Task1.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"B3U4CBFZsdvQ","executionInfo":{"status":"ok","timestamp":1693245355083,"user_tz":-330,"elapsed":3,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}}},"outputs":[],"source":["#importing all libraries\n","import pandas as pd\n","import numpy as np\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","from warnings import filterwarnings\n","filterwarnings(action='ignore')"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mhlGUeDXsjIq","outputId":"7ed91254-8225-4d2a-cd21-baf4866917b2","executionInfo":{"status":"ok","timestamp":1693245444526,"user_tz":-330,"elapsed":89445,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n","0      1            5.1           3.5            1.4           0.2   \n","1      2            4.9           3.0            1.4           0.2   \n","2      3            4.7           3.2            1.3           0.2   \n","3      4            4.6           3.1            1.5           0.2   \n","4      5            5.0           3.6            1.4           0.2   \n","..   ...            ...           ...            ...           ...   \n","145  146            6.7           3.0            5.2           2.3   \n","146  147            6.3           2.5            5.0           1.9   \n","147  148            6.5           3.0            5.2           2.0   \n","148  149            6.2           3.4            5.4           2.3   \n","149  150            5.9           3.0            5.1           1.8   \n","\n","            Species  \n","0       Iris-setosa  \n","1       Iris-setosa  \n","2       Iris-setosa  \n","3       Iris-setosa  \n","4       Iris-setosa  \n","..              ...  \n","145  Iris-virginica  \n","146  Iris-virginica  \n","147  Iris-virginica  \n","148  Iris-virginica  \n","149  Iris-virginica  \n","\n","[150 rows x 6 columns]\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')  # Mounting the entire Google Drive\n","\n","#navigating to the directory where your Iris.csv file is located\n","import os\n","os.chdir('/content/drive/MyDrive/Project_O/Task_1')\n","\n","# Loading the CSV file\n","iris_data = pd.read_csv(\"Iris.csv\")\n","print(iris_data)"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Js40XYHwt-rz","outputId":"ff676aee-c370-4a95-ec3b-9654d50610a5","executionInfo":{"status":"ok","timestamp":1693245444526,"user_tz":-330,"elapsed":8,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["(150, 6)\n"]}],"source":["# Display the dimensions of the dataset (rows, columns)\n","print(iris_data.shape)"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NIomBUbduW3W","outputId":"67b25327-93bd-4ebe-a139-2929ca0dfe3c","executionInfo":{"status":"ok","timestamp":1693245445610,"user_tz":-330,"elapsed":1089,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n","count  150.000000     150.000000    150.000000     150.000000    150.000000\n","mean    75.500000       5.843333      3.054000       3.758667      1.198667\n","std     43.445368       0.828066      0.433594       1.764420      0.763161\n","min      1.000000       4.300000      2.000000       1.000000      0.100000\n","25%     38.250000       5.100000      2.800000       1.600000      0.300000\n","50%     75.500000       5.800000      3.000000       4.350000      1.300000\n","75%    112.750000       6.400000      3.300000       5.100000      1.800000\n","max    150.000000       7.900000      4.400000       6.900000      2.500000\n"]}],"source":["# Displaying descriptive statistics of the dataset\n","print(iris_data.describe())"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"W_rFQnbYu9WP","outputId":"a97a48be-84f3-4d73-9fcf-42682d5191e8","executionInfo":{"status":"ok","timestamp":1693245445610,"user_tz":-330,"elapsed":44,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n","0   1            5.1           3.5            1.4           0.2  Iris-setosa\n","1   2            4.9           3.0            1.4           0.2  Iris-setosa\n","2   3            4.7           3.2            1.3           0.2  Iris-setosa\n","3   4            4.6           3.1            1.5           0.2  Iris-setosa\n","4   5            5.0           3.6            1.4           0.2  Iris-setosa"],"text/html":["\n","  <div id=\"df-c74712c4-86f6-4b47-9155-1eafb63bd336\" 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>Id</th>\n","      <th>SepalLengthCm</th>\n","      <th>SepalWidthCm</th>\n","      <th>PetalLengthCm</th>\n","      <th>PetalWidthCm</th>\n","      <th>Species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button 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float64\n"," 4   PetalWidthCm   150 non-null    float64\n"," 5   Species        150 non-null    object \n","dtypes: float64(4), int64(1), object(1)\n","memory usage: 7.2+ KB\n"]}],"source":["# Displaying information about the dataset, including data types and non-null counts\n","iris_data.info()"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vTpqSTVswM4K","outputId":"bc0d1e48-eb1c-4580-a599-151bf249c1d3","executionInfo":{"status":"ok","timestamp":1693245445611,"user_tz":-330,"elapsed":38,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n","       'Species'],\n","      