Final_Project_Source_Code.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Source_Code.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "815mpDkQ0ma7"
      },
      "source": [
        "Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x84__x8Mi00J"
      },
      "source": [
        "from google.colab import files\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from numpy import unique\n",
        "from numpy import where\n",
        "from sklearn.metrics import silhouette_score\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from sklearn.cluster import Birch\n",
        "from sklearn.cluster import KMeans\n",
        "from matplotlib import pyplot\n",
        "from sklearn import preprocessing\n",
        "import scipy.cluster.hierarchy as shc\n",
        "import plotly.express as px"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ZmlEH680qx-"
      },
      "source": [
        "Fetch Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "8W2wHZfeh_pB",
        "outputId": "68d28ecc-6059-43bf-d9d3-f38f823b67a1"
      },
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5846194e-da86-4097-a204-e77af345c82d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5846194e-da86-4097-a204-e77af345c82d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Indian_IPO_Data.csv to Indian_IPO_Data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQZyNNsLuxur"
      },
      "source": [
        "Explanation : The file of \"Indian_IPO_Data.csv\" is uploaded"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6hudIYp0t1E"
      },
      "source": [
        "Create Raw Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0prCuTYi0IT"
      },
      "source": [
        "raw_df = pd.read_csv('Indian_IPO_Data.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bAzz9kJJ0x08"
      },
      "source": [
        "Define Constant Variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_oQt6pZi_b-"
      },
      "source": [
        "# LIST OF ATTRIBUTES THAT ARE RELEVANT FOR CLUSTERING\n",
        "selected_list = ['Srno','Company_Name','Face_Value','Bid_Price_From','Bid_Price_To','Market_Lot','NSE_Listing_day_open','NSE_Listing_day_low','NSE_Listing_day_high','NSE_Listing_day_last_trade','NSE_Listing_day_volume','NSE_SME_Listing_day_open','NSE_SME_Listing_day_low','NSE_SME_Listing_day_high','NSE_SME_Listing_day_last_trade','NSE_SME_Listing_day_volume','BSE_Listing_day_open','BSE_Listing_day_low','BSE_Listing_day_high','BSE_Listing_day_last_trade','BSE_Listing_day_volume','BSE_SME_Listing_day_open','BSE_SME_Listing_day_low','BSE_SME_Listing_day_high','BSE_SME_Listing_day_last_trade','BSE_SME_Listing_day_volume']\n",
        "# LIST OF ATTRIBUTES THAT ARE RELEVANT FOR BIDDING PRICE\n",
        "bid_average_list = ['Bid_Price_From', 'Bid_Price_To']\n",
        "# LIST OF ATTRIBUTES THAT ARE RELEVANT FOR OPENING PRICE\n",
        "open_average_list = ['NSE_Listing_day_open', 'NSE_SME_Listing_day_open','BSE_Listing_day_open','BSE_SME_Listing_day_open']\n",
        "# LIST OF ATTRIBUTES THAT ARE RELEVANT FOR CLOSING PRICE\n",
        "close_average_list = ['NSE_Listing_day_last_trade', 'NSE_SME_Listing_day_last_trade','BSE_Listing_day_last_trade','BSE_SME_Listing_day_last_trade']\n",
        "# LIST OF ATTRIBUTES THAT ARE RELEVANT FOR HIGH ON FIRST DAY OF LISTING\n",
        "high_average_list = ['NSE_Listing_day_high', 'NSE_SME_Listing_day_high','BSE_Listing_day_high','BSE_SME_Listing_day_high']\n",
        "# LIST OF ATTRIBUTES THAT ARE RELEVANT FOR LOW ON FIRST DAY OF LISTING\n",
        "low_average_list = ['NSE_Listing_day_low', 'NSE_SME_Listing_day_low','BSE_Listing_day_low','BSE_SME_Listing_day_low']\n",
        "# LIST OF ATTRIBUTES THAT ARE RELEVANT FOR OVERALL VOLUME IN MARKET\n",
        "volume_average_list = ['NSE_Listing_day_volume', 'NSE_SME_Listing_day_volume','BSE_Listing_day_volume','BSE_SME_Listing_day_volume']\n",
        "\n",
        "# LIST OF ATTRIBUTES THAT ARE AFFECTING STOCK PERFORMANCE OVERALL\n",
        "summarised_feature_columns = ['Bid_Price_Average','Open_Price_Average','High_Price_Average','Low_Price_Average','Close_Price_Average','Volume_Average','Profit_Margin']\n",
        "# LIST OF ATTRIBUTES THAT ARE MOST PROMINENT FOR AFFECTING STOCK PERFORMANCE\n",
        "selected_summarised_feature_columns = [summarised_feature_columns[0],summarised_feature_columns[6]]\n",
        "scaler = preprocessing.MinMaxScaler()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSuUestx04dJ"
      },
      "source": [
        "Filter Raw Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxJPuu6eDLkW"
      },
      "source": [
        "# DATAFRAME IS SELECTED ONLY FOR RELEVANT ATTRIBUTES\n",
        "selected_df = raw_df.filter(selected_list, axis=1)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4GX3X9qI09Wk"
      },
      "source": [
        "Pre-Process Data \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxICkuKzG9S-"
      },
      "source": [
        "# AMPUTING NAN WITH 0\n",
        "selected_df = selected_df.replace(np.NaN,float(0))\n",
        "\n",
        "# PRE-PROCESSING ATTRIBUTE FOR TYPE CASTING INTO FLOAT\n",
        "selected_df['NSE_Listing_day_open'] = [float(str(i).replace(\",\", \"\")) for i in selected_df['NSE_Listing_day_open']]\n",
        "selected_df['BSE_Listing_day_open'] = [float(str(i).replace(\",\", \"\")) for i in selected_df['BSE_Listing_day_open']]\n",
        "selected_df['NSE_Listing_day_last_trade'] = [float(str(i).replace(\",\", \"\")) for i in selected_df['NSE_Listing_day_last_trade']]\n",
        "selected_df['BSE_Listing_day_last_trade'] = [float(str(i).replace(\",\", \"\")) for i in selected_df['BSE_Listing_day_last_trade']]\n",
        "selected_df['NSE_Listing_day_high'] = [float(str(i).replace(\",\", \"\")) for i in selected_df['NSE_Listing_day_high']]\n",
        "selected_df['BSE_Listing_day_high'] = [float(str(i).replace(\",\", \"\")) for i in selected_df['BSE_Listing_day_high']]\n",
        "selected_df['NSE_Listing_day_low'] = [float(str(i).replace(\",\", \"\")) for i in selected_df['NSE_Listing_day_low']]\n",
        "selected_df['BSE_Listing_day_low'] = [float(str(i).replace(\",\", \"\")) for i in selected_df['BSE_Listing_day_low']]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOoGDzD51h0c"
      },
      "source": [
        "Explanation : Dataset contains data about various IPO features that are completely unrelated to other IPO. Hence, Imputation of dataset had been done as 0 rather than mean/median."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPAhuDvO1A9b"
      },
      "source": [
        "Create Average_Bid_Price Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8EdpcMJAGMn"
      },
      "source": [
        "# FINDING AVERAGE VALUE OF BIDDING PRICE AND SCALING IT ACCORDINGLY\n",
        "selected_df[summarised_feature_columns[0]] = selected_df[bid_average_list].mean(axis=1)\n",
        "selected_df[summarised_feature_columns[0]] = scaler.fit_transform(selected_df[[summarised_feature_columns[0]]])"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvpVCCgz1HIm"
      },
      "source": [
        "Create Average_Open_Price Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSdmGkUErU86"
      },
      "source": [
        "# FINDING AVERAGE VALUE OF OPENING PRICE AND SCALING IT ACCORDINGLY\n",
        "selected_df[summarised_feature_columns[1]] = selected_df[open_average_list].mean(axis=1)\n",
        "selected_df[summarised_feature_columns[1]] = scaler.fit_transform(selected_df[[summarised_feature_columns[1]]])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rUm2MPsd65Kn"
      },
      "source": [
        "Create Average_High_Price Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GluFsI7v6u5w"
      },
      "source": [
        "# FINDING AVERAGE VALUE OF HIGH PRICE ON LISTING DAY AND SCALING IT ACCORDINGLY\n",
        "selected_df[summarised_feature_columns[2]] = selected_df[high_average_list].mean(axis=1)\n",
        "selected_df[summarised_feature_columns[2]] = scaler.fit_transform(selected_df[[summarised_feature_columns[2]]])"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgGMWa4J7DAI"
      },
      "source": [
        "Create Average_Low_Price Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVXJqIcs7UmP"
      },
      "source": [
        "# FINDING AVERAGE VALUE OF LOW PRICE ON LISTING DAY AND SCALING IT ACCORDINGLY\n",
        "selected_df[summarised_feature_columns[3]] = selected_df[low_average_list].mean(axis=1)\n",
