzTUz0lkyM4PZ2cP75R8C0v2cc+w7VIcDEmOjSBgSqT+oelhLq49n3qzknvVFvPL2ISLDjUXT0vnw2aOZO1rTikROZuncLIoO1/Hr594mZ+RQPn3ROK8jdVlb/7t/bjhAac0JkuOj+dzF4/nA/FFkDh/idbxBTcUsEREZsJ7dWUlDs48fXDeNL963hfvzilXMkm5XdayR59+q5GPn5XRLT5zoiHAun5LK5VNSOdHUynM7K1lTUMa9Gw7w11f3kzl8CEtmZrBkZjpT0ofpj+dBpNXneLP8KG/sq2bDvsNs3H+E6rqmd/aHGQyPjWJEbCSJcVGMiA0scVEkxkUyIjbKvz0uisTA9mExEfoZ6oKqY438640D/PONA5QHGrp/5YqzWDlvFMnxmlYk0pkvXX4W+w7X86MndjI6KZarp6d7HemUnHNs2FfNPeuLeHJ7Bc2tjnPGJfHNayZz+ZRUjfLvI1TMEhGRAWtNQRmpw6JZODaJpXOzuOulvVQebSBlmJ4kJd3nkS2ltPocy0OYYthVQ6LCuWZGOtfMSOdYQzNP7zjImoIy/vTyXv7w4h7GJsexZIa/sDU+Jb7b7y/eam71sbW0ljf2VfPGvmo27q/mWEMLANmJQ7hkUgrzxowgJjKc6romjtQ1UV3fxJG6ZqrrmjhQXc+W4hqO1DfR3HryPrkRYcbw2HeLXR0Vv9rW46LCB0UBzDlHXtER/vF6EY9v8zd0P2/8SO64diqXTlJDd5FQmBk/WTaD0iP1fOm+LaQnxDB71AivY71H7YlmHtxUwr0bDvB25XGGxURwy8Ix3LRwFOOS1f+ur1ED+G6gBvAiIn3P0YZmcr//DDcvHM13lkxh36E6Lv7pC3z1qol85qLxXseTAcI5x1W/fJmYqHAe+ey5vXbfI3VNPLG9gjUFZby+9zDOweT0YSyZ6e+xlZ0Y22tZpPs0NLdSUFzDhkDxKr/oCCeaWwEYlxzH/JwkFo5NZN6YRDJCmN7inKOuqdVf7Hqn4OVfP1LfRHVdc1AhzL/tSH0zrb6T/50QFR7GiOBiV2wUI+Ii3xnt1bYtuBA2JKr/PKWzrrGFh7eU8o/Xi9hZcYz4mAiWBRq66w9akTNz+Hgj1//uVU40tfLQZ87tE/+/2lpSyz3ri3i0oIwTza3MzB7OzQtGsXhGRr/6t2ug0tMMe5CKWSIifc/q/BK+vKqABz9zDnMC7/yt+N/XqTrWyHNfvnBQjCqQnldYUsO1v3mVH1w/jZsXjvYkQ+XRBh7bWs6awnLyi44AMDN7OEtmpLN4RgZpCRqJ2FfVNbaw6cAR/7TBvdVsKa6hqdWHGUxKG8aCnETm5/iLV709lc3ncxxraPEXuzopfrV9rDnRzKn+tIiJDHun2PXO6K/YyPcXvwIfh8dGEhPZu39Evl3pb+i+Or+EY40tTE4fxofPHs11szKIjdKEFpHu8nblMW743WukJ8TwwKfPYVhMZK9nONHUGpjCX0RBSS1DIsO5blYGNy8czbTMhF7PI6emYlYPUjFLRKTvufWvb7D74HFe+drF7xSu2gpc931yIQvGJnmcUAaCbz+8jfvzinnjm5eRMKT3fxlvr+RIPesKy1lTWMa20qOYwbwxiSyZmcHV09JI0iPDPVV7opm8/dWBnlfVbCutpcXnCA8zpmUMY8HYJOaPSSR3zAiGx0Z5HTdkrT5H7Qn/FMea+nbFr7bX7UaGHQ1MmzyZuKjwd4pdw2ODil8nHQHmHykWai8bf0P3g/z99SJe23OYyHDj6un+hu5zRqmhu0hPefXtQ3zkL29w9rgk/nrrvF6btvt25XHu3eAvWh9taGFCylBuXjiaG+ZkelJUk86pmNWDVMwSEelbjtQ1Me/OZ/jY+Tl8Y9Hkd7afaGpl/p3PcPnUVH6+YpZ3AWVAaGj2/zxdNDGFX39wttdx3mdv1XHWFpbzaEEZb1ceJzzMOGdcEktmZnDl1LQ+UXwb6A4fb2Tj/mrW7/UXsN6sOIpz/ml6M7MTmJ+TyIKcJOaMHsHQ6ME58qe51UdN/bvFrpqOil+BfmDHG09dAIuPiehS8SsuOpxn36zknxsOUHG0gYyEGG5aOJoVudlq6C7SS+7beICvrd7KTQtG8YPrp/VY8bipxcdTOyq4Z30R6/dWExluXDUtnZsXjGJ+TqKK1n3cqYpZg/P/miIiMqA9ub2CFp9jyYyM92wfEhXOklkZPLiphDuunap34OSMPL3jIEcbWlie2/2N37vD2OShfP7SCfzHJeN56+Ax1hSUsaagnK8+UMi3HtrGBWcls2RmOpdNTiVukBZSultFbQMb9h1+Z+TV25XHAf8Uu7mjR/DFS89ifk4is0cN7/UpdH1VZHgYyfHRIRWQGltaqalvfl+x60jbtkAhrOp4I7sOHqe6rumd3mPtnT9hJN+7biqXqKG7SK9bOW8U+w7V84cX95AzMo6Pnz+2W69fcqSef71RzL82FnPoeCNZI4bw1asmsiI3m5Eaqdzv6TcXEREZcNYUljEmKZapGcPet29lbjb/3HCANQVl3LTAmx5HMjCsyi8hPSGGc8aN9DpKh8yMSWnDmJQ2jK9cMZHCklrWFJSxtrCcZ948SExkGJdOTmXJjAwumpisIksXOecoOXKCDfuq2bD3MG/sr6bocD0AQ6MjyB0zghvnZLIgJ4npmQlERahQ0l2iI8JJHRZOaghPpj3R1PruaK9Ag/tpGcMYq4buIp766pUTKTpcx52PvcmoxFiumJp2Rtdr9Tle2lXFPeuLeP6tShxwycQUbl44mgvOSiY8TKOwBgoVs0REZECpOtbI63sO89mLx5902PiMrAQmpcVz/8ZiFbPktFXUNvDK7io+c9H4fvWLsZkxM3s4M7OHc/vVk8krOsKagjIe21rOusJyhkZHcMXUVJbMzOC88SND7j80kDnn2FNVxxv7qnlj32E27KumvLYBgOGxkcwfk8gtC0ezICeJyenxGuXTxwyJCmdI1JCQngIpIj0vLMz4+YpZlP1xPV/41xbu/9TZTM8KvQH7oeON3LexmP974wAlR04wcmg0n7loPB+Yn03WCO+fmCjdT8UsEREZUJ7YVo7PweJ2UwzbmBkrcrP53tod7Kw4yqS094/eEunM6k0l+Bwsm9s3pxh2RViYMT/wtLz/WjKF1/ceZk1BGY9vq+DBTaUMj41k0bR0lsxMZ0FOUr8q2nUHn8/x1sFj74y6emNfNYeONwGQHB/N/JxEFuYkMj8niQkpQwkbZF8fEZHuMiQqnD9+eC43/PY1Pva3jTzyuXNJT+i88Oyc44191dyz4QBPbCunudVx9tgkvr5oEldMSdOI2AFODeC7gRrAi4j0HSv+8DpH6pt4+rYLT3nMkbomFvz3s9y0cBT/tWRqL6aTgcA5xyU/e5HkodHc/+9nex2n2zW2tPLyrkOsKSzj6R0HqW9qJTk+mmump7NkZgZzRg0fkM1yW1p9bC87Guh35e971fakvczhQ1gQKPwtGJvEmKTYAfk1EBHx0lsVx1j6+9fIToxl1b+ffcoHYxxtaObB/BLu3XCA3ZXHiY+JYNncLG5aMJrxKZo6PNCoAbyIiAx4FbUNbCyq5kuXndXhcSPiorh8aioPbS7l64smER2hHkHSdflFR9h3qI5PXzTO6yg9IjoinMumpHLZlFRONLXy3M5K1hSU8c83DnD3a/vJHD6ExTPTWTIjg6kZw/ptUaexpZXCktp3mrXn76+mrsnfJDxnZByLpqWzYKy/gKUpKiIiPW9iWjy/vWkOH717I5//v83cdcvc90zZ3lZayz3ri3hkSxknmluZmZXAj5fOYMnMDIZE6Xe5wUbFLBERGTDWbS3HOVg8I73TY1fmZrOusJyndxw85ZREkZNZlVdCbFQ410zv/OesvxsSFc41M9K5ZkY6xxqaeXrHQdYUlPHnl/fxvy/uZezIOBbPzGDJjHQmpMZ7HbdDJ5pa2XzgCOsDPa82H6ihscUHwMTUeG6ck+UfeZWTSEoIjcVFRKT7XHhWMndcO5VvP7yNH6x7k69dNYk1hWXcu76IgpJaYiLDuG5mJjcvHH1avbVk4FAxS0REBow1BWVMSe/a06nOGz+SzOFDuG9jsYpZ0mX1TS2sLSzj6unpxJ1i+sNAFR8TyY1zsrhxThZH6pp4YnsFawrK+H/P7ebXz+5mUlo8S2ZmsGRGBqOSvB/JdKyhmbyiI/6RV3sPs7W0luZWR5jBlIxh3LxwNPNzEpk3JpHEuCiv44qISMAtC0ez/1Adf35lH6vyiqlramV8ylDuWDKFG+ZkkTAk0uuI0gcMrt/CRERkwCqurmdLcQ1fu2pSl44PCzOWzc3i18/tpuRIvaYRSZc8vrWCuqZWlvfjxu/dYURcFB+cP4oPzh9F5dEGHttazprCcn7y5Fv85Mm3mJmVwJKZGVwzI71LTXy7w5G6pncatb+xr5rtZbX4HESEGTOyEvjYeWNZMDaRuaNHMCxGfwiJiPRlt189mfqmVuoaW/jQglEsyEnst9PapWeoAXw3UAN4ERHv/eHFPfzP4zt5+asXk53YtcJUyZF6zv/x83zh0gl8sZM+WyIAH7jrdcpqGnjxPy/SL9UnUXKknnWF5awpLGNb6VEA5o9JZMnMdBZNT2fk0Ohuu1flsYbAqCt/8eqtg8cAiIoIY3b2cBaMTWJBTiKzRw0nNkrv34qIiPRHagAvIiID2pqCMmZmD+9yIQsga0Qs540fyaq8Ej5/yQTCwlSckFM7cLie9Xurue3ys1TIOoWsEbF86sJxfOrCceytOs7awnIeLSjj249s578e3c6540eyZEYGV05NIyE2tNFRpTUn2LD38Dsjr/YeqgMgNiqcuaNHsGRmOgvGJjEjK0EPdRARERngVMwSEZF+b9+hOraXHeVb10wO+dwVudn8x/9t5tU9hzh/QnIPpJOBYvWmEsxg6SCfYthVY5OH8vlLJ/Afl4znrYPHWFNQxpqCcr66upBvPryVC89KZsnMDC6bnPq+/mPOOfYfrueNfYfZsNf/tMHSmhMADIuJYH5OIh+Yn838nCSmZgwjMuhpVyIiIjLwqZglIiL93tqCMgCu6cJTDNu7Ymoqw2MjuW9jsYpZcko+n+OB/BLOHed/cIB0nZkxKW0Yk9KG8ZUrJlJYUsuagjLWFpbzzJuVxESGcemkVK6alkZNfRMbAiOvKo81ApAUF8X8nEQ+fn4OC3KSmJgWT7hGUYqIiAxqKmaJiEi/t6awjHljRpxWo+noiHCun5XJPzcc4EhdEyP0VDM5ifV7D1Nac4KvXjXR6yj9mpkxM3s4M7OHc/vVk8krOsKagjIe21rOuq3lAKQNi+HscUnMz0lkQU4i45KHalqniIiIvIeKWSIi0q/tOniMXQeP873rpp72NVbOy+bu1/bz8JZS/u3cnG5MJwPFqvwS4mMiuHJqmtdRBoywMGN+TiLzcxL5ryVT2FJcQ0p8DNmJQ1S8EhERkQ6pwYCIiPRrawvKCDNYNC30KYZtJqcPY0ZWAvdtLEZP+ZX2jjY08/i2cpbMzCAmUo3Fe0JEeBi5YxIZlRSrQpaIiIh0SsUsERHpt5xzrCksZ+HYJJLjo8/oWitys9lZcYytpbXdlE4GinWF5TQ0+1iuxu8iIiIifYKKWSIi0m9tLzvKvkN1LJmZccbXunZWBjGRYdy3sbgbkslAsiqvmPEpQ5mVPdzrKCIiIiKCilkiItKPrS0sJyLMuKob+hgNi4nk6mnpPLqljBNNrd2QTgaCtyuPs+lADcvmZmn6m4iIiEgfoWKWiIj0S8451haWce74kd32BMIV87I51tjC49vKu+V60v89kF9CeJhx4+xMr6OIiIiISICKWSIi0i9tKa6h5MiJbpli2GZBTiJjkmI11VAAaPU5HtpcwoVnJZMyLMbrOCIiIiIS0CeKWWZ2rZk9bWbVZtZgZrvN7GdmltTF8xPM7PNm9qCZ7TKzGjNrMrNiM7vXzGad5JwLzOznZrbBzErNrNHMjprZG2b2JTPrnrf5RUSkR6wtLCcqPIwrpqZ22zXNjOW52WzYV83+Q3Xddl3pn17aXcXBo41q/C4iIiLSx3hezDKz7wKPAJcBI4BoYDxwG5BnZtlduMxk4FfADcAEIAGIBLKADwFvmNmidufcDnwJmA9kAFFAPDAP+DnwlJlFnNEnJyIiPcLnc6wrLOeCs5IZFhPZrddeNjeLMIP78zQ6a7B7IK+EEbGRXDq5+wqmIiIiInLmPC1mmdn5wHcCL334C0w3AOsD28YAf+ri5XzAWuCjwOXA14G2t9Ujgd+c5JxS4L+BawL3fT5o34X4C2EiItLH5BUdoeJoA0tmpnf7tVOHxXDxxBQeyC+hpdXX7deX/qGmvomndxzkulmZREV4/t6fiIiIiATxeuTRF4PW/+Kc+yGAmeUDRYABV5jZVOfc9g6uUwLMds4VBm17xsyagZ8FXo81sxTnXGXg9c+AF51zTW0nmNmTwH4gJbBpIfD30/rMRESkx6wtLCMmMozLemjEzIp52Ty7s5IXd1VpVM4g9ciWMppafSzP1RRDERERkb7G67caLw5af6VtxTlXDBwI2ndJRxdxzpW0K2S1eavd63caoDjnng4uZAW2nWh33+Md3VdERHpfS6uPx7aWc8mkFOKie+Y9mUsmpTByaLQawQ9iq/KLmZI+jKkZCV5HEREREZF2PCtmmdkI/D2y2lS0OyT49bjTvM3KoPVnnXMddvM1s7OAWUGbHj3N+4qISA/ZsK+aQ8ebWDKj+55i2F5keBhL52Ty3M5Kqo419th9pG96s/wo20qPalSWiIiISB/l5cisuHavmzp4PTTUi5vZV4BbAi9rgf/o5PhM/MWrtrf573LOvdLB8Z80szwzy6uqqgo1noiInKa1hWXERYVz8aSUzg8+A8tzs2nxOR7cVNKj95G+Z1VeCZHhxnWzMr2OIiIiIiIn4WUxq/0oqegOXnd5up/5/Qz4SWBTDbDIOfdmB+dMw990fmJg0z+Bz3R0H+fcXc65XOdcbnJyclfjiYjIGWhu9fH4tgoum5JKTGR4j95rfMpQckeP4L68YpxzPXov6TuaWnw8vKWUSyelkhgX5XUcERERETkJz4pZzrkjwJGgTWntDgl+RNWerlzTzKKBfwG3BTaVABc4517v4JyL8PfraptL8EvgZudca1fuKSIiveeVtw9RU9/co1MMg62Yl83eqjryi450frAMCM+/VUl1XZOmGIqIiIj0YV43gH8+aP38thUzywGyg/Y919mFzGw48BSwIrCpEFjonNvawTkfBJ4EEgAf8EXn3Jec3oIXEemT1hSUER8TwflnjeyV+10zPZ24qHA1gh9EVuWVkBwfzYVnadS1iIiISF/ldTHr10Hrt5rZ7WZ2PXBf0PZnnHPbAczsbjNzgeWOtgPMLAV4FbggsKkY+AaQY2bnBS0JQed8DrgXaJtD8Csgv93x07v30xURkdPV0NzK09sPcuXUNKIjenaKYZu46AiWzMxg3dZyjje29Mo9xTtVxxp5/q1KbpydSUS4178iiYiIiMipePqbmnPuReDOoCx3Ag8B8wLbDgAf78KlpgSWNtnAOuDldsvsoGOWARb0+ksnOf7/df2zERGRnvTSriqONbawZGbvTDFss2JeNvVNrawtKOvV+0rve3hzKa0+pymGIiIiIn2c5287Oue+BdyAfyphDf6nGO4BfgHkOueKvEsnIiJ9xZrCckbERnLOuKReve/s7OFMSBnKfXmaajiQOedYlV/MrOzhjE+J9zqOiIiIiHQgwusAAM65h4GHu3DcrcCtJ9n+Au8dZdWVe14UyvEiIuKdE02tPPvmQa6blUlkL0//MjNWzsvmB+veZPfBY0xIVaFjICosqWXXwePcecM0r6OIiIiISCc8H5klIiLSmed2VlLf1MqSmemdH9wDbpidSWS4qRH8ALYqv5joiDAW99KTMkVERETk9KmYJSIifd6agjKS46NZkNO7UwzbJA2N5rLJqTy4uZSmFp8nGaTnNDS38uiWMq6cmkbCkEiv44iIiIhIJ1TMEhGRPu14YwvPv1XJ1dPSCA8LaUZ5t1oxL5vquiaeffOgZxmkZzy94yBHG1rU+F1ERESkn1AxS0RE+rRndhykscXX608xbO+CCcmkJ8SoEfwAtCq/hIyEGM4ZN9LrKCIiIiLSBSpmiYhIn7amoIz0hBjmjBrhaY7wMGPZ3Cxe2lVFee0JT7NI9ymvPcHLu6tYOjfL05F/IiIiItJ1KmaJiEifVVvfzEu7q7hmejphfaDQsHxuNj4HD+SVeB1FusmDm0pxDpbN1RRDERERkf5CxSwREemzntxRQXOr83yKYZtRSbGcMy6J+/OL8fmc13HkDDnnWJVXzPycREYnxXkdR0RERES6SMUsERHps9YUlDEqMZYZWQleR3nHynnZFFefYP3ew15HkTOUV3SE/YfrWa5RWSIiIiL9SkjFLDOLN7PvmNkrZrbbzM4ObB8Z2D6pZ2KKiMhgc/h4I6/tOcw1M9Ix836KYZsrp6YxLCZCjeAHgFV5xcRGhXP19HSvo4iIiIhICLpczDKzZCAP+DaQBIwFhgA45w4BHwE+2QMZRURkEHpiewWtPseSGX1jimGbmMhwrp+dyePbKqitb/Y6jpym+qYW1hWWc830dOKiI7yOIyIiIiIhCGVk1g+ANGABcD7Q/m3yR4BLuymXiIgMcmsKyhibHMfk9Hivo7zPitxsmlp8PFJQ6nUUOU2Pb62grqlVjd9FRERE+qFQilmLgd855zYBJ+t6uxfI7pZUIiIyqFUebWDDvmoWz8joU1MM20zLTGBqxjDu26iphv3VqvxiRifFMj8n0esoIiIiIhKiUIpZI4G3O9jvA2LOLI6IiAg8trUc52DJjL7by2jlvGy2lx1lW2mt11EkRAcO17N+bzXL5mT1yWKpiIiIiHQslGJWBTCug/2zgQNnFkdERATWFJYzKS2eCal9b4phm+tmZhIVEcb9agTf7zywqQQzWKophiIiIiL9UijFrMeAj5nZ+94mN7MFwIfx980SERE5bWU1J8gvOsKSmX2r8Xt7CbGRLJqWxsObS2lobvU6jnSRz+dYnV/CeeNHkjF8iNdxREREROQ0hFLM+i7QAmwGfoi/b9ZHzOz/gJeAMuBH3Z5QREQGlXWF5QAs7sNTDNuszM3maEMLT26v8DqKdNHrew9TWnNCjd9FRERE+rEuF7OccxXA2cAG4KP4n2Z4C7ACeAo43zlX3RMhRURk8FhTWMb0zARGJ8V5HaVTC8cmkZ04RI3g+5FVecXEx0Rw5dQ0r6OIiIiIyGkKZWQWzrkDzrnrgERgAbAQSHbOLXHOlfREQBERGTyKDtdRWFLLkpl9f1QWQFiYsWJuNq/tOcyBw/Vex5FOHG1o5vFtFVw7M4OYyHCv44iIiIjIaepSMcvMhppZq5l9G8A5d9Q5t9E594ZGY4mISHdZG5hieM2Mvt0vK9iy3CzCDFbla3RWX7e2oJzGFp+mGIqIiIj0c10qZjnnjgM1QGWPphERkUFtTUEZc0YNJ7MfNeZOTxjCBWcl80B+Ca0+53Uc6cAD+cWMTxnKrOzhXkcRERERkTMQyjTD54ELeyqIiIgMbm9XHmdnxbE+/xTDk1mZm015bQMv7a7yOoqcwtuVx9l0oIblc7MwM6/jiIiIiMgZCKWY9Z/AeWb2XTMb1lOBRERkcFpbWIYZXD29f/TLCnbp5FSS4qK4X43g+6wH8ksIDzNumJPpdRQREREROUOhFLOeBWKAbwFHzKzCzPa2W/b0TEwRERnInHOsKShj/phEUofFeB0nZFERYdwwO5Nn3jzI4eONXseRdlpafTy4qYSLzkomJb7//XyJiIiIyHuFUsw6AGwHXgosbwJF7ZYD3R1QREQGvp0Vx9hTVdcvpxi2WTkvm+ZWx0ObS72OIu28vPsQlccaWZ6rxu8iIiIiA0FEVw90zl3UgzlERGQQW1tYRniYsWhamtdRTtuE1HhmjxrOvzYW87HzctSXqQ9ZlV9MYlwUl0xK9TqKiIiIiHSDUEZmiYiIdDv/FMNyzhmXRNLQaK/jnJGVudnvNBqXvuFIXRPP7KjkulkZREXo1x4RERGRgSDk3+rMbJyZ3WZmvwkst5nZuJ4IJyIiA9/W0loOVNezZEb/nWLYZvHMDGKjwtUIvg95ZEspTa0+ls/N9jqKiIiIiHSTkIpZZvZ9YCfwU+AzgeWnwFtm9r3ujyciIgPd2sJyIsONK6f23ymGbYZGR3DN9HTWFpZR19jidRwBVuWXMCV9GFMy9CBmERERkYGiy8UsM/so8E1gA3A9MCGwXA+8DnzTzG7t9oQiIjJg+XyOtQVlnD8hmYTYSK/jdIuV87Kpa2plXWG511EGvR1lR9ledlSN30VEREQGmFBGZn0WfyHrIufco865PYHlUeBi4A3gP3oipIiIDEybi49QVtvAkpnpXkfpNnNHj2Bschz35WmqodceyC8hMty4blam11FEREREpBuFUsyaDPzLOfe+eROBbf8KHCMiItIlawrKiYoI47LJA+cpc2bGytxs8ouO8HblMa/jDFpNLT4e3lLKZZNTSYyL8jqOiIiIiHSjUIpZTcDQDvbHB44RERHpVKvPsW5rORdPTCY+ZmBMMWxz45wsIsKM+/NKvI4yaD23s5LquiZNMRQREREZgEIpZm0EPmVm73v73MxSgE/in4YoIiLSqTf2VVN1rJElM/v/UwzbS46P5pJJKTy4qYTmVp/XcQalB/KLSYmP5oIJyV5HEREREZFuFkox6/tAOvCmmf3EzP4tsPwUeBNIA37QEyFFRGTgWVtYxpDIcC6ZlOJ1lB6xcl42h4438eyblV5HGXQqjzXw/FtV3DAnk4jwkB7cLCIiIiL9QERXD3TOvWRmNwK/Ab7cbvcB4CPOuZe7M5yIiAxMLa0+Ht9WwaWTU4iN6vL/ivqVC89KJiU+mvvzirlqWprXcQaVhzeX0upzLJ+b7XUUEREREekBIf0F4ZxbY2brgLlATmDzXmCTc07zKEREpEte23OY6rqmATnFsE1EeBjL5mbxhxf3UFHbQFpCjNeRBgXnHKvySpg9ajjjUzpq9SkiIiIi/VXIY++dcz7n3Ebn3P2BJU+FLBERCcXawjLioyO48KyB3c9oRW42PgerN6kRfG8pLKlld+Vxls1V43cRERGRgarLxSwzu9TMftjB/h+a2cWnE8LMrjWzp82s2swazGy3mf3MzJK6eH6CmX3ezB40s11mVmNmTWZWbGb3mtmsU5xnZvYRM3vFzGrNrN7MtpnZd8ws7nQ+FxER6VhTi48ntlVw+ZRUYiLDvY7To8aMjGNBTiL35xXj8zmv4wwKq/KLiY4IG9Cj/kREREQGu1BGZn0NGN/B/pzAMSExs+8CjwCXASOA6MB9bgPyzKwrDS8mA78CbgAmAAlAJJAFfAh4w8wWneS8uwPLucAwYAgwFfgu8LKZJYT6+YiISMde3l3F0YaWQVNsWDkvm6LD9WzYV+11lAGvobmVR7eUcdW0NIbFRHodR0RERER6SCjFrJnA+g72bwgc02Vmdj7wncBLH3A7/oJU233GAH/q4uV8wFrgo8DlwNeBusC+SPyN64PvfTPw4cDLE8BngRXArsC22cCPu/zJiIhIl6wtLCdhSCTnjh/pdZResWhaOvHREdyfV+x1lAHvqR0HOdrQosbvIiIiIgNcKMWsBN4tDp3MCfwjq0LxxaD1vzjnfuicexh/UaltPsYVZja1k+uUALOdc0ucc391zj3jnPsR7xbKAMaaWfDz378UtH6nc+53zrlV+IthbT5iZomhfEIiInJqDc2tPLW9gqumphEVEXLbxn5pSFQ4187K4LGt5dSeaPY6zoC2Kq+YzOFDOGdcl7oUiIiIiEg/FcpfEqX4n2J4KnOBihDvH9xj65W2FedcMXAgaN8lHV3EOVfinCs8ya632r2uAzCz4fhHXr3v3vhHmLUE1qPxT0EUEZFu8MJbldQ1tQ6aKYZtVs7LprHFx6MFZV5HGbDKak7wytuHWDonk7Aw8zqOiIiIiPSgUIpZ6/CPVLqs/Q4zuxT4CPBYVy9mZiN470iu9oWw4NfjQsgZbGXQ+rPOubaRZTlA8G+679zLOdcCHO6Ge4uISDtrCstJioti4djBNeh1emYCk9LiuX+jphr2lAc3leAcLNMUQxEREZEBL5Ri1p1AFfCkma01sx8ElrXAU4F93w/heu2fFtjUweuhIVwXADP7CnBL4GUt8B/deW8z+6SZ5ZlZXlVVVajxREQGnbrGFp598yCLpqcRET44phi2MTNWzstma2ktO8qOeh1nwHHO8UB+CQtyEhmVFOt1HBERERHpYV3+a8I5dxA4B3gSWIS/WfvtgfXHgXOdc+Uh3Lt9/63oDl4f7+pFze9nwE8Cm2qARc65N7vz3s65u5xzuc653OTk5K7GExEZtJ7dWUlDs48lMwbXFMM218/KJCo8TI3ge8DG/UfYf7ieZXOzvI4iIiIiIr0gpLfGnXNFzrmrgZHAgsAy0jm32Dm3P8RrHQGOBG1Ka3dIetD6nq5c08yigX8BtwU2lQAXOOdeb3foPt5tMP+ee5tZJBDcObZL9xYRkY6tLSgjdVg088YMrimGbUbERXHF1FQe2lxKQ3Or13EGlAfyi4mNCufq6emdHywiIiIi/d5pzfNwzh1xzm0MLEc6P+OUng9aP79txcxygOCmF891dqFAU/en8D8JEaAQWOic29r+WOdcDbD5ZPfG3/A9PLDeCLza2b1FRKRjRxuaeeGtKq6enj6om3OvnJdN7Ylmntpx0OsoA0Z9UwvrCsu5Zno6cdERXscRERERkV5w2k1LzOxsM7vLzNaZ2Y/N7HTeDv110PqtZna7mV0P3Be0/Rnn3PbAPe82MxdY7gjKkoK/6HRBYFMx8A0gx8zOC1oSgq77q6D1b5jZZ8xsOfCnoO3/cM5Vn8bnJSIiQZ7efpCmVt+ge4phe+eOG0nm8CFqBN+NHttaQV1TK8tz1fhdREREZLDo8C1MM/sq8HVgknOuMmj7h4C/8e4IpkXAB81sbvBxnXHOvWhmdwLfxF9Yu7PdIQeAj3fhUlMCS5ts/E9fbO9i4IXAvf9uZlcANwFDgN+2O7YA+M8u3FtERDqxtrCMzOFDmJ093OsongoLM5bnZvHLZ3ZTXF1PdqKalZ+pVXnFjEmKZd6YEZ0fLCIiIiIDQmcjsy4G8toVsiKAnwOtwCeBGcB/ARnAV0IN4Jz7FnAD/qmENfifJLgH+AWQ65wrCvWaIfgw8FHgdfyN3huAHcB3gfMC0xFFROQMHKlr4uXdh1g8Ix2zwTvFsM3y3GzMYFV+iddR+r0Dh+vZsK+aZXOz9LMlIiIiMoh01lxiCvCPdtsuBFKA3zjn2qbkbTOzOfhHaH011BDOuYeBh7tw3K3ArSfZ/gIQ8m+xzjkf8NfAIiIiPeDJ7RW0+Nygn2LYJnP4EM4bP5IH8or5wqUTCB/EPcTO1AP5xZjBjXP0FEMRERGRwaSzkVnJ+J/8F+wc/E8CfLjd9heAMd0RSkREBo61heWMSYplasYwr6P0GSvnZVNW28Arbx/yOkq/5fM5Vm8q5bzxI8kYPsTrOCIiIiLSizorZtUBQ9ttm4+/mPVGu+21dD7SS0REBpGqY428tucQi2dkaBpYkMunpDIiNlKN4M/Aa3sOU1pzQo3fRURERAahzopZ+4DL2l6YWQxwHrDVOXe83bFpQJebv4uIyMD3xLZyfA5NMWwnOiKc62dn8tSOCqrrmryO0y89kF9MfEwEV0xJ9TqKiIiIiPSyzopZ/wCuNrOfmtnVwF+AYcD9Jzn2XODtbs4nIiL92JrCciakDGViWrzXUfqclfOyaW51PLS51Oso/c7RhmYe31bBtTMziIkM7/wEERERERlQOitm3QWsB24D1gAfADYDvwo+yMzSgCuAZ3ogo4iI9EMVtQ1s3F/N4hkalXUyk9KGMTMrgfs3FuOc8zpOv7K2oJzGFp+mGIqIiIgMUh0Ws5xzjcAFwDLgdmAFcLZzrr7doamB/ff2REgREel/1m0txzlYPDPd6yh91op52bx18BgFJbVeR+lXVuUXMyFlKDOzEryOIiIiIiIe6GxkFs65Vufcg865HznnHnDONZ/kmALn3M+ccwd6JqaIiPQ3awvLmJI+jHHJ7Z8jIm2WzMwgJjKM+9QIvsverjzG5gM1LM/N0kMFRERERAapTotZIiIioSqurmfzgRqNyurEsJhIrp6ezpqCMuqbWryO0y+syi8hPMy4fnam11FERERExCMqZomISLdbt7UcgCXql9WplbnZHG9s4bGtFV5H6fNaWn08uKmUiycmkxIf43UcEREREfGIilkiItLt1haWMTN7ONmJsV5H6fPm5ySSMzKO+zXVsFMv7a6i6lgjy+aq8buIiIjIYKZiloiIdKt9h+rYVnqUJTM0xbArzIzluVm8sb+avVXHvY7Tp63KKyExLopLJqV4HUVEREREPKRiloiIdKu1BWUAXKNiVpctm5NFeJhxf16J11H6rCN1TTzz5kGum5VBVIR+fREREREZzPTboIiIdKu1heXMGzOC9IQhXkfpN1KGxXDxxGRWbyqhpdXndZw+6ZEtpTS3OpZriqGIiIjIoBdyMcvMLjCzH5jZH81sUmDb0MD24d2eUERE+o1dB4/x1sFjLFbj95CtyM2m6lgjz79V5XWUPmlVfglTM4YxJWOY11FERERExGNdLmaZWbiZ3Qc8D9wOfBRo+2ulBXgY+Ex3B5TeU3mswesIItLPrS0oI8xg0fQ0r6P0OxdPSmHk0GjuUyP499lRdpTtZUdZPjfL6ygiIiIi0geEMjLra8BS4DZgMmBtO5xzDcBDwNXdmk56zSNbSlnw38+yR82HReQ0OedYW1jOwrFJpMTHeB2n34kMD2Pp3Eyef6uSyqN6cyHYqvxiosLDuG5WptdRRERERKQPCKWY9WHg7865XwGHTrL/TWBct6SSXnf22CQMeHCTmg+LyOnZXnaUvYfqWDJTUwxP14rcbFp9jtWbSr2O0mc0tfh4ZEsZl01JYURclNdxRERERKQPCKWYNQZ4vYP9NcCIMwkj3kkZFsP5E5J5aFMpPp/zOo6I9ENrC8uJCDOumqophqdrXPJQ5o0Zwaq8YpzTv8UAz+08SHVdkxq/i4iIiMg7QilmHQMSO9g/HlDX2n5s6dwsymobeH3vYa+jiEg/459iWMa540dq9MwZWpGbzd5DdWzcf8TrKH3CqrwSUuKjOX/CSK+jiIiIiEgfEUox6xXgZjOz9jvMbAT+hvDPd1cw6X1XTEklPiaC1fmaaigiodlSXEPJkROaYtgNrpmRztDoCDWCx/9gkhd2VXHjnCwiwkN+ALOIiIiIDFCh/GZ4JzABeA5YHNg208w+BWwC4oD/6d540ptiIsNZPCOdx7dVcLyxxes4ItKPrC0sJyo8jCumpnodpd+LjYpgycx0HttazrGGZq/jeOrhzaW0+hzL9BRDEREREQnS5WKWcy4P/9MMJwF/DWz+KfB7YAhwg3NuR7cnlF61dE4WJ5pbeXxruddRRKSf8Pkc6wrLueCsZIbFRHodZ0BYkZvNieZW1hQM3n+LnXOsyith9qjhjE8Z6nUcEREREelDQhqz75xbh78R/LXA14Bv4C9wjXXOPdXt6aTXzR09gjFJsazWUw1FpIvyio5QcbSBJTPTvY4yYMzKHs5ZqUO5L2/wTjUsKKlld+VxNX4XERERkffpcjHLzEaZ2RDnXKNzbq1z7ifOuR875x5yztWb2RAzG9WTYaXnmRk3zsli/d5qiqvrvY4jIv3A2sIyYiLDuGyyphh2FzNjRW42BcU1vFVxzOs4nliVV0xMZBiLVSQVERERkXZCGZm1D7ihg/3XBo6Rfu6G2ZkAPLS51OMkItLXtbT6eGxrOZdMSiEuOsLrOAPKjXOyiAy3QdkIvqG5lUcLyrhqapqmroqIiIjI+4RSzHrfUwxPci13Blmkj8hOjGXh2EQe3FSCc/qWisipbdhXzaHjTSyZoacYdrfEuCgun5LKQ5tLaGxp9TpOr3pyewXHGlpYnqsphiIiIiLyfqE+57qjysZkoOb0o0hfsnROFvsP15NfdMTrKCLSh60tLCMuKpyLJ6V4HWVAWpGbzZH6Zp7ZUel1lF71QH4JmcOHcPbYJK+jiIiIiEgf1GExy8w+YmbPmdlzgU3fanvdbtkC3A4839OBpXcsmp7OkMhwNYIXkVNqbvXx+LYKLpuSSkxkuNdxBqTzJySTkRAzqBrBl9ac4JW3D7F0bhZhYZ0NChcRERGRwaizkVnDgZzA4oDkoNdty5jAdf4CfKaHckovGxodwaJpaawtKKeheXBNbxGRrnnl7UPU1DdrimEPCg8zls3N4uXdVZTWnPA6Tq94aFMJzsGyOVleRxERERGRPqrDYpZz7lfOuRznXA7+nllfbHsdtIx1zs1wzn3SOVfVO7GlNyydm8Wxxhae2nHQ6ygi0getLSgnPiaC888a6XWUAW15bjbOwQN5A3+krHOOB/JLWJCTyKikWK/jiIiIiEgfFUrPrBzgoVPtNLMhZjbqzCNJX3H22CQyEmJYnT/w/4ASkdA0trTy1PYKrpyaRnSEphj2pOzEWM4dn8Sq/GJ8voH9UI6N+4+w/3C9Gr+