dtype='object')"]},"metadata":{},"execution_count":8}],"source":["# Displaying the column names of the dataset\n","iris_data.columns"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"ZVJoNt5p9Zio","outputId":"3334b7a7-4312-41f8-a8ae-f05bb9080139","executionInfo":{"status":"ok","timestamp":1693245445611,"user_tz":-330,"elapsed":35,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n","0    False          False         False          False         False    False\n","1    False          False         False          False         False    False\n","2    False          False         False          False         False    False\n","3    False          False         False          False         False    False\n","4    False          False         False          False         False    False\n","..     ...            ...           ...            ...           ...      ...\n","145  False          False         False          False         False    False\n","146  False          False         False          False         False    False\n","147  False          False         False          False         False    False\n","148  False          False         False          False         False    False\n","149  False          False         False          False         False    False\n","\n","[150 rows x 6 columns]"],"text/html":["\n","  <div id=\"df-05b0fa9f-4387-44fb-8c14-a1601d1f985b\" 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>Id</th>\n","      <th>SepalLengthCm</th>\n","      <th>SepalWidthCm</th>\n","      <th>PetalLengthCm</th>\n","      <th>PetalWidthCm</th>\n","      <th>Species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 6 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05b0fa9f-4387-44fb-8c14-a1601d1f985b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 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column\n","iris_data.isnull().sum()"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sZiTcYxi9wAx","outputId":"75b52b85-258b-4ccc-eb98-0e1fdc587ca0","executionInfo":{"status":"ok","timestamp":1693245445612,"user_tz":-330,"elapsed":31,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["No of Iris-virginica in Dataset: 50\n","No of Iris-setosa in Dataset: 50\n","No of Iris-versicolor in Dataset: 50\n"]}],"source":["# Counting the number of occurrences of each species\n","n1 = len(iris_data[iris_data['Species'] == 'Iris-virginica'])\n","print(\"No of Iris-virginica in Dataset:\",n1)\n","n2 = len(iris_data[iris_data['Species'] == 'Iris-setosa'])\n","print(\"No of Iris-setosa in Dataset:\",n2)\n","n3 = len(iris_data[iris_data['Species'] == 'Iris-versicolor'])\n","print(\"No of Iris-versicolor in Dataset:\",n3)"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":516},"id":"aQW6Yw2p-Pzd","outputId":"35f3538d-c3b0-49e2-8211-e02f399a9f23","executionInfo":{"status":"ok","timestamp":1693245445612,"user_tz":-330,"elapsed":28,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAApQAAAHzCAYAAACe1o1DAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABXYklEQVR4nO3dd3hUZeL28Xtm0nsvhBR6B2mKZekIFlQURURd1nUVLKtrfXd1dy2/tYCLrHV1XQVF1gUUVERRRCyoK6L0XkOAkEJ6m2Rm3j+iURQwySR5pnw/15VLknPmzJ0Rknue5zznWFwul0sAAABAM1lNBwAAAIB3o1ACAADALRRKAAAAuIVCCQAAALdQKAEAAOAWCiUAAADcQqEEAACAWyiUAAAAcAuFEgAAAG6hUAIAAMAtFEoAAAC4hUIJAAAAt1AoAQAA4BYKJQAAANxCoQQAAIBbKJQAAABwC4USAAAAbqFQAgAAwC0USgAAALiFQgkAAAC3UCgBAADgFgolAAAA3EKhBAAAgFsolAAAAHALhRIAAABuoVACAADALRRKAAAAuIVCCQAAALdQKAEAAOAWCiUAAADcQqEEAACAWyiUAAAAcAuFEgAAAG6hUAIAAMAtFEoAAAC4hUIJAAAAt1AoAQAA4BYKJQAAANxCoQQAAIBbKJQAAABwC4USAAAAbqFQAgAAwC0USgAAALiFQgkAAAC3UCgBAADgFgolAAAA3EKhBAAAgFsolAAAAHALhRIAAABuoVACAADALRRKAAAAuIVCCQAAALdQKAEAAOAWCiUAAADcQqEEAACAWyiUAAAAcEuA6QAA0NJq6hzKK61RSVXtMR+lP/m8pKpWpdV1Kv1um93hlNPpksPlktMpOVwuOZwuSZLNapHNYpHVqu/+a1GQzaqo0MD6j5AARYcGHvMR9ZPPo0MDlRQVrOAAm+FXCABalsXlcrlMhwCApnC5XMorq1H20UodOFqp7O8+vv9zXlmNPPUnm8UiJUUGKyMuTOlxYcr40Ud6XJiSIoNlsVhMxwSAJqFQAvBYVXaHthwu0aaDpdpbUNFQHHOKKlVd6zQdr1WEBFrVPvaHktkhIVy906LUMzVaoUGMbALwTBRKAB6h0l6nLYdKtfFgiTYeLNGmgyXanV/RMOXs72xWizolhqt3WrT6fPfRs12UwoI4c6mpLBaLFi9erIsuuqjFj71q1SqNGDFCRUVFiomJcetYTc05Z84c3XrrrSouLnbreYHmoFACaHOV9jptPlSqjTn1xXHjwRLtzi8X3bFprBapU2KE+qRF1xfN9tHq5eclc+rUqSouLtaSJUtOuE9ubq5iY2MVHBzc4s9vt9t19OhRJScnu33qQlNzVlVVqaysTElJSW49L9AcFEoAra7W4dQ3+4v02a4CfbarQBtyShh5bCU2q0V920frV50TdGbnBA3IjFWgzX8u6HGyQmm32xUUFNT2oX7E4XDIYrHIavWf/yfwD/yNBtAqtueW6d+f7dVvXvpK/e5/X5Oe/1JPrtylb7OLKZOtyOF06dvsYj2xcpcmPf+lTrn/ff3mpa/078/2antumel4bWr48OG66aabdOuttyohIUFjx46VVD+V/H3htNvtuummm5SamqqQkBBlZmbq4YcfPu7xduzYIYvFom3bth3z9ccff1ydOnWSVD/lbbFYGqad58yZo5iYGL311lvq2bOngoODlZ2drcOHD+u8885TaGioOnTooPnz5ysrK0uzZ89uOO6Pc+7bt08Wi0VvvPGGRowYobCwMPXr109ffPFFw/7fP9ePvf322xo8eLBCQkKUkJCgCRMmNGx75ZVXNGjQIEVGRiolJUVXXHGF8vLymvoyA5K4bBCAFnKktFqf7awfgVy9q0B5ZTWmI0FShd2hj7bn66Pt+ZLqV5if9d3o5a+6JCgpKsRwwtY1d+5cTZ8+XatXrz7u9ieeeEJvvfWWFixYoIyMDB04cEAHDhw47r5du3bVoEGD9Oqrr+rBBx9s+Pqrr76qK6644oQZKisr9eijj+qFF15QfHy8kpKSdOGFF6qgoECrVq1SYGCgbrvttkaVuXvuuUePPfaYunTponvuuUeTJ0/Wrl27FBDw81/n77zzjiZMmKB77rlHL7/8sux2u5YtW9awvba2Vg8++KC6deumvLw83XbbbZo6deox+wCNRaEE0Gw7jpRp6fpDem9zrnYcKTcdB42QV1ajN749qDe+PShJ6pocoXG9UnR+v3bqmhxpOF3L69Kli2bMmHHC7dnZ2erSpYvOOussWSwWZWZmnvR4U6ZM0VNPPdVQKHfs2KG1a9dq3rx5J3xMbW2tnnnmGfXr10+StG3bNq1YsUJr1qzRoEGDJEkvvPCCunTp8ovfzx133KHzzjtPknT//ferV69e2rVrl7p37/6zff/2t7/p8ssv1/3339/wte8zSNI111zT8OeOHTvqiSee0ODBg1VeXq6IiIhfzAL8GFPeAJpkd365/rFip85+/GOd/fgnemLlLsqkF9txpFxPrNylsx//RGc//rGe+HCn9uT7zv/PgQMHnnT71KlTtW7dOnXr1k2///3v9f777zdsmzZtmiIiIho+JOnyyy/Xvn379OWXX0qqH50cMGDAcQvd94KCgtS3b9+Gz7dv366AgAANGDCg4WudO3dWbGzsL34/Pz5OamqqJJ1wZHPdunUaNWrUCY+1du1ajR8/XhkZGYqMjNSwYcMk1ZdsoKkYoQTwi7ILK/X2hkNauuGwth4uNR0HrWTHkXLN+mCHZn2wQz1So3R+31SN79tOGfFhpqM1W3h4+Em3DxgwQHv37tW7776rFStW6LLLLtPo0aO1aNEiPfDAA7rjjjuO2T8lJUUjR47U/PnzNWTIEM2fP1/Tp08/6XOEhoa22MXqAwMDG/78/TGdzuNfkzU0NPSEx6moqNDYsWM1duxYvfrqq0pMTFR2drbGjh0ru93eIlnhXyiUAI4rp6hS72w4rKUbDmvjwRLTcdDGth4u1dbDpZq5fLv6to/W+X1TdV7fdkqLOXFJ8VZRUVGaNGmSJk2apIkTJ2rcuHE6evSokpKSjnsJnilTpuiuu+7S5MmTtWfPHl1++eVNer5u3bqprq5O3377bcMI6q5du1RUVNQi38/3+vbtqw8//FC/+c1vfrZt27ZtKiws1COPPKL09HRJ0tdff92izw//QqEE0KC61qGlGw7rP19l65vsIo+9fSHa1oacEm3IKdHD727TgIxYTT41Q+f3TVVIoPffuWfWrFlKTU1V//79ZbVatXDhQqWkpJz0ouQXX3yxpk+frunTp2vEiBFq165dk56ze/fuGj16tK677jo9++yzCgwM1O23396iI5mS9Ne//lWjRo1Sp06ddPnll6uurk7Lli3T3XffrYyMDAUFBenJJ5/UtGnTtGnTpmMWGgFNxTmUALS/sEJ/e2eLhjz8oe5YuF5r91Mm8XMul7R2f5HuWLhepz/8oR5atlXZhZWmY7klMjJSM2bM0KBBgzR48GDt27dPy5YtO+l1IiMjIzV+/HitX79eU6ZMadbzvvzyy0pOTtbQoUM1YcIE/e53v1NkZKRCQlpu1f3w4cO1cOFCvfXWWzrllFM0cuRIffXVV5KkxMREzZkzRwsXLlTPnj31yCOP6LHHHmux54b/4cLmgJ9yOl36cFueXvlyvz7dmU+BRLNYLNLQLom6akimRnZPktXaciNs/iQnJ0fp6elasWLFSRfSAJ6KQgn4mYLyGv13zQHN/1+2DhZXmY4DH5IWE6orTsvQpMHpSoho+dsa+pKVK1eqvLxcffr00eHDh3XXXXfp4MGD2rFjxzELbwBvQaEE/MSafUf1yhf79d6mXNkdx18VCrSEIJtV43qn6KrTMzU4K850HI+0fPly3X777dqzZ48iIyN1xhlnaPbs2b94HUzAU1EoAR/3wZYjenLlTm3IYaU22l7f9tH6/cguGt0z2XQUAK2IQgn4IJfLpeWbc/Xkyl3afIjrRsK8Xu2idPPILhrbK7lFVzID8AwUSsCHuFwuLduYqydX7tS23DLTcYCf6Z4SqZtHdtG5fVIoloAPoVACPsDpdOntDYf09EfcBhHeoWtyhG4c0Vnj+7ZjZTjgAyiUgBdzOF16c91BPfXRLu3JrzAdB2iyTonhumlkZ13QL002iiXgtSiUgBdyuVxa/O1BPblyl/YWUCTh/TokhOvmkZ01oX8aU+GAF6JQAl7mi92F+tuyLdp0kMU28D2906J073k9NaRjvOkoAJqAQgl4ib0FFXpo2VZ9sOWI6ShAqzu7Z7L+dG4PZSWEm44CoBEolICnqyrWoysP6IXPD6jWwT9X+I9Am0VXDcnSLaO7KDqUu8cAnsxqOgCAE3A6pbVzpScHqv/hBZRJ+J1ah0svrt6rkY+t0mtfZYvxD8BzMUIJeKKctdKyO6RD30iSXMFRGl37uHZXhhoOBpjTr3207r+wt05JjzEdBcBPUCgBT1JRIK34q/Ttq5KO/ae5K/0Sjd55iZlcgIewWKRLB7bX3eO6Kz4i2HQcAN+hUAKeYuMiadmdUtXR4252Way6OWK2luYntHEwwPPEhgXq/gt764J+7UxHASAKJWBeeb609FZp29Jf3LU0+VT13X9rq0cCvMW4Xin6vwm9lcBoJWAUi3IAkzYukp4+tVFlUpKijnyle7K2tXIowHu8tzlXZz/+id5ef8h0FMCvMUIJmFCeL71zm7T1rSY/tC6yvfoXPaSyuoBWCAZ4r3N6p+j/LurNuZWAARRKoK1teqN+BXdlYbMPsTr9ek3ZOawFQwG+IS48SA9c2Evn9+XcSqAtUSiBtlJRUD8queVNtw/lCgzThZbZ2lAa0QLBAN9zXp9UPXBhL0YrgTZCoQTawuYl0ju3S5UFLXbIA+3P0692TWmx4wG+Jj48SA9e1Fvn9kk1HQXweRRKoDXV1dRfCuibua1y+P8X+3e9dphflsDJTD41Xfdd0EvBATbTUQCfRaEEWkvRPmnB1dLh9a32FJUJ/dTr4F1yuSyt9hyAL+idFqVnpwxUelyY6SiAT+KyQUBr2P6u9NzQVi2TkhRWsF4Pd9jUqs8B+IJNB0t1/pOfacWWI6ajAD6JEUqgJTkd0ocPSKv/oZ/eOrG1OMKTdHr5TOXVBLbJ8wHezGKRpg3rpDvO7iablZF9oKVQKIGWUp4nLbpG2vdpmz/1N+m/1sU7x7b58wLeakjHOD05eYASI1kFDrQECiXQEvatri+T5blGnt5lC9blgf/Q/4qjjDw/4I2SIoP11BUDdGqHONNRAK/HOZSAuz6bLc0db6xMSpLFUaMn4hYZe37AG+WV1eiKf32p5z7ebToK4PUYoQSaq6ZcWnx9o+/D3RYeSnhEz+dkmI4BeJ2zeybr8UmnKDyYW5oCzUGhBJqjLFd69VIpd4PpJMeoieum3rl/Ua2TxQZAU/VOi9KLUwcrKTLEdBTA6zDlDTRV3jbphTEeVyYlKfjods3qsNZ0DMArbTpYqglPf65deWWmowBehxFKoCn2fir9d4pUXWI6yQk5Q+M0tOrvyqlm9SrQHFEhAXr+6kEa0jHedBTAazBCCTTWxkXSvIs9ukxKkrXqqJ5Pf990DMBrlVbX6ep/f6U31x00HQXwGhRKoDE+nSW9fq3ksJtO0ig9Di7UqPijpmMAXsvucOrW/67TM6t2mY4CeAWmvIGTcTqkZXdIX79oOkmTHU05SwP23WA6BuD1rhySofsv6M2ddYCTYIQSOBF7hfSfyV5ZJiUpLvcz3Z7B6ArgrnlfZuu6l79Wpb3OdBTAYzFCCRxPeb40/1Lp0Lemk7ilNrqDTil4UBUO3jsC7urbPlovTh2shAgWvAE/xW8Z4KfKjkhzzvP6MilJgSV79UTHL03HAHzChpwSXf78l8orqzYdBfA4FErgx8qO1N9GsWC76SQtZmTeXHUNrzIdA/AJu/LKdcW//kepBH6CQgl8zwfLpCRZasr0z1TPuT0k4O0olcDPUSgByWfL5Pc6HHxTFyXnmY4B+AxKJXAsCiXg42VSkiwupx4Mnmc6BuBTKJXADyiU8G9+UCa/F5n3tf6atdV0DMCnUCqBehRK+C8/KpPfu6riRUUHci09oCVRKgEKJfyVH5ZJSQooO6h/Zn1qOgbgcyiV8HcUSvif8ny/LJPfG3J4nk6JKjcdA/A535fKgvIa01GANkehhH+xV0ivTvTbMilJlroqPZW0xHQMwCftyivXNXPWqMruMB0FaFMUSvgPp0NadI10eJ3pJMa1z1mmq9odNB0D8Ekbckp083++kcPJnY3hPyiU8B/L7pB2vGc6hcf4k3WubBan6RiAT1qxNU9/fWuT6RhAm6FQwj98Okv6+kXTKTxKaMEmPdJhg+kYgM+a92W2nl2123QMoE1QKOH7Ni6SPnzAdAqPdEnxS0oJtpuOAfisGcu36c11nF4C30ehhG/b+6m0ZLokzmU6Hmtlvv6ZsdJ0DMBnuVzSnQs36Ms9haajAK2KQgnflbdN+u8UycEI3Mn0O/SazowtMR0D8Fl2h1PXv7JWu/LKTEcBWg2FEr6pLLf+8kDVFKVfYnHYNTt2oekYgE8rqarVr19cw4XP4bMolPA9NeXSq5dKJQdMJ/EaiYdWanr6PtMxAJ92sLhK18xZo4oabn8K30OhhO9ZfL2Uy+rlprq1bo6CrVxGCGhNmw6W6g//XWc6BtDiKJTwLav/IW1bajqFVwou2qHHO641HQPwee9vOaLnP+FyQvAtFEr4jn2rpRX3m07h1cYVvKSsUM7xAlrbjPe266u9R03HAFoMhRK+oeyItOg3kov757rDWl2sf7ZfbjoG4PPqnC7dNP8b5ZfVmI4CtAgKJbzf9/foLj9iOolP6JazSGcnMHICtLa8shru+Q2fQaGE9/vwAWn/Z6ZT+AyLy6EZEa+ajgH4hS/3HNVj7283HQNwG4US3m37u/ULcdCiYnK/0F2ZO03HAPzCPz/erRVbmGGBd6NQwnsV7au/RBC3VWwVv6t+SeEBnJMKtDaXS7p94XodOFppOgrQbBRKeKe6GmnB1dwJpxUFluzT0x2+MB0D8AslVbW64dVvVFPHmzh4JwolvNOyO6XD602n8HnDjsxVjwhGTYC2sPFgie57a4vpGECzUCjhfTYvkb6ZazqFX7DYK/RsytumYwB+4z9fZevdjYdNxwCajEIJ71JRIL1zu+kUfiUz5y1dksyCAaCt/PnNTTpaYTcdA2gSCiW8yzu3SZUFplP4FYtcuj/oFdMxAL9RUG7Xn5dsMh0DaBIKJbzHpjekLW+aTuGXIvK/0QMdNpuOAfiNdzYe1jsbmPqG96BQwjuU50vL7jCdwq9NKXtR8UG1pmMAfuMvb25SYTm3ZoR3oFDCO7xzm1RZaDqFX7OVH9azmZ+YjgH4jcIKu/78JlPf8A4USni+Ta9LW98ynQKSBh9+VQOiy0zHAPzGso25WrrhkOkYwC+yuFwubjMCz1WeLz1zGqOTHuRQ2jidsftq0zH8Stm3y1T27TLVldSvtg9MyFDMGZMV2mmQJKnwvadUvX+dHOVHZQkMUXBaD8UOn6rA+PQTHrP4s1dVsfVTOcryZbEGKCils2KGXq3gdt0a9sl7/QHZj+yVo7JYtpAIhWSdophhUxUQGS9Jqis5ooKls2Q/sktByZ2VcP5tCohO/uHxi+5XeJ/RCu92Zmu8LH4jLjxI7/9hqBIigk1HAU6IQgnP9t8rpa1cB9HT/DVupuYeSjMdw29U7vqfLBarAmLbSZLKN32o0v+9odSp/1BQYqbK1r2nwPj2CohKlKOqTCWr58t+ZK/Spr0gi9V23GNWbFkla1iMAmJS5KqtUdnXb6pi22dKu/5fsoVFS5JK1yxRcLvuskXEqa6sUMUf/VuSlHLVY5Kk/MUPSbYAxZw1RcWfzpOcDiVO+FP98bd+oootq5R0yV9a++XxC+f0TtGzVw40HQM4Iaa84bk2LqJMeqj/Z5kjm8VpOobfCOt8mkI7DVZgXJoC49IUO/RqWYNCVHNouyQp8pRxCknvrYDoZAWndFbMr66SoyxfdSV5JzxmeM/hCs06RYExKQpKzFTsyGvlslfKnre3YZ+owRcpOK27AqKTFNK+h6KGXKqaQ9vlctRJkmoLDyii9ygFxqUpovco1RbmSJKc1eUq/vQVxY2Z3oqvin95d1Ou3lrP1Dc8F4USnqmioP72ivBIoYWbNbMjt740weV0qGLLx3LWVis4rfvPtjvt1SrfuEIB0ckKiEpo3DEdtSpb954sweEKSupw3H0cVWWq2LJKwWk9ZLEFSJICkzqoet86uVxOVe37VoFJWZKkoo9eVGT/8xUQldi8bxLH9VdWfcODMeUNz/TmjdK380ynwEk4QxN0ZtVjOlwdZDqKX7Dn71PuK3fIVWeXJShUiePvUGinwQ3by755R0WrXpKrtloBce2VNPGvCoxNPekxK3d9pYK3ZshVWyNbRKwSL75Xwaldj9mnaNVLKvtmqVy1NQpq101JE/8qW2iUJKmurEBHlz8te94+BSVlKW7sjaorzlXRyn8r6bIH6rfl7lRIh/6KG329LLbAln9h/MykQel6dGJf0zGAn6FQwvPkrJVeGCWJv5qebkP6lbpg57mmY/gFl6NWdaX5ctZUqnL7Zypf/76Sr3hEQQkZkiRnTYUcFcVyVBSp9Ks35CgrVMqVM2UJOHHhd9qr5ag4KmdlqcrWL1d19galXvV32cJjGvZxVJbIWV2uupI8laz+j6zBYUqc+FdZLJafZ6yr1eG5tyj+vNtUsfkjOWsqFT/2RuUt/ItCu5yuqIHjW/x18TcWi7T4hjN1SnqM6SjAMZjyhmdxub67gDll0hv0OfhfDY0rNh3DL1hsgQqMbafglM6KHTZVQUkdVPb1D5fTsgaHKzAuTSHpvZV40R9VezRHlTu+OOkxrUEh9cdM666Ec2+RxWpV+Yb3j9nHFhatwLg0hXbor4QL7lLVnq9lP7TtuMcr+WKBQrL6Kzils2oObFJ4tzNksQUorOsZqsne6P6LALlc0l/f2izGguBpKJTwLN+8LB36xnQKNJLFWatZ0f81HcMvuVwuuRwnuHORq/7jhNtPfNBfeEz9Qqzj7VNbcEAVW1cp5ldX1e/jdMjldHy3v0MuF4u4Wsr6A8Va8PUB0zGAY1Ao4TmqiqUPHzCdAk2UcPhj3Zyx95d3RLMVfTxH1Qc2qa7kiOz5+1T08RzVZG9UeM/hqi3OVckXC1STu0t1pXmqztmq/DcfliUgSKEdBzUc4+C/pqlyx+eS6qe6iz6eq5qD21RXkqea3F0qWDZbdWWFCut2liSp5tB2la59W/Yje1RXkqeq/etV8NZMBcSkKrhdj2PyuVwuFS5/UrEjfydrUIgkKbh9T5WvX15fNDevVHDasY+Be2a8t10lVdwKFZ4jwHQAoMHHj0qVBaZToBlutr+k5633qcbJe9TW4KgoUcHSWXJUHJU1OFxBiVlKuuwBhXbor7qyQlXnbFbp12/JWV0uW3iMgtN7KeXKmcecC1l3NEfOmkpJksVqVe3RHOUv+VCOqlLZQqMUlNJFKVMeVVBiZv0+gcGq3PGFSj6bL2dttWwRcQrtMEAJF06SJeDYxTXl69+TLSxGYZ1PbfhazJlXqODtmTr8ym0K7TBQkQPOa/0Xyo8UVtj1jxU79ZfxPU1HASSxKAeeonC39PRpkpN33N7qvfa3aNqu00zHAPxGoM2i9/8wTB0Swk1HAZjyhod4/8+USS83Nn+OOoZVm44B+I1ah0sPL9tqOgYgiUIJT7D3U2n7O6ZTwE2WmhL9M+1d0zEAv/L+liP6ck+h6RgAhRKGOZ3S8j+ZToEW0iXndZ2byHmwQFv6v3e2cBkhGEehhFkbF0i5G0ynQAuxuJx6OGy+6RiAX9l0sFRL1h00HQN+jkIJc5wO6eMZplOghUUf+VJ/zNxhOgbgV578cJccTkYpYQ6FEuZsXCQd3W06BVrBb6teVGRAnekYgN/YU1Cht9cfMh0DfoxCCTOcTunTx0ynQCsJKM3WMx0+Nx0D8CtPrtwpJ6OUMIRCCTM2vyEVMC3qy87KfUW9IitMxwD8xu78Ci3deNh0DPgpCiXansslfTLTdAq0MktthZ5Jftt0DMCvPLVyJyu+YQSFEm1vyxIpf5vpFGgDGTlv69KUXNMxAL+x40i53t3Evzm0PQol2pbLJX3CuZP+wiKX7gt8WRYLIyZAW3ly5S5GKdHmKJRoW9uWSkc2mU6BNhSev05/y+L/OdBWth4u1ftbjpiOAT9DoUTb+vhR0wlgwKTSl5QYxL3agbbyxIc7TUeAn6FQou1sWyblbjSdAgbYKnL1bNbHpmMAfmPzoVKtYJQSbYhCibbzCXfF8WcDD83XqTGlpmMAfuPJlYxSou1QKNE29n8hHfrWdAoYZKmr1hNxr5uOAfiN9Tkl+nrfUdMx4CcolGgba14wnQAeIOXQB/pt2gHTMQC/8cqX+01HgJ+gUKL1ledLW98ynQIe4k7XHAVauaQJ0Bbe3ZirwvIa0zHgByiUaH3fzJUcdtMp4CFCjm7VYx04/QFoC3aHU6+tYVYArY9CidbldEpr55hOAQ8z/uhLSgth1ARoC/P/ly2nk1kBtC4KJVrXjvekEt4d41jWqkI9l/6B6RiAXzhYXKWV2/JMx4CPo1CidbEYByfQ6+ACjYwvMh0D8AsszkFro1Ci9RzdI+1eaToFPJTFWafHol4zHQPwC5/szFd2YaXpGPBhFEq0nq9flMR5OzixuMOf6taMPaZjAD7P5ZJe/R+jlGg9FEq0jtpq6dtXTaeAF7jB/qJCbQ7TMQCft+DrA6qu5d8aWgeFEq1j8xtSFXdowC8LKt6jJzp8ZToG4POKKmv1zobDpmPAR1Eo0Tq4VBCaYHT+XHUJrzIdA/B587/KNh0BPopCiZZXnC0d+J/pFPAilppSPdtumekYgM/7JrtIB4t584aWR6FEy9u82HQCeKFOOYt1fmKB6RiAT3O5pHc2HDIdAz6IQomWR6FEM1hcTj0UNs90DMDncR4lWgOFEi3r6B7pEPdpRvNEHflKf87aZjoG4NPW55RwTUq0OAolWhajk3DTryteVHRgnekYgE9bupFpb7QsCiVa1iYKJdwTUJajZ7M+Mx0D8GlL1zPtjZZFoUTLKdgpHdloOgV8wOm589Q3qtx0DMBnbTlcqj35/BtDy6FQouUw3Y0WYqmt1NNJb5qOAfg0FuegJVEo0XI2vWE6AXxIes47uiKVX3hAa1lKoUQLolCiZeRtlfK3mk4BH3Ovba4sFpfpGIBP2n6kTDuPlJmOAR9BoUTLYHQSrSCsYIMe6cB5uUBreZtRSrQQCiVaxta3TSeAj5pY8qKSgmtNxwB80vJNuaYjwEdQKOG+0sNMd6PV2Cry9M+MlaZjAD5p+5Ey5ZVWm44BH0ChhPv2rDKdAD6u/6HXdHpsiekYgE/6bFeB6QjwARRKuG/PR6YTwMdZHDWaHbvIdAzAJ322k0IJ91Eo4b49H5tOAD+QfOhDXd8+23QMwOes3k2hhPsolHDPkS1SOSd1o23c5nxJgVYuIwS0pCOlNdrB5YPgJgol3MP5k2hDwUe3a1aHtaZjAD6HaW+4i0IJ93D+JNrYeYUvKSOUValAS2JhDtxFoUTzOWql/Z+bTgE/Y60u0nPt3zcdA/Ap/9tTqFqH03QMeDEKJZrvwFeSvdx0Cvih7gcXaUzCUdMxAJ9RYXfo2+xi0zHgxSiUaD6mu2GIxVmnGRH/MR0D8Cmf7cw3HQFejEKJ5mNBDgyKzV2t2zN2mY4B+AzOo4Q7KJRoHnuFdPAb0yng56bVvKRwG+d9AS1hfU6JKu11pmPAS1Eo0TyHN0guh+kU8HOBJXv1ZMcvTMcAfILD6dKWQ6WmY8BLUSjRPIfXmU4ASJJGHJmrruFVpmMAPmHjwRLTEeClKJRonkPrTCcAJEkWe7n+mbrUdAzAJ1Ao0VwUSjQPI5TwIB0OvqmLkvNMxwC83iYKJZqJQomms1dIBTtMpwAaWFxOPRg8z3QMwOvtzq9QlZ3z49F0FEo0Xe5GycXKWniWyLyvdV+HraZjAF7N4XRpy2FGKdF0FEo0HedPwkNdWf6iYgO57Angjo05FEo0HYUSTcf5k/BQAWUH9WzWp6ZjAF5t40EuHYSmo1Ci6RihhAc77fA8nRLFPeaB5mJhDpqDQommsVeyIAcezVJXpacTF5uOAXitXfnlLMxBk1Eo0TS5G7lDDjxe2sF3dVW7g6ZjAF6pfmEO095oGgolmubwetMJgEb5k3WubBauRgA0B9PeaCoKJZqmcJfpBECjhBZs0qMdNpqOAXilvQUVpiPAy1Ao0TRF+0wnABrt4uIXlRJsNx0D8DoHjlaajgAvQ6FE01Ao4UWslfl6LuND0zEAr5NNoUQTUSjReC6XVJxtOgXQJH0P/VdnxXE+GNAUB4oolGgaCiUaryxXqqsynQJoEovDrsejF5iOAXiV6lqn8kqrTceAF6FQovGY7oaXSjz8kaan7zMdA/AqTHujKSiUaLzi/aYTAM12a90cBVu5jBDQWEx7oykolGg8RijhxYKLdmh2x69NxwC8RnYhpzih8SiUaDwKJbzc2II5ygrlvDCgMZjyRlNQKNF4FEp4OWt1sZ5r/57pGIBX4FqUaAoKJRqPQgkf0DXndY1LLDQdA/B4jFCiKSiUaJza6vrLBgFezuJy6JHw+aZjAB7vSFm1auocpmPAS1Ao0TjluZJcplMALSIm9wvdlbnTdAzAo7lcUl5pjekY8BIUSjROVbHpBECL+l3ViwoPYPQFOJmSqlrTEeAlKJRonOpi0wmAFhVYul/PdPjcdAzAo5VSKNFIFEo0DiOU8EFDj7ysHhEsPABOhBFKNBaFEo3DCCV8kMVeoWdT3jYdA/BYFEo0FoUSjVNdYjoB0Coyc97SJclHTMcAPFJpNYUSjUOhROMw5Q0fZZFL9we9LIuFqxgAP8UIJRqLQonGYcobPiwi/1s9kLXZdAzA41Ao0VgUSjQOI5TwcVeUvaT4IH55Aj9WUlVnOgK8BIUSjcMIJXycrfywns38xHQMwKMwQonGolCicViUAz8w+PCrGhBdZjoG4DG4DiUai0KJxmHKG37AUletpxPeMB0D8BgUSjQWhRKNwwgl/ETqweWa2i7HdAzAI3DZIDSW3xRKi8WiJUuWmI7hvRz8UIH/uNsyVzaL03QMwDh7Hf8O0DgBpgO0hKlTp6q4uPikhfHw4cOKjY1tu1A/ct9992nJkiVat26dkedvES6H6QQt6tk1dj37tV37iut/WPZKsukvQ4N0TpdASdL1b1dpxd46HSpzKSLIojPSbXp0dLC6J9hOeMz7VlXrtU11OlDqVJBNGphq099GBuu09j/8M7vgP5Val+tQXoVLsaEWje4YoEdHB6tdZP17u33FTl29uEprDzs0MNWmlyeEKivmh/d958+v1G9OCdQlPQNb42XBd0ILN2tmx/W6bXd/01EAo5xcnhWN5PMjlHa7XZKUkpKi4OBgw2m8mNO3CmX7KIseGR2stdeF6+vrwjUyy6YLX6vS5rz673NgO5teujBUW2+M0PIrw+RySWe/UinHSX66do236alzQ7RxeoQ++024smKsOntepfIrfniHPyLLpgWXhmr7TRF6/bJQ7T7q1MQFVQ3bb3+/WmlRFq27PlypkRbd8X51w7b/bqqV1SLKZBu56OhLSg2xm44BGHWyn3nAj/lcoRw+fLhuuukm3XrrrUpISNDYsWMlHTvlbbfbddNNNyk1NVUhISHKzMzUww8/fMJj/tL+xcXFuvbaa5WYmKioqCiNHDlS69evlyTNmTNH999/v9avXy+LxSKLxaI5c+ZIkrKzs3XhhRcqIiJCUVFRuuyyy3TkyA+3gFu/fr1GjBihyMhIRUVFaeDAgfr6668lSYWFhZo8ebLS0tIUFhamPn366D//+U9LvpTH8rERyvHdAnVul0B1ibepa7xNfxsVoogg6cuc+u/zuoFBGpoZoKwYqwak2vR/I4N1oNSlfcUn/uF6RZ9Aje4YoI6xVvVKsmnW2BCV1kgbjvxQKP9werCGtA9QZoxVZ6QH6P+dFaQvcxyqddQfd2u+U7/uF6Qu8TZN7ReorQX1jy2udunej2r09Lkhrfiq4MesVQV6Pv0D0zEAoxwuCiUaxyemvH9q7ty5mj59ulavXn3c7U888YTeeustLViwQBkZGTpw4IAOHDhwwuP90v6XXnqpQkND9e677yo6OlrPPfecRo0apR07dmjSpEnatGmT3nvvPa1YsUKSFB0dLafT2VAmP/74Y9XV1enGG2/UpEmTtGrVKknSlClT1L9/fz377LOy2Wxat26dAgPrR6eqq6s1cOBA3X333YqKitI777yjq666Sp06ddKpp57aQq/kj/jYCOWPOZwuLdxSp4pa6fT0n09pV9hdeunbWnWIsSg92tKoY9odLj2/1q7oYKlfyvHftx2tcunVjbU6I92mQFv9cfulWLViT53O7mTT+7sd6ptc/9g736/WjYMDlR7tc+8BPVrvgws0LP4MfVxo5nQZwDQnI5RoJJ8slF26dNGMGTNOuD07O1tdunTRWWedJYvFoszMzJMe72T7f/bZZ/rqq6+Ul5fXMKX+2GOPacmSJVq0aJGuu+46RUREKCAgQCkpKQ2P++CDD7Rx40bt3btX6enpkqSXX35ZvXr10po1azR48GBlZ2frzjvvVPfu3Ru+r++lpaXpjjvuaPj85ptv1vLly7VgwYLWKZQ+NkIpSRuPOHT6vytUXSdFBEmLJ4WqZ+IPhfKZNXbd9UG1KmqlbvFWfXBVuIJsJy+US3fU6vJFVaqslVIjLfrgqnAlhB1bAu/+oFpPrbGrslYa0t6mpZNDG7Y9NiZE1y+tVtbscvVNtum580P0yf46rTvi0KNjQnTZwkp9fcihszsF6IlzQn4xD9xjcdZqVtQCDSy83nQUwAhGKNFYPlkoBw4ceNLtU6dO1ZgxY9StWzeNGzdO559/vs4++2xJ0rRp0zRv3ryGfcvLy0+6//r161VeXq74+PhjnqOqqkq7d+8+YYatW7cqPT29oUxKUs+ePRUTE6OtW7dq8ODBuu2223TttdfqlVde0ejRo3XppZeqU6dOkiSHw6GHHnpICxYs0MGDB2W321VTU6OwsLCmvVh+rFuCVeumRaik2qVFW2r16yXV+niqtaFUTukTqDEdbTpc7tJjn9t12aJKrb4mXCEBJy5xI7ICtG5ahAoqnfrX2lpdtqhS/7s2XEnhP5TKO88M0m8HBGp/sUv3f1yjq5dUa+nkUFksFqVFWbX0ih/+H9bUuTR2XrXmXhSq//ukRpFBFm2/KULjXq3Uc1/X6ubTglrvBYIkaXOYXZE9/p/pGIARFlkknWc6BryAT86fhYeHn3T7gAEDtHfvXj344IOqqqrSZZddpokTJ0qSHnjgAa1bt67h45f2Ly8vV2pq6jGPWbdunbZv364777zTre/jvvvu0+bNm3Xeeedp5cqV6tmzpxYvXixJmjlzpv7xj3/o7rvv1kcffaR169Zp7NixDYuQWpzlxKubvVWQzaLOcVYNbGfTw6ND1C/Zqn98+cPrFx1iUZd4m4ZmBmjRZaHaVuDU4q0nv69teFD9MYe0D9C/LwxVgNWif39z7CWXEsKs6hpv05hOAXptYqiW7axrOHfzpx76tEZndwrQwHY2rdpXp0t6BijQZtHF3QO1aj/32G1ttdZAzQz95f0AX2XzwZ/9aB0+OULZGFFRUZo0aZImTZqkiRMnaty4cTp69KiSkpKUlJTU6P0HDBig3NxcBQQEKCsr67jPFRQUJIfj2MLQo0ePhnMxvx+l3LJli4qLi9WzZ8+G/bp27aquXbvqD3/4gyZPnqyXXnpJEyZM0OrVq3XhhRfqyiuvlCQ5nU7t2LHjmMe2KKtNcvjetPePOV1SzQm+RZer/qPG0bTpH6fLddLHfH960vGed2u+Q/M31Wnd9fVvkBwuqfa7/WqdLjm4PFyrm99rlPaVbzEdAzDGavHJcSe0Ar8slLNmzVJqaqr69+8vq9WqhQsXKiUlRTExMU3ef/To0Tr99NN10UUXacaMGeratasOHTqkd955RxMmTNCgQYOUlZWlvXv3at26dWrfvr0iIyM1evRo9enTR1OmTNHs2bNVV1enG264QcOGDdOgQYNUVVWlO++8UxMnTlSHDh2Uk5OjNWvW6JJLLpFUfz7lokWL9Pnnnys2NlazZs3SkSNHWq9Q+ti71D+uqNY5XQKUEW1VWY1L8zfWatU+h5ZfGaw9RU79d1Otzu4UoMRwi3JKnXrkM7tCAy06t8sP/2S6P1Wuh0cFa0KPQFXYXfrbpzW6oFuAUiOsKqh06ek1dh0sdenS7y7z87+cOq055NRZGTbFhli0u8ipP39Uo06xFp3e/tjX1+Vy6bql1Xp8bLDCg+qn2M9Mt+lf39Sqa7xVL6+v1eTeXD6oNR0NT9BzNdmmYwBG2ay+9bMfrccvC2VkZKRmzJihnTt3ymazafDgwVq2bJms1uO/E/ul/ZctW6Z77rlHv/nNb5Sfn6+UlBQNHTpUycnJkqRLLrlEb7zxhkaMGKHi4mK99NJLmjp1qt58803dfPPNGjp0qKxWq8aNG6cnn3xSkmSz2VRYWKirr75aR44cUUJCgi6++GLdf//9kqR7771Xe/bs0dixYxUWFqbrrrtOF110kUpKWukWiT72QyWvwqWrF1fpcLlL0cEW9U22avmVYRrTKUCHypz6NNuh2f+zq6jKpeQIi4Zm2vT5NWHHnAu5vdCpkpr6IUabVdpW4NTc9VUqqHQpPtSiwWk2ffqbcPVKqn/twgItemNrrf66qkYVdpdSIy0a1ylA904MVfBPzst8fm2tksMtOr/rD6XxvuHBuuL1Kp32QoXGdQ7Qjady/mRreqLbaSor2mg6BmAUI5RoLIvLxRIuNMLDGVIN9/OGf9iW2lOTQivldHFeAfxbZFCkPp/8uekY8AK89UDj2Jhehf94NDmVMglICrTysx+NQ6FE44REm04AtInl3Ybq65KdpmMAHiEqKMp0BHgJCiUaJzTGdAKg1dUEhGiWrdJ0DMBjUCjRWBRKNA4jlPADL/UaqUNVeaZjAB4jMijSdAR4CQolGickxnQCoFUdiW6nFytPfHcrwB8xQonGolCicZjyho97vNMpqqqrMh0D8ChRwRRKNA6FEo3DCCV82Lr0U7SsaLPpGIDHYYQSjUWhROMwQgkf5ZJFj8ZFyyUuyQv8FIUSjUWhROOwKAc+6s0eI7WpdK/pGIBHYlEOGotCicZhyhs+qDI4Qk+o0HQMwGNxDiUai0KJxmHKGz7o+Z7DlF991HQMwGMx5Y3GolCicRihhI85EJ+pV0q3m44BeDQKJRqLQonGYYQSPuaxzB6yO+2mYwAejSlvNBaFEo0TmSrJYjoF0CK+7HCqVhZtMR0D8GgWWZQYmmg6BrwEhRKNExAsRaaYTgG4zWGx6dGIQNMxAI+XGJaoIFuQ6RjwEhRKNF5slukEgNsW9hqlXeUHTMcAPF77iPamI8CLUCjReBRKeLmS0Bg9XXvYdAzAK7SPpFCi8SiUaDwKJbzcM93PVLG9xHQMwCtQKNEUFEo0HoUSXmx3UlctKNlqOgbgNZjyRlNQKNF4MZmmEwDNNiMtU3WuOtMxAK/BCCWagkKJxmOEEl5qVecz9XkxFzEHmoIRSjQFhRKNF5kiBYSaTgE0Sa0tSI+FOEzHALxKiC1EiWFcgxKNR6FE41ksUkyG6RRAk8zrNVL7Kw6ZjgF4lbSINNMR4GUolGgapr3hRQoikvR89X7TMQCvw/mTaCoKJZqGQgkv8mTXwSqvrTAdA/A6FEo0FYUSTRPf2XQCoFG2tOulJcWbTccAvFJGJKc3oWkolGia1H6mEwCN8mhSspwup+kYgFfqGd/TdAR4GQolmialj2SxmU4BnNS73Ybrm5JdpmMAXslmsalbXDfTMeBlKJRomqAwKaGr6RTACVUHhmqWrcx0DMBrdYjuoFAuEYcmolCi6dqdYjoBcEIv9Rqp3Kp80zEAr8V0N5qDQommSz3FdALguHJj2uul8p2mYwBejUKJ5qBQoukYoYSHmtWxr6oc1aZjAF6tV3wv0xHghSiUaLqUPpKFvzrwLN9kDNC7RZtMxwC8Ggty0Fy0AjRdUDgLc+BRnBarHomJMB0D8HosyEFzUSjRPJxHCQ/yZo+R2lq2z3QMwOtx/iSai0KJ5uE8SniIiuBI/cPJqm6gJVAo0VwUSjQPI5TwEM/1HKrCmiLTMQCfQKFEc1Eo0TypfbljDozLTuigeaXbTMcAfILNYlO3WBbkoHkolGieoHApbYDpFPBzM9O7qtZZazoG4BN6J/RWWGCY6RjwUhRKNF/H4aYTwI993uE0rSreajoG4DOGpA4xHQFejEKJ5us4wnQC+Kk6a4BmRgSYjgH4lNPbnW46ArwYhRLNl36qFMS1/9D2/ttrlHaVHzAdA/AZYQFh6pvY13QMeDEKJZrPFihlnmE6BfxMcVicnrHnmI4B+JRBKYMUaA00HQNejEIJ9zDtjTb2VPfTVWovMx0D8CmnpzLdDfdQKOEeFuagDe1M7qZFxVtMxwB8Dgty4C4KJdyT3FOKSDGdAn7i0XbpcrgcpmMAPiUpNEmdYzubjgEvR6GE+zoOM50AfuDDLmfpf8U7TMcAfM5pqaeZjgAfQKGE+ziPEq3MbgvW34PspmMAPonLBaElUCjhPs6jRCt7uddIHajMNR0D8EmcP4mWQKGE+6JSpcQeplPARxVEJutfVXtNxwB8UueYzkoMSzQdAz6AQomW0WO86QTwUbO7DFRlXaXpGIBPGp052nQE+AgKJVpG74tNJ4AP2pzWR28VbTYdA/BZ47LGmY4AH0GhRMtI6sG0N1rcwwkJcsllOgbgkzrHdFanmE6mY8BHUCjRchilRAta2n2E1pfuNh0D8FmMTqIlUSjRcnpNMJ0APqIqKEyzrSWmYwA+bWzWWNMR4EMolGg5CV2k5D6mU8AH/LvnCB2pKjAdA/BZ3eO6Kys6y3QM+BAKJVpWb0Yp4Z5DsRmaW84dcYDWxOgkWhqFEi2rF+dRwj1/79BL1Y4a0zEAn8b5k2hpFEq0rLgOUrv+plPAS32dOVDvc5kgoFX1ju+t9pHtTceAj6FQouWxOAfN4LRY9Wh0mOkYgM9juhutgUKJlkehRDO80XOktpXtNx0D8GkWWSiUaBUUSrS8mAwp/TTTKeBFykKi9aQj33QMwOf1S+yn1IhU0zHggyiUaB0Dp5pOAC/yzx5n6WhNkekYgM+7tNulpiPAR1Eo0Tp6XSyFxplOAS+wL7GT5pduNR0D8HkxwTGs7karoVCidQSGSP2nmE4BLzCzfWfVOetMxwB83oTOExRkCzIdAz6KQonWM+gaSRbTKeDBPut0uj4pZnQSaG0WWZjuRquiUKL1xHWUOo00nQIeqs4aoBmhplMA/uGMtDOUHpluOgZ8GIUSrWvwtaYTwEP9p9do7a04aDoG4Bcu73a56QjwcRRKtK6u46ToDNMp4GGKwuP1bE226RiAX2gX3k5D2w81HQM+jkKJ1mW1SgN/bToFPMxT3YaorLbcdAzAL1za7VJZLfy6R+vibxha34BfS6wsxHe2p/TQ68VbTMcA/EKgNVAXd7nYdAz4AQolWl9EotTjAtMp4CFmpKTJ4XKYjgH4hTGZYxQXwjWB0foolGgbLM6BpA+6/kpflewwHQPwG5d3ZzEO2gaFEm0j83SpXX/TKWBQTUCI/h5YbToG4Dd6x/dW/yR+7qJtUCjRdobeZToBDHq510gdrDxiOgbgN67vd73pCPAjFEq0ne7nSil9TKeAAXnRqXqhco/pGIDf6BHXQ8PTh5uOAT9CoUTbGna36QQwYHan/qqsqzQdA/AbjE6irVEo0ba6ny8l9zadAm1oQ/t+Wlq02XQMwG90i+2mkenc9hZti0KJtmWxSEPvMJ0CbcQlix6Nj5VLLtNRAL9xXd/rZLFYTMeAn6FQou31vEhK7GE6BdrA0h4jtKGUcyeBttI5prPGZI4xHQN+iEKJtscopV+oDArXbBWZjgH4FUYnYQqFEmb0ulhK6Go6BVrRCz2HK6+60HQMwG90jO6osVljTceAn6JQwgyrVfoVo5S+KicuQy+Xc0ccoC39ru/vZLXwax1m8DcP5vSZKMV1Mp0CreDvWT1V46gxHQPwG1lRWTon6xzTMeDHKJQwx2qThnH3HF+zJmuwVhRtMR0D8CvX9b1ONqvNdAz4MQolzOo7SUrtZzoFWojDYtOjUSGmYwB+pWd8T53f8XzTMeDnKJQwy2KRzv6b6RRoIa/3HKntZftNxwD8yh2D7mBlN4yjUMK8Dr+Sup1nOgXcVBoarafqck3HAPzKyPSRGpwy2HQMgEIJD3H2g5I10HQKuOHZ7mepyF5iOgbgNwKsAbp90O2mYwCSpADTAQBJUnwn6dTfSV8+YzoJmmFPUme9VrLVdAyfVbiyUEdXHlVtQa0kKTgtWEkXJimyb6Qk6eCcgyrfXK664jpZQ6wK6xymlEtTFNwu+ITHPLL4iEr+V6Lao7WyBFgUmhWq5EuSFdYprGGf/bP3qzq7WnWldbKF2xTeM1wpl6UoMLb+zZ89366cf+Woal+VQrNC1f537RWUGPTD4x/fr5izYhQ9OLo1Xha/d3m3y5URlWE6BiBJsrhcLm6yC89QVSw9OVCqLDCdBE00rf/ZWl28zXQMn1X6baksVouCkuvLWvFnxSp4t0CdHuikkLQQHV11VMGpwQqMC5SjwqG8JXmqzq5W18e6ymI9/rl1xV8UKyAqQEGJQXLWOlW4vFAla0rU9dGuCoiqH2soWF6gsE5hCogJUF1RnQ7/97AkqdO99Zf7yn4yW5YAi5ImJOnIG0ckp5RxU33BKflfiYq/KFbmrZmt/fL4pbiQOL094W1FBUWZjgJIYsobniQ0Rhr1F9Mp0ESfdDqDMtnKovpHKbJfpIJTghWcEqzkicmyhlhVuatSkhQ3PE7h3cIVlBjUMNJYe7RW9gL7CY8Zc3qMInpFKCgpSCFpIUqZnCJnlVPVOdUN+ySMTVBY5zAFJQQprEuYEs9LVNXuKrnq6schag7XKObMGAWnBCv2rFjVHKq/9qijwqEjbxxR6lWprfiq+LdbBtxCmYRHoVDCswy4Wmo3wHQKNFKtNVAzQ52mY/gVl9Ol4i+L5axxKqxz2M+2O2ucKvq0SIGJgQqMa9x5yc46p4pWFckaalVI+vEv+1RXXqfiL4oV1jlMloD6Uc+Q9BCVbymXy+lS+abyhsfm/jdXcSPjFBQfdNxjwT19EvpoQucJpmMAx2DKG54nZ630wihJ/NX0dHP7jtNjZVzEvC1UH6jWnv/bI2etU9Zgq9KnpSuyX2TD9sIPC3VkwRE5a5wKSglS5m2ZCk468TmUklS6rlQ5z+bIaXcqIDpAGb/PUFjHY0tq7oJcFa4olMvuUminUGX+IVMBEfVT4rVFtTo055CqD1QrJD1E7aa2kz3PrtzXcpV5e6YOzT2kqn1ViugVodQrU2UNYAzDXRZZNP+8+eqd0Nt0FOAYFEp4pjdvlL6dZzoFTuJoeILOT0tSWW256Sh+wVnnVG1hrZxVTpWsKVHRJ0Xq8P86KCStflTQUelQXWmd6krqVPBugWqLatXxno6yBp24xDlrnKotrpWjzKGjHx9VxdYKdfpLp4ZzKCWprqxOjgqHagtqlfdmnqyhVmX+IfO41z101jq1+77dav+79ir+oliOSofSfp2mfX/fp6gBUYofE9/yL4yfubjLxbr/jPtNxwB+hreL8Eyj75dC40ynwEk80e00ymQbsgZYFZwcrNCsUKVcmqKQ9BAVflDYsN0WZlNwSrDCu4Ur/aZ01RyuUek3pSc/ZnD9McM6h6n9b9vLYrOo6JOiY/YJiAxQcEqwInpHKH16uso3lKtqd9Vxj5e/NF8RvSMUmhWqim0Vih4ULUuARVEDo1SxrcL9F8HPxQTH6JYBt5iOARwXhRKeKTxBOnem6RQ4gW2pPbW4eLPpGP7NJblqTzDB9N2XT7j9RId0uuSsPfE5sd9PaB1vn+pD1Sr5skTJFyfXf8EpuRz1+7scLrmcTIa560+n/UlxIbzRhmeiUMJz9Zko9RhvOgWO45HkVDldLMZpK7kLc1WxvUL2fLuqD1TXf76tQjGnx8ieZ1f+0nxV7auSvdCuyp2VOvD0AVkDrcecY7nj/+1Q6dr6EUtnjVO5i3JVuatS9gK7qvZVKeffOaorqlP0qfXXjKzcXanCFYWq2l8le4Fd5VvKlfNsjoKSgn62GMjlcunQnENKmZwia3D9r5WwLmEq+rhI1YeqVfx5scK6/HwBERpvTOYYndPhHNMxgBPiwubwbOc9Lu3/XKos/OV90Sbe6zpMa0t2mo7hV+pK65TzfI7qSuoaVmJn3Z6liN4Rqi2qVcWOChW8XyBnhVO2aJvCu4ar470djzkX0p5rl6PSUf+JRbIftiv7s2w5yh2yRdgU2iFUHf70wzmZ1iCrSteWKm9xnpw1TgXEBCiyT6QSL0iUNfDYsYiiVUUKiApQ1Ck/XMYm6aIkHfjnAe15YI8i+kQofhTnTzZXbHCs7jntHtMxgJNiUQ4836bXpUXXmE4BSTUBIbqga28dqsozHQXwGzOHzdS4rHGmYwAnxZQ3PF/vS6QeF5hOAUkv9RpJmQTa0JjMMZRJeAVGKOEdyvOlZ05j6tug3Jg0XZAQripH9S/vDMBtcSFxWnzhYhbiwCswQgnvEJEonfuY6RR+7fGO/SiTQBtiVTe8CYUS3qP3xVLPi0yn8Evr0vtrWdEm0zEAv3F25tkamzXWdAyg0SiU8C7n/V0KSzCdwq+4ZNEjcVG/vCOAFhEXEqd7h9xrOgbQJBRKeJfwhPpSiTbzZo+R2ly613QMwG/cO+RexYbEmo4BNAmFEt6n10XSgF+bTuEXKoIj9Q8XC6GAtjKx60SNyRxjOgbQZBRKeKdzZ0qp/Uyn8HnP9xiqgpqjpmMAfqFnfE/98dQ/mo4BNAuFEt4pIFi67GUpJNp0Ep91ID5L88q2m44B+IWooCjNGj5LQbYg01GAZqFQwnvFZkkTnpNkMZ3EJz2W2V12p910