        "selected_df[summarised_feature_columns[3]] = scaler.fit_transform(selected_df[[summarised_feature_columns[3]]])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJsJ3Q_27HSx"
      },
      "source": [
        "Create Average_Close_Price Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmOt5pnx6yg3"
      },
      "source": [
        "# FINDING AVERAGE VALUE OF CLOSING PRICE(LAST TRADE PRICE) ON LISTING DAY AND SCALING IT ACCORDINGLY\n",
        "selected_df[summarised_feature_columns[4]] = selected_df[close_average_list].mean(axis=1)\n",
        "selected_df[summarised_feature_columns[4]] = scaler.fit_transform(selected_df[[summarised_feature_columns[4]]])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzYR9G5s7Lsh"
      },
      "source": [
        "Create Total_Volume Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1W4sxv4t618p"
      },
      "source": [
        "# FINDING TOTAL VOLUME OF STOCK AS ON LISTING DAY IN MARKET AND SCALING IT ACCORDINGLY\n",
        "selected_df[[summarised_feature_columns[5]]] = selected_df[volume_average_list].sum(axis=1)\n",
        "selected_df[summarised_feature_columns[5]] = scaler.fit_transform(selected_df[[summarised_feature_columns[5]]])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-P6r3S9y7yL"
      },
      "source": [
        "Expalnation :\n",
        "\n",
        "Average Price for Each of Open, High, Low, Last Trade(Close) are calculated as (corresponding value for NSE + NSE_SME + BSE + BSE_SME) / (1 or 2 based on non null values in above parameters)\n",
        "\n",
        "Ex: Average Open Price = (NSE_OPEN + NSE_SME_OPEN + BSE_OPEN + BSE_SME_OPEN) / (1 or 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y5W-9RjstC-_"
      },
      "source": [
        "Create Profit Margin Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmDuSauEfEME"
      },
      "source": [
        "# FINDING PROFIT MARGIN ON LISTING DAY AND SCALING IT ACCORDINGLY\n",
        "selected_df[summarised_feature_columns[6]] = selected_df[['Bid_Price_Average','Open_Price_Average']].pct_change(axis=1)['Open_Price_Average']\n",
        "selected_df[summarised_feature_columns[6]] = selected_df[summarised_feature_columns[6]].replace([np.inf,np.nan],float(0))\n",
        "selected_df[summarised_feature_columns[6]] = scaler.fit_transform(selected_df[[summarised_feature_columns[6]]])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HaTstj6qylTo"
      },
      "source": [
        "Explaination :\n",
        "\n",
        "Profit Margin = (Average Opening Price - Average Bid Price) / Average Bid Price"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KIdr1hAbz_cH"
      },
      "source": [
        "Finding Optimum Value for \"K\" using Elbow method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "C_GDa-VkzVtv",
        "outputId": "2780aa39-d870-4b18-ef75-ae27605c340b"
      },
      "source": [
        "# COMPARING ERROR TO NUMBER OF CLUSTER USING KMEANS [VISUALLY] TO UNDERSTAND OPTIMUM VALUE FOR \"K\"\n",
        "x = selected_df[selected_summarised_feature_columns].iloc[:,:].values\n",
        "sum_sq_dist =[]\n",
        "for i in range(2, 11):\n",
        "    kmeans = KMeans(n_clusters = i)\n",
        "    kmeans.fit(x)\n",
        "    sum_sq_dist.append(kmeans.inertia_)\n",
        "pyplot.plot(range(2, 11), sum_sq_dist)\n",
        "pyplot.title('Elbow method')\n",
        "pyplot.xlabel('No of clusters')\n",
        "pyplot.ylabel('Sum Squared Error')\n",
        "pyplot.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qDNaMYL7mdWaM+3dgkplNN7OrGyZycsrKSOO+ywawbfdefvzc3LDjiEgTEOuAH0dqmLsPAs4FvmNmp9VewcyuNrNpZjZtw4bUvtOkb7tCbhrei5dmr+GFWavDjiMiKS6uBd7dVwV/1wPjgZMOss5D7j7Y3QcXFxfHM05CuOa0bgzs1IIfPzeX9eUVYccRkRQWtwJvZnlmVlAzD5wDNPm2iYz0NO69bAC791Zxx7g56qtGROImnmfwJcBUM5sFvA+85O4T43i8pNG9OJ/bRvXh9QXreXa6BuoWkfjIqGtBcLdMnaeX7l5Y347dfQkw4MijpbYrhpbyyry1/OKFjxjavRUdWzYLO5KIpJj67qIpCIr4b4HbgQ5AR+A24IHGiZe60tKMey4dgLtz65jZVFerqUZEGlYsTTQXuvsf3X27u5e7+5+Ai+IdrCnoVNSMH13Qj3cWb+LJ95aHHUdEUkwsBX6nmf2XmaWbWZqZ/RewM97Bmoovn9iJ03sV86uXF7B0oz5WEWk4sRT4rwKXAeuC6dLgPWkAZsavLzmOzHTj5mdnUaWmGhFpILH0B7/M3S9y99buXuzuF7v7skbI1mS0bZ7Dzy86hunLt/DwW0vCjiMiKeKQBd7MepnZ62Y2N3h9nJn9KP7RmpaLB3Zg5DEl3DtpIQvXbQ87joikgFiaaP5KpD/4fQDuPpsD+4qXBmBm/PILx5Kfk8EPnpnFvqrqsCOJSJKLpcA3c/f3a72nQUbjoHV+Nnd+oT9zVm3jj/9ZHHYcEUlysRT4jWbWneChJzP7IrAmrqmasFH923HxwPY8+MYi5q7aFnYcEUlisRT47wB/AfqY2SrgJuDauKZq4n5+YX9a5Wfx/Wc+ZE9lVdhxRCRJ1VvgzSwduM7dhwPFQB93H+bueionjpo3y+SuS45j4bod3P/qorDjiEiSqrfAu3sVMCyY3+nuur2jkZzZuw1fOakTD01ZzPTlm8OOIyJJKJYmmplm9ryZfd3MRtdMcU8m/PD8frRvkcsPnpnFrr26ri0ihyeWAp8DbALOAj4fTBfEM5RE5GdncPcXB7Bs0y5+M/HjsOOISJKps7vgGu7+zcYIIgc3pHsrvnlKKY+9vYxz+pUwtEfrsCOJSJKI5UnWHDP7jpn90cwerZkaI5xE3DqyD91a53HLmNlsr9gXdhwRSRKxNNH8HWgLjAQmE+kTXhdbG1FuVjr3XDaANdt2878vzg87jogkiVgKfA93/zGw092fAM4HPhffWFLboM4tueb07vxr2kreWLAu7DgikgRiKfA1bQJbzaw/0BxoE79IUpebhvekd0kBt4+dw9Zde8OOIyIJLpYC/5CZtQR+DDwPfAT8Jq6p5KCyM9K597IBbN65l58+Py/sOCKS4GLpD/5hd9/i7pPdvZu7t3H3P8d6gGAkqJlm9uLRRRWA/h2ac8PZPXnuw9W8PEddAolI3Q55m6SZ/eRg77v7L2I8xo3AfKDwMHJJPf7fGd15bf46fvTvuZxYWkRxQXbYkUQkAcU0JmvUVAWcC5TGsnMz60jkouzDR5hPDiIzPY17Lx3Ajj2V/HD8HNw1zJ+IfFYsTTT3Rk2/BM4AusW4/weAW4E6R68ws6vNbJqZTduwYUOMu5WeJQXcck5vJn20jvEzV4UdR0QSUCxn8LU1I3IvfL3M7AJgvbtPr289d3/I3Qe7++Di4uIjiNN0fWtYV04sbclPn5/Hmm27w44jIgkmlidZ55jZ7GCaB3xM5Mz8UE4BLjSzZcA/gbPM7MmjSisHSE8z7rl0AJVVzq1jZqupRkQOEMsZ/AV82snYOUB7d//9oTZy9zvcvaO7lxIZw/UNd//a0YSVz+rSKo//Pr8vby3ayFPvrwg7jogkkFgK/PaoaTdQaGZFNVNc00lMvva5zpzaszW/fGk+KzbtCjuOiCSIWAr8DGADsBBYFMxPD6ZpsRzE3d90d3UxHCdmxq8vOY50M25+dhbV1WqqEZHYCvyrwOfdvbW7tyLSZDPJ3bu6e6x300ictW+Ry08vPIb3l23m0beXhh1HRBJALAX+ZHd/ueaFu08AhsYvkhypSwZ1YHjfNtz9ysd8sn5H2HFEJGSxFPjVZvYjMysNph8Cq+MdTA6fmXHn6GPJzUrnB8/OorKqzscPRKQJiKXAfwUoBsYHU5vgPUlAbQpy+N+L+zNr5Vb+MmVJ2HFEJESxDNm3mUh/MgS9Sm513XCd0C44rj0T567lgdcWcmbvNvRrr26ARJqiOs/gzewnZtYnmM82szeAT4B1Zja8sQLKkfmfi/rTPDeL7z/zIXsr1VQj0hTV10TzJSJPrQJcHqzbBjgduDPOueQotczL4q7Rx7Jg7XZ+9/qisOOISAjqK/B7o5piRgJPu3uVu88nhqYdCd/wfiVcekJH/vjmJ8xcsSXsOCLSyOor8HvMrL+ZFQNnApOiljWLbyxpKD/+fD/aFubwg2dnUbGvKuw4ItKI6ivwNwJjgAXA/e6+FMDMzgNmNkI2aQCFOZn85osDWLJhJ3e/8vGhNxCRlFFnU4u7vwf0Ocj7LwMvf3YLSVTDerbmG0O68OjbSxnRr4STu7UKO5KINIIj6Q9ektDt5/ahc1Ezbhkzix17KsOOIyKNQAW+iWiWlcG9lw6gbMtu7nx5fthxRKQRqMA3IYNLi7j61G489d4KJs1bG3YcEYmzQ97uaGbpRAbOLo1e393vi18siZfvjejFlEUbuebJ6Vw+pJRbR/WmWZbuehVJRbGcwb8AXAG0AgqiJklCOZnpjLl2CJcPKeXxd5Yx6oG3eHfJprBjiUgc2KG6lTGz2e5+XGOEGTx4sE+bFtMYItIA3luyiVvHzmb5pl18Y0gXbhvVh7xsnc2LJBMzm+7ugw+2LJYz+Almdk4DZ5IE8LlurZh442l865Su/P3d5Yx8YArvfLIx7Fgi0kBiKfDvAuPNbLeZlZvZdjMrj3cwaRy5Wen85PP9ePaaIWSmp/HVh9/jh+Pn6FZKkRQQS4G/DxgCNHP3QncvcHf1P5tiBpcW8fINp/LtU7vy1PsrGHn/FKYu0tm8SDKLpcCvBOYebh/wZpZjZu+b2Swzm2dmPz+yiNJYcrPS+eH5/Rhz7RCyM9P42iPvcce42Wyv2Bd2NBE5ArFcUVsCvGlmE4A9NW/GcJvkHuAsd99hZpnAVDOb4O7vHnlcaQwndImczd//6kL++tYSJn+8gV9dchyn9yoOO5qIHIZYzuCXAq8DWRzGbZIeUTPyc2YwaSSoJJGTmc4d5/Vl7P8bSrPsDC5/9H1uHTOLbbt1Ni+SLA55m+RR7TzykNR0oAfwB3e/7SDrXA1cDdC5c+cTli9fHrc8cmQq9lXxu9cX8efJi2lTkMOvRh/LmX3ahB1LRKj/NslY7oP/Dwc583b3sw4jQAsiA3Zf7+5z61pP98Entlkrt3LLmFksXLeDSwZ15CcX9KN5s8ywY4k0afUV+Fja4G+Oms8BLgEO6x46d98afFGMAuos8JLYBnRqwQvXD+PB1z/hT5MX89aiDdz5hWMZ3q8k7GgichCHbIN39+lR09vu/n3gjENtZ2bFwZk7ZpYLjCAyeIgkseyMdG4e2Zt/X3cKRXlZXPW3aXz/Xx+yddfesKOJSC2HLPBmVhQ1tTazkUDzGPbdDviPmc0GPgBedfcXjzKvJIhjOzbn+e8O44aze/L8rNWMuH8Kr6iHSpGEEksb/FIibfBGpGlmKfALd5/a0GHUBp+c5q7axi1jZjN/TTkXDmjPzy48hqK8rLBjiTQJR3WRtTGpwCevvZXV/OnNxTz4xiJaNMvkfy/uz6j+7cKOJZLyjqizMTM70czaRr3+hpk9Z2a/M7OieASV5JWVkcaNw3vywvXDaNs8h2ufnMF3n5rBph17Dr2xiMRFfW3wfwH2ApjZacBdwN+AbcBD8Y8myahvu0LGX3cKN5/Ti1fmreWc+6fw0uw1YccSaZLqK/Dp7r45mP8S8JC7j3X3HxN5cEnkoDLT0/juWT158fpTad8il+88NYPr/jGdjTqbF2lU9RZ4M6u5T/5s4I2oZRoVQg6pd9sCxl83lFtH9ea1j9Yz4r7JPD9rNYl03UckldVX4J8GJpvZc8Bu4C0AM+tBpJlG5JAy0tO47owevHTDMDq3yuOGp2dy7ZPTWb+9IuxoIimv3rtozOxkIvezT3L3ncF7vYB8d5/R0GF0F01qq6yq5uGpS7nv1YU0y0rnZ58/hosGtsfMwo4mkrR0m6QklE/W7+CWMbOYuWIrw/uWcOcX+tOmMCfsWCJJ6WjHZBVpUD3a5DPm2qH86Py+vLVoA8Pvm8y4GWVqmxdpYCrwEor0NOOqU7sx4cZT6VVSwPefmcWVT0xj1dbdYUcTSRkq8BKqbsX5/OuaIfz4gn68s3gjp//mP9z0z5nMKdN1fJGjpdsdJXTpacaVw7oy8pgSHnt7Gf/6YCX//nA1J3Ut4sphXRnet4T0NF2IFTlcusgqCae8Yh/PfLCSx95exqqtu+nSqhnfHFrKpYM7kZetcxKRaLqLRpJSZVU1r8xbxyNTlzBjxVYKczL4yuc6c8XQUto1zw07nkhCUIGXpDd9+RYenbqUCXPXkGbGece246pTu3JcxxZhRxMJ1dEO2ScSuhO6tOSELi1ZuXkXT7wTaad/ftZqTixtyZXDujGin9rpRWrTGbwkpe0V+3hmWhmPvb2Usi276VzUjG+eEmmnz1c7vTQhaqKRlFVZVc2rH63j4alLmb58CwU5GXzlpM5cPrSUDi3UTi+pTwVemoSZK7bwyNSlTJgbGRv23P5tuerUbgzspHZ6SV0q8NKkrNq6myfeWcbT761g+55KBndpyZXDunLOMW3VTi8pJ5QCb2adiIwAVUJk0O6H3P239W2jAi8Naceeysj99O8sZeXm3XQqyuWKoV25bHBHCnIyw44n0iDCKvDtgHbuPsPMCoDpwMXu/lFd26jASzxUVTuvfrSWR6Yu5YNlWyjIzuDLJ3Xi8qGldGzZLOx4IkclIZpogoFDfu/ur9a1jgq8xNuHK7fyyNSlvDwnMk7sqP5tuWpYV47v3DLkZCJHJvQCb2alwBSgv7uX11p2NXA1QOfOnU9Yvnx53POIrA7a6Z96fwXbKyoZ1LkFV53ajXP6lZCRrj74JHmEWuDNLB+YDPzS3cfVt67O4KWx7dxTybPTVvLYO8tYvmkXHVrk8s1TSvnSiZ3UTi9JIbQCb2aZwIvAK+5+36HWV4GXsFRVO6/NX8cjby3l/WWbyc/O4EsnduKKoaV0KlI7vSSusC6yGvAEsNndb4plGxV4SQSzyyLt9C/NXkO1O6P6t+XyIaWcWFpEmm6zlAQTVoEfBrwFzAGqg7f/291frmsbFXhJJGu27eaJd5bz1HvLKa+opGPLXEYf34EvDOpI19Z5YccTARLgImusVOAlEe3aW8mkeesYO6OMtz/ZSLXDoM4tGD2oIxcc144WzbLCjihNmAq8SANZu62C5z5cxdgZZSxct4Os9DTO7tuG0YM6cnqvYrIydAeONC4VeJEG5u7MW13OuBmreH7WKjbu2EtRXhYXDmjP6EEdOLZDcyKXoUTiSwVeJI72VVXz1qINjJ2xilc/Wsfeymp6tMln9KAOXDywA+3Vq6XEkQq8SCPZtnsfL89Zw7gZZXywbAtmMKRbK0YP6si5/dtqTFlpcCrwIiFYsWkX42euYtzMMpZv2kVuZjqj+rdl9KAODO3eWj1bSoNQgRcJkbszY8UWxs5YxYuzVlNeUUlJYTYXH9+BSwZ1pFdJQdgRJYmpwIskiIp9VbyxYD3jZpTx5scbqKx2+ncoZPTxHblwYHta52eHHVGSjAq8SALauGMPL8xazbgZq5izahvpacYZvYoZPagjZ/dtQ05metgRJQmowIskuIXrtjNuxir+PXMVa8srKMjJ4ILjIrdcDu7SUrdcSp1U4EWSRFW183+LNzFuRhkT5q5l974qOhc14wvHd2D0oA50aaUuEuRAKvAiSWjnnkomzl3LuJllvLN4E+4wuEtLRg/qyPnHtaN5rrozFhV4kaS3Zttu/j1zNWNnlPHJ+h1kZaQxom8JI/qV0K99IV1b55GpgUqaJBV4kRTh7sxdVc7YGWU8P9MS5nMAAAtOSURBVGs1m3fuBSArPY0ebfLp066Avm0L6dOugD5tCyku0F05qU4FXiQF7auqZvGGHSxYs535a8tZsGY7C9aWs658z/51Wudn0adtIX3aFtCnXeRvjzb5ukMnhdRX4PXctEiSykxPC4p3IRfTYf/7m3fuZUFUwZ+/Zjt/f3c5eyojwzKkpxndi/Mi20ad8bctzNHdOilGBV4kxRTlZTG0e2uGdm+9/73KqmqWbdp1QOGfvnwLz89avX+d5rmZ9GlbQN92n57x9yrJp1mWykSy0v9yIk1ARtBG36NNPhcc9+n723bvY+G67SxYU878tZG/z0xbya69VQCYQWmrvAMKf992hXRokavhC5OACrxIE9Y8N5MTS4s4sbRo/3vV1c7KLbuYH5zpL1iznflrypk4by01l+zyszPo3bZg/5l+37YF9G5bQEGObt1MJLrIKiIx2bmnMnK2v/bAM/7yisr963RsmUuftoX0aJNPp6JcOrZsRseWuXRokasLu3Gii6wictTysjM4vnNLju/ccv977s6abRX7L+bOX1PO/DXlTF64nn1VB548Fhdk07Hlp0U/el5fAPERtwJvZo8CFwDr3b1/vI4jIuExM9q3yKV9i1zO6lOy//2qamf99grKtuymbMsuyjbvjsxv3cXssq1MnLtGXwCNIJ5n8I8Dvwf+FsdjiEgCSk8z2jXPpV3z3APa92sc6gtgwpw1VFbrC+Boxa3Au/sUMyuN1/5FJHnpC6BxhN4Gb2ZXA1cDdO7cOeQ0IpIIYvkCWFce9QUQ9XfWyrq/ANq3yKVtYTZtC3MoaZ4T+RtMbZvnkJ9iY+bG9S6a4Az+xVjb4HUXjYg0hLq+AFZvrWBdeQVryyvYHnX3T4387AxKCrMjBb/Wl0Db5jmUFGZTnJ9NRgJ17Ka7aESkSUlP+/Ti70ldP/sLAGDX3krWbosU+/Xle1hbXsHabZ9+Aby7ZBPrt+/5zC+BNIPW+dlBwY8U/egvgZovhoLsjNC7flCBF5EmqVlWBt2K8+lWnF/nOtXVzqadeyNFf/+XQeTv2vI9rNi0i/eXbmbb7n2f2TY3M33/WX9N0S8pyNn/xdC2eQ5tCrLj2s1zPG+TfBo4A2htZmXAT939kXgdT0SkoaWlGcUF2RQXZNO/Q/M616vYV3XAl8C68grWBb8K1m2rYNryLawv38PequoDtjODVnlZdGudzzPXDmnw/PG8i+Yr8dq3iEgiyclMp0urvHqHVHR3tuzad0Az0NptFazfXkG8LoWqiUZEpBGYGUV5WRTlZdGvfWGjHDNxLgWLiEiDUoEXEUlRKvAiIilKBV5EJEWpwIuIpCgVeBGRFKUCLyKSolTgRURSVEKNyWpmG4DlR7h5a2BjA8ZpKMp1eJTr8CjX4UnFXF3cvfhgCxKqwB8NM5tWV5eZYVKuw6Nch0e5Dk9Ty6UmGhGRFKUCLyKSolKpwD8UdoA6KNfhUa7Do1yHp0nlSpk2eBEROVAqncGLiEgUFXgRkRSV1AXezDqZ2X/M7CMzm2dmN4adCcDMcszsfTObFeT6ediZoplZupnNNLMXw84SzcyWmdkcM/vQzKaFnaeGmbUwszFmtsDM5ptZw4+tdviZegefU81UbmY3hZ0LwMy+F/z/fq6ZPW1mOWFnAjCzG4NM88L8rMzsUTNbb2Zzo94rMrNXzWxR8LdlQxwrqQs8UAn8wN37AScD3zGzfiFnAtgDnOXuA4CBwCgzOznkTNFuBOaHHaIOZ7r7wAS7V/m3wER37wMMIAE+O3f/OPicBgInALuA8SHHwsw6ADcAg929P5AOfDncVGBm/YFvAycR+d/wAjPrEVKcx4FRtd67HXjd3XsCrwevj1pSF3h3X+PuM4L57UT+w+sQbirwiB3By8xgSoir2WbWETgfeDjsLMnAzJoDpwGPALj7XnffGm6qzzgbWOzuR/oUeEPLAHLNLANoBqwOOQ9AX+A9d9/l7pXAZGB0GEHcfQqwudbbFwFPBPNPABc3xLGSusBHM7NS4HjgvXCTRATNIB8C64FX3T0hcgEPALcC1YdaMQQOTDKz6WZ2ddhhAl2BDcBjQbPWw2ZW98jK4fgy8HTYIQDcfRVwD7ACWANsc/dJ4aYCYC5wqpm1MrNmwHlAp5AzRStx9zXB/FqgpCF2mhIF3szygbHATe5eHnYeAHevCn4+dwROCn4ihsrMLgDWu/v0sLPUYZi7DwLOJdLcdlrYgYicjQ4C/uTuxwM7aaCfzw3BzLKAC4Fnw84CELQdX0Tki7E9kGdmXws3Fbj7fODXwCRgIvAhUBVqqDp45N71BvnFn/QF3swyiRT3f7j7uLDz1Bb8nP8Pn21zC8MpwIVmtgz4J3CWmT0ZbqRPBWd/uPt6Iu3JJ4WbCIAyoCzqF9gYIgU/UZwLzHD3dWEHCQwHlrr7BnffB4wDhoacCQB3f8TdT3D304AtwMKwM0VZZ2btAIK/6xtip0ld4M3MiLSNznf3+8LOU8PMis2sRTCfC4wAFoSbCtz9Dnfv6O6lRH7Wv+HuoZ9dAZhZnpkV1MwD5xD5WR0qd18LrDSz3sFbZwMfhRiptq+QIM0zgRXAyWbWLPjv82wS4KI0gJm1Cf52JtL+/lS4iQ7wPHB5MH858FxD7DSjIXYSolOArwNzgvZugP9295dDzATQDnjCzNKJfIk+4+4JdUtiAioBxkdqAhnAU+4+MdxI+10P/CNoDlkCfDPkPMD+L8IRwDVhZ6nh7u+Z2RhgBpG73GaSON0DjDWzVsA+4DthXSw3s6eBM4DWZlYG/BS4C3jGzK4k0mX6ZQ1yLHVVICKSmpK6iUZEROqmAi8ikqJU4EVEUpQKvIhIilKBFxFJUSrwkvDMzM3s3qjXN5vZzxpgv9lm9lrQG+OXYli/NLoHwMM81hVm1v5IthU5Uirwkgz2AKPNrHUD7/d4gKBXxn818L5ru4LIo/sxCzrrEjliKvCSDCqJPCzzvdoLgrPqN8xstpm9HjylWHudIjP7d7DOu2Z2XPBU45PAicEZfPda2/QIzu5nmdmMgyy/wsx+H/X6RTM7I+hk7vGg3/E5Qd/oXwQGE3lY6kMzyzWzE8xsctCx2itRj6m/aWYPWKQ//BvN7NJgX7PMbEoDfJbShOgMQZLFH4DZZvabWu8/CDzh7k+Y2beA3/HZrlZ/Dsx094vN7Czgb+4+0MyuAm529wsOcrx/AHe5+3iLDFiRBrSJIedAoEPQFzpm1sLdt5rZd4NjTQv6T3oQuMjdNwTNQ78EvhXsI6umP3wzmwOMdPdVNd1fiMRKBV6SgruXm9nfiAwmsTtq0RA+7df770DtLwCAYcAlwX7eCLqMLazrWEGfOB3cfXywTUXwfixRlwDdzOxB4CUivRfW1hvoD7wa7DOdSNe6NaKbi94GHjezZ4h03CUSMzXRSDJ5ALgSSIT+2Cs58L+fHAB330JkxKA3gWs5+MAqBsyrGZHJ3Y9193Oilu+smXH3a4EfEem7fHrQl4pITFTgJWm4+2bgGSJFvsY7fDok3H8Bbx1k07eCZZjZGcDG+sYNCEYHKzOzi4NtsoNBIqItAwaaWZqZdSLo2ji4EJzm7mOJFOaaroW3AwXB/MdAsQVju5pZppkdc7AsZtbd3d9z958QGXgkkQapkASnJhpJNvcC3416fT2R0ZZuIVIAD9bT48+AR81sNpGxSy8/yDq1fR34i5n9gkjvg5dy4ChYbwNLiXQdPJ9I74kQGTLyMTOrOXm6I/j7OPBnM9tNpFnpi8DvLDIkYAaRXyfzDpLjbjPrSeSs/3VgVgzZRQD1JikikrLURCMikqJU4EVEUpQKvIhIilKBFxFJUSrwIiIpSgVeRCRFqcCLiKSo/w8XO51OAD0jHAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NGe0-dEXtN6c"
      },
      "source": [
        "Explanation : \n",
        "\n",
        "Elbow method shows the significant decrease in error compared to value of K (Number of clusters), Here value of optimum K can be 3,4,5 afterwards error rate seems quite normal and change is insignificant to varying value of \"K\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1T7Cz9AD0qiA"
      },
      "source": [
        "Silhouette Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "kzPp8jem0veF",
        "outputId": "723de746-87b7-48b7-d151-4ad3adfd130e"
      },
      "source": [
        "silhouette = []\n",
        "for k in range(2, 11):\n",
        "  kmeans = KMeans(n_clusters = k)\n",
        "  kmeans.fit(x)\n",
        "  labels = kmeans.labels_\n",
        "  silhouette.append(silhouette_score(x, labels))\n",
        "pyplot.plot(range(2, 11), silhouette)\n",
        "pyplot.title('Silhouette Method')\n",
        "pyplot.xlabel('No of clusters')\n",
        "pyplot.ylabel('Silhouette Score')\n",
        "pyplot.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4SXBmtzbwDHu"
      },
      "source": [
        "Explaination : \n",
        "\n",
        "Silhouette score is favourable for value of K as 2, 3, 4 and 5, however it drops significantly for K = 6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_RRiQQy0ftt"
      },
      "source": [
        "Dendogram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "DJYeuxqdQXpU",
        "outputId": "51f05b51-cc0c-4966-ee83-0885586585b8"
      },
      "source": [
        "# CREATING DENDOGRAM FOR UNDERSTANDING CLUSTERING PROCESS AT EACH STEPS\n",
        "Dendrogram = shc.dendrogram((shc.linkage(selected_df[selected_summarised_feature_columns], method ='ward')))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxdjCEDq0Y8O"
      },
      "source": [
        "# SELECTING RANGE OF OPTIMUM VALUES FOR \"K\" BASED ON ELBOW METHOD AND SILHOUETTE\n",
        "optimum_k = [3,4,5]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BL9DqTpFwotK"
      },
      "source": [
        "Explanation :\n",
        "\n",
        "Optimum value are selected based on value of K obtained by results of Elbow method and silhouette method collectively"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A1OLhvU90pMB"
      },
      "source": [
        "Agglomerative Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HcFWKIIx1iQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7438cb62-0dc5-4384-9183-b928433ec0fc"
      },
      "source": [
        "# APPLYING AND VISUALISING DATA FOR AGGLOMERATIVE CLUSTERING ALGORITHM FOR VARIOUS VALUES OF \"K\"\n",
        "for i in optimum_k:\n",
        "\n",
        "  pyplot.figure(figsize =(10, 6))\n",
        "  model = AgglomerativeClustering(n_clusters=i)\n",
        "  y_predicted = model.fit_predict(selected_df[selected_summarised_feature_columns])\n",
        "  clusters = unique(y_predicted)\n",
        "\n",
        "  for cluster in clusters:\n",
        "    row_ix = where(y_predicted == cluster)\n",
        "    pyplot.scatter(selected_df[selected_summarised_feature_columns[0]], selected_df[selected_summarised_feature_columns[1]],c=model.labels_.astype(float))\n",
        "  pyplot.title('Hierarchical Clustering [K = '+ str(i) +' ]')\n",
        "  pyplot.xlabel('Bidding Price')\n",