LiIiISIdCKWbtBW7oYP+1wL4ziyN9SViYccOcTF7eXcXBow1exxGRPuTFt6o41tjCkpmaYtgbVuRmU3LkBK/tOex1lB61Kq+YuKhwrp6e5nUUEREREenDQilmddaFNYyOn3Yo/dCNc7LwOXh4c6nXUUSkD1lbWM6I2EjOGaenzfWGK6emkTAkckA3gq9rbGHd1nKumZFObFSE13FEREREpA8LpZgFHRerJgM1px9F+qJxyUOZPWo4qzeV4JxqlSICJ5paeebNg1w1LZ3I8FD/NyKnIyYynOtnZfDk9gpq6pu8jtMjHttaTn1Tq6YYioiIiEinOvwrxMw+YmbPmdlzgU3fanvdbtkC3A4839OBpfctnZPFroPH2VZ61OsoItIHPLezkvqmVpbMTPc6yqCyYl42TS2+ATtSdlV+CTkj48gdPcLrKCIiIiLSx3X2lvpw/I3fc/CPykoOet22jAlc5y/AZ3oop3hoyYwMoiLCWL1JjeBFBNYWlpEcH82CHE0x7E1TMxKYljmM+/IG3kjZosN1vLGvmmVzszDrrKuBiIiIiAx2HRaznHO/cs7lOOdy8PfM+mLb66BlrHNuhnPuk865qt6JLb0pITaSyyen8siWUppafF7HEREPHW9s4bmdlVw9LY3wMBUdetvK3GzeLD864EbKPpBfghncOCfT6ygiIiIi0g90udmJcy7MOffPnghhZtea2dNmVm1mDWa228x+ZmZdftvfzC4ws9+bWZ6ZNZqZCywvdHDONDO728z2mNkJM2s2swoze8zMOnpy46CzdG4mR+qbef6tSq+jiIiHntlxkMYWn55i6JFrZ2USHRHGfXkHvI7SbXw+x+r8Es4bP5L0hCFexxERERGRfiDkzr1mNsbMPm5m3zSzMYFtUWY2ysyiTuN63wUeAS4DRgDRwHjgNiDPzLraCfZG4N+BuUCnOcxsPrAR+AgwFogBIoBUYBHwoJl9NaRPZgC7YEIyI4dGszpfUw1FBrO1hWWkJ8QwZ5T6GnkhYUgki6al8ciWMhqaW72O0y1e23OYstoGNX4XERERkS4LqZhlZj8CdgN3Ad/DXwQCfyFoByH2zDKz84HvBF768DeRvwFYH9g2BvhTFy9XCTwauN6qLhz/efy5AUqADwDXAK8FHfPlLt57wIsID+P6WRk8/1Yl1XUD80laItKx2vpmXtxVxTXT0wnTFEPPrJiXzbGGFh7fVu51lG6xKr+YYTERXDEl1esoIiIiItJPdLmYZWafAv4T+C1wBf4eWgA4547iLyQtCfH+Xwxa/4tz7ofOuYeBFfgbzgNcYWZTO7uQc+6/nXPXOee+j7+w1pnhQev3Oefuc849BvwiaHvII80GsqVzs2hudTy6ZWA+SUtEOvbkjgqaW52mGHpsYU4SoxJjuW9jsddRzljtiWae2FbBtbMyiIkM9zqOiIiIiPQToYzM+gzwkHPui8Dmk+wvBCaGeP+Lg9ZfaVtxzhUDwQ1BLgnxul3xVND6SjNbaWaLgC8Fbf9HD9y335qcPowp6cNYvUnFLJHBaG1hOaMSY5mRleB1lEEtLMxYkZvF+r3VFB2u8zrOGVlbWEZji4/lczXFUERERES6LpRi1lnA0x3srwJGdvViZjYCf4+sNhXtDgl+Pa6r1w3Bb4EfAnVAFvAv4DHgHOAo/lFoXzrl2YPU0rlZbC2tZdfBY15HEZFedPh4I6++fYhrZqRjpimGXls2N5swg/vz+vforFV5JZyVOlQFUhEREREJSSjFrAYgroP9o4GaEK7X/lrtGzEFvx4awnW7xDnXChTh77XV3jDgQ3Qw0szMPhl4cmJeVVVVd8frs66blUFEmKkRvMgg88T2Clp9jiUzNMWwL0hLiOHCs5J5IL+Ellaf13FOy9uVx9hSXMPyudkqkIqIiIhISEIpZr2Bvzn7+5hZDHAL8GoI12s/NyK6g9fHQ7hul5jZN4A/ADnA6/ifoBiPvwk9wGzgKTM7aQHPOXeXcy7XOZebnJzc3fH6rJFDo7loYjIPbS7tt39AiUjo1haUMzY5jsnp8V5HkYCV80Zx8GgjL+3un2+orMorITzMuH52ptdRRERERKSfCaWY9RPgbDP7BzAjsC3NzK4EXsA/Ve+nXb2Yc+4IcCRoU1q7Q9KD1veEkLOrgp+8+GPn3B7n3HHn3A95d4RZJnBeD9y7X1s6J4vKY4288vYhr6OISC+oPNrA+n2HWTwjQyNo+pBLJ6cwcmhUv2wE39Lq48HNpVw8MYXk+PbvZYmIiIiIdKzLxSzn3DPAp4FlwDOBzf/A32dqJvAJ59zrId7/+aD189tWzCwHCO4G+1yI1+2K4OFUw4LuHc17R4WpkUc7l0xOIWFIpBrBiwwSj20txzlYMiO984Ol10SGh3HjnCyefbOSqmONXscJyUu7q6g61siyuVleRxERERGRfiiUkVk45+7CPy3vi8Dvgf8FvgKMd87dfRr3/3XQ+q1mdruZXQ/cF7T9GefcdgAzu9vMXGC5I/hCZjbJzK4PnD8paNfItu1mNidoe0HQ+g/M7BYzuwr4P2BI26cM5J/G5zWgRUeEc+3MDJ7aXsHRhmav44hID1tbWM6ktHgmpGqKYV+zIjebFp/joc39q4/hqrwSEuOiuGRSitdRRERERKQfCqmYBeCcq3DO/T/n3Gedc59xzv3COXdaQ3Sccy8CdwZluRN4CJgX2HYA+HgXL/eBwLkPASuDtk8N2v75oO1fw9/UHvyjwP4OPM57+4L9wjnXE1Mc+72lc7NobPGxrrDc6ygi0oPKak6QV3SExRqV1SeNTxnK3NEjuG9jMc45r+N0SXVdE8+8eZDrZ2USFRHyryEiIiIiIqEXs7qbc+5b+AtIz+HvVdWEv0fWL4Bc51xRD933BSAX+AvwNtAItAJVwJPASufcl3vi3gPBzKwExiXH6amGIgNcW8F6sZ5i2GetzM1mT1Udmw4c6fzgPuCRLaU0tzqW52qKoYiIiIicnoiuHmhmXelb5Zxzl4Yawjn3MPBwF467Fbj1FPvuAO4I8b7bgY+Fco74mRlL52bx4yfeYv+hOsaMPOlDH0Wkn1tbWMb0zAT9N96HXTMjne+u2c59G4uZOzrR6zidWpVXwrTMYUxOH9b5wSIiIiIiJxHKyKyx+PtlBS8TgAuAi4BpgWNkkLhhdiZm8OAmjc4SGYiKDtdRUFKrKYZ9XFx0BItnZLC2sJzjjS1ex+nQ9rJadpQfZfnc7M4PFhERERE5hVCeZjjGOZfTbskG4oBv4p8ieE4P5ZQ+KD1hCOeNH8nqTaX4fP2jV4uIdN3awBTDa1TM6vNWzMumvqmVdYVlXkfp0Kq8EqLCw7hulqatioiIiMjpO+OeWc65RufcD4ENwM/PPJL0J0vnZFFac4IN+6q9jiIi3WxtYTlzRg0na0Ss11GkE3NGDWd8ylDu21jsdZRTamrx8ciWUi6fksrw2Civ44iIiIhIP9adDeBfAa7sxutJP3Dl1DSGRkewWlMNRQaUtyuP82b5UTV+7yfMjJW52Ww6UMPblce8jnNSz+08yJH6Zpap8buIiIiInKHuLGblAHqrdZAZEhXO1dPTeHxrOfVNfbtXi4h03drCMsw0xbA/uWFOJhFh1mdHZ63KKyElPprzx4/0OoqIiIiI9HNdLmaZ2ahTLLPM7CvA54GXei6q9FVL52RR19TKE9sqvI4iIt3AOceagjLmj0kkdViM13Gki0YOjeayyak8uKmUphaf13Heo/JYAy/squLGOVlEhHfn+2giIiIiMhiF8hvlfmDfSZZ84MdAEf6Clgwy88Ykkp04RFMNRQaInRXH2FNVx+KZmmLY36ycl83huiae23nQ6yjv8dCmUlp9juWaYigiIiIi3SAihGO/B7R/ZJ0DqoFdwDPOub71VrD0irAw48bZWfz6ud2U1ZwgY/gQryOJyBlYW1hGeJixaFqa11EkRBeclUzasBju21jMVdP6xhRR5xyr8kuYM2o445KHeh1HRERERAaALheznHN39GAO6eeWzsniV8/u5qHNpXz24vFexxGR0+ScY21hOeeMS2Lk0Giv40iIwsOMZXOz+N0Lb1NR20BagvfTRLcU1/B25XF+eON0r6OIiIiIyAChxhXSLUYlxTJ/TCKr80twrv0APhHpL7aW1lJ0uJ7Favzeb63Izcbn4IH8vtEIflV+CTGRYfqZEhEREZFuE1Ixy8zizOy7ZlZoZscDS6GZ3WFmcT0VUvqHpXMz2Xuojs3FNV5HEZHTtLawnMhw48qpmmLYX41KiuXssUncn1eCz+ftmwsNza2sKShj0bR04mMiPc0iIiIiIgNHKE8zTATeAL4NpAKbA0sq8B3gjcAxMkhdPT2dmMgwVuerEbxIf+TzOdYVlnP+hGSGx0Z5HUfOwMp52Ryormf9vsOe5nhyewXHGlpYPleN30VERESk+4QyMut7wCTgc0CGc+5859z5QAbwWWAicEe3J5R+Iz4mkiunprGmoIyG5lav44hIiDYXH6G05oSmgw0AV01LIz4mgvs3ejvV8IH8EjKHD2Hh2CRPc4iIiIjIwBJKMeta4E/Oud85596pVDjnWp1zvwf+Alzfzfmkn1k6J4ujDS08+2al11FEJERrCsqJigjj8impXkeRMxQTGc71szJ5fFsFtSeaPclQWnOCV94+xNK5WYSFmScZRERERGRgCqWY1Ta18FQ2BY6RQezc8SNJGxbD6k2aaijSn7T6HI9tLefiicnqbTRArJyXTWOLj0e3lHpy/wfzS3AOTTEUERERkW4XSjHrIDC7g/2zA8fIIBYeZlw/O5MXd1VRdazR6zgi0kVv7Kum8lgji2dkeB1Fusm0zASmpA/jvrzen2ronOOBTSUsHJtIdmJsr99fRERERAa2UIpZa4CPmdmnzOyd88wszMw+CXwUeLS7A0r/s2xuJq0+xyMejQYQkdCtLSxjSGQ4l05O8TqKdKOV87LZVnqU7WW1vXrfN/ZVU3S4nuVzs3v1viIiIiIyOIRSzPoOsBf4HVBmZi+a2YtAGfD7wL7/6v6I0t+MT4lnZlYCD+iphiL9Qkurj8e3VXDp5BRioyK8jiPd6PpZmURFhPV6I/hV+SUMjY5g0fS0Xr2viIiIiAwOXS5mOecOA7nA/wCHgXmB5RDwQ2Be4BgRls7NYmfFsV4fDSAioXttz2Gq65o0xXAASoiN5KqpaTy8pfeeMlvX2MJjW8u5Znq6iqMiIiIi0iNCGZmFc+6oc+6bzrmpzrnYwDLNOfct59zRngop/c+SGRlEhhsPbtJUQ5G+bm1hGUOjI7hoYrLXUaQHrJyXTe2JZp7cXtEr91u3tZz6plaW56rxu4iIiIj0jJCKWSJdNSIuiksnpfLIllKaW31exxGRU2hq8fHEtgqumJJKTGS413GkB5w9NonsxCHc30uN4B/ILyFnZBxzR4/olfuJiIiIyOAT0vh/MzPgMmACkARYu0Occ+773ZRN+rmlc7N4YnsFL+2q4tLJqV7HEZGTeHl3FUcbWlgyU1MMB6qwMGP53Gx+/vQuiqvre/TpgkWH63hjXzX/eeVE