DicomOpacitiesOriginalWithResize_rev1_05_07_2020

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.3"},"colab":{"name":"DicomOpacitiesOriginalWithResize_rev1_05_07_2020","provenance":[],"collapsed_sections":[],"toc_visible":true,"machine_shape":"hm"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"MNkZrE8gbmED","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947265606,"user_tz":-330,"elapsed":7328,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["# Install packages that are not available by default in google colab\n","!pip install -q pydicom"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"id":"8J2MnBEJby3U","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947269927,"user_tz":-330,"elapsed":11622,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"cbbb9e51-c173-4517-fd1a-6f92b71f1e22"},"source":["!pip install pydicom\n","import matplotlib.pyplot as plt"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: pydicom in /usr/local/lib/python3.6/dist-packages (2.0.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"cXtEDS4tm1XZ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947273692,"user_tz":-330,"elapsed":15344,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"46998521-8acc-4a6d-91b0-aaea3fe81926"},"source":["pip install dicom"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: dicom in /usr/local/lib/python3.6/dist-packages (0.9.9.post1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"LuBkSNVgIWq9","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947278013,"user_tz":-330,"elapsed":19625,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"7ab43f7d-c2ac-4509-88f5-8e62f06e2674"},"source":["pip install --trusted-host pypi.python.org --upgrade pip"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Requirement already up-to-date: pip in /usr/local/lib/python3.6/dist-packages (20.1.1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"uZd4Y_Q9WAI1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1593947278620,"user_tz":-330,"elapsed":20211,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"81898089-ae24-4865-9cba-4b0f47c8540a"},"source":["import numpy as np\n","import pandas as pd\n","import pydicom as npydicom\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import os\n","import csv\n","from csv import writer\n","from tqdm import tqdm\n","#import tryy\n","import cv2\n","import pydicom\n","#import random\n","#import pydicom as dicomio\n","from os.path import join  \n","import dicom \n","import numpy"],"execution_count":5,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/dicom/__init__.py:53: UserWarning: \n","This code is using an older version of pydicom, which is no longer \n","maintained as of Jan 2017.  You can access the new pydicom features and API \n","by installing `pydicom` from PyPI.\n","See 'Transitioning to pydicom 1.x' section at pydicom.readthedocs.org \n","for more information.\n","\n","  warnings.warn(msg)\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"0V3UYNKrx9vL","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":54},"executionInfo":{"status":"ok","timestamp":1593947278622,"user_tz":-330,"elapsed":20149,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"be97cd16-51f3-4d70-f36b-612eadfe8f5e"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ZTPEcSJRw-uG","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947278623,"user_tz":-330,"elapsed":20113,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["Capstone_Project_Path= \"/content/drive/My Drive/Colab Notebooks/Capstone Project:- Pneumonia /data from Akshay/\""],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"V668yf9AWAJ1","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947278624,"user_tz":-330,"elapsed":20092,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["#paths on pc\n","train_dir = r'/content/drive/My Drive/Colab Notebooks/Capstone Project:- Pneumonia /data from Akshay/stage_2_train_images/'\n","#folder_path = r'/content/drive/My Drive/Colab Notebooks/Capstone Project:- Pneumonia /data from Akshay/stage_2_train_images'\n","#imgs = os.listdir(folder_path)"],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OzWZau4KSsTF","colab_type":"text"},"source":["##**EDA**##"]},{"cell_type":"code","metadata":{"id":"-H3tYG3iaYUR","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":88},"executionInfo":{"status":"ok","timestamp":1593947280174,"user_tz":-330,"elapsed":21621,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"cfbe6883-9856-44f5-c0a7-83ea6eedf0d9"},"source":["import pandas\n","import pandas as pd\n","import numpy as np\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","%cd {Capstone_Project_Path}\n","\n","\n","class_1= pd.read_csv('stage_2_detailed_class_info.csv')"],"execution_count":9,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n","  import pandas.util.testing as tm\n"],"name":"stderr"},{"output_type":"stream","text":["/content/drive/My Drive/Colab Notebooks/Capstone Project:- Pneumonia /data from Akshay\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-C-VJvkQ-Tjm","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"status":"ok","timestamp":1593947280175,"user_tz":-330,"elapsed":21604,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"d2ece230-b7c4-4871-98f6-5bfa01bf4029"},"source":["class_1.head()"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>patientId</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0004cfab-14fd-4e49-80ba-63a80b6bddd6</td>\n","      <td>No Lung Opacity / Not Normal</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>00313ee0-9eaa-42f4-b0ab-c148ed3241cd</td>\n","      <td>No Lung Opacity / Not Normal</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>00322d4d-1c29-4943-afc9-b6754be640eb</td>\n","      <td>No Lung Opacity / Not Normal</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>003d8fa0-6bf1-40ed-b54c-ac657f8495c5</td>\n","      <td>Normal</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>00436515-870c-4b36-a041-de91049b9ab4</td>\n","      <td>Lung Opacity</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                              patientId                         class\n","0  0004cfab-14fd-4e49-80ba-63a80b6bddd6  No Lung Opacity / Not Normal\n","1  00313ee0-9eaa-42f4-b0ab-c148ed3241cd  No Lung Opacity / Not Normal\n","2  00322d4d-1c29-4943-afc9-b6754be640eb  No Lung Opacity / Not Normal\n","3  003d8fa0-6bf1-40ed-b54c-ac657f8495c5                        Normal\n","4  00436515-870c-4b36-a041-de91049b9ab4                  Lung Opacity"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"Um4HDZh5-yaQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947280177,"user_tz":-330,"elapsed":21565,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"e1607c10-31fc-4a95-c01f-90dfec9547ff"},"source":["class_1.shape"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(30227, 2)"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"GfHxGpNU85aL","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947281691,"user_tz":-330,"elapsed":23035,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["train_label=pd.read_csv(Capstone_Project_Path +'stage_2_train_labels.csv')"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"b0xwQ8HF-8FD","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"status":"ok","timestamp":1593947281692,"user_tz":-330,"elapsed":23018,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"eae4b532-cecb-4484-e2be-54e360edc223"},"source":["train_label.head()"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>patientId</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>Target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0004cfab-14fd-4e49-80ba-63a80b6bddd6</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>00313ee0-9eaa-42f4-b0ab-c148ed3241cd</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>00322d4d-1c29-4943-afc9-b6754be640eb</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>003d8fa0-6bf1-40ed-b54c-ac657f8495c5</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>00436515-870c-4b36-a041-de91049b9ab4</td>\n","      <td>264.0</td>\n","      <td>152.0</td>\n","      <td>213.0</td>\n","      <td>379.0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                              patientId      x      y  width  height  Target\n","0  0004cfab-14fd-4e49-80ba-63a80b6bddd6    NaN    NaN    NaN     NaN       0\n","1  00313ee0-9eaa-42f4-b0ab-c148ed3241cd    NaN    NaN    NaN     NaN       0\n","2  00322d4d-1c29-4943-afc9-b6754be640eb    NaN    NaN    NaN     NaN       0\n","3  003d8fa0-6bf1-40ed-b54c-ac657f8495c5    NaN    NaN    NaN     NaN       0\n","4  00436515-870c-4b36-a041-de91049b9ab4  264.0  152.0  213.0   379.0       1"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"GWTkr8Vv-8dv","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947281694,"user_tz":-330,"elapsed":22990,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"a7d70267-97f1-4b2c-e89d-2db5397b1d74"},"source":["train_label.shape"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(30227, 6)"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"SgqwYvfIaaD4","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947281695,"user_tz":-330,"elapsed":22967,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["#missing values\n","def check_data(dataframe):\n","  print('\\nIs NA:\\n',dataframe.isna().sum())\n","  print('\\nUnique Patients:\\n',len(dataframe['patientId'].unique()))\n"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"id":"z-C5hxYVmN5-","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":357},"executionInfo":{"status":"ok","timestamp":1593947281695,"user_tz":-330,"elapsed":22947,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"1a7daefb-ce7e-4fba-9b6b-185afcb79d95"},"source":["check_data(class_1)\n","check_data(train_label)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["\n","Is NA:\n"," patientId    0\n","class        0\n","dtype: int64\n","\n","Unique Patients:\n"," 26684\n","\n","Is NA:\n"," patientId        0\n","x            20672\n","y            20672\n","width        20672\n","height       20672\n","Target           0\n","dtype: int64\n","\n","Unique Patients:\n"," 26684\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Y-gJHlDgALlU","colab_type":"text"},"source":["**Above data imples that for 20.6K patients x & y values are missing** \n","\n"," **Above data have 26.6K unique patients data**\n"]},{"cell_type":"code","metadata":{"id":"mJRZ5cehR6ve","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947281696,"user_tz":-330,"elapsed":22920,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["class_1_train_label_integrating = train_label.merge(class_1, left_on='patientId', right_on='patientId', how='inner')"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"id":"eDuPiUO9TKIm","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"status":"ok","timestamp":1593947281696,"user_tz":-330,"elapsed":22873,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"8b836404-b97c-4fff-c4fb-8a972e0f4fd8"},"source":["class_1_train_label_integrating.head()"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>patientId</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>Target</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0004cfab-14fd-4e49-80ba-63a80b6bddd6</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>No Lung Opacity / Not Normal</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>00313ee0-9eaa-42f4-b0ab-c148ed3241cd</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>No Lung Opacity / Not Normal</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>00322d4d-1c29-4943-afc9-b6754be640eb</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>No Lung Opacity / Not Normal</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>003d8fa0-6bf1-40ed-b54c-ac657f8495c5</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>Normal</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>00436515-870c-4b36-a041-de91049b9ab4</td>\n","      <td>264.0</td>\n","      <td>152.0</td>\n","      <td>213.0</td>\n","      <td>379.0</td>\n","      <td>1</td>\n","      <td>Lung Opacity</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                              patientId  ...                         class\n","0  0004cfab-14fd-4e49-80ba-63a80b6bddd6  ...  No Lung Opacity / Not Normal\n","1  00313ee0-9eaa-42f4-b0ab-c148ed3241cd  ...  No Lung Opacity / Not Normal\n","2  00322d4d-1c29-4943-afc9-b6754be640eb  ...  No Lung Opacity / Not Normal\n","3  003d8fa0-6bf1-40ed-b54c-ac657f8495c5  ...                        Normal\n","4  00436515-870c-4b36-a041-de91049b9ab4  ...                  Lung Opacity\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"dZioZ8TGTKO_","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":238},"executionInfo":{"status":"ok","timestamp":1593947281697,"user_tz":-330,"elapsed":22835,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"de7c0907-bbed-432f-d67b-589255446d4c"},"source":["check_data(class_1_train_label_integrating)"],"execution_count":19,"outputs":[{"output_type":"stream","text":["\n","Is NA:\n"," patientId        0\n","x            20672\n","y            20672\n","width        20672\n","height       20672\n","Target           0\n","class            0\n","dtype: int64\n","\n","Unique Patients:\n"," 26684\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"YhEOPfaRTKSc","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1593947281698,"user_tz":-330,"elapsed":22800,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"0049d96f-72cb-4efc-8097-472224475ae0"},"source":["print(class_1_train_label_integrating[class_1_train_label_integrating['Target'] == 0].isna().sum())"],"execution_count":20,"outputs":[{"output_type":"stream","text":["patientId        0\n","x            20672\n","y            20672\n","width        20672\n","height       20672\n","Target           0\n","class            0\n","dtype: int64\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8oPi-aQkUuuB","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1593947281698,"user_tz":-330,"elapsed":22759,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"e01dfe41-ce6d-44d9-d44e-8c74efcec7d8"},"source":["print(class_1_train_label_integrating[class_1_train_label_integrating['class'] == 'Normal'].isna().sum())"],"execution_count":21,"outputs":[{"output_type":"stream","text":["patientId       0\n","x            8851\n","y            8851\n","width        8851\n","height       8851\n","Target          0\n","class           0\n","dtype: int64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"3etl6kZTmblA","colab_type":"text"},"source":["There are total 8851 missing values for Normal class"]},{"cell_type":"code","metadata":{"id":"gEQBtP8Rmth-","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1593947281699,"user_tz":-330,"elapsed":22722,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"e6206b8a-b8fb-4017-d376-5eb0a0fe50a9"},"source":["print(class_1_train_label_integrating[class_1_train_label_integrating['class'] == 'No Lung Opacity / Not Normal'].isna().sum())"],"execution_count":22,"outputs":[{"output_type":"stream","text":["patientId        0\n","x            11821\n","y            11821\n","width        11821\n","height       11821\n","Target           0\n","class            0\n","dtype: int64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"dhtMXsqKm4K6","colab_type":"text"},"source":["There are total 11821 missing values for No Lung Opacity / Not Normal class"]},{"cell_type":"code","metadata":{"id":"CwdjjHnRnMSb","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1593947281699,"user_tz":-330,"elapsed":22679,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"ecec8f6d-d3bc-4a54-81d6-56c2cde49e71"},"source":["print(class_1_train_label_integrating[class_1_train_label_integrating['class'] == 'Lung Opacity'].isna().sum())"],"execution_count":23,"outputs":[{"output_type":"stream","text":["patientId    0\n","x            0\n","y            