        "  pyplot.ylabel('Profit Margin')\n",
        "  pyplot.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GzRMteunuHhO"
      },
      "source": [
        "Explanation : \n",
        "\n",
        "clusters = unique(y_predicted) stores all unique value of predicted (Here it can be considered as Cluster Class)\n",
        "\n",
        "y_predicted = model.fit_predict(selected_df[selected_summarised_feature_columns]) predicts the value of Cluster Class based on selected attributes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZcFpp5rDjTfe"
      },
      "source": [
        "BIRCH Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nvgDDMmZjXYF",
        "outputId": "11d99c4d-0666-4a8a-adad-659d5a036579"
      },
      "source": [
        "# APPLYING AND VISUALISING DATA FOR BIRCH CLUSTERING ALGORITHM FOR VARIOUS VALUES OF \"K\"\n",
        "for i in optimum_k:\n",
        "\n",
        "  pyplot.figure(figsize =(10, 6))\n",
        "  model = Birch(threshold=0.01, n_clusters=i)\n",
        "  y_predicted = model.fit_predict(selected_df[selected_summarised_feature_columns])\n",
        "  clusters = unique(y_predicted)\n",
        "\n",
        "  for cluster in clusters:\n",
        "    row_ix = where(y_predicted == cluster)\n",
        "    pyplot.scatter(selected_df[selected_summarised_feature_columns[0]], selected_df[selected_summarised_feature_columns[1]],c=model.labels_.astype(float))\n",
        "  pyplot.title('BIRCH Clustering [K = '+ str(i) +' ]')\n",
        "  pyplot.xlabel('Bidding Price')\n",
        "  pyplot.ylabel('Profit Margin')\n",
        "  pyplot.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EmVkL4Dagcd7+zrmzivhoA+D3PO9XAScV6NMieIzPgWTgQefc+yFq6A/0B2jUqFFRJYuIiIiUedFcjpwGjAXGA3ticPzmwJlAQ+BTMzvaObclbyfn3DhgHEBGRoYr5RpERERE4i6aELbbOTemBPteDRyS533D4La8VgFfO+f8wHIzW0oglM0rwfFEREREDhjRzAl7x8wGmll9M6u19yuKz80DmptZUzOrAPQAZhTo8xaBUTDMLJ3A5clfoy9fRERE5MAUzUhY7+Cvt+XZ5oBDI33IObfbzG4APiAw3+tF59xCMxsKzHfOzQi2dTCzRQQudd7mnNtY3G9CRERE5EBjzh1YU6wyMjLc/PnzvS5DREREpEhm9q1zLiNUW9iRMDM7yzn3kZl1CdXunHuztAoUERERKW8iXY48A/gIuCBEmwMUwkRERERKKGwIc849EPz1qviVIyIiIlI+RLNY6y0hNmcB3zrnviv9kkREREQSXzRLVGQA1xFYAb8BcC3QERhvZrfHsDYRERGRhBXNEhUNgeOdc9sAzOwBYCZwOvAt8GTsyhMRERFJTNGMhNUBduZ57wfqOuf+KrBdRERERKIUzUjYa8DXZvZ28P0FwGQzqwIsilllIiIiIgksYggzMwNeBt4DTg1uvs45t3e11F6xK01EREQkcUUMYc45Z2aznHNHA1qmXkRERKSURDMn7L9mdmLMKxEREREpR6KZE3YS0MvMVgDbASMwSHZMTCsTERERSWDRhLBzY16FiIiISDlTZAhzzq0AMLM6QKWYVyQiIiJSDhQ5J8zMLjSzn4HlwCfAbwTulhQRERGREopmYv7DwMnAUudcU+Bs4KuYViUiIiKS4KIJYX7n3EYgycySnHMfE3iepIiIiIiUUDQT87eYWVXgU+A1M1tP4C5JERERESmhaEbCOgN/AYOB94FfCDy6SERERERKKJq7I/OOek2MYS0iIiIi5UbYEGZmWwGXd1Pw/d7FWqvHuDYRERGRhBVpJGwOUA94E5jinFsZn5JEREREEl/YOWHOuYsIrJa/ARhvZp+Y2UAzqxW36kREREQSVMSJ+c65LOfcS0An4HlgKNAnDnWJiIiIJLSIE/PN7BSgJ9AWmAtc7Jz7LB6FiYiIiCSySBPzfwO2AFOA/sDu4PbjAZxz/41DfSIiIiIJKdJI2G8E7oY8F+hA4K7IvRxwVuzKEhEREUlsYUOYc+7MONYhIiIiUq5Es2K+iIiIiJQyhTARERERDyiEiYiIiHigyBBmZnOi2SYiIiIi0Yu0REUlIBVIN7Oa7Ls7sjrQIA61iYiIiCSsSEtUXAvcDBwM5F0T7E/guVgWJSIiIpLoIi1RMQIYYWY3OudGxrEmERERkYQX6XLkWc65j4DVZtalYLtz7s2YViYiIiKSwCJdjjwd+Ai4IESbAxTCREREREooUgjbHPx1gnNubjyKERERESkvIi1RcVXw12fjUYiIiIhIeRJpJGyxmf0MHGxmP+TZboBzzh0T29JEREREElekuyN7mlk94APgwviVJCIiIpL4Io2E4ZxbCxxrZhWAFsHNS5xz/phXJiIiIpLAIoYwADM7A5gE/EbgUuQhZtbbOfdpjGsTERERSVhFhjDgKaCDc24JgJm1AF4HTohlYSIiIiKJrMgHeAO+vQEMwDm3FPDFriQRERGRxBfNSNi3ZvYC8GrwfS9gfuxKEhEREUl80YSw64DrgUHB958Bo2NWkYiIiEg5EDGEmVky8L1z7nACc8NEREREpBREnBPmnNsDLDGzRnGqR0RERKRciOZyZE1goZl9A2zfu9E5pwVcRUREREoomhB2X0l3bmYdgRFAMvCCc+7xMP26Am8AJzrnNOlfREREEl7YEGZmlQhMym8G/A+Y4JzbHe2Og/PJRgHtgVXAPDOb4ZxbVKBfNeAm4Ovil1/6bjv7Qb7/ZCE4aHVKS4Z99AA+n1bkEBERkdIVaU7YRCCDQADrBAwv5r7bAMucc78653YBU4DOIfo9DDwB7Cjm/kvduRUu5buPF+JywDlY9PkSzq/cy+uyREREJAFFCmFHOOcud849D1wCtC3mvhsAv+d5vyq4LZeZHQ8c4pybGWlHZtbfzOab2fwNGzYUs4zoPHPtWHJ25xTa7nIcQ7sXN3+KiIiIRBYphOU+pLs4lyGjZWZJBJa9uLWovs65cc65DOdcRu3atUu7FADmTJ4btu3rd7+NyTFFRESk/Io0Mf9YM/sz+NqAysH3BjjnXPUi9r0aOCTP+4bBbXtVA44C/mNmAPWAGWZ2oReT81N8KcDOkG1JKcnxLUZEREQSXtiRMOdcsnOuevCrmnMuJc/rogIYwDyguZk1NbMKQA9gRp79Zznn0p1zTZxzTYCvAE8CGMC1w68M23bFfV3jWImIiIiUB9E8wLtEgpcwbwA+ABYDU51zC81sqJmVuTXGOl51FvUPrVNoe/ohteh+20UeVCQiIiKJzJxzXtdQLBkZGW7+/NgNli34+EeGXz0a5xw3j+3HiR2Pj9mxREREJLGZ2bfOuYxQbdEs1lqutG53FK8u1/PJRUREJLZidjlSRERERMJTCBMRERHxgEKYiIiIiAcUwkREREQ8oBAmIiIi4gGFMBEREREPKISJiIiIeEAhTERERMQDCmEiIiIiHlAIExEREfGAQpiIiIiIBxTCRERERDygECYiIiLiAYUwEREREQ8ohImIiIh4QCFMRERExAMKYSIiIiIeUAgTERER8YBCmIiIiIgHFMJEREREPKAQJiIiIuIBhTARERERDyiEiYiIiHhAIUxERETEAwphIiIiIh5QCBMRERHxgEKYiIiIiAcUwkREREQ8oBAmIiIi4gGFMBEREREPKISJiIiIeEAhTERERMQDCmEiIiIiHlAIExEREfGAQpiIiIiIBxTCRERERDygECYiIiLiAYUwEREREQ8ohImIiIh4QCFMRERExAMKYSIiIiIeUAgTERER8YBCmIiIiIgHFMJEREREPKAQFkJWZhZXNrueDind6FSpJxMfnOJ1SSIiIpJgUrwuoKzp1WQA61dm5r7fvWs3rw6dzmfTv+aF/z3tYWUiIiKSSDQSlscF1S7PF8DyWrFwFZlrQreJiIiIFJdCWNDyH1ewY/vOiH2GXTU2TtWIiIhIolMIC5o2/J0i+1StkRqHSkRERKQ8iGkIM7OOZrbEzJaZ2Z0h2m8xs0Vm9oOZzTGzxrGsJ5JjzzyyyD53vHJjHCoRERGR8iBmIczMkoFRQCfgCKCnmR1RoNsCIMM5dwzwBvBkrOopyrm920Vsb9/7DHw+X5yqERERkUQXy7sj2wDLnHO/ApjZFKAzsGhvB+fcx3n6fwVcHsN6ilTz4Bps/mNLoe3jFg2j6eGeDdKJiIhIAorl5cgGwO953q8KbgunL/BeDOuJaMm8n0MGMIBHumppChERESldZWKdMDO7HMgAzgjT3h/oD9CoUaOY1PBAl3+GbVu5eHVMjikiIiLlVyxHwlYDh+R53zC4LR8zOwe4B7jQORdyjQjn3DjnXIZzLqN27doxKXbTmtCjYCIiIiKxEMsQNg9obmZNzawC0AOYkbeDmbUGnicQwNbHsJYi+Soke3l4ERERKWdiFsKcc7uBG4APgMXAVOfcQjMbamYXBrv9E6gKTDOz78xsRpjdxdzpl5wStq3N+cfFsRIREREpD8w553UNxZKRkeHmz58fk313SOmOyynw+2Ewe8+0mBxPREREEpuZfeucywjVphXz85j512s0O75p7vsmRx3CrB2TQ/Z99LJnaJ/cjfZJ3big+uUs+PjHeJUpIiIiCUAjYSXQq+kA1q8o/DDvoe/cwd/ODxl2RUREpByKNBJWJpaoKGuys7PpXLV3vm0166Ux9Y8XyFyTGTKAAQztOpz3drwejxJFRETkAKcQFkLBAAaweW0Wlx86kFYntwj7ud27dseyLBEREUkgmhNWwEdT5oZtW/fbBhq2qBfHakRERCRRKYQVMPmxNyO2936wR9i2+ofWKe1yREREJEEphBVwbp92RfYZ8HThy5XJKUlMWPxMLEoSERGRBKQQVkC3Wy4I21alRmUAutz0d2btnMxpXdrQ+MiGDHlpIO/v+hc+n6/Yx/P7/cx6YTY/fLa4xDWLiIjIgUdLVISw8qeV9D3i1nzbUiqklOqdj5lrMunb6hay//wr3/bHZt3FiR2PL7XjiIiIiHciLVGhEBbBHys3sODDBbS77DRSU1NLbb/9jxvC8h9WhG2fnaMV+kVERBKBVswvoYMb1eb8azqUagB77dHpEQMYwIiB40rteCIiIlI2KYRFIXNNJu+/9BGZa0Iv0locrz3yRpF9Fn6xdL+PIyIiImWbFmuNwO/3c3GNPuz8a1futgqVK/DWlpdLNAkfYLd/T5F9OvU7u0T7FhERkQOHQlgea1du4NbT7yfzj01UTavCzr925gtgALv+2kXX2n2ZsWVSiY5Rr2kd1vyyLmy7JcHF13cq0b5FRETkwKHLkUFz3/qaK5oMZP3KTHJ25/Dnxq3szN4Vsu9fwTsal/+4gqn/fIsl//2Fno2upX1SN86tcCljBr8U9jgjv3w0bFuVtFTe+nNisep+qt9Y2id3o31SNzqkdOe1R6cX6/NlycgbJ3Bpg/7c1PZesrOzvS5HREQkpnR3ZFCHlG64nOj7J6UkkbM7/AfqNq3Nq7+MDtk24vpxvDtmdu77lArJzNj6SrEvcd7U9l4Wfb6k0Pa/X9eem0b3L9a+vJSVmcUlda4ptH3A073pctPfPahIRESkdOjuyCgUJ4ABEQMYwLrlG/jjlzWFto8Z/FK+AAawe9ceeje7sXgFQMgABvDu2Nkht5dVVxx2fcjtYwZPLJWbIURERMoihbAYerr/84W2vTliVsi+G37fSFbW9qj3vWTezyWuKx76tBxE+6RuuV99jxqc2/bElSPp03IQL90XWPz2r607w+6nZ4MBXHP04LDtIiIiByqFsKCklNL/rUirXT3fe7/fH7H/hxM+inrf9ZqW3YeF92o6gNU/5x8FXLloFZc27E/7pG78+9VPWf3zGiY/+ibtk7oVub8VC1cx8cEpsSpXRETEEwphQc9+/kip7/OOV/JfYux31K1hegYceUqL3NeZazLpXr9f7kjSwBPvyNc3LT0Ns/D7mv7MO8UvOIxZL8zm0gb9ueKw66MagVu/IvQlxE1/bC5xDa//460Sf1ZERKQsUggLanlic2btnMzBzeuV2j4n3T813/uCo0MFLfn2VyAwUb1ngwFsXrclt+3nb3+lU6Ueue+zs7OJdE/F2Fsm5Qa4zMySz6u6MO0Knu4/jk1rNrN2+XpuOOluBp16T9j+RY32ldSe3UWvr1ZejJv8GRf2HUOfIRPZnJUYd5FuzsqO2Z8dEZGySiEsj41rtuDfUXo/CN4rxuVFgMkPB1bTH3jinSHbd+/aw7SnAiNcl9aN/u7HnnUGFKuOvR7p8RR/bd1RaPviL5fy83fLAbi29ZDcsHdx+lVsWFmywPfc14+RUjH8snV1G9Uu0X4BsrP9XNxvLKd1HcZpXYfRvtcIVqwKXefTE+bQa9CLPPfSxyU+Xqz4/X7adh3GpOlfs2nLdpYt38AFV48uk7VG675hb3Na12FccPVo2vUYQduuw1i6PPw6eiIiiURLVAT9/N1yBh5/e6nus0Hz+ry85Nnc951rXEl2cI2xUFKrVyL7z8KhJ6/KVSqSXCGFbZujn8QP0OfhS+l1zyVR9c3Ozubao25j7W/rw/apf1hd1q/YwJ5Qd4kaUMw/VnsfWh5uqZDXV48hvX568XYadFrXYSG3v/PiQGqmBZ4LunT5Oq4e8kqhPq+N6EPjhiU7bmnr0u951m/aGrLt4yk3lfgpDl6ZOO0Lxk/5ImTb3OlD4lyNiEhsaImKKNzS9r5S3+djs+7K937k1/+I2L+oAAbw1/adxQ5gAO88H92yFX6/n85Ve0cMYACb120JHcCg2AGsz8P7LrPO/GsydRvvG/WqXK0S434YVuIA9s8I3/d1d72W+7rvbYUDGMAVg1/O9/6DTxfR9pJhuaNqZ/V4mjUbskpUW3GFC2AAz036NC41lKYJU0MHMIBnXyreKLKIyIFIjy0K2rE9/DIJJXHWZZGczucAACAASURBVG05+LD6+bY1atmA11ePoWeDwpcHk1OSYzrv6aTzWkfV74HOT0bVb8e2/f/98lXycdtLA2l36Wn7tvl8vLo89CK3JfHxF6HXUgP4Y30gPL32f1+GnV+XkydnLv75Dx4usMTILv8eul033vORm02bt3l6/JLIibDU3rzvfotbHSIiXtFIWCmzFOPtbRO569VBIdvT66czO2cat04YQM26Nah9yEEM/2Qosb4sPHjsdVH1+/4/C4vsU7HK/l/2mvjzs8zKnpwvgAF8OXM+P3y2eL/3v1e1qpXCtvmSkxlwz+uMefXzqPZ1yyPhHwn18LMzi11bcVWskBy27ZZ+7WN+/NKWnBT+9t62JzWPYyUiIt5QCCtlbrfj4rSrinz2YcerzmLqmvFMXjGWY9q2ImdPMZfsLyjCchVPzon+UquvUoUi+1xyywVR7y+c3s0H0bXO1bnvn7zqOdondeP+C57g1jPup31SN6Y99Q7TnnqHi2v14eJafXhzxLsR95m5JpOh3Ycz9rZ9z98cfm/XsP07nHE4//tpddQ1b40w+hePkZvnhl4acvuhjdNz57YdSAZd1S5sW//L2saxkvJnbfY67vr+Xm5ZcDtzN3zldTki5ZZCWAzk7Mmhc9XeIdv8fj8rl6zOdzv+yBsn7Nfxjmt3JLP3TGN2zjSufuwyqtRIJbV6ZW549mpm50