/L8yiIiIiIh0vy4Xs8xsAvAwMIn3F7HaOEDFLAHgoonJJMVFsXpTiYpZIn3U2sJyEoZEcu74kV5HkR60bG4Wv3hmF6vyirntiok9dp8H8ksIM7hxTmaP3UNEREREJJRphv8PGAd8DX8j+JyTLGO7O6D0X5HhYVw7K4NndlRSU9/kdRwRaaehuZWndxzkqqlpREVo1vlAljF8CBdMSGZVfgmtPtcj92j1OVbnl3DehGTSE4b0yD1ERERERCC0Ytb5wC+dcz91zm1yzhWdbOmpoNI/LZ2TRVOrjzWF5V5HEZF2XnirkuONmmI4WKycl015bQMv767qkeu/tucQZbUNLJ+rxu8iIiIi0rNCKWY1Avt6KogMTFMzhjEpLZ7V+SVeRxGRdtYUlpMUF8XCsYleR5FecNnkVBLjonqsEfyqvBKGxURw+RRNKxcRERGRnhVKMetJ4NyeCiIDk5mxdE4WW4pr2FN13Os4IhJQ39TCc29Wsmh6GhHhmmI4GERFhHHD7Eye3nGQw8cbu/XatSeaeXJ7BdfNytRTMUVERESkx4XyF8xtwNlm9mUzi+qpQDLwXDc7g/Aw0+gskT7kmTcrOdHcypIZmmI4mKycl01zq+OhzaXdet01BWU0tvhYnqsphiIiIiLS805ZzDKzvcEL8CqQAPwYqDOzovbHmNme3gou/UdKfAwXTBjJQ5tLe6zxsIiEZm1BGanDopk3RlMMB5OzUuOZlT2c+/OKca77/j1elV/CxNR4pmcmdNs1RUREREROpaORWQeAonbLduAl4BVg70n2H+jJsNJ/LZ2bRXltA6/vOex1FJFB72hDMy/squLq6emEhZnXcaSXrZyXza6Dx9lSXNMt19t98BgFxTUsz83CTD9PIiIiItLzIk61wzl3US/mkAHussmpDIuJYPWmEs6bMNLrOCKD2tPbD9LU4tNTDAepxTPS+d6aHdyfV8zsUSPO+HoP5JcQHmZcNyuzG9KJiIiIiHROXX+lV8REhrN4ZgZPbKvgeGOL13FEBrW1hWVkDh/C7OzhXkcRD8THRHLNjHTWFJRT33Rm/x63tPp4cHMpF09MITk+upsSioiIiIh0TMUs6TVL52RxormVx7aWex1FZNCqqW/i5d2HWDwjXVPCBrGV87I53tjCusIz+/f4xV1VVB1rVON3EREREelVp5xmGGj6HirnnBt3BnlkAJszajg5I+NYnV/Citxsr+OIDEpPbKugxec0xXCQyx09grHJcdyfV8zyM/j3eFVeCUlxUVwyKaUb04mIiIiIdCzUBvCdLWoAL6dkZiydk8mGfdUUV9d7HUdkUFpbWM6YpFimZgzzOop4yMxYkZvNxv1H2FN1/LSuUV3XxLM7D3L97EwiwzXQW0RERER6zyl/+3TOXeScuzjUpTfDS/9zw5wszODBTaVeRxEZdA4db+S1PYdYPCNDUwyFG+dkEh5m3J9XfFrnP7y5lOZWpymGIiIiItLr9Faq9KrM4UM4e2wSD24uwTnndRyRQeXxreX4HJpiKACkxMdwyaQUVueX0tzqC/n8VfklTM9MYFKaRvmJiIiISO9SMUt63dI5WRQdriev6IjXUUQGlTWF5UxIGcrEtHivo0gfsTI3m0PHG3l+Z2VI520vq+XN8qMalSUiIiIinjhlMcvMnjOzZ80sIuh1Z8uzpxPCzK41s6fNrNrMGsxst5n9zMySQrjGBWb2ezPLM7NGM3OB5YVOzksws++Y2SYzO2pm9Wa2z8weNrMrTufzkY5dNS2N2KhwVueXeB1FZNCoqG1g4/5qFs/QqCx510UTk0mJjw55quGqvBKiwsO4VqP8RERERMQDp3yaITAW8AEW9Lrb54WZ2XeB77TbPB64DbjRzC5wznXlt+wbgX8P8d6TgSeAUe12jQksbwNPhXJN6VxcdASLpqWzrrCcO66dSkxkuNeRRAa8dVvLcQ4Wz0z3Oor0IRHhYSydm8VdL+2l8mgDKcNiOj2nqcXHI1tKuXxKKsNjo3ohpYiIiIjIe3XUAH6Mc26sc6456HVOZ0soNzez83m3kOUDbgduANYHto0B/tTFy1UCjwaut6oL944FHuHdQtYrwK3AZcBy4AfAm128t4Ro6dxMjjW28OT2Cq+jiAwKawvLmJI+jHHJQ72OIn3MitxsWn2OBzZ1bbTss28e5Eh9M8s0xVBEREREPNLRyKze8MWg9b84534IYGb5QBH+UWFXmNlU59z2ji7knPvvtnUzu6ML9/43YEJg/TngcudccAfcB7pwDTlNC3OSyBw+hNWbSrluVqbXcUQGtOLqejYfqOGrV030Oor0QTkj45ifk8iqvBI+feG4Tp90uSq/hNRh0VwwIbmXEoqIiIiIvNdpN4A3swgzO8fMlpvZ1NO8zMVB66+0rQSmFR4I2nfJaV6/IzcGre8AHjezw2Z23MxeMrNreuCeEhAWZtw4J5NXdldx8GiD13FEBrR1W8sBWKJ+WXIKK3Oz2Xeojjf2VXd4XOXRBl54q5Ib52QRHtZx0UtEREREpKd0WMwys4vM7NdmltJuew6QD7wM/AsoNLO/hHJjMxsBjAja1H6+WfDrcaFcu4tmBK1/DrgCSATigPOBtWb26VOdbGafDDSbz6uqquqBeAPfjXOy8Dl4aHOp11FEBrS1hWXMzB5OdmKs11Gkj7p6ejrx0RHc10kj+Ac3l+JzsHyuphiKiIiIiHc6G5l1K3Clc679M7vvBqYDrwG/wD+y6SNm9pEQ7h3X7nVTB697osnL8HavfwBcDTwUtO2np3qionPuLudcrnMuNzlZUy1OR87IOOaOHsHq/BKc6/ZnC4gIsO9QHdtKj7Jkhhq/y6kNiQpnyawMHttaztGG5pMe45xjVV4xc0ePYKx6r4mIiIiIhzorZs2n3dP8zGwS/pFLLznnznfOfSVw3G7gwyHcu67d6+gOXh8P4bpdFTy3bYNz7tvOucfxF/DafpOPBS7sgXtLwNI5WeyuPM7W0lqvo4gMSGsLygC4RsUs6cTK3Gwamn2sCfzMtLe5uIY9VXUalSUiIiIinuusmJWGv0gV7CLAEfSUQefcCeCfvHfqXoecc0eAI+3uFSz4L689Xb1uCIqC1vcF5ToKHA7al9AD95aAa2akExURxur8rj1FS0RCs7awnHljRpCeMMTrKNLHzchKYFJaPPdvPPlUwwfyS4iJDFNhVEREREQ811kxKxo40W7bvMDHF9ttLyb0ws/zQevnt60EenJlB+17LsTrdkVw/jFB944HgqcWBhe9pJslDInkiimpPFpQRlOLr/MTRKTLdh08xlsHj7FYjd+lC8yMFbnZFJTUsrPi6Hv2NTS3sqagjEXT0omPifQooYiIiIiIX2fFrANA+ycVngdUBp44GCwWqAnx/r8OWr/VzG43s+uB+4K2P+Oc2w5gZnebmQssdwRfyMwmmdn1gfMnBe0a2bbdzOYEbf890BJYX2hm3zWzq4C/Am2/qRcT9JRF6RlL52ZxpL6Z53a2b80mImdibUEZYQaLprcf+CpycjfMziQqPIz72o3OenJ7BccaWjTFUERERET6hM6KWS8DHzazaQBmdgMwAXj8JMdOB0J6LJ1z7kXgzqAsd+JvwN42+usA8PEuXu4DgXMfAlYGbZ8atP3zQffeBnwl6Ljv4P+8lgZe1wG3OOfaN6aXbnb++JEkx0ezepOmGop0F+ccawvLWTg2iZT4GK/jSD8xIi6Ky6em8tDmUhpbWt/ZviqvhKwRQ1g49qTPRBERERER6VWdFbN+iH+qYYGZVQIP4H/K4M+CDzKzcOBaTmMUk3PuW8AN+KcS1gSuvwf/UxJznXM9Ns3POfcr4DL8Raxq/CO1SoG/AXMCxTbpYRHhYdwwO5Pnd1Zy+Hij13FEBoQd5UfZe6hOUwwlZCtzs6mpb+bpHQcBKK05wat7DrF0ThZhYeZxOhERERGRTopZzrl9+J/m9xj+puiPAxe1TfsLcnFg/yOnE8I597Bz7lLn3AjnXLRzbrxz7jbnXFW74251zllguaPdvjuC9p1qufUk937WOXe1cy7JORfpnMsK3GfX6XwucnqWzsmixed49BRP0RKR0KwpKCcizLhqmqYYSmjOGz+SzOFD3plquDq/BOdgmaYYioiIiEgf0dnILJxzec65Jc65yc65xc659Sc55hnn3HTn3DM9E1MGuolp8UzLHKaphiLdwD/FsIxzx48kMS7K6zjSz4SFGcvmZvHK24corq7ngfwSzh6bRHZirNfRRERERESALhSzRHrL0jlZbCs9ylsVx7yOItKvFZTUUnLkBItnpHsdRfqp5bn+UVjfeHArB6rr33ktIiIiItIXqJglfca1MzOICDONzhI5Q2sKyogKD+OKqZpiKKcna0Qs540fyStvH2JodASLpqkwKiIiIiJ9h4pZ0mckDY3m4kkpPLS5lJZWn9dxRPoln8+xrrCcC85KJmFIpNdxpB9bkZsNwOIZ6QyJCvc4jYiIiIjIu1TMkj5l6Zwsqo418vLbh7yOItIv5R84QsXRBpbM1EgaOTNXTE3l1nPG8O8XjvM6ioiIiIjIe6iYJX3KJZNSGB4byep8TTUUOR1rCsqIiQzjssmpXkeRfi46Ipw7rp3KmJFxXkcREREREXkPFbOkT4mKCOPamRk8teMgtSeavY4j0q+0+hyPba3gkkkpxEVHeB1HRERERESkR6iYJX3O0jlZNLX4WFdY7nUUkX5lw97DHDreyOIZGV5HERERERER6TEqZkmfMyMrgfEpQ/VUQ5EQrSksIy4qnIsnpngdRUREREREpMeomCV9jpmxdE4W+UVH2Heozus4Iv1Cc6uPx7dVcNmUVD15TkREREREBjQVs6RPumF2JmEGD2p0lkiXvPr2IWrqmzXFUEREREREBjwVs6RPSkuI4dzxI3lwUyk+n/M6jkift6agnPiYCC44a6TXUURERERERHqUilnSZy2bm0VpzQnW7zvsdRSRPq2xpZWndlRw5dQ0oiM0xVBERERERAY2FbOkz7piShpDoyNYnV/qdRSRPu2lXYc41tDC4hnpXkcRERERERHpcSpmSZ81JCqca6an8/i2cuoaW7yOI9JnrSkoY0RsJOeO1xRDEREREREZ+FTMkj5t6dws6ptaeWJbhddRRPqkE02tPPPmQa6alk5kuP5JFxERERGRgU9/+UifNm/MCEYlxrJaTzUUOann36qkvqmVJZpiKCIiIiIig4SKWdKnmRk3zsnk9b2HKa054XUckT5nTUEZI4dGs2BsktdRREREREREeoWKWdLnLZ2ThXPwkEZnibzH8cYWnttZyTXT0wgPM6/jiIiIiIiI9AoVs/5/e/cdX2V993/8/ckm7BEMJAxxIBsy3Nu6RYV4W6ktglVrrT9Xx91a66jW+rtb62rrXVfAUa02glLrrKtaByEswYHIMAkj7Jn9vf84J3qIjARyzjfXOa/n45FHrn294+PyQN5c1/dCu9evR6YO3b+HSsoq5JzzHQdoN/718SrV1DfqrFF9fUcBAAAAgJihzEIgnJeXqyVrtqps+QbfUYB2Y8bcSvXpmqH8/t