DMDnWWTRQ2c9pLSINNNRgGajUMK7dTtHOvMW0yl8zpcdTtXKoi2mYwB+4Zre12hY+jDTMQC3UCjh/Ub9Rcr6lekUPsNhsenRiEDTMQC/MDhlsG7uf7PpGIDbKJTwflabNPFFKSLFdBKfsKDXKO0qP2A6BuDzEkMTNWPoDNmsNtNRALdRKOEbIpLqS6U1wHQSr1YSFqunaw+ZjgH4vABLgGYOm6mEUG7UAN9AoYTvyDqzfvobzfZM9zNVYi81HQPwebcMuEUDkweajgG0GAolfMuZt0jdzzedwivtTuqqBcUsxAFa26iMUZrae6rpGECLolDC90x4joueN8OMtEzVuepMxwB8Wo+4HnrorIdMxwBaHIUSvic4QrpigRSdYTqJ1/io85n6vJiLmAOtqV14Oz0z+hmFBYaZjgK0OAolfFNkijRlIXfSaYRaW5AeC2ZkEmhNUUFRenb0syzCgc+iUMJ3JXWXLp8vcSuzk5rXa5SyKw+bjgH4rEBroGaPmK2OMR1NRwFaDYUSvi3rLOmiZ8XtGY+vICJJz1fvMx0D8FkWWfS3s/6mwSmDTUcBWhWFEr6vz0QuJ3QCT3QdrPLaCtMxAJ91y4BbdE6Hc0zHAFodhRL+4Ve3SYOuMZ3Co2xu11tvFm82HQPwWZO6TdJv+/zWdAygTVAo4T/OfUzqOs50Co/xaFKSnC6n6RiATxrefrj+eOofTccA2gyFEv7j+3t+t+tvOolx73Yfrm9LdpmOAfikXvG9NGMY9+iGf6FQwr8EhUtXLJQSuplOYkx1YKhmWctMxwB8Usfojnp61NMKDQg1HQVoUxRK+J+IROnXb/ttqXyx5wjlVuWbjgH4nI7RHfXvsf9WfGi86ShAm7O4XC6X6RCAEWVHpLnjpQL/uUNMbkx7jU8IVbWjxnQUwKd8Xya5cDn8FSOU8F+RyX43Uvn3jn0ok0ALo0wCjFACfjNS+U3GAP3aVmA6BuBTKJNAPUYoAT8YqXRarHokJsJ0DMCnUCaBH1AoAcnnS+WSHiO0tWyf6RiAz6BMAseiUALf89FSWR4SpSecTHUDLYUyCfwchRL4MR8slc/1+JUKa4pMxwB8AmUSOD4W5QDHU54vzb9UOvSt6SRuyU7ooIuirap11pqOAni93vG99dSop7jOJHAcjFACxxORKE19x+vv/T0zvStlEmgBw9sP14vjXqRMAidAoQROJChcuny+NOga00ma5fOOQ7SqeKvpGIDXm9RtkmaPmM3tFIGTCDAdAPBoVpt0/uNSTIa04n5J3nGGSJ01QDPCrVK56SSA97LIolsH3qprenvnm0qgLTFCCTTGWX+QLnlBsgWZTtIo/+05SrvLc0zHALxWkDVIjw59lDIJNBKLcoCm2PeZ9NoVUnWJ6SQnVBwWp/PSU1VqLzMdBfBKUUFRmj1itganDDYdBfAajFACTZF1lnTN+1J0hukkJ/RU99Mpk0AztQtvp1fOeYUyCTQRI5RAc5TlSvMvkw6vN53kGDuTu+nScLscLofpKIDX6RHXQ8+MfoZrTALNwAgl0ByRKdLUZVL3800nOcajqemUSaAZRqaP1JxxcyiTQDMxQgm4a/U/pA8fkJx1RmN82OVXurVuv9EMgLcJsATolgG3aGrvqaajAF6NQgm0hH2rpUXXSOW5Rp7ebgvWhd37KafSzPMD3igxNFEzh83UwOSBpqMAXo8pb6AlZJ0pTftUyvqVkad/uddIyiTQBINTBmvB+AWUSaCFMEIJtCSnQ1r5oPTZbLXVRdDzo1J0fnKMKusq2+T5AG9mkUW/7fNb3XTKTbJZbabjAD6DQgm0hu3vSouvb5PrVd4z4Fy9VbSp1Z8H8HZRQVF66KyHNCx9mOkogM+hUAKtpWiftODqVr200Ka0ProiqFQuL7klJGBKz/ie+vuwv6t9ZHvTUQCfxDmUQGuJzZJ++4E0cGqrPcUjCQmUSeAXTOw6Ua+c8wplEmhFjFACbWHzEumd26XKghY75NLuI/THmt0tdjzA18SFxOneIfdqTOYY01EAn0ehBNpKRUF9qdyyxO1DVQaFa3ynbsqrbrmCCviSszPP1r1D7lVsSKzpKIBfoFACbW3z4u9GKwubfYgnTzlPz5dsbMFQgG+IC4nTn077k8ZmjTUdBfArFErAhIoC6Z3bpC1vNvmhh2IzdEF8sGocNa0QDPBeYzLH6N4h9youJM50FMDvUCgBkza9Li27s0mjlbcNOEcfFG1uxVCAd4kNjtWfhvxJ47LGmY4C+C0KJWBaeb70zh+krW//4q5fZw7Ub6z5bRAK8A5jMsfontPuUXxovOkogF+jUAKeYuOi+tHKqqPH3ey0WDWpz5naVra/jYMBnicmOEb3nHaPxnVgVBLwBBRKwJNUFEgr7pO+naef3rpxYa8xeqByu5FYgKewyKIJXSbolgG3cK4k4EEolIAnylkrLbtDOvSNJKksJFrnZ2XqaE2x2VyAQb3je+tPp/1JfRL7mI4C4CcolICncrmkb1+RVtyvGd1O0yvFXCYI/ikuJE63DLhFEzpPkMViMR0HwHEEmA4A4AQsFmnA1VKPCxSw6QUFlG5VnbPOdCqgzQRYAzS5+2RN6zdNUUFRpuMAOAlGKAEvsb90v2Z9PUsrD6w0HQVodaMyRum2gbcpIyrDdBQAjUChBLzMmtw1mrlmprYe3Wo6CtDiesT10J2D79TglMGmowBoAgol4IVcLpeW7lmq5zY8p/2lXEYI3i8zKlPX971e53c8n/MkAS9EoQS8mMPp0LK9y/Svjf/S3pK9puMATdYhuoOu63udzsk6RzarzXQcAM1EoQR8gNPl1PJ9y/X8hue1q3iX6TjAL+oc01nX9b1OY7PGymqxmo4DwE0USsCHuFwufbD/Az234TntKNphOg7wM11ju+r6vtdrTOYYprYBH0KhBHyQy+XSyuyVem7DcyzegUfoEddD1/e7XiPTR1IkAR9EoQR83KoDq/Tc+ue0qXCT6SjwQ73je2tav2kalj7MdBQArYhCCfiJb/O+1WvbXtMH+z9QrbPWdBz4sEBroMZkjtHl3S9X/6T+puMAaAMUSsDPFFYVavGuxVq4faEOVRwyHQc+pF14O13a7VJd3OVixYXEmY4DoA2xtA7wM/Gh8bq2z7V695J39eTIJ3Vm2pmyiHPa0DxWi1VnpZ2lp0Y+pXcveVfX9rnW48ukxWLRkiVLTMdokqysLM2ePdtjjwdwL2/AT1ktVg1PH67h6cN1oOyAFmxfoCW7lqi4pth0NHiBmOAYTeg8QZd2u1Tpkemm4zSYOnWqiouLT1oYDx8+rNjY2LYL1QLWrFmj8PBw0zGAE6JQAlB6ZLpuH3S7bup/k5bvW66F2xdqff56ucQZMfiBRRb1S+ynS7tdqrFZYxVsCzYdqUnsdruCgoKUkpJiOsoxvs91MomJiW2UpnEakxn+hSlvAA2CbcG6oNMFeuXcV7T8kuW6feDt6hXfy3QsGNY7vrfuGHSHll+yXK+c+4ou6HSBV5TJ4cOH66abbtKtt96qhIQEjR07VtKxU952u1033XSTUlNTFRISoszMTD388MPHPd6OHTtksVi0bdu2Y77++OOPq1OnTg2fb9q0Seecc44iIiKUnJysq666SgUFBSfN5XK5dN999ykjI0PBwcFq166dfv/73zc85qdT1MXFxbr++uuVnJyskJAQ9e7dW0uXLm3Y/vrrr6tXr14KDg5WVlaW/v73v5/0tcrOztaFF16oiIgIRUVF6bLLLtORI0catt9333065ZRT9MILL6hDhw4KCQk56fHgfxihBHBcqRGpmtp7qqb2nqoDZQe0fN9yLd+3XNuObvvlB8PrdY/rrrFZYzU2a6xHTWk31dy5czV9+nStXr36uNufeOIJvfXWW1qwYIEyMjJ04MABHThw4Lj7du3aVYMGDdKrr76qBx98sOHrr776qq644gpJ9UVv5MiRuvbaa/X444+rqqpKd999ty677DKtXLnyhLlef/11Pf7443rttdfUq1cv5ebmav369cfN4XQ6dc4556isrEzz5s1Tp06dtGXLFtls9beuXLt2rS677DLdd999mjRpkj7//HPdcMMNio+P19SpU497vO/L5Mcff6y6ujrdeOONmjRpklatWtWw365du/T666/rjTfeaHgu4HsUSgC/KD0yXdf2uVbX9rlW+0r26b1972n5vuXc5tHHdI7prHFZ4zQ2a6yyorNMx2kRXbp00YwZM064PTs7W126dNFZZ50li8WizMzMkx5vypQpeuqppxoK5Y4dO7R27VrNmzdPkvTUU0+pf//+euihhxoe8+KLLyo9PV07duxQ165dj5vrnXfeUUpKikaPHq3AwEBlZGTo1FNPPW6GFStW6KuvvtLWrVsbjtexY8eG7bNmzdKoUaP05z//WVJ9Ed6yZYtmzpx53EL54YcfauPGjdq7d6/S0+vfPLz88svq1auX1qxZo8GDB0uqH819+eWXPW76HZ6BKW8ATZIVnaVp/aZp8YWLteTCJZrWb5o6x3Q2HQvN1Dmms6b1m6YlFy7R4gsX6/p+1/tMmZSkgQMHnnT71KlTtW7dOnXr1k2///3v9f777zdsmzZtmiIiIho+JOnyyy/Xvn379OWXX0qqH50cMGCAunfvLklav369Pvroo2Me9/223bt3nzDXpZdeqqqqKnXs2FG/+93vtHjxYtXV1R0387p169S+ffuGMvlTW7du1ZlnnnnM184880zt3LlTDofjuPunp6c3lElJ6tmzp2JiYrR16w932srMzKRM4oQYoQTQbJ1iOunGU27UjafcqPzKfH15+Et9cegLfXn4S+VX5ZuOh+NICk3SkHZDNCS1/iMxzLcLwi+tjB4wYID27t2rd999VytWrNBll12m0aNHa9GiRXrggQd0xx13HLN/SkqKRo4cqfnz52vIkCGaP3++pk+f3rC9vLxc48eP16OPPvqz50pNTT1hrvT0dG3fvl0rVqzQBx98oBtuuEEzZ87Uxx9/rMDAwGP2DQ0NbfT335JYZY6ToVACaBGJYYka32m8xncaL0naVbSrvmAe/kJf536tyrpKwwn9U1hAmAalDNLpqadrSOoQdY5lNPmnoqKiNGnSJE2aNEkTJ07UuHHjdPToUSUlJSkpKeln+0+ZMkV33XWXJk+erD179ujyyy9v2DZgwAC9/vrrysrKUkBA037FhoaGavz48Ro/frxuvPFGde/eXRs3btSAAQOO2a9v377Kyck5Zgr9x3r06PGzc0ZXr16trl27Hvfcxx49ejScO/r9KOWWLVtUXFysnj17Nul7gP+iUAJoFZ1jO6tzbGdd2fNK1TprtSF/Q8Po5aaCTXK4fj71BvfZLDb1Tuit09vVF8i+iX0VaA385Qf6qVmzZik1NVX9+/eX1WrVwoULlZKSopiYmBM+5uKLL9b06dM1ffp0jRgxQu3atWvYduONN+pf//qXJk+erLvuuktxcXHatWuXXnvtNb3wwgsnXMwyZ84cORwOnXbaaQoLC9O8efMUGhp63HM6hw0bpqFDh+qSSy7RrFmz1LlzZ23btk0Wi0Xjxo3T7bffrsGDB+vBBx/UpEmT9MUXX+ipp57SM888c9znHj16tPr06aMpU6Zo9uzZqqur0w033KBhw4Zp0KBBTXtB4bcolABaXaA1UAOTB2pg8kDd1P8mVdZWanvRdm0p3NLwsbdkLyWziWwWmzpEd1DP+J4NH91iuyksMMx0NK8RGRmpGTNmaOfOnbLZbBo8eLCWLVsmq/XESwwiIyM1fvx4LViwQC+++OIx29q1a6fVq1fr7rvv1tlnn62amhplZmZq3LhxJz1mTEyMHnnkEd12221yOBzq06eP3n77bcXHxx93/9dff1133HGHJk+erIqKCnXu3FmPPPKIpPpR0gULFugvf/mLHnzwQaWmpuqBBx447oIcqf4ySm+++aZuvvlmDR06VFarVePGjdOTTz75C68e8APu5Q3AI1TVVWn70e3aXLiZknkcPy2PveJ7qVtcN4UGmDmfDgB+jEIJwGN9XzK3FG5Rdlm2cspylFOWo4PlB1XtqDYdr1WE2EKUFpGm9pHt1T6yvTIiM+pHHimPADwYhRKA13G5XCqoKlBOeX3BbPjvd3/Or8z32NtGWmRRYlii2ke0byiN7SPaKz0yXWkRaT6/6hqAb6JQAvA5dodd+VX5Kq0pVan9u4+aUpXZy475vNR+7NdqnbVyupxyupxyOB31//1uyt1msclqscpmrf+v1WJVoDVQUUFRigqKUmRQZP2fg6OO/dqPPo8KjlJiaKKCbNwDGYBvoVACAADALdwpBwAAAG6hUAIAAMAtFEoAAAC4hUIJAAAAt1AoAQAA4BYKJQAAANxCoQQAAIBbKJQAAABwC4USAAAAbqFQAgAAwC0USgAAALiFQgkAAAC3UCgBAADgFgolAAAA3EKhBAAAgFsolAAAAHALhRIAAABuoVACAADALRRKAAAAuIVCCQAAALdQKAEAAOAWCiUAAADcQqEEAACAWyiUAAAAcAuFEgAAAG6hUAIAAMAtFEoAAAC4hUIJAAAAt1AoAQAA4BYKJQAAANxCoQQAAIBbKJQAAABwC4USAAAAbqFQAgAAwC0USgAAALiFQgkAAAC3UCgBAADgFgolAAAA3EKhBAAAgFsolAAAAHALhRIAAABuoVACAADALRRKAAAAuIVCCQAAALdQKAEAAOAWCiUAAADcQqEEAACAWyiUAAAAcAuFEgAAAG6hUAIAAMAtFEoAAAC4hUIJAAAAt1AoAQAA4BYKJQAAANxCoQQAAIBbKJQAAABwC4USAAAAbvn/Vlkd6m7WFB8AAAAASUVORK5CYII=\n"},"metadata":{}}],"source":["# Creating a pie chart to visualize the distribution of species\n","fig = plt.figure()\n","ax = fig.add_axes([0,0,1,1])\n","ax.axis('equal')\n","l = ['Iris-virginica','Iris-setosa','Iris-versicolor']\n","s = [50,50,50]\n","ax.pie(s, labels = l,autopct='%3.2f%%')\n","plt.show()"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"Pk5W6j8SIxNj","executionInfo":{"status":"ok","timestamp":1693245445612,"user_tz":-330,"elapsed":27,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}},"colab":{"base_uri":"https://localhost:8080/","height":449},"outputId":"f050aac6-8d0b-4a04-b9ef-1c784f1f54f0"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Visualizing the relationship between Petal Length and Petal Width\n","X = iris_data['PetalLengthCm'].values.reshape(-1,1)\n","Y = iris_data['PetalWidthCm'].values.reshape(-1,1)\n","plt.xlabel(\"Petal Length\")\n","plt.ylabel(\"Petal Width\")\n","plt.scatter(X,Y,color='g')\n","plt.show()"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8fie9ijsKcKU","outputId":"9bed10d3-f700-448a-dea4-9e2ee9e26c93","executionInfo":{"status":"ok","timestamp":1693245446371,"user_tz":-330,"elapsed":785,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["                     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n","Id             1.000000       0.716676     -0.397729       0.882747   \n","SepalLengthCm  0.716676       1.000000     -0.109369       0.871754   \n","SepalWidthCm  -0.397729      -0.109369      1.000000      -0.420516   \n","PetalLengthCm  0.882747       0.871754     -0.420516       1.000000   \n","PetalWidthCm   0.899759       0.817954     -0.356544       0.962757   \n","\n","               PetalWidthCm  \n","Id                 0.899759  \n","SepalLengthCm      0.817954  \n","SepalWidthCm      -0.356544  \n","PetalLengthCm      0.962757  \n","PetalWidthCm       1.000000  \n"]}],"source":["# Calculating the correlation matrix among features\n","corr_mat = iris_data.corr()\n","print(corr_mat)"]},{"cell_type":"code","source":["# Importing necessary libraries for model training and evaluation\n","from sklearn.model_selection import train_test_split\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn import metrics\n","\n","\n","# Splitting the data into training and testing sets\n","train, test = train_test_split(iris_data, test_size=0.75)\n","\n","# Define features and target variables for training and testing sets\n","features = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']\n","train_X = train[features]\n","train_y = train['Species']\n","test_X = test[features]\n","test_y = test['Species']\n","\n","# Initialize the KNN classifier\n","knn_model = KNeighborsClassifier(n_neighbors=3)\n","\n","# Fit the model on the training data\n","knn_model.fit(train_X, train_y)\n","\n","# Make predictions on the test data\n","predictions = knn_model.predict(test_X)\n","\n","# Calculate and print the accuracy\n","accuracy = metrics.accuracy_score(predictions, test_y)\n","print('KNN Accuracy:', accuracy)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gZVsWrcSViyq","executionInfo":{"status":"ok","timestamp":1693245446372,"user_tz":-330,"elapsed":780,"user":{"displayName":"BHAKTI _201030","userId":"02947347682794080068"}},"outputId":"dd98339d-7215-4a27-aafb-bcb7a2b3d38c"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["KNN Accuracy: 0.9646017699115044\n"]}]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","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":"3.11.1"}},"nbformat":4,"nbformat_minor":0}