yjdbtjot7XjSP7Ftln+jOlM+rzZ+ZWBp9+H3Pf+prZEz8p1D5uyCTGDZnEti3b2bZlO2MGT6RL+lUh99Wn5SB6NhjAZ298xfTh79I+qRsTH5xCw/o1Of+sI0N+5t05RY/6de5wbO7rCr7wI1EH16tZ5L72V6vmBzPlub4cVLMqEBhJurLrSUx6qk/Mjx0LXc87ni4dj8u3LTnZmDa2n0cVlQ/DFg/njh/v5o+da9jo38j45eO5dl7oR4eJJKIc/1Jy1p1EztoW5Kw9nJyshz2rRXdHBkWzcntx+Sol49+xb55XSoVkdu/ak6fdxxuZL/D4Zc/y5YxvS3QMSzJm/vVaxDvjsrOzmXTfVN5/8SN2bN9JeoODGPbJQ9QLs+zD9W3uZOn8XyIet2rNVLZtLp01qipXqxRyKYxw+jzcg1737BvhGjFwXNjnZb69bSIrVm+h352Ti13X4L5n0fW84/fta/b3/DPMcT585SZSUw+suxOl9GRmZzLs52fYuvtPTqiZwdWHXul1SSGtzV7HHT/eHbLthBonMKjFwDhXJBJfOTvmw5bLCjckNSOpTujHCu6vSHdHKoQFxSKERSOlYgovLXmWK5oU/x+/tpeczP1TI6/C36vJANaHWbvrgTeHcNpFJ4Vsu/ig3hFD1uycaVzedCDrVmzI3dawZT1WLVkbReX5WZLhcqL/c1i5aiVm/LnvbsYOvu64PaE/f+wZR7CjbSu+W7iq2HVB4CrvMw9154SjGgFw9xNv8ek3y/L1ue269nRuHxgxmzt/GXO/WUaXTq1p0bRuiY55IOs16EVWrN6U+75BvRr8a9Q1HlYUe5OWv8qcDYXXahvTeiSpvrJ1mfiu7+/lj52hF4024OU2+zcqL1LW5aw9CtgVuvGgL0jylf6SRAphRfAqgO2POo3See23MRH73NT2XhZ9Hv7uQEuCD3dPC9l2+WEDWbd8Q8g22Leu115//LKG3s1D34wQSXJKEjXqpLGxGI808lVKYVb267nvI52/ek3qcOSQC3h3zo/Fri2vgutwzZm7mBo1quSGs1VrNtPjhvw/wCr4kvnglRsOuPW7SqrnDRP4fU3h81g3vSrTn4/uxpDS5vf7eXrZCH7LXkn9SvW4tcXNIYPRkqwlzM2cyynpp9AqrVWx9n/NgtDfW7WUajx3/DMlrj0WbllwOxv9G8O2T1QIkwSXs7ZF+MaKF5BUc3ipH1PrhCWgTeu2MGLAOO6/6HGeu3lC7hwzv9/P8h9XkJ2dHTGAASEXRd2r09Vnh22rVKVioW0DTrgjRM+iDR5/HU/OeaBYn/Hv2E37pG5cmHYFP3y2mKq1qobtu/73DQzqc3qJasvrxgfyh86zT2uVG8AAeoaY17fLv4cu144Lub+ly9fRpd/zdLj8WR56JvINBweKUAEMYF2mN8tn/PznMq5ZcB0Lty5m+57tLNv+CwMW3Mg3mfv+E5ftz6bPN9fw2JIn+XTjFzy+ZBh9vrmGbH/kS+3vrp5J72/6hg1gAFt3h1/XraRGLR3DVd/0o8831/DE4n8W+/NdGnYJ21avYvkbuRXJJ7l+0X1KmUbCgg7E0bDS0PyEQ+l847ns2u6n4zVn4fP5WLlkNe+/+BHT/jkj5Gd6D+3G5fd2z7etJL9/qdUrs2vHrnzz5Eqi4eH1WfVT+OdyVq1VlUteGMCYV0q+oGnV1Aq8/0rokb4VqzLpddPLYT9bcA2x+4a9zcdfFn4Qerzmla1as5krbn4Jf3CxXTN48ObzOfu06EaAsrZnk1rBlzvCtzkrm4uuGcOeCJeUvVjRv8831+DCrBy8d8TnmnnX4ne7C7WnkMKENs+H/OxXmV8z5tfQ4TrccUrDNfOuw+/yP1bNMF5u80Kx9nPtvOvZ4QrPwSyLl09FSlvOutPBhZ42k1RvaUyOGWkkTIu1lnM/f/srw/oELms+e310/5hPefztQiGsJCI9wqk4IgUwgG2bttHrojZ8/MVP/PRL6CcBpKVVIisr/M0B27J30WfIREYPvaxQUPrp18hPF8grO9sfMoAB9LppAv83PvaX7QpeNnUOHnh6Jk0PSefQxuGf0fnwszP54JP8a7g980AXBg99M+LD5AHa9RhB4wa1eO3Zq/NtX7Mhi2vvnMymLYGnQJxxcjMeve2iYnw34YULYBAYAUv1pYYMYAC7Cb0dYPyvL0Z1/Aq2b7mX+Zu+5b01H9C0SmMub9Irqs/n9X+r3i4UwCDwPT6zZCQ3t7wx6n09f+IoRix5jv9mLQCgboU6PHjkfQdcAMv2Z3Pn/+4ha/efAFSySjx65EOkp5aNx4xJGVVrDmw8GihwKSjVmzuEdTkyKClZvxXR2pldeFJjzfo1PKgken6/P2wAMyNiANtr2fINdLhiBD8uyb+2WNuM6BYWfWr8v+lwxYiw7Rs2xf6y3RU3vxS27eahoecHAkx999tCAQzg5oeKDmB7rVi9ib6377uhYtWazXS7bnxuAAP45KtldOo9Mrod7gc/hQNNoT7+0H0iBbS87jniTvx+P1d/05+Ry0azbPsvzF7/Eb2/6Zvvkmg0Plwb/vFb32f9UKx9AdzU8gYmtpnAxDYTePK4fxxwAQxgwIIbcwMYwA63g1t/vKPIS8lSviX5fCTV+wmq3Ap2MKScAAd9R1L1mzypRyNhQfu9WGo58/eqvahasyonnd+aWePmxOw4yb4kOl1zNmZJvDP6gxLv57FR4T9b3Cvy1987hU+m7bsrNTXVR1KSkRPictzeNcpu/8d0vpi/vHgHKkWRltfYa/OW8D+8Rk38T6nUseSXddw/fAYff7E07DjV1m07+fbHlfnm3JW2NF9akX32Xj71+/38Y+mT/LL916j2nV4hnXtb3UXNijW4ZcHt7KHw5fZRv46hTXr0lyqTLPx/Eh2O3t/sW9/Ph4/hrZ+I6ns8UI35OfSlYoBHFj/OY8cMjWM13snx+2FzR8j5PbilAtR4maRKIa98SR5J1a6Fatd6XYZGwqRkdmbvYuPqTTENYAB7/Dm8O2Y2p3VpU+zPOsAlGTtOPZxly9eVXk0Fwtb3i1aFDGAAm//M5sWpn0cVwKpV3XfDQ9b2bMa++ilz5oZ/hJPf72fu/GWsCjMhPu/NGkUFsLzHLejXFRsizvcqro8iBLC9RryY/wHemZlZ/P2qUbkPTr+w72hmfvR9xH1cdkiPkNs71u3A/616O19wKahF1Wa5r/stGBB1AJvYZgLDj3uCmhUDI8OR7kScu35u7utsfzaTlr/K9JVvhux7RePwlzALXnb142fQgluiqvdA9V2E0b81OyJPT0goG4/JE8AAdsGWy8jxL/KsJCkejYTJAeGOc0q2ovHug2vh6qSxfFX0S2AU1yMjwy/w98X85VGPgL04LLDAZ4/rx7NqbVbu9geensljt3fm9DzPU+xzy8ssW5FZ4PNX0LRhLc7vM5rsHfsupVWuFN2EeAecf+VoMo5pxDMPBOb8XXr9eFbnqSVelq/MxO/34/P5yM72c9G14/O1b9qSzT9GzeYfo2Zz2omH8fidFxfaxwdrPiy0Ld2XTnrFg3h15euF2vbKqJHBjS0GAPDqb69FnFuW1y3N81/OGLcs8kjXV5lfc1LNk3jy5+Es3bZvnuCMtTPpUOccejXpmbvt5PSTmLZyOpm7w4e6gqaunE73RuEf23UgS02uzI6c0FMIUiwxf6zl/PkUZI8H9gCpUPHi4OsQNvaGevPiWJ2UlEbCJGEZYDG4+zepwN+a3bv371L2oY3T+fCVm6hfO437h8/IF8D2uvvJt3Nf3zJ0aqEABnD1kFc4q+eIfAEM4K8dRc9/ymv+DyuZ9/1vTJz2hScBDAKXiNv3CswNu+7e1yL2nTvvF96enX9U7O3V77Bx96ZCfTP9mRED2PWHDsgNYABz1hdehDWvm5vdyCNHPcTENhM4tua+x4OtzV7H55u+iPjZ/21bxDULrssXwPb6cP2/2bxzS75tw49/kpub3UiaL41qKdXo26RPxP3P31T6d5GXFTe1CH8jwhWNQ6yGfoDLybwMsseyL3Rlw85Ify+8+XsrxacQtleEB2DLgcklJ5FTt/RvGMjJgWdf2ne5LNKDqIuSkpzEyIe789GXi1mzIYuPvgh/i/Q1dwQmtX/z/cqwfUorcz70zEwmvvF16eyshHbvyWHyW1/zW5gnPuQ1YkL+y5fvrC7Z801H/Zp/AeScIkbBWtc6jkNSGxba/tTS/V+k9YmfhoU83rOtn+K545/h9DptSYrwT3jTKk32u4Z4GrV0DL2/6Zv71feb/mT5Q4eJJlUac3LNwlMUmlU5jNPrJNbD33P8fthd3EBdtn+052y+h5wN3cnJjvyfnPKgbJ+pOEpKVgo7oBU4fS7JcFUqsqfhQSXa3R3Xto/YPvXd/3LD/VMAaHdKS6yEf3wqVEjm/CtH8/joD+l23fiIfX9ato7MzPj8D3fHTj+79+zf+m2lYfQrn5GSEv7B6Xvt8uevNZq7H8MZuvDR3NdVk8IvBBwpAG3bsz1sW7T+zHPnXzgXNbgwbNuA5t5POo7WlJXT+GZL/qCxmz0R57YNaH4tL7Qey6m1TiGjRgZjWo/kviNDPxfzgLZzSvE/U7FsXobO2f5GYMX6ndNgz3fw57XBxwiVXwphuRTCDmgO6h9WF2dGTloq/lYN2XnGkVDCpUdq1qrC3OlDaBhh6Y28z6Osllr4KQKR3HPDuaTXqkL2X8ULCzcNnV6s/iXV5tgmNGtap0SfrVqlQolDaSgntW4SVb9wS0oUFCk8Afkm4d/WKnwI6Ns0/MT+NrX2/+6049KOLbJP5wYXcHT1wj/ECs5PK+veW/t+2LZP138Wts3n89G/WV9ubDHggFxmIypJhUdaI2tAUs1Hi+7mha2hQvIuctZ3jHspZYVCWFDOfs7rkdKRlGy8vW0ibc47niNObcmExc/QeVB0f0Hb9TiVf++ZypA3h+AOPxiiGEEJp85B1cjMzGLVmi0R+82d9wttLxnGn9t3Rr3vv599FJ3aHU3mpuKPlmzcEp9HAD12x0U891DPojuG8P6kQXz2xpCiO0apoi+FQxsVvQDn86/OLbIPQIPKB5MS5T1JTao05pRaJxfa3qpaS06rXXj7Xn2aXhnV/iPp3yx8yMtryOGDmdhmArc0v4l/HPVIoflpB7rPMyPPrUt0SamRpjtUCazyXrkPVDgHDvqMpHpl8xJfztbRERqju/s4ESmESZly68sDSE1N5dF372LEZ4/QqGUDbngmuh9G897/DoBO7Y7m02lDCj0uqDhaNK3L0y8V/Y/ZfcPeLtY8rMMPq8udA0v+v76jWzZgxEP7/7SCSKaN7QcE1j+bOWkgtSM8m7OgE44+JPd1xjGls87XVd3/xqSn++TWFU6Kb1/orukLP4J5dZM+3Hn4bWHbU5Pyj6hc26wfL7QeS7vaZ3BqrVMY03okd7a6vci6x7QeSU1fzdz3lawiFsWIe2pSKmNaF3/B2mNrHsPBqfF/9l2sZdQ6wesSvFe98PxAAA76DwBJaXeTVGs0Sb4y/PzP3bF5JNCBLjHv5S0BS4r8QGuJwGDWjsmFng046NS7WRzmET3hHH/W0SG3PznnAW4/+6GInz3n8sITco9oXpdFPxdvjbDH7wo8NuegGkWHD38xR1A3lGD0K6/Hbr8Qn8/HOy8OZOA9r7Nm/RaqVqnE43dexKAHphaaGxVOkkHBpb/q1a7GhOFXkFZlXwhJq5Ka71FKp3UN88MACi0V8cwD3Tn9kmGFjlMcVatU4NeVG+kz5BX8RXxvfS/9W+7rh466P+R8orTkNA6t1jSw7+SqbNtTeGTxviMKXzLx+XzFHt1K9aXyTOv8v19+v5+bvr+F7Tn71mM7I/0Mrj50/0fODmRn127HnA2h/9PTvt45ca6m7ElKvZAcXyfYej3sXgaVOnu2wnuJVb0ddoZbzqf8RhE9wDvo3IqXkuNXCovGmT1O4dcfVrJ14zYuGNCeK+7fNzKTlRUIGWlpVfD7/ZxXMfrbxVMqpPDejvDLB2RlZnF1q8H8uXFr4UaD2XsKP3YnO9sf8VFBAJd1PpH/fLWUlofW4eEhnXO3+/1+2vUI/9mxj/XkurvD1xtK66MOYeRDlwLQe/DL/BLFnX8AFXzJTHq6Dw3r14zY77mXPuat2d+TZEm0P+Nw3vv4R3btyv/numKFZOa8Phi/309WVjbp6dGvrB7qYd0nHH0IIx68NOxnwgW3erWr0apZvbDP0jz8sHp0OvMIni5w52Mo5591JHdd3ynftmx/NkMXPcaanWtIIomz65xZ6LmNI5eOYX5wQnhaSnXuPvxO6qWW4dGEBDbsp6f5358/5tt2X8u7aZZ2mEcVSWnLWXsqsKFwQ/WRJKWeG/d64iXSA7wVwoLaJ3Ur9X3GQ/Xa1Rg25wH6H1N6c3COObMVP/wn/Erts3MKh513xnxQ6AHg/Z7oxcU3n891rW9n5aJVYNDq5BY8+/mj9GjYn41/7FtA1VcxhTc2TiA1tejJtSNvnMCMUfsm8voq+3h9xRjSwoSJf4x6j5kfLQzZllrZx4evhv8f5ehJnzD57cKLHn485SZ8Pl/EkaFQ9n5urzO6D2fPntB/Bw8/rB4vPHl5sfYfygtT5vKvd+bjHHTp2JqBV56x3/sEchdTLcrUd7/l2QKXdpOSjDmTB+Hz+Xh6whymz1qQ23buGa24b9D5ALTtOizsIhEVKyRTvWplnrj7Ylo0VXA60A1bPJz/bS280vu9h99F8+rNQnxCDkQ5G/uD/z/BdxWCAazky/wcCBTConCghrC9WrRpxtJvlpXKvhq0qMfqpWvDtr+x+WXS0qrkvs9ck0nPBgNC9p2w+BkatWyQb9sHEz9mzOCJ+Hfuov5hddi4egs46HnXRXS/7aJC+/i/Ue/xf0+/S93GtXnondv5feFqHu35DLv9exg44ipOu+ikIr+nNWs30u36F8l7F2xqJR8zXx5YZJDI2p7NbUPfJHPTdjp3OJre3U7Jbbvt0el8+d/CK+JXrVKRbQUm699zw7l0alf4cuvoSZ8wbea37NmTw+GH1eXR2y6kdjFGpw4Efr+f+4a/y+9/bKb7BSfQuX3Rd/5B5MufA644nV4XFf9xViU1f9O3vLnqbQ6qUItBh10fVQCV6Pj9fq5ZcF3IthSSmdBmXJwrEik9CmFRONBDWLwNfecO/nZ+4M/Ulc1vYM0voeddpTesxesr9z1st++RN7Ny8eqw+61crRIzsgKLkvr9fv6eenmRD1dPq12NN9a9GLLtlaFTmfRgnpE7g37P9+fi3meW2g/Rx0e/z7tz9l1GOefUljx4ywUAzJm7mPRa1Tj2iOLeZi4QOYT9854u/O34Q+NSR79517HL5V8C48J659O1UZe4HD/RTV/5JjPWhl9gd2Kb6B92LlLWRAphujsy6PhzEueW7ni4/8Incl9vCvMAaYAt6/ctOLlk3s8RAxjAX1t3MGbwSwBc3fLmIgMYQNaGrYy9bWKh7T98tjh/AANwML5/6f6v+s6BHZk7fUju194ABnD2aa0UwPZD3oeaFxSvAPbAj0MLBTAIPOMx2rXJJLKKyQm6xpdIERTCgs7qdZrXJRxY8gyg1m1cO2y39Aa1cl8/cUV0t92/Mzbw4OW1v62Pupy3RxZe7PH+zo+H7X/rmQ9GvW/xzvQx14Vc+PXhWy8ovDFGfsteEbZtwm8vxa2ORPb3BuGXbalkleJYiUh8xTSEmVlHM1tiZsvM7M4Q7RXN7F/B9q/NrEks64nk+/+EnrgtRXvqi/CrMz89d9+yEtEuVZBTgjUNQo2YZf+ZHaJnwO8/RR6Rk7IhNdXHZ28MYXDfszi0UTodzzyCudOH0O6Ull6XBsBG/0avS0gYneqFDmJPHvdYnCsRiZ+YhTAzSwZGAZ2AI4CeZnZEgW59gc3OuWbA08ATeKRbHP9nnWjS0qrwwJtD8j/5yeDOVwaRXn/fSue3T7w+qv217RpYidxXMfo5W0efdnihbVVqVAnRM6CRLhEeULqedzyTnu7DvTeeF/djV7AKYdt6NY5+CRaJrEejbjxz7HDqV6xPalIqbWpkMLHNBNJ8iXWTikheMZuYb2Z/Ax50zp0bfH8XgHPuH3n6fBDs86WZpQBrgdouQlGxmpgPcF7ly/DvLF9zPGrUqZ5v3lZBlapWZMe2wo/kGfLSQM7tXfzbinu3uIE/loVfPDU5JYn3d/0LgAUf/1jkAq1A2MVif/hsMbeecX/Ij8zaWbi/SCg//7mMR376R6HtVZOrMuqEyGvQiYh4NTG/AfB7nvergttC9nHO7QaygIMK7sjM+pvZfDObv2FDiIXeSsmsvyaTlHyAP8g7RPlValRm4s/PMjtnGuf3O5tKVStRrWYV7n79JqatncDgcf1JSin8R6H+oXV4589XGffDMGrVr0myL4k6jdJ55bfRJQpgABOXPsegUdeQWr0yFSr5qNcknZSKKST7kjmj+99yAxhA63ZHMfq/T1K1RmDSriUZ5/c7mxM7HoclBb7X5iccGjKAARzTthVXP1ZgpMLg8Q/uVQCTqDWv3oyHjrw/39yk1mnHKYCJyH6L5UjYJUBH59w1wfdXACc5527I0+fHYJ9Vwfe/BPuEXUY8liNhe5XWchUZHY/htx9X4XBUqFiBE9ofw8ARV+Hz+fjwtY8ZduXoQs8dbNCiHts2Z3PZvV3ocuP5hfaZuSaTb2YuoM35rfNd6hMREZGyJ9JIWCwf2LQaOCTP+4bBbaH6rApejkwDPJ/pen6/s5k5fk6R/Y4/5xh6D+3OWyPf54dPfmTjH1swg+PbH8vj798b8bMderWjQ692fDlzPm8+9S5HtT2c3g/2KPKY6fXTOe+a9lF/LyIiIlI2xXIkLAVYCpxNIGzNAy5zzi3M0+d64Gjn3HVm1gPo4pzrHnKHQfEYCQNY/uMK7j7vMbZt3k5anTSSDA5uXo8d23dSKbUSd/1rcL5V40VEREQK8mQkzDm328xuAD4AkoEXnXMLzWwoMN85NwOYALxiZsuATUDRQ0Fx0vSoxvlWehcREREpTbG8HIlzbhYwq8C2+/O83gHoeUEiIiJS7mjFfBEREREPKISJiIiIeEAhTERERMQDCmEiIiIiHlAIExEREfGAQpiIiIiIBxTCRERERDygECYiIiLiAYUwEREREQ/E7NmRsWJmG4AVMT5MOpAZ42NI8em8lD06J2WTzkvZo3NSNsXjvDR2ztUO1XDAhbB4MLP54R62Kd7ReSl7dE7KJp2XskfnpGzy+rzocqSIiIiIBxTCRERERDygEBbaOK8LkJB0XsoenZOySeel7NE5KZs8PS+aEyYiIiLiAY2EiYiIiHigXIcwM+toZkvMbJmZ3RmivaKZ/SvY/rWZNYl/leVPFOflFjNbZGY/mNkcM2vsRZ3lSVHnJE+/rmbmzEx3gcVYNOfEzLoH/64sNLPJ8a6xPIri369GZvaxmS0I/ht2nhd1lidm9qKZrTezH8O0m5k9GzxnP5jZ8fGqrdyGMDNLBkYBnYAjgJ5mdkSBbn2Bzc65ZsDTwBPxrbL8ifK8LAAynHPHAG8AT8a3yvIlynOCmVUDbgK+jm+F5U8058TMmgN3Aac6544Ebo57oeVMlH9X7gWmOudaAz2A0fGtslx6GegYob0T0Dz41R8YE4eagHIcwoA2wDLn3K/OuV3AFKBzgT6dgYnB128AZ5uZxbHG8qjI8+Kc+9g5lx18+xXQMM41ljfR/F0BeJjAf1R2xLO4ciqac9IPGOWc2wzgnFsf5xrLo2jOiwOqB1+nAX/Esb5yyTn3KbApQpfOwCQX8BVQw8zqx6O28hzCGgC/53m/KrgtZB/n3G4gCzgoLtWVX9Gcl7z6Au/FtCIp8pwEh+8Pcc7NjGdh5Vg0f09aAC3M7HMz+8rMIo0ESOmI5rw8CFxuZquAWcCN8SlNIijuz51SkxKPg4jEgpldDmQAZ3hdS3lmZknAU0Afj0uR/FIIXF45k8Bo8admdrRzbounVUlP4GXn3HAz+xvwipkd5ZzL8bowib/yPBK2Gjgkz/uGwW0h+5hZCoGh441xqa78iua8YGbnAPcAFzrndsaptvKqqHNSDTgK+I+Z/QacDMzQ5PyYiubvySpghnPO75xbDiwlEMokdqI5L32BqQDOuS+BSgSeXyjeiernTiyU5xA2D2huZk3NrAKBCZIzCvSZAfQOvr4E+MhpYbVYK/K8mFlr4HkCAUzzXGIv4jlxzmU559KdrJX9FQAABDtJREFUc02cc00IzNO70Dk335tyy4Vo/v16i8AoGGaWTuDy5K/xLLIciua8rATOBjCzVgRC2Ia4VikFzQCuDN4leTKQ5ZxbE48Dl9vLkc653WZ2A/ABkAy86JxbaGZDgfnOuRnABAJDxcsITOrr4V3F5UOU5+WfQFVgWvA+iZXOuQs9KzrBRXlOJI6iPCcfAB3MbBGwB7jNOaeR/BiK8rzcCow3s8EEJun30X/uY8vMXifwH5L04Fy8BwAfgHNuLIG5eecBy4Bs4Kq41aZzLyIiIhJ/5flypIiIiIhnFMJEREREPKAQJiIiIuIBhTARERERDyiEiYiIiHhAIUxEygQz22Nm35nZ92b2XzM7Jbj9YDN7I8xn/hNqUVgz62NmzwVfX2dmV5ZSjf8xsyXBGj83s5Zh+r0Q6iHnIiJ5ldt1wkSkzPnr/9u7exC7ijAO489jTLEJiCgp0khEQQ0xKIIa1A2IX4gfhWhhClFEMJAqdjYRC7EQ1EILQVEQVDCphUjCgsRdjEnWmFWbWCkYMAp+RGF5Lc5cuIarbDYbzt3L/9ecwzBnZthieXln7rxVdQOAei/wErC9qn6guyx5Wdo9QCtpR1V9oT5Dd2fdv+6oU9dU1dMrPGdETKBkwiJiHF0CnAZQN6nH2/uU+oG6oO4DpgYfqE+q36lzwG1D7XvU59r7QfVlda71vaO1r1M/Uk+o+9TZJZRdmgGubt//pr6iHgO2DWfo1PtaZu+Y+mlrW6++3dZxRH14Zf5sEbGaJBMWEeNiSj1KV8ZlI3DniD7PAn9U1XXqVuBLAHUj8AJwE/ArcAA48h/zXFxVN6v3092cfRewEzhdVZvVLcDRJaz3QeCr9r4emK2q3W09tOcG4C1guqpOqpe1/s/TlUF7Sr0UmFP3V9XvS5g3IiZEgrCIGBfD25HbgPdaQDRsGngdoKrm1fnWfgtwsKpOte8/pKuVOMre9jwMbGrvtwOvtXGPD407yvvqn8D3wK7Wtgh8PKLvrcBMK6BNVf3c2u8BHhpk6OgCzyuAhf+ZNyImTIKwiBg7VXWoFZ3ecAGG/6s9F1ne/8AdI4qTn6mqxXMYQ+CRqvp2GfNHxITImbCIGDvqtXQFkM8uOD0DPN76bAG2tvZZYLt6uboWePQcp/wMeKyNuxm4fplLP9vnwLR6ZRt7sB35CbDLtm+p3rhC80XEKpJMWESMi8GZMOgyRU9U1eLgfFXzJvCOukC3dXcYoKp+VPcAh4BfWNqZrmFvAO+qJ4BvgK/pzpadl6o61X5FuVe9CPgJuBt4EXgVmG/tJ4EHzne+iFhdrKq+1xAR0St1DbC2qs6oVwH7gWuq6u+elxYREyyZsIgIWAccaFuZAjsTgEXEhZZMWEREREQPcjA/IiIiogcJwiIiIiJ6kCAsIiIiogcJwiIiIiJ6kCAsIiIiogcJwiIiIiJ68A+PlwKfZ2ouCwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7IKWbTkRFFP"
      },
      "source": [
        "K-Means Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LIK3KE92REKC",
        "outputId": "36fd894a-9d32-423c-bb2b-ac52d0e2f22e"
      },
      "source": [
        "# APPLYING AND VISUALISING DATA FOR KMEANS CLUSTERING ALGORITHM FOR VARIOUS VALUES OF \"K\"\n",
        "for i in optimum_k:\n",
        "\n",
        "  pyplot.figure(figsize =(10, 6))\n",
        "  model = KMeans(n_clusters=i)\n",
        "  y_predicted = model.fit_predict(selected_df[selected_summarised_feature_columns])\n",
        "  clusters = unique(y_predicted)\n",
        "\n",
        "  for cluster in clusters:\n",
        "    row_ix = where(y_predicted == cluster)\n",
        "    pyplot.scatter(selected_df[selected_summarised_feature_columns[0]], selected_df[selected_summarised_feature_columns[1]],c=model.labels_.astype(float))\n",
        "  pyplot.title('KMeans Clustering [K = '+ str(i) +' ]')\n",
        "  pyplot.xlabel('Bidding Price')\n",
        "  pyplot.ylabel('Profit Margin')\n",
        "  pyplot.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FMBEREREPKISJiIiIeEAhTERERMQDCmEiIiIiHlAIExEREfFAQkOYmfU3s7lmNt/MRkXpM9DMfjGzOWb2UiLrEREREaku0hN1YDNLAx4D+gHLgB/MbKpz7pewPt2A64H/c85tMLOWiapHREREpDpJ5EhYL2C+c26Bcy4fmAQMKNXnfOAx59wGAOfcmgTWIyIiIlJtlDsSZmbdgWuATuH9nXNHlPPSdsDSsO1lwJ9L9ekeOse/gTTgVufcBxFqGA4MB+jYsWN5JYuIiIhUe/FcjnwVeAJ4CtiRgPN3Aw4H2gNfmtm+zrmN4Z2cc08CTwJkZ2e7Kq5BREREJOniCWEFzrmxlTj2cqBD2Hb70L5wy4DvnHMBYKGZzSMYyn6oxPlEREREaox45oS9bWYjzKyNmTUr+ojjdT8A3cysi5llAIOAqaX6vElwFAwzyyJ4eXJB/OWLiIiI1EzxjIQNCf15Tdg+B+wW60XOuQIzuwT4kOB8r2ecc3PM7HZghnNuaqjtKDP7heClzmucc+sq+kWIiIiI1DTmXM2aYpWdne1mzJjhdRkiIiIi5TKzmc657EhtUUfCzOwI59xnZnZKpHbn3OtVVaCIiIhIbRPrcmRv4DPghAhtDlAIExEREamkqCHMOXdL6M9zk1eOiIiISO0Qz2KtIyPszgVmOud+rPqSRERERFJfPEtUZAMXElwBvx1wAdAfeMrMrk1gbSIiIiIpK54lKtoDBzjnNgOY2S3Au8BhwEzg3sSVJyIiIpKa4hkJawlsD9sOAK2cc1tL7RcRERGROMUzEvYi8J2ZvRXaPgF4yczqA78krDIRERGRFBYzhJmZAc8B7wP/F9p9oXOuaLXUMxNXmoiIiEjqihnCnHPOzN5zzu0LaJl6ERERkSoSz5yw/5jZQQmvRERERKQWiWdO2J+BM81sMbAFMIKDZPsltDIRERGRFBZPCDs64VWIiIiI1DLlhjDn3GIAM2sJ1E14RSIiIiK1QLlzwszsRDP7DVgIfAEsIni3pIiIiIhUUjwT8+8A/gLMc851AfoC3ya0KhEREZEUF08ICzjn1gE+M/M556YRfJ6kiIiIiFRSPBPzN5pZA+BL4EUzW0PwLkkRERERqaR4RsIGAFuBK4EPgN8JPrpIRERERCopnrsjw0e9JiSwFhEREZFaI2oIM7NNgAvfFdouWqy1UYJrExEREUlZsUbCPgVaA68Dk5xzS5JTkoiIiEjqizonzDl3EsHV8tcCT5nZF2Y2wsyaJa06ERERkRQVc2K+cy7XOfcscAwwDrgdGJqEukRERERSWsyJ+Wb2V2AwcCgwHTjZOfdVMgoTERERSWWxJuYvAjYCk4DhQEFo/wEAzrn/JKE+ERERkZQUayRsEcG7IY8GjiJ4V2QRBxyRuLJEREREUlvUEOacOzyJdYiIiIjUKvGsmC8iIiIiVUwhTERERMQDCmEiIiIiHig3hJnZp/HsExEREZH4xVqioi6QCWSZWVN23h3ZCGiXhNpEREREUlasJSouAK4A2gLha4L9ATyayKJEREREUl2sJSrGAGPM7FLn3CNJrElEREQk5cW6HHmEc+4zYLmZnVK63Tn3ekIrExEREUlhsS5HHgZ8BpwQoc0BCmEiIiIilRQrhG0I/TneOTc9GcWIiIiI1Baxlqg4N/Tnw8koRERERKQ2iTUS9quZ/Qa0NbOfw/Yb4Jxz+yW2NBEREZHUFevuyMFm1hr4EDgxeSWJiIiIpL5YI2E451YB+5tZBtA9tHuucy6Q8MpEREREUljMEAZgZr2BicAigpciO5jZEOfclwmuTURERCRllRvCgAeBo5xzcwHMrDvwMnBgIgsTERERSWXlPsAb8BcFMADn3DzAn7iSRERERFJfPCNhM83saeCF0PaZwIzElSQiIiKS+uIJYRcCFwOXhba/Ah5PWEUiIiIitUDMEGZmacBPzrk9Cc4NExEREZEqEHNOmHNuBzDXzDomqR4RERGRWiGey5FNgTlm9j2wpWinc04LuIqIiIhUUjwh7KbKHtzM+gNjgDTgaefcPVH6nQq8BhzknNOkfxEREUl5UUOYmdUlOCm/K/BfYLxzriDeA4fmkz0G9AOWAT+Y2VTn3C+l+jUELge+q3j5Ve+MKZP5bvlSAHq2bsvLJ5+G368VOURERKRqxZoTNgHIJhjAjgEeqOCxewHznXMLnHP5wCRgQIR+dwD/ArZV8PhVrtvDD/Lt8qU4wAH/WbWCvcY+7HVZIiIikoJihbC9nXNnOefGAacBh1bw2O2ApWHby0L7ipnZAUAH59y7sQ5kZsPNbIaZzVi7dm0Fy4jPDZ9+xA5cmf2FwMXvvZWQc4qIiEjtFSuEFT+kuyKXIeNlZj6Cy15cVV5f59yTzrls51x2ixYtqroUAKbO/TVq22cLFybknCIiIlJ7xZqYv7+Z/RH63IB6oW0DnHOuUTnHXg50CNtuH9pXpCHQA/jczABaA1PN7EQvJuenp6VBQeSsmRasT0RERKTKRB0Jc86lOecahT4aOufSwz4vL4AB/AB0M7MuZpYBDAKmhh0/1zmX5Zzr7JzrDHwLeBLAAG74v95R2y7tdXASKxEREZHaIJ4HeFdK6BLmJcCHwK/AZOfcHDO73cyq3RpjA3vsS8eGZbNlm/r1uSC7lwcViYiISCoz58pORq/OsrOz3YwZiRss+2bpEq795ANwcEefIzm8y24JO5eIiIikNjOb6ZzLjtQWz2KttcrBHTry1bnDvS5DREREUlzCLkeKiIiISHQKYSIiIiIeUAgTERER8YBCmIiIiIgHFMJEREREPKAQJiIiIuIBhTARERERDyiEiYiIiHhAIUxERETEAwphIiIiIh5QCBMRERHxgEKYiIiIiAcUwkREREQ8oBAmIiIi4gGFMBEREREPKISJiIiIeEAhTERERMQDCmEiIiIiHlAIExEREfGAQpiIiIiIBxTCRERERDygECYiIiLiAYUwEREREQ8ohImIiIh4QCFMRERExAMKYSIiIiIeUAgTERER8YBCmIiIiIgHFMJEREREPKAQJiIiIuIBhTARERERDyiEiYiIiHhAIUxERETEAwphIiIiIh5QCBMRERHxgEKYiIiIiAcUwkREREQ8oBAmIiIi4gGFMBEREREPKISJiIiIeEAhTERERMQDCmEiIiIiHlAIExEREfGAQpiIiIiIBxTCIsjNyeWcrhdzVPrpHFN3MBNuneR1SSIiIpJi0r0uoLo5s/NFrFmSU7xdkF/AC7dP4asp3/H0f0d7WJmIiIikEo2EhTmh4VklAli4xXOWkbMycpuIiIhIRSmEhSycvZhtW7bH7HP/uU8kqRoRERFJdQphIa8+8Ha5fRo0yUxCJSIiIlIbJDSEmVl/M5trZvPNbFSE9pFm9ouZ/Wxmn5pZp0TWE8v+h+9Tbp/rnr80CZWIiIhIbZCwEGZmacBjwDHA3sBgM9u7VLdZQLZzbj/gNeDeRNVTnqOH9InZ3m9Ib/x+f5KqERERkVSXyLsjewHznXMLAMxsEjAA+KWog3NuWlj/b4GzElhPuZq2bcKGFRvL7H/yl/vpsqdng3QiIiKSghJ5ObIdsDRse1loXzTDgPcTWE9Mc3/4LWIAA7jzVC1NISIiIlWrWqwTZmZnAdlA7yjtw4HhAB07dkxIDbeccl/UtiW/Lk/IOUVERKT2SuRI2HKgQ9h2+9C+EszsSOAfwInOuYhrRDjnnnTOZTvnslu0aJGQYtevjDwKJiIiIpIIiQxhPwDdzKyLmWUAg4Cp4R3MrCcwjmAAW5PAWsrlz0jz8vQiIiJSyyQshDnnCoBLgA+BX4HJzrk5Zna7mZ0Y6nYf0AB41cx+NLOpUQ6XcIed9teobb2O+1MSKxEREZHawJxzXtdQIdnZ2W7GjBkJOfZR6QNxhaW+HwYf73g1IecTERGR1GZmM51z2ZHatGJ+mHe3vkjXA7oUb3fu0YH3tr0Use8/z3iIfmmn0893Oic0OotZ02Ynq0wRERFJARoJq4Qzu1zEmsVlH+Z9+9vXcfBxEcOuiIiI1EKxRsKqxRIV1U1eXh4DGgwpsa9p68ZMXvE0OStzIgYwgNtPfYD3t72cjBJFRESkhlMIi6B0AAPYsCqXs3YbwV5