19RwEAAACAmKHMQiCcPiJbGalJDAQPhG3cXqe3PqvSmSP6KIlHDAEAAAAkEMosBELnjFSdNixb/5hbqeq6Bt9xAO9eWbBSdQ1OY3nEEAAAAECCocxCYBTl52pTdb1e+3iV7yiAdzPmrVD/HpkamdvVdxQAAAAAiCnKLATGkQf0UnaXDJXM4lFDJLZ1W2v17udrdObIPjLjEUMAAAAAiYUyC4GRnGQal5ejtxet0erN1b7jAN689NFKNTQ6jR3JI4YAAAAAEg9lFgKlKC9XDY1Oz82u9B0F8GbG3EoNyuqoIX06+44CAAAAADFHmYVAObB3J43q100lZeVyzvmOA8Tc6s3V+mDJWp01si+PGAIAAABISJRZCJzz8nL0ycrNWlC5yXcUIOZenL9SjU4aO7KP7ygAAAAA4AVlFgJn7Ki+SktOUkkZA8Ej8cyYW6lDsjvroP14xBAAAABAYkrxHQBorW6ZaTppSG89P6dS158xRKnJdLJoHxoanWrqG1Rd1/jV9+q6BtXUh75HTtfsapv60Lrq+sZm24emP1m5WT855WDfPyoAAAAAeEOZhUAqysvVix+t1JufVunkofv5joN2xjmn2obGr0qlmm+USnsqknZTNn3jOI2qCe9b17D347glmZSRmqz0lCRlpCZ/NZ2emqyMlCR1zUxTRkqShvXtqm8X9m/D/1oAAAAAECyUWQik4wZnqWfHNJXMKqfMCqhFqzZrzZbaHe5kaiqSmu5cqt7NXU47lE3N961v1L68HyAtJSmiVEpSekroe0ZKsjqkJat7ZtqOZVPkNjsUUjvum/7V/De3SU02BnQHAAAAgBagzEIgpSYn6ZzROXrs/aVav7VW3Tum+Y6EVnjg7cW6/Z+f7HG75CRTRsTdSRmpyUqLKIG6ZabtpED6ejq92b672iayVEpPSVJSEqUSAAAAALRXlFkIrKL8HD3y7hLNmFepiUcM9B0HLfTCvBW6/Z+f6LRh2Zp45IBd3NEUmmY8NAAAAABAc5RZCKxhfbvqkOzOKplVTpkVEKVL1+nap+eoYEB33X3BaGWkJvuOBAAAAAAIGG57QKCdl5+rueUb9fnqzb6jYA++qNqiSx4tVU63DnpwYgFFFgAAAABgr7SLMsvMzjazV81snZlVm9kiM7vTzHq24hjHmtn9ZlZqZjVm5sJfb7Zw/x9G7NPi/eDXOaNzlJxk+vusCt9RsBtrt9RoUvFMJZtpyuRCxjgDAAAAAOw172WWmd0i6TlJ35LUXVK6pAMlXSep1Mz6tfBQ4yVdLilfUqt+UzazgyT9vjX7oH3I6pyu4w7O0rTZ5Wpo3IfX1yFqqusadMmjpVq1qVoPXlSgAT07+o4EAAAAAAgwr2WWmR0j6cbwbKOk6yWNk/R+eNlASQ+18HCrJT0fPt4zrciQIukxSZmSqlu6H9qPorxcrdpUo3c/X+M7CpppaHS6+qnZmvPlBt1zwRjl9e/uOxIAAAAAIOB835l1TcT0I8653zrnpks6X1LTbTanmNmwPR3IOXe7c+4c59ytkha2IsP1kg6TtEzS/a3YD+3ESUN6q0tGikrKyn1HQTO/eeFjvbxglW44c6hOG57tOw4AAAAAIA74LrNOiJh+p2nCOfelpOUR606MxsnNrEDSrxS6K2yipE3ROA+iKyM1WWNH9dXLC1Zqc3Wd7zgIK353iR55d4kmHTlQ3z96f99xAAAAAABxwluZZWbdFRojq8nKZptEzh8QhfN3kPS4pBRJv3fOvd3W50DsFOXnqrquUf+cv8J3FEh6ecFK/fofC3XK0P30q7OG+o4DAAAAAIgjPu/Maj4KdO1u5jtF4fy/kzRY0lyF7s5qFTO7LPzmxNKqqqo2D4fWGdOvmwb16qgS3mro3ezl63X1U7M1Kreb7rlgjJKTzHckAAAAAEAc8VlmbW02n76b+S1teWIzK5R0haQaSd91zjUv0vbIOfeAc67AOVeQlZXVlvGwF8xMRfm5+nDpOi1fu813nIS1bO1WXTK1VL07Z+ihiwrUIS3ZdyQAAAAAQJzxVmY559ZLWh+xqPno0H0iphe38elzJJlChdl8M3Nm5iTdFLHNceHld7fxuREl48bkyEwMBO/J+q21mlw8Uw3OqXhyoXp1at5PAwAAAACw73wPAP9GxPQxTRNmtr+kfhHrXo9ZIgRW324ddOQBPfXs7HI1Nro974A2U13XoMseK1X5hu16cGKBDsiKxpPBAAAAAACEBj/36V5J48PTk8xssaSFkq6P2OY159wCSTKzKZIuCi+/xTl3c9NGZnaIpEPCs4dE7N/LzM4NTy93zpVJmi/p2p3kOU3SqeHpxZL+KGl2q38qeFOUl6vrnp6rmUvX6bBBPX3HSQiNjU4/eWauZi5dr/smjFHhwB6+IwEAAAAA4pjXMss595aZ/UbSLxW6S+w3zTZZLumSFh7uAu34mGCTYZKmhaenSprknFss6e7mG5pZN31dZpU7576xDdq304Zn61fTP1JJWTllVoz8/5c/0T/mrdDPTz9EY0f19R0HAAAAABDnfD9mKOfcDZLGKfQo4QaF3mK4WNJdkgqcc8v8pUPQZKal6PQRffTP+Su1vbbBd5y49/j7y/SXt77QhYf11w+OHeQ7DgAAAAAgAZhzjC20rwoKClxpaanvGAh7b/FaTXjwfd397dE6d0yO7zhx6/VPVumSqaU6fnBvPfC9fKUke+/GAQAAAABxxMxmOecKmi/nt0/EncP276Gcbh14q2EUzS/fqCv/OltD+3bRfRPGUGQBAAAAAGKG30ARd5KSTEV5OXrn8zVasXG77zhxp3z9Nl08daa6Z6bpkYsK1THd93skAAAAAACJhDILcWl8Xq6ck6bNrvAdJa5s3F6nycUzVV3XoOLJherdJcN3JAAAAABAgqHMQlwa2KujCgZ0V8mscjEuXNuorW/U5Y/N0tK1W/WX7+Xr4P06+44EAAAAAEhAlFmIW0X5uVpctVVzyzf6jhJ4zjn9vGSe3vtirf7nvJE68oBeviMBAAAAABIUZRbi1pkj+yg9JUklsxgIfl/d9epnenZ2hX588sEaNybXdxwAAAAAQAKjzELc6pKRqlOGZev5uZWqqW/wHSewnp75pe59/XN9u6CfrjzxQN9xAAAAAAAJjjILca0oL0cbt9fp9Y9X+44SSG9/VqVfTJuvYw7qpdvGDZeZ+Y4EAAAAAEhwlFmIa8cclKXendNVUsajhq21sHKTrniiTAf17qQ/X5in1GQ+LgAAAAAA/vHbKeJacpJp3JgcvflpldZsqfEdJzBWbNyui6fMVKf0FBVPLlTnjFTfkQAAAAAAkESZhQRQlJ+r+kan5+ZU+o4SCJur6zS5eKa21NSreHKh+nTt4DsSAAAAAABfocxC3Dt4v84akdOVtxq2QF1Do654okyLVm/Rny/M05A+XXxHAgAAAABgB5RZSAhFeTlauGKTPl6xyXeUdss5p19Om69/L1qj344boWMPzvIdCQAAAACAb6DMQkI4e3SOUpONu7N244+vf66nS8t11YkH6vzCfr7jAAAAAACwU5RZSAg9OqbphMG9NX1OpeobGn3HaXeeLSvXna9+pvFjcnTtyQf7jgMAAAAAwC5RZiFhFOXnas2WGr29qMp3lHblP5+v0X+XzNMRg3rqjqKRMjPfkQAAAAAA2CXKLCSMEwb3VvfMVJXMqvAdpd34bNVm/eDxWRrYs6P+93v5SkvhIwEAAAAA0L7xmysSRlpKks4ZnaNXF67Sxm11vuN4t3pTtSYXz1RGarKKJxeqa4dU35EAAAAAANgjyiwklKK8XNU2NGrGvErfUbzaWlOvi6fO1PpttSqeVKjc7pm+IwEAAAAA0CKUWUgow3O66OD9OqmkLHHfaljf0Kgr/1qmhZWb9Kfv5Gl4TlffkQAAAAAAaDHKLCQUM1NRXq5mL9+gxVVbfMeJOeecbnp+gd74tEq3njtcJxzS23ckAAAAAABahTILCWfcmBwlmfRsAt6d9Ze3v9ATHyzX5ccdoAsPG+A7DgAAAAAArUaZhYTTu0uGjjkoS9PKKtTY6HzHiZkZcyt1x4ufaOyovvrZqYN9xwEAAAAAYK9QZiEhFeXnqnJjtd77Yq3vKDHx4ZJ1+vHTc1U4sLt+d95IJSWZ70gAAAAAAOwVyiwkpFOG7qfOGSkqmRX/jxourtqiSx8tVW6PDnpwYoEyUpN9RwIAAAAAYK9RZiEhZaQm66yRffTiRyu1pabed5yoWbOlRpOKP1RKkmnKpEPVLTPNdyQAAAAAAPYJZRYSVlFerrbXNejF+St8R4mK7bUN+v7UUlVtrtHDkwrVv2em70gAAAAAAOwzyiwkrPwB3TWwZ6ZK4vCthg2NTlc9NVvzyjfo3gvGaHS/br4jAQAAAADQJiizkLDMTOPzcvX+F+v05bptvuO0qVv/sVCvLlylm84aqlOGZfuOAwAAAABAm6HMQkIbNyZHkjRtdoXnJG3n4XeWaMp/lur7R++vSUft7zsOAAAAAABtijILCa1fj0wdPqiHni0rl3POd5x99tJHK3TbCwt12rBs/fKMIb7jAAAAAADQ5iizkPCK8nK1dO02zVq23neUfTJr2Xpd/dQcje7XTXdfMFpJSeY7EgAAAAAAbY4yCwnv9BF91CE1OdADwS9ds1WXPlqq7K4ZemhigTJSk31HAgAAAAAgKiizkPA6pafo9OHZ+sfcFaqua/Adp9XWba3V5Ckz5ZxT8aRC9eyU7jsSAAAAAABRQ5kFSCrKz9Xmmnq9snCV7yitUl3XoEsfLVXFhu166KICDcrq5DsSAAAAAABRRZkFSDpiUE/17ZqhklnBedSwsdHpx0/P1axl63XX+aOVP6CH70gAAAAAAEQdZRYgKSnJNC4vR/9eVKVVm6p9x2mRO176RC/MX6HrzzhEZ47s4zsOAAAAAAAx0S7KLDM728xeNbN1ZlZtZovM7E4z69mKYxxrZvebWamZ1ZiZC3+9uZvt/2BmH5hZRXifTWb2oZlda2ZpbfYDIhDG5+Wq0UnTZ1f4jrJHj723VA+8/YUmHjFAlx4zyHccAAAAAABixnuZZWa3SHpO0rckdZeULulASddJKjWzfi081HhJl0vKl9SSIup6SddKOlRS3/A+nSUVSvqDpFfMLKXlPwmC7oCsThrTv5tKysrlnPMdZ5deW7hKNz2/QN8a0ls3jR0mM/MdCQAAAACAmPFaZpnZMZJuDM82KlQwjZP0fnjZQEkPtfBwqyU9Hz7eMy3cp0LS7ZLODJ/3jYh1x0n6TguPgzhRlJerz1Zt0UcVm3xH2al55Rv0/56creE5XXXvhDFKTqLIAgAAAAAkFt93Zl0TMf2Ic+63zrnpks6X1HRrzClmNmxPB3LO3e6cO8c5d6ukhS04952SBjnnfumc+2f4vGcqVIo1ObwFx0EcGTuyr9JSklRS1v4Ggv9y3TZdPKVUPTqm6aGLCpSZxo2DAAAAAIDE47vMOiFi+p2mCefcl5KWR6w7sa1P7Jx71TlX22zZ9mbn3dLW50X71jUzVScP2U/PzalQbX2j7zhf2bitTpOnzFRtfYOmXlyo3p0zfEcCAAAAAMALb2WWmXVXaIysJiubbRI5f0D0E0lmdrCk0RGLno/FedG+FOXnaP22Or3x6eo9bxwDNfUNuuyxUi1fu00PTCzQgb07+44EAAAAAIA3Pu/M6thsvnY3852inEVmlqNQedX07NYDzrl3drP9ZeE3J5ZWVVVFOx5i6NiDstSrU7pKZvl/1NA5p5/9fZ4+WLJOv/uvkTp8UItf8AkAAAAAQFzyWWZtbTafvpv5qD7uZ2bDFRp0fnB40V8lXbG7fZxzDzjnCpxzBVlZWdGMhxhLSU7SuaP76o1PV2vd1uYda2z9/pVP9dycSv301ME6Z3SO1ywAAAAAALQH3sos59x6SesjFmU326RPxPTiaOUws+MVGq8rN7zobknfdc41ROucaP+K8nNV1+D0/JwKbxme/HC5/vTGYk04tJ+uOD4mT9oCAAAAANDu+R4A/o2I6WOaJsxsf0n9Ita9Ho2Tm9kESS9L6iqpUdI1zrlrnXNu93si3g3p00VD+3RRSZmfMuvNT1frhukf6biDs3TrOcNlZl5yAAAAAADQ3vgus+6NmJ5kZteb2bmS/hax/DXn3AJJMrMpZubCXzdHHsjMDjGzc8P7HxKxqlfTcjPLi9j+SklPSEoLL7pH0iwzOzria0Rb/aAInqL8XM2v2KjPVm2O6XkXVG7Uj54o0+D9OutPF+YpJdn3/6YAAAAAALQfXn9Lds69Jek3EVl+I2mapMLwsuWSLmnh4S4I7ztN0rcjlg+LWH5VxPLzJEXe7nKtpH83+7qvhedGHDpndF+lJFlMB4Kv3LBdF0+ZqS4dUlU8uVCd0lP2vBMAAAAAAAnE+y0fzrkbJI1T6FHCDQq9xXCxpLskFTjnlvlLh0TWq1O6jh+cpWmzK1Tf0Bj1822qrtPk4pnaVtOg4smF2q9LRtTPCQAAAABA0LSL2z6cc9MlTW/BdpMkTdrFupsl3dyKcx7f0m2RuIrycvXax6v1zudrdPzg3lE7T11Do654vEyLq7Zo6sWH6pDsLlE7FwAAAAAAQeb9ziygPTtxSG917ZAa1YHgnXP6xbPz9c7na3RH0UgddWCvqJ0LAAAAAICgo8wCdiM9JVlnj+qrVxas1Kbquqic455/LdLfZ5Xr6pMO0nn5uVE5BwAAAAAA8YIyC9iDovxc1dQ36oV5K9r82H+fVa67X1ukorxcXfOtg9r8+AAAAAAAxBvKLGAPRuV21QFZHdv8rYbvfr5GPy+Zp6MO7Knfjh8hM9vzTgAAAAAAJDjKLGAPzExF+bkqXbZeS9dsbZNjfrpysy5/bJYOyOqk+7+br7QU/lcEAAAAAKAl+A0aaIFxY3JkJj1btu93Z63aVK3JxR+qQ1qyiicXqktGahskBAAAAAAgMVBmAS3Qp2sHHX1gL5WUVaix0e31cbbU1Gty8Uxt3F6nRyYVqm+3Dm2YEgAAAACA+EeZBbRQUV6uKjZs1wdL1u3V/vUNjfrRE2X6dNVm/fHCPA3P6drGCQEAAAAAiH+UWUALnTosW53SU1SyF48aOuf0q+cW6K3PqnTbucN1wuDeUUgIAAAAAED8o8wCWqhDWrLOGJGtF+ev0Lba+lbte/9bi/Xkh8t1xfEHaMKh/aOUEAAAAACA+EeZBbRCUV6uttY26KWPVrZ4n+fmVOh/XvpUZ4/qq5+cMjiK6QAAAAAAiH+UWUArFA7soX49OrT4UcMPvlirnz4zT4ft30O/+6+RSkqyKCcEAAAAACC+UWYBrZCUZBo/Jlf/WbxWlRu273bbz1dv0WWPzVK/Hh30wPcKlJ6SHKOUAAAAAADEL8osoJWK8nLlnDRtdsUut6naXKNJxR8qNdk0ZfKh6pqZGsOEAAAAAADEL8osoJX698zUoQN7qKSsXM65b6zfVluv70+dqbVbavXwRYXq1yPTQ0oAAAAAAOITZRawF4ryc/RF1VbN+XLDDssbGp2uenK2PqrYqHsnjNGoft285AMAAAAAIF5RZgF74YwRfZSRmrTDQPDOOf16xgK99vFq3Xz2MJ08dD+PCQEAAAAAiE+UWcBe6JyRqlOHZWvG3BWqqW+QJD38zhJNfW+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Run the silhouette_plot here\n","silhouette_plot(reduced_data)"]},{"cell_type":"code","execution_count":null,"id":"aa35ac07-7180-47b0-9928-51f989c8ffd1","metadata":{"id":"aa35ac07-7180-47b0-9928-51f989c8ffd1"},"outputs":[],"source":["# Implement a K-means clusterer here. \n","# There are few hyperparameters that you can tweak.\n","# Look at scikit-learn documentation.\n","clusterer = KMeans(n_clusters=8, n_init='auto', max_iter=10000)\n","cluster_labels = clusterer.fit_predict(reduced_data)"]},{"cell_type":"markdown","id":"7702df1e-73cc-4507-bc53-8d40a33c9227","metadata":{"id":"7702df1e-73cc-4507-bc53-8d40a33c9227"},"source":["### Evaluate the results of clustering\n","In most cases you really don't have well defined categories so you can't evaluate your model like you would do in supervised learning. Nonetheless we still have [other methods](https://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation) that we can use to reason about our model. One of those methods is [Calinski-Harabasz Index](https://scikit-learn.org/stable/modules/clustering.html#calinski-harabasz-index). Reading the documentation in the previous link use this method on your model. What do you think about the results?"]},{"cell_type":"code","execution_count":null,"id":"5972e206-94dc-4e9e-b1e1-cd04832efad7","metadata":{"id":"5972e206-94dc-4e9e-b1e1-cd04832efad7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677832628864,"user_tz":-120,"elapsed":514,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"b3bf1ecd-7f03-4db8-aeb5-45456e96ba3e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["68171.53052946531"]},"metadata":{},"execution_count":62}],"source":["from sklearn.metrics import calinski_harabasz_score\n","calinski_harabasz_score(reduced_data, cluster_labels)"]},{"cell_type":"code","source":["calinski_harabasz_score(data, cluster_labels)"],"metadata":{"id":"CqO7TJpSgDhZ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677832635343,"user_tz":-120,"elapsed":6507,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"75e57514-6753-47c4-b49c-d961d447056b"},"id":"CqO7TJpSgDhZ","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["56695.6629330603"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["del data"],"metadata":{"id":"YgtnpbJWYNbr"},"id":"YgtnpbJWYNbr","execution_count":null,"outputs":[]},{"cell_type":"markdown","id":"c01fc8e2-2a7a-482e-ba6c-89e5f3a05e23","metadata":{"id":"c01fc8e2-2a7a-482e-ba6c-89e5f3a05e23"},"source":["Another useful way to reason about your model is by visualizing the clusters. Unfortunately our model is in higher dimensions so we can't visualize the result with normal methods. A common technique used in these situations is the [TSNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html). TSNE in its essence is a dimensionality reduction algorithm but it is used quite often to convert variables to 2D space for visualization. Unfortunately this method takes hours to terminate sometimes. So we will another method namely [UMAP](https://umap-learn.readthedocs.io/en/latest/basic_usage.html) to reduce our data to 2D space and plot your results using libraries like matplotlib."]},{"cell_type":"code","source":["from umap import UMAP"],"metadata":{"id":"nBYqf14nJe5x"},"id":"nBYqf14nJe5x","execution_count":null,"outputs":[]},{"cell_type":"code","source":["data_2d_umap = UMAP(verbose=True, low_memory=True)\n","data_2d = data_2d_umap.fit_transform(reduced_data)"],"metadata":{"id":"YOtTOQqzhG__","colab":{"base_uri":"https://localhost:8080/","height":502},"executionInfo":{"status":"error","timestamp":1677833031826,"user_tz":-120,"elapsed":372932,"user":{"displayName":"عبدالرحمن الخنجاري","userId":"03587907462715703891"}},"outputId":"97b02490-ff72-4470-bcea-9f09bf1b3523"},"id":"YOtTOQqzhG__","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["UMAP( verbose=True)\n","Fri Mar  3 08:37:38 2023 Construct fuzzy simplicial set\n","Fri Mar  3 08:37:38 2023 Finding Nearest Neighbors\n","Fri Mar  3 08:37:38 2023 Building RP forest with 53 trees\n","Fri Mar  3 08:38:34 2023 NN descent for 20 iterations\n","\t 1  /  20\n","\t 2  /  20\n","\tStopping threshold met -- exiting after 2 iterations\n","Fri Mar  3 08:40:09 2023 Finished Nearest Neighbor Search\n","Fri Mar  3 08:40:22 2023 Construct embedding\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-66-b5e182f7c779>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdata_2d_umap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUMAP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m 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                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/umap/umap_.py\u001b[0m in \u001b[0;36m_fit_embed_data\u001b[0;34m(self, X, n_epochs, init, random_state)\u001b[0m\n\u001b[1;32m   2715\u001b[0m         \u001b[0mreplaced\u001b[0m \u001b[0mby\u001b[0m \u001b[0msubclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2716\u001b[0m         \"\"\"\n\u001b[0;32m-> 2717\u001b[0;31m         return simplicial_set_embedding(\n\u001b[0m\u001b[1;32m   2718\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2719\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/umap/umap_.py\u001b[0m in \u001b[0;36msimplicial_set_embedding\u001b[0;34m(data, graph, n_components, initial_alpha, a, b, gamma, negative_sample_rate, n_epochs, init, random_state, metric, metric_kwds, densmap, densmap_kwds, output_dens, output_metric, output_metric_kwds, euclidean_output, parallel, verbose, tqdm_kwds)\u001b[0m\n\u001b[1;32m   1076\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0minit\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"spectral\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1077\u001b[0m         \u001b[0;31m# We add a little noise to avoid local minima for optimization to come\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1078\u001b[0;31m         initialisation = spectral_layout(\n\u001b[0m\u001b[1;32m   1079\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m             \u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/umap/spectral.py\u001b[0m in \u001b[0;36mspectral_layout\u001b[0;34m(data, graph, dim, random_state, metric, metric_kwds)\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m         return multi_component_layout(\n\u001b[0m\u001b[1;32m    307\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m             \u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/umap/spectral.py\u001b[0m in \u001b[0;36mmulti_component_layout\u001b[0;34m(data, graph, n_components, component_labels, dim, random_state, metric, metric_kwds)\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0mnum_lanczos_vectors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miparam\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    539\u001b[0m                                 self.ipntr, self.workd, self.workl, self.info)\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["plt.figure(figsize=(25, 10))\n","clusters = sns.scatterplot(x=data_2d[:,0], y=data_2d[:,1], hue=cluster_labels.astype(str))\n","clusters.set_title(\"Clusters\", pad=12, size=25)"],"metadata":{"id":"Po2zeH2HHnBC"},"id":"Po2zeH2HHnBC","execution_count":null,"outputs":[]},{"cell_type":"markdown","id":"4FQOLd8O57sk","metadata":{"id":"4FQOLd8O57sk"},"source":["## Finished early?\n","Don't worry we've got you backed up. Here are few things to consider:\n","- We talked about reducing dimensionality methods. We used PCA but it has its drawbacks the most serious one is that it assumes that the data is linear which is usually not the case. A more powerful method for reducing dimensionality is using autoencoders. Autoencoders are a variant of neural networks that learn how to represent the data in a smaller dimension using machine learning methods. [Read here about autoencoders and try implementing one by yourself](https://blog.keras.io/building-autoencoders-in-keras.html)\n","\n","- In this notebook we used K-means. K-means has two serious drawbacks:\n","      1. It assumes spherical clusters.\n","      2. It is sensitive to noise and outliers.\n","      \n","\n","If you want to read further: [Click here](https://developers.google.com/machine-learning/clustering/algorithm/advantages-disadvantages).\n","Our data had some outliers that made K-means not the preferred method for this problem. A more resilient method to outliers is DBSCAN. [Read here about DBSCAN and try applying it by yourself](https://scikit-learn.org/stable/modules/clustering.html)"]},{"cell_type":"markdown","id":"b721c45b-1893-4c20-b455-d80ee06f490d","metadata":{"id":"b721c45b-1893-4c20-b455-d80ee06f490d"},"source":["## Give us your feedback!\n","[Please fill in this form and give us your feedback](https://forms.gle/hg9VRLUdnocU4G3Y7)"]}],"metadata":{"colab":{"provenance":[{"file_id":"1MUTXhu9y5ie1OAzjJUFg527S7vYpxtUg","timestamp":1677833038341}]},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.7"},"gpuClass":"standard"},"nbformat":4,"nbformat_minor":5}