/6R71dQX5BYksS0RERFKI5oSV8tmk6VHbVi9aS/vurZNYjYiIiKQqhbBSXrrr9ZjtQ24dFLWtzW4tq7ocERERSVEKYaUcPbRPuX0uGl32cmVauo/xvz6UiJJEREQkBSmElXL6yBOittVvUg+AUy4/nve2v8Qhp/Si0z7tufrZEXyQ/wp+v7/C5wsEArz39Mf8/NWvla5ZREREah4tURHBkv8tYdjeV5XYl56RXqV3PuaszGHYXiPJ+2Nrif13vXc9B/U/oMrOIyIiIt6JtUSFQlgMK5asZdZHs+hzxiFkZmZW2XGH/+lqFv68OGr7x4VaoV9ERCQVaMX8SmrbsQXHnXdUlQawF/85JWYAAxgz4skqO5+IiIhUTwphcchZmcMHz35GzsrIi7RWxIt3vlZunzlfz9vl84iIiEj1psVaYwgEApzcZCjbt+YX78uol8GbG5+r1CR8gILAjnL7HHN+30odW0RERGoOhbAwq5as5arDbiZnxXoaNK7P9q3bSwQwgPyt+ZzaYhhTN06s1Dlad2nJyt9XR203H5x88TGVOraIiIjUHAphIdPf/I7bTrm/ePuPdZui9t0auqNx4ezF/PD+LPbvuy+3nnQvOcvW40v3cdLF/blo9LkRX/vIN//ktJbnRWyr3ziTl5aPrVDd439/ji/XfQWAYZzcbgAD2kVfZqM6m7jwBWZs/A8tMrK4qvsVZPqrbi6eiIhIdaO7I0OOSj8dVxh/f1+6j8KC6C9o1aUFL/z+eMS2MRc/yTtjPy7eTs9IY+qm5yt8ifOOOXcxf8vvZfb3adGboV3OqdCxvJQbyOWyWSPL7D+jwyCObtPPg4pERESqhu6OjENFAhgQM4ABrF64lhW/ryyzf+yVz5YIYAAF+TsY0vXSihUAEQMYwLS1X1T4WF665sfrI+5/aekkNmzfmORqREREkkOXIxNo9PBx3PfprSX2vT7mvYh91y5dR27uFho3rh/XsRdsWrir5SXUdT/dwKrtO+e+ta3Thrv3vxOAcfOfYsGWhfRqms2pHU9hu9se9ThX/HQVbeu25e797kh4zSIiIsmkEBZS3uXFymjcolGJ7UAgELP/R+M/i/nYpHDN6zardF2JNvI/17CuYH2JfSu2r+TymVexccfOka2pq95l6qp3yz3eim0reH3pG5zS4eQqr1VERMQruhwZ8vC/76zyY173fMlLjOf3uCpKz6B9/tq9+POclTkMbHM+/Xyn0893OiMOuq5E38b+xjGP9f6yDytYbXTTVn3BZbNGcvWP18U1Alc6gBUJD2AV9fbKyCOIIiIiNZVCWMgeB3Xjve0v0bZb6yo75sSbJ5fYXv5b2Tli4ebOXABAbk4ug9tdxIbVO0PLbzMXcEzdQcXbeYG8mMeatGIyQ74fxpDvh5GTV/lFZof/MILnlkwkN5DL2vwcbvv1Tu6Yc1fU/uWN9lVWIVU7SlmTPfnSV5w4bCxDr57AhtzYPwc1xYbcvIT97IiIVFe6HBlm3cqNBLZV3S+C98d/xrC7z4y7/0t3vMbJFx/DiINGRWwvyN/Bqw++zekjT+DyH2OPqoW7avZ1TOg1Pu7+RR6bNzbifK35W35n0ZbFdK7fiRv/ewtLty4DINOXyS1731jh8wDcsteN3Pnr3ewg8mK2zf3NK3VcCAbWG2bfxIZAMNTWsTrcus9NtM1sU6bvC4teZHbur+zfZF8Gd/pbpc+ZCIFAgCMGjaHofub1G7dwwt8fZ9DxB3LJuX08ra2ybrr/LaZ981vxtgHj7z+b7l1aeVeUiEiSaImKkN9+XMiIA66t0mO269aG5+Y+XLw9oMk55IXWGIsks1Fd8v7YFvOY9erXIS0jHRtUQL0+6aR3TIurlpPbnMRJHeKbb5YXyOOmObeRkx99BK1lRgty8tdV2QhVUUgc8v2wiO0P7f8ATes0qdSxox3z4Z4PFl/WXbRlMbfMub1Mn7t73BkxrHnhlPPHsWZ95PXrpk26vNJPcfDKhFe/5qg0IqsAACAASURBVKlJX0dsmz7l6iRXIyKSGFqiIg4jD72pyo9513sll1545Lu7Y/YvL4ABbN2ync0btrDp6e2sOXsLf4wr/zUA09Z+Hle/QCDARbMujRnAADbm51ZZADul3UnFnz/d84kSo151rA539rit0gHsuYXRn2xw55yd78etEQIYwD9ml/y5mL722+LLvEO+H8aw7y/Ypcu9FREtgAE8OvHLpNRQlcZPjhzAAB5+9rMkViIi4g1djgzZtiX6MgmVccQZh9J295IjKB33aMfLy8cyuN1FZfqnpaexo6D850oWC1013fJyPnUP8ZOxT+wRsf0b7xfXYcf8/mhc/fLJL79TOdJJ5/zd/s5fsv5cvM/v9/Ngz3t3+dhFvl8ffdR0Tf5aAN5Z8g7RxoMLw1oWbFrIUwufKtFeQEGlL/dWpfUbNnt6/soojJHhf/hxUdLqEBHxikbCqpilG29tnsD1L1wWsT2rTRYfF77KVeMvommrJrTo0JwHvridyl4Wdtsh76PyA9Hfdx8S1/H+t2luuX387Pplr3/1uIvxvcaVCGAAs9b/yNzc8muIV4O06OuupZPOP3+5m1dXvRHXse6b+2DUtnHzn4raVlXqZEQP2iPPr3lPFkjzWdS2Q//cLYmViIh4QyNhVcwVOE5ufC5v5D5LZmb0Zx/2P/cI+p97RPF24Y5KXtpzUJgbO8Bds/uVcR/Ob+kEXOybE/q36sfbq3dtyYjrZt9Ag7QGPHbgGACenD+ef68veXlqcPuBALy14h0ATmp3YszHGG3YvpEXFr9MVp1mxZPqR3a/gutm3xCx/8FN/8JXG6bHXXNeYfQ7EWf/8Uvcx6msR2//G+ePeqnM/t06ZdG0cc17zuZl5/Zh9PjIlx2Hn3FokqupXQoDS2DDheC2QsMr8WWe6HVJIrWSRsISoHBHIQMaRB55CgQCLJm7vMTt+I9cumuXsvbcvgcTeo1nQq/xnN7uFDKtHnWtLme0H8yEXuPp0bxH3Mc6s9NZ5fb5cPUnu1Jusc07NvPPX+5mxvqZZQIYwMvLJvPyssnkFeaRV5jHS0snMWJG5BHG6366gSt+uooZG2fwweqPGPL9MF5f+gatM1txWPO/RnxNPAGsT4vexZ+nx/g/S8s6Lco91q7aq1tbJj06jOZNGwDBkaRzTv0zEx8cmvBzJ8Kpxx7AKf3/VGJfWprx6hPne1RR7VC47u+w7kgonA9uOfxxNYWrenpdlkjSFAbmUbj6zxSu6k7hqj0pzPXuiSy6OzKkn+/0Kj+mv24agW0753mlZ6RRkL8jrN3PazlPc88ZD/PN1JmVOof5jHe3vhjzzri8QB5vLp/Kl2u/YrvLp6m/KTfscS1ZmVkR+986+w4W5i2Ked5MX2bMkaGKqGN1Yj66qLRT2p3EgHY77/R8buHEqM/LHNvzEVZtW81tv1Z8Md6zOg6mX+sji7enrfqC55ZEnug/tucjZPpr3miUVI2Va3O5+o4pbMzNo/fB3bj2wqO9LimiwsCSYACLpM5R+JrGNydUpKYq3DYDNp5RtsHXFV/LxCwKHuvuSIWwkESEsHik10nn2bkPc3bnERV+7aGn/YWbJ8deLyzSI4SKXNp1BNnNDozYdtEPl5Dnoi+nMaHXeEbOupZ1gXXF+1r5W7I6sCaOyksyDBd1anxZdX11GJf9ePH20O/Pi/r6PRsEn0Lwv83zKlxXkVF7XM1ejfcCYMzcR/lP7qwS7UM7nkOf1sERs1nrf+Q/G36kb+s+dK7fqdLnrKnOvOwZFi/f+fPWrnUTXnnsPA8rSrwHn/qE1z/4scz+j56/nMzM6rVsSOGaY4MjYBEZvtZVNx9TpDoqXNUDot1Y1vxrfP7IgxO7QiGsHF4FsF3RsmMWLy4aG7PPHXPuYv6W32P2iXZX38iZ17Jux7qIbZFetypvddS5V7H48NHI35CNgdy4X5NOOuN7jSvejrYOGEBWRhZ7N9yLL9d9VeHawj3d84kSo43f5nxHY3+j4nAW6ev3k87Yno/WuPW7KmvwJeNZunJDmf2tshowZdyFHlQUvPx/zV1vMG/BGjq0bcbom06PGIx++mUZ7037L0f13ocDe3Ss0PH7DBoTsa1po3q8/ezFla49EQpXHw5uRdR2X+vK/2dFpCYoXNU9emOdE/A1faDKzxkrhGlifg21fvVGxlz0JOtWrqdl5xZccN85+P1+AoEAqwKrae5vVm4Ai6V360N5ffmbEdsyLKPMvkgLncZjWOch7NZgd66fHf9K+wUUMOT7YdS1OozsfnnMS6Pr8tcxuM3AXQ5h98y7j5v22RmySt/VGSmABihg5M/X8MiBD5Vpm7dwNaPuepPNW7fzf9m7ccsVx+9SfdVBpAAGsDrHm+UzZs9dzoU3vFy8PWfeCo46ewx3XHUCff66BwB5eQH6DxlTvFzGu5/NwQzemTCCxvWjX15+/vVvGfdi7DmFG2IszFxZNz8wlc+//Q2co2ePDoy5tYJPdWg4Ev6IshCur/Mu1ydSo6Ulf2FujYSF1MTRMAD/vmk0vqYu/q7Beywsxm3/kXTO7ETfrCMIEOCw5ofg9/tZkbeSL3Om8/6qDyK+5qQ2J3JyhwEl9sUajYqmHvXIJz/qo4ri1TqjNavyV0Vtz/Rlclyb43h1+auVPkc9Xz2eyI48X2ZF3sqYIbL0qGHpR/UUSdblq2UrN3D2Fc8SKAgmDzO49Yrj6HvIXnG9PndLHpkZ/uIRvg25eZx03lh2FEb/t8SLFf0PPe1+ov3zVrQif99Bo9keKPvz5/f7mDZpZMTXfjr9V24Z/W5cNVTlyv99B49me37JWs3gq9cqdo7gJPwtZRua/4Av9AQJkVRVuPowcJF/XyRqJFgjYSkqvYuP5g9n4qtXseAVblHeYsYveRaAiUteiOs17636oEwIq4ytVM1IQawABsGlJY5v158fcn5g0fZFEfs0oAGbiT5is7VwKzf991au3/PaMhPwF2xZHHeteXmBiAEM4MzLx/PGU4m/bDfokpKh0Dm4ZfS7dOmQxW6dot/lecfD7/LhF7+W2PfQLadw5e2vRw07RfoMGkOnds148eG/l9i/cm0uF4x6ifUbg6Gg91+68s9rTop0iAqLVVPuljwa18+MGMAAAoHoS8bc+Wjk/5yUFr6u25ff/caLb/7Anl1bceWwvnG9Ptwzk/9dJoBB8Gscdc8b3DPq5LiP5Ws9i8L1IyD/U8CBrxM0fa3GBbDCQB6sPxJc0RMrGkDzd/D523pal1RzzT6FdftC6Se+ZHozdUBLVIT40mret6LBkAwsA9wOR+G25I1o5ruykxobpzVK2vkrIxAIRA1ghsUMYEWWbF3KRbMu5bc/Sk5sPqBJfE8jePCpTzjq7MjzhwDWrk/8Zbuzr3g2atsVt0cfKZz8zswyAQzgitvKD2BFFi9fz7Brny/eXrZyA6df+FRxAAP44tv5HDPkkfgOuAsKC8rvE76MTMn98Y3cjr3rDAKBAIf/7UFuuPct5sxbwZT3ZnHIqfcz7euKTYCf/E70u6e/+c+CCh0LwNfscXyt5+JrPQ9fy49rXAADYN2fwgIYwGZYd3gwnIlE4fP78bX+H9S/CqwtpB8IzX/E1+hyT+rRSFhIpRdL9ZDbDmsGbyYty0fzx5K7PML5P1xE/fRM/tR4P6blJO65hT6M3lmH4cP4NOfzSh9n/KLo4aMid2YC3PW/f/Fsr50r5Gf6M/Hhi/gszaI1yq69ewpfz1hYofNUpbc+/on7nvg4Zp8NG6P/8npswudVUsfc31dz8wNTmfb1vKjf9U2btzNz9pIKTZCvqHgWty26fBoIBLjkpsnM+W1lXMdu07IRY+/4G1lZjTn1wnEUFJT9ubjpgbeZHpqXFo80X/SnJbhCxyGn3l+8neFPY8q4C2rkAr7xKtwQYwHqjQOhxTvJK8ZDhYEAbOgPhUtDezKgyXP46ka88iVhfA0vgIYXeF2GRsJqstx7t7FjiaPhhXUwq/wlycrId/lsCGxMaACD4LMbp+V8wYHNDtil4yzevKSKKqJM2JqbOzfqw8z/KNjMM5P/HVcAa9igTvHnuVvyeOKFL/l0etnRpyKBQIDpM+azLMqE+KKRnEAgUG4ACz9vaQsWr40536uiPosRwIqMeabkSvo5Obkcf+5jHHLq/Rxy6v2cOOxx3v3sp5jHuOzcPhH3Dzr+QJ6Z/O8SwaW0ffdsV/z5EYPHxB3Apk+5mlfHDicrKziytHpt9Ieuvz/tv8Wf5+UFePCpT3jypcg3kIw874iI+wFKvzX5gR2c8PfHI3dOFdsjrwsIwI7K35BU46zbLyyAAeTDxjMoDCT+CR5SNTQSVpOFroqkd0z9LH3vvOjPbYzHikB8v0Qr48mFz0Tc7xxM/24hq9+K/RioIs/cfw4Agy5+imWrdi7Zccvod7nr2gEcFvY8xaEjn2P+4pxSrz+bLu2bcdzQx8nbtvOc9erGNyHeAced8zjZ+3XkoVuCj4z628VPsXxV/MuHVJWFS3IIBAL4/X7y8gKcdEHJZ3Ou35jH3Y99zN2PfcwhB+0ecU7UK2//UGZf25aNaNOqcdTHJQH0Obgbd1wdnPM4evyncV9uve8fp5TYvvOR2As/fvLv/3HkIXtyxe1T+OmXZcX7J075joHHH8BlYY8163vIXjzx4lesXPNHfMUAT7zwJReedVjc/WsUawAu2uX7sndvp4LCPx6EvKcI/sOfCXVOhmg3Na0bAq3L/vxL9ZP6v71rgYIlNe9Sak1mlBx13OGizxGyjPLfm906ZfHR85fTpkVjbn5gaokAVuSGe98q/nzk7ZPLBDCAv1/9PEcMHlMigAFs3RZfCCwy4+cl/PDTIia8+rUnAQyCAbbfmcG5YRfe+GLMvtN/+J23Pi45Kjbh1a8jLo2xYs0fPBQjgN1x1QnFAQzgjQiLsIa75/qTmPjgEKZPuZqDD9iteP+ylRv44PPYoxHfzVpMn0FjSgSwIpPf+Q85OSW/96+OHc49159E86b1adqoHqNGHBXz+NO+SeE1v5o9Eb2t4U3JqyNJCnPOgLwn2Bm68mB7rL8X3vy9lYpTCCuS3Kt5VWrTuO1JnZhf2zkcLy7auf7UGR0GRexnBvU6biG9SZTVmYH0NB+P3DGQz775lZVrc/ns6+i/OM+7Ljip/fufol9araoVZ2576F0mvPZd1Ryskgp2FPLSm9+xaEnZwFnamFLBasKU6LXH+hbd9MDbJbYLy7kMe0h214h3lF5155SYr4vH5beXPcYh2V156+mLePvZizm+736kxViSZq/dW+1yDclUuOGK0LP8ij72oTAQ+b33+feGOseVbUg7AF/90xJcaXIVBgJQUNFlmar3r/bCDf+gcO1ACvOmeV2K56r3O5VEvrSam8LyZ+5gww1bKcwrpKat+1Zdndvp7JjtH635hLt/+RcAvbKiT4I1oNH+kedsAWRkpHHcOY9zz+MfcfqFT0XtB/C/+avLjI4kyrbtAQp27Nr6bVXh8ee/Ij09+qT0Ivml7lgsvV0RF4zaOcLQqGHdqP1iBaBNm7dV+vxFNuSWf5ff3wceHLXt1pEnRG2rbgr/uBe2l758G4B1/xf1Nb6mo6H5nOBluTr9g+uctZiU2EK9sL0SX1OdU6u+jipQuOW14Ir121+FHT/CHxeEHiNUeymEFau5IQyAAgdY0ifoVyctM6KvcVVRjf1NmNBrPK0yoo8mhD+PMtNK3olWsCWNNR+0ZtHYbmz8vnmZ1/7jkqPJalafvK0Vu1QYaXQkEXrt35muXVpW6rUN6mdQlT+Gf+7ZOa5+0ZaUKM2fHvufvfBJ+A/dEn0R5xsuPSZqW58K3PkYzV+zdyu3z5DT/xrx+1N6flq1lxf58WngKNzyWtSX+fx+fE3/ha/pwzVzmY14+NpX8AXt8DX9Z0JK2WWbIj3aLp/CNf2TXkp1oRAWUhjhNvIaIx3q9k7Hov+nvcYwjLE9H2G/RvvStf7u3N3jTvq1jH5nWLi/NOvFhF7jOb/zufh2MVQ3rdOUDds3sjp/dcx+s3J+Yuj355Hndo5aFG73sWxCFzb9twmFeekUbi15/8vxfXtwTJ99yVkfYdXycqzbmJxHAN113Uk8etvgSr32g4mXVXgV91jq+NPZrWP5D9Ud90LsxwgV6di+OX5/+aNrAN27tOLo3mWfJNCzRweOPmzvqK+75oJ+cR0/lhsvPTaufg/ceBrTp1zNff84hRfHDC0zP61miDGCvzXy49NqC19m5Lt8g+oHV3mvNxQyjoTmX+FrXT0v8RVuinHHbmHF17pLFQphKcDXEArzqfGDeQDndRxKpj+Tq/a8gpv2uYG2mW04q/OZcb325z9mA3BIy0N4ttfTUR9OHo/O9Tvx/OKXyu336ILHy6wz9secxhRuS4PCsm/Inru3YtSIyv+vb9892jHmtoGVfn08Xn3ifAAyM/28O3EELZo1iPu1B+7bofjz7P2qZp2vcwcezMTRQ4vriiY9LFjFqnnUhf14JMb3MHypEICbLjuOaZMuZ8BR+9P/8L356PnLeeS28p/Z+NHzl5eoI7Oun2hXMMNHDhs2qMO7E0eUe/zSDj5gNzq1Lz+sVk8x/vGqe3TyyqiuGkVZTqX55wD4Gt8QXIDXX43nARak8I0iu0BLVISYD1wNHQwr3AD+Pb3N00/3fKLMswFvn/1Pfs+r2P9w9mkaeX7AqD2u5p650dd1Avhr87LzY3ar15kFWxdVqIYrul4KQBN/+U8BKKDs0uvbltbDBSK/H2srMfoV7q5rT8Tv9/P2MyMY8Y+XWblmIw3q1+WeUSdx2S2T454L5bOy60u1btGQ8Q+cXeLB1Y3rZ5Z4lFKstbVKLxXx0C0DOey0+8ucpyIa1M9gwZJ1DL36+XJXqh/2t53v/zP3nxNxraysJvXZq1vwsTaNG9Yld1PZuVtP/LPsCKDf76/w6FZmpr/MY6gCgQAnnvcEmzZvL953Yr99ufbCWh406p4B2yLf7edrEHt+Zm3gyzyRQv8xsOliKJgPdQd4tsJ7pTW4NsK8vyK1N4roAd4hR9f5G4UxnhdX3TW+oS71T0zO+ji9mhzEsq3L2LJjC31aHs7J7Xfe0p8XemRIpj+TQCDAebPifxZiGmk80+vJqO25gVyu/+lGthRGnrAcaeQrL5DHRbMujXneY1r3Z8b6GXTK7Myl3S8q3l9e/TfueT13/u/uMvvXf5XFhu+aw46yQaxnjw7FoyhDrnyO3+O48w+Cq6BPHD2U9m2axuz36LPTePPjn/CZj3699+T9abPJzy/5c10nI41PX76SQCBAbm5e8cKi8Yj0sO4D9+3AmFujjwxFC26tWzRkr66toz5Lc8/dW3PM4XvHXNOryHFH7MP1F5eco5W7JY8R109iyfL1+HzGSf3/VOa5jTfd/xaff/MbDmjWJJPH7xxc7vdYEqNw3XkQKLX4c6NX8WXu701BUuUKV/0fsLZsQ6NH8GWm7n9EYj3AWyEspJ8v+gTc6qxRi4bc/+kt3LT6FnwNfFUyMX+Put2Yuy3yL0aIHHY+XTWtzAPAT293Oke37MvNv9zGiu3Byc5d6+/OTfvcwOUzr2Ljjo3FfdNJ55Geo8s8HDuSiQtf4NO1O+c9pJPOgz3vpXGUibnjfx/Pl+u+jthW1+oy7qDHop5r0pJXeX9V2Qc2F438Dfl+WJm2gk3pLHlyd1xB2RA2bdLlJUYMew98gB07Iv8d3HP31jx971lRa4vX05Om88rbM3AOTunfkxHn9N7lYwLFi6mWZ/I7M3n42ZLzVHw+49OXLsPv9zN6/KdMeW9WcdvRvffipsuCyw8ceur9UWcL1clIo1GDevzrhpPp3qUaX4aRuBSu+zsEIszra/oKvjo9k1+QJEThuuEQ+Dy0lREKYLHmvdV8CmFxqKkhrMh+l+3B2oErsPRdD2GtMlqyOn9N1PaxPR8pEZY2bN/IFT9dFbHv3T3upG1mmxL7pq+ZzotLJhEoLKBlRhYbCoLLLhzX5jiOb1d2vtTHqz7hw1Ufk1WnOZd1vYRV21bz+O9PsMMVcmanQWQ3O7Dcr2nNlrVcM2dUiX11rS6P/umhcoNEXiCP++Y+wMZALoe37M2Adjtv/X/gfw/x8x//LfOa7d92YNkXJecl/eOSozmmz75l+j4+8QtefXcmO3YUsufurfjnNSfSogKjUzVBIBDgpgfeYemKDQw84UAG9ItvdCPW5c+Lzj6MM0/qVVUlluvL737j6Un/plVWw+LLwlI1CgMBWLdPlFY/vtZzklqPSFVSCItDTQ9hAL7djVYTGxTfbpHI5Squ6HopPZv9CYBrfhzFmvwIQ8xAU39THuq58xfpqJ9uZOX26I8QCh+ZCgQCDJ91EYXlPGmwYVoDHj1wTMS2N5a9xZsrppbY97d2A+nX8ogq+yU6/vfn+HLdzmf+/aVpLy7qFnww7KfTfyWrWUP237uit5kLxA5h9/3jlKTdBdh38Gi255eck3bOqX9m+BmHJuX8qS74SJ7oq+D7WmtSt9RcsUKY7o4MOeDI/bwuYZf56hj4guEr0euFPTT/keLPNwaiLyC6KbDzWXcLNi2MGcAAtrltxavRj5pzY7kBDGDTjs28vPiVMvvn5s4tE8AAXlk+udxjVsSw3Ycyodf44o+iAAbBZ/4pgFVe6TsVwyUrgA279vkyAQyCz3iMd20yKYc19LoCEU8ohIUcceYhXpewywrmFhLhZr2Ea55RdjHSIk0zdk5yHrfg6biO99mazwHIyY9v0jrAJ6s/LbPvoXmPROgZdM+8++I+tnhnytgLIy78esdVyVsNfu7v0deKu+uxD5NWRyrzNYy1/Ej9pNUhkmwJDWFm1t/M5prZfDMbFaG9jpm9Emr/zsw6J7KeWH76PEXmHHhwdfnmvSOtghz0j72uD9uKrzhHxe9SjTRilue2Ru2/YmvsETmpHjIz/Xz12tVcOewIduuYRf/D92b6lKurZEX6qrBq7SavS0gdmedF3t/84+TWIZJECQthZpYGPAYcA+wNDDaz0ktMDwM2OOe6AqOBfyWqnvKcnsT/WSdKelcfeDBXONOfyaVdyy4ueX6X82lap0nx9nld/h7X8Q5qGrx0nl6BtWO6N+hati5f9Dst29ZrE7VNqp9Tjz2AiaOHxr2KfFWqWyf6z+GV58X3NAcpn6/RtdD8K/DtBtYI6hyLr/U8fP6augCtSPkSORLWC5jvnFvgnMsHJgEDSvUZAEwIff4a0Nc8evhhlx6d8Nep2Xc77VhTCPlU6CHejdJiz8WoQ+Q5Oed3PrfEdnazA0vMi5rQazyHtPhLiT7dGnWlhT/28x19+IrnVF29xxXllV/s6m4jy+y7otslUfuP6n5N3MeW2i3a8yMbN6yrpTGqmM/fCl/LD/C1moGv6UNelyOScIkMYe2ApWHby0L7IvZxzhUAuUCZCUZmNtzMZpjZjLVrI9+FVxXe2/oSvrSa++wftwHWnLCFwpySIawudflXj7uY0Gs8vbN6U8dXh/q+TC7abTiPHPgQQzuegy/Cj0KLjCye7PU4d/a4jcb+xvjw0dzfnAd6/ItDWlZuDt39Pe/hnI5nUddXF7/5ycpoRjrp+PDRq0k2z/Z6qrjvXo334rZ9bibT6gHB50r2zurNvo12rqrfObNTxNX6AfZovAentyv7IONrul+p5QUkbj32aMcz959N/Xo7f2YOOWh33n0uesgXEYlHwpaoMLPTgP7OufNC22cDf3bOXRLWZ3aoz7LQ9u+hPlFnZCdqiYpwVbVcRXb//Vg0exkOR0adDA7stx8jxpyL3+/noxencf85j1P629+ue2s2b8jjjBtP4ZRLjytzzJyVOXz/7ix6HdeTrDYaphcREanOYi1RkcgHNi0HOoRttw/ti9RnmZmlA42BdQmsKS7Hnd+Xd58qe7ddaQccuR9Dbh/Im498wM9fzGbdio2YwQH99ueeD26M+dqjzuzDUWf24Zt3Z/D6g+/Q49A9GXLroHLPmdUmi2PPq9gz7ERERKT6SeRIWDowD+hLMGz9AJzhnJsT1udiYF/n3IVmNgg4xTk3MNZxkzESBrBw9mJuOPYuNm/YQuOWjfEZtO3Wmm1btlM3sy7Xv3IljRvr1mkRERGJzpORMOdcgZldAnwIpAHPOOfmmNntwAzn3FRgPPC8mc0H1gPlDwUlSZcenXh5yTivyxAREZEUlcjLkTjn3gPeK7Xv5rDPtwE1/3lBIiIiIhWkFfNFREREPKAQJiIiIuIBhTARERERDyiEiYiIiHhAIUxERETEAwphIiIiIh5QCBMRERHxgEKYiIiIiAcUwkREREQ8kLBnRyaKma0FFif4NFlAToLPIRWn96X60XtSPel9qX70nlRPyXhfOjnnWkRqqHEhLBnMbEa0h22Kd/S+VD96T6onvS/Vj96T6snr90WXI0VEREQ8oBAmIiIi4gGFsMie9LoAiUjvS/Wj96R60vtS/eg9qZ48fV80J0xERETEAxoJExEREfFArQ5hZtbfzOaa2XwzGxWhvY6ZvRJq/87MOie/ytonjvdlpJn9YmY/m9mnZtbJizprk/Lek7B+p5qZMzPdBZZg8bwnZjYw9Hdljpm9lOwaa6M4/v3qaGbTzGxW6N+wY72oszYxs2fMbI2ZzY7Sbmb2cOg9+9nMDkhWbbU2hJlZGvAYcAywNzDYzPYu1W0YsME51xUYDfwruVXWPnG+L7OAbOfcfsBrwL3JrbJ2ifM9wcwaApcD3yW3wtonnvfEzLoB1wP/55zbB7gi6YXWMnH+XbkRmOyc6wkMAh5PbpW10nNA/xjtxwDdQh/DgbFJqAmoxSEM6AXMd84tcM7lA5OAAaX6DAAmhD5/DehrZpbEGmujct8X59w051xeaPNboH2Sa6xt4vm7AnAHwf+obEtmcbVUPO/J+cBjzrkNAM65NUmusTaK531xQKPQ542BFUmsr1ZyJeI6IwAABYZJREFUzn0JrI/RZQAw0QV9CzQxszbJqK02h7B2wNKw7WWhfRH7OOcKgFygeVKqq73ieV/CDQPeT2hFUu57Ehq+7+CcezeZhdVi8fw96Q50N7N/m9m3ZhZrJECqRjzvy63AWWa2DHgPuDQ5pUkMFf29U2XSk3ESkUQws7OAbKC317XUZmbmAx4EhnpcipSUTvDyyuEER4u/NLN9nXMbPa1KBgPPOeceMLODgefNrIdzrtDrwiT5avNI2HKgQ9h2+9C+iH3MLJ3g0PG6pFRXe8XzvmBmRwL/AE50zm1PUm21VXnvSUOgB/C5mS0C/gJM1eT8hIrn78kyYKpzLuCcWwjMIxjKJHHieV+GAZMBnHPfAHUJPr9QvBPX751EqM0h7Aegm5l1MbMMghMkp5bqMxUYEvr8NOAzp4XVEq3c98XMegLjCAYwzXNJvJjviXMu1zmX5Zzr7JzrTHCe3onOuRnelFsrxPPv15sER8EwsyyClycXJLPIWiie92UJ0BfAzPYiGML+v737CdGqisM4/n2aXIxGROXCFmEUWGJSBKX9GSHKQsoWUUSCaUSU5CKsVYusFhERVEQFllEQ/VdwJxgOA2Iz+GfGdCZDMFokJGRCqUXD0+KegbfhnXgdZ+a+zjyfzb0czj3nx1wYfu85555zfEqjjNG2AavLV5JLgJO2j01FxzN2OtL2P5KeAbYDHcBm24ckvQzssb0N+JBqqPgI1aK+R+qLeGZo8b28DlwEfFW+k/jZ9sragp7mWnwnMYVafCfbgeWSBoFh4HnbGcmfRC2+lw3AJknPUi3SX5Mf95NL0mdUP0guL2vxXgRmAdh+n2pt3grgCHAKWDtlseXdR0REREy9mTwdGREREVGbJGERERERNUgSFhEREVGDJGERERERNUgSFhEREVGDJGER0RYkDUvqlzQgaZ+kW0v5FZK+HuOZ7mabwkpaI+mdcv+UpNUTFGO3pMMlxl2SFoxR74Nmh5xHRDSasfuERUTbOW37BgBJ9wCvAsts/0K1WfK4lH2AJtIq23skPUm1Z91/9qiT1GH7iQnuMyKmoYyERUQ7uhg4ASBpvqSD5b5T0ueShiRtBTpHHpC0VtKPkvqA2xrKN0p6rtx3S3pNUl+pe0cpny3pS0mDkrZK6m3h2KUe4Jry/B+S3pA0ACxtHKGTdG8Z2RuQ9G0pmyNpc4ljv6QHJubPFhHnk4yERUS76JTUT3WMyzzgziZ1ngZO2b5O0mJgH4CkecBLwE3ASWAnsH+Mfi60fbOkFVQ7Z98FrANO2F4oaRHQ30K89wPfl/s5QK/tDSUeynUusAnosn1U0qWl/gtUx6A9LukSoE/SDtt/ttBvREwTScIiol00TkcuBT4pCVGjLuBtANsHJB0o5bcA3baPl+e/oDorsZkt5boXmF/ubwfeKu0ebGi3mU8lnQZ+AtaXsmHgmyZ1lwA95QBtbP9WypcDK0dG6KgSzyuBof/pNyKmmSRhEdF2bO8uh07PnYTm/yrXYcb3P3BVk8PJz9gePos2BDxo+/A4+o+IaSJrwiKi7Ui6luoA5NEHTvcAj5Y6i4DFpbwXWCbpMkmzgIfOsstdwMOl3YXA9eMMfbTvgC5JV5W2R6YjtwPrVeYtJd04Qf1FxHkkI2ER0S5G1oRBNVL0mO3hkfVVxXvAR5KGqKbu9gLYPiZpI7Ab+J3W1nQ1ehf4WNIg8ANwiGpt2Tmxfbx8RblF0gXAr8DdwCvAm8CBUn4UuO9c+4uI84ts1x1DREStJHUAs2yfkXQ1sANYYPvvmkOLiGksI2ERETAb2FmmMgWsSwIWEZMtI2ERERERNcjC/IiIiIgaJAmLiIiIqEGSsIiIiIgaJAmLiIiIqEGSsIiIiIgaJAmLiIiIqMG/pdKD6gS4IsgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SU_tNPNR3aRr",
        "outputId": "4da281d5-eda6-4ab6-d426-9e95774bdd33"
      },
      "source": [
        "# APPLYING AND VISUALISING DATA FOR KMEANS CLUSTERING ALGORITHM [OVER MULTIPLE FEATURES] FOR VARIOUS VALUES OF \"K\"\n",
        "cluster_list = []\n",
        "for i in optimum_k:\n",
        "\n",
        "  model = KMeans(n_clusters=i)\n",
        "  model.fit(selected_df[summarised_feature_columns])\n",
        "\n",
        "  clusters=pd.DataFrame(selected_df[summarised_feature_columns],columns=summarised_feature_columns)\n",
        "  clusters['label']=model.labels_\n",
        "  polar=clusters.groupby(\"label\").mean().reset_index()\n",
        "  polar=pd.melt(polar,id_vars=[\"label\"])\n",
        "  fig4 = px.line_polar(polar, r=\"value\", theta=\"variable\", color=\"label\", line_close=True, title= 'Line Polar for K = '+ str(i),height=400,width=400)\n",
        "  fig4.show()\n",
        "  cluster_list.append([clusters,i])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "Explanation : \n",
        "\n",
        "In order to understand clustering of data in higher dimension line polar method are used for data visualisation"
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      "source": [
        "# VISUALISING DATA FOR STAKE OF EACH CLUSTER\n",
        "color_list = ['blue','red','green','purple','orange']\n",
        "for clusters in cluster_list:\n",
        "  pie=clusters[0].groupby('label').size().reset_index()\n",
        "  pie.columns=['label','value']\n",
        "  fig = px.pie(pie,values='value',names='label',color=color_list[:clusters[1]],title='Pie Chart for K = '+ str(clusters[1]),height=300,width=300)\n",
        "  fig.show()"
      ],
      "execution_count": 22,
      "outputs": [
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              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AETVTyS5yHxe"
      },
      "source": [
        "Explanation : \n",
        "\n",
        "Above Pie charts depicts the contribution of types of IPO till date in each category"
      ]
    }
  ]
}