0\n","width        0\n","height       0\n","Target       0\n","class        0\n","dtype: int64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bwbXW-TAoHKQ","colab_type":"text"},"source":["There are no missing values for Lung Opacity class"]},{"cell_type":"code","metadata":{"id":"ronByDKtnM2l","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947281700,"user_tz":-330,"elapsed":22629,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["sns.set_style(\"dark\")"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"id":"uncKrKArpmWS","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"status":"ok","timestamp":1593947281700,"user_tz":-330,"elapsed":22611,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"258224cd-16db-4b58-a2ff-9aeb23b6d3c9"},"source":["sns.countplot(train_label['Target'])\n"],"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f3d62c0f128>"]},"metadata":{"tags":[]},"execution_count":25},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"dBGf4lIhqshi","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947281701,"user_tz":-330,"elapsed":22580,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"d6ccd9e2-a9fa-4592-fe5e-a22b224d1720"},"source":["train_label['Target'].count()"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["30227"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"code","metadata":{"id":"zeFJ-DWexzCZ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":142},"executionInfo":{"status":"ok","timestamp":1593947281701,"user_tz":-330,"elapsed":22542,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"b83c0f5b-d7fa-4048-edf5-32edc12dcc0b"},"source":["train_label.groupby(['Target']).count() "],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>patientId</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>width</th>\n","      <th>height</th>\n","    </tr>\n","    <tr>\n","      <th>Target</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>20672</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9555</td>\n","      <td>9555</td>\n","      <td>9555</td>\n","      <td>9555</td>\n","      <td>9555</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        patientId     x     y  width  height\n","Target                                      \n","0           20672     0     0      0       0\n","1            9555  9555  9555   9555    9555"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"markdown","metadata":{"id":"nVoqFS7twuPN","colab_type":"text"},"source":["From total patients data of 30227, 9555 patients are having Lung opacity"]},{"cell_type":"code","metadata":{"id":"GMFHj3M743VA","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":301},"executionInfo":{"status":"ok","timestamp":1593947281702,"user_tz":-330,"elapsed":22514,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"73d4d06a-58e3-4d2c-9983-756e71d75eaa"},"source":["# Bifurcation of Target & Class\n","fig = plt.figure(figsize = (10, 6))\n","ax = fig.add_subplot(121)\n","g = (train_label['Target'].value_counts()\n","    .plot(kind = 'pie', autopct = '%.0f%%', \n","          labels = ['Negative', 'Pneumonia Proofs'], \n","          colors = ['green', 'red'], \n","          startangle = 90, \n","          title = 'Bifurcation of Target', fontsize = 12)\n","    .set_ylabel(''))"],"execution_count":28,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"_n-X70Zbmev6","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"status":"ok","timestamp":1593947281703,"user_tz":-330,"elapsed":22496,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"1745d850-0a98-44f3-c97d-7dde22ce8481"},"source":["ax = fig.add_subplot(122)\n","g = (class_1['class'].value_counts().sort_index(ascending = False)\n","    .plot(kind = 'pie', autopct = '%.0f%%', \n","          colors = ['green', 'yellow', 'red'], \n","          startangle = 90, title = 'Bifurcation of Class', \n","          fontsize = 12)\n","    .set_ylabel(''))\n","plt.tight_layout()"],"execution_count":29,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"rAx3awGI7pDw","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947282308,"user_tz":-330,"elapsed":23072,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["sample_sub=pd.read_csv(Capstone_Project_Path +'stage_2_sample_submission.csv')                                          "],"execution_count":30,"outputs":[]},{"cell_type":"code","metadata":{"id":"XRn2BidQ7548","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"status":"ok","timestamp":1593947282310,"user_tz":-330,"elapsed":23052,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"9746bb39-42cf-4017-f5f5-629233bf2461"},"source":["sample_sub.head()"],"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>patientId</th>\n","      <th>PredictionString</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0000a175-0e68-4ca4-b1af-167204a7e0bc</td>\n","      <td>0.5 0 0 100 100</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0005d3cc-3c3f-40b9-93c3-46231c3eb813</td>\n","      <td>0.5 0 0 100 100</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>000686d7-f4fc-448d-97a0-44fa9c5d3aa6</td>\n","      <td>0.5 0 0 100 100</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>000e3a7d-c0ca-4349-bb26-5af2d8993c3d</td>\n","      <td>0.5 0 0 100 100</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>00100a24-854d-423d-a092-edcf6179e061</td>\n","      <td>0.5 0 0 100 100</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                              patientId PredictionString\n","0  0000a175-0e68-4ca4-b1af-167204a7e0bc  0.5 0 0 100 100\n","1  0005d3cc-3c3f-40b9-93c3-46231c3eb813  0.5 0 0 100 100\n","2  000686d7-f4fc-448d-97a0-44fa9c5d3aa6  0.5 0 0 100 100\n","3  000e3a7d-c0ca-4349-bb26-5af2d8993c3d  0.5 0 0 100 100\n","4  00100a24-854d-423d-a092-edcf6179e061  0.5 0 0 100 100"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"TUl9TM3j43Yq","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947282311,"user_tz":-330,"elapsed":23031,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"78c45691-44f6-4bc8-cf2b-5300ed169909"},"source":["print('PatientId is linked with {} class'.format(class_1.groupby(['patientId'])['class'].nunique().max()))"],"execution_count":32,"outputs":[{"output_type":"stream","text":["PatientId is linked with 1 class\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"PrIHbJLvlVhJ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":238},"executionInfo":{"status":"ok","timestamp":1593947282312,"user_tz":-330,"elapsed":23004,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"8f03ea63-04b6-4e12-9643-7d86cbd2a32e"},"source":["boundingboxes = train_label.groupby('patientId').size().to_frame('number_of_boxes').reset_index()\n","train_label = train_label.merge(boundingboxes, on = 'patientId', how = 'right')\n","print('\\nNumber of patientIDs per boundingboxes in the dataset')\n","(boundingboxes.groupby('number_of_boxes')\n",".size()\n",".to_frame('number_of_patientIDs_per_boxes')\n",".reset_index()\n",".set_index('number_of_boxes')\n",".sort_values(by = 'number_of_boxes'))"],"execution_count":33,"outputs":[{"output_type":"stream","text":["\n","Number of patientIDs per boundingboxes in the dataset\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["<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>number_of_patientIDs_per_boxes</th>\n","    </tr>\n","    <tr>\n","      <th>number_of_boxes</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>23286</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3266</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>119</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>13</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 number_of_patientIDs_per_boxes\n","number_of_boxes                                \n","1                                         23286\n","2                                          3266\n","3                                           119\n","4                                            13"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"markdown","metadata":{"id":"T3LVtvcCodFe","colab_type":"text"},"source":["### Summarization from CSV\n","1. Out of 30227 patient IDs, 26684 patients are having unique IDs. This means that remaining are duplicates.\n","2. As per our understanding, CSV data has patient IDs and Bounding boxes.\n","3. Class variable contains No Lung Opacity/Not Normal, Normal and Lung Opacity.\n","4. Out of Given patient data, 32% people have Pneumonia Proofs/Symptoms."]},{"cell_type":"code","metadata":{"id":"lLgvm58AskUK","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947282313,"user_tz":-330,"elapsed":22978,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["import pydicom as dcm\n","import codecs"],"execution_count":34,"outputs":[]},{"cell_type":"code","metadata":{"id":"k6_LNAT6m1T1","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947282314,"user_tz":-330,"elapsed":22961,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["sample_patient_id = train_label['patientId'][1]\n","dcm_file = train_dir + '{}.dcm'.format(sample_patient_id)"],"execution_count":35,"outputs":[]},{"cell_type":"code","metadata":{"id":"XxP39gA9zHqk","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947282315,"user_tz":-330,"elapsed":22949,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["dcm_data = dcm.read_file(dcm_file)"],"execution_count":36,"outputs":[]},{"cell_type":"code","metadata":{"id":"7uUQ0CjUzOTO","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":765},"executionInfo":{"status":"ok","timestamp":1593947282316,"user_tz":-330,"elapsed":22935,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"901849fe-c38e-4651-cf86-cdb24476c5f1"},"source":["print(dcm_data)"],"execution_count":37,"outputs":[{"output_type":"stream","text":["Dataset.file_meta -------------------------------\n","(0002, 0000) File Meta Information Group Length  UL: 200\n","(0002, 0001) File Meta Information Version       OB: b'\\x00\\x01'\n","(0002, 0002) Media Storage SOP Class UID         UI: Secondary Capture Image Storage\n","(0002, 0003) Media Storage SOP Instance UID      UI: 1.2.276.0.7230010.3.1.4.8323329.26024.1517874469.23011\n","(0002, 0010) Transfer Syntax UID                 UI: JPEG Baseline (Process 1)\n","(0002, 0012) Implementation Class UID            UI: 1.2.276.0.7230010.3.0.3.6.0\n","(0002, 0013) Implementation Version Name         SH: 'OFFIS_DCMTK_360'\n","-------------------------------------------------\n","(0008, 0005) Specific Character Set              CS: 'ISO_IR 100'\n","(0008, 0016) SOP Class UID                       UI: Secondary Capture Image Storage\n","(0008, 0018) SOP Instance UID                    UI: 1.2.276.0.7230010.3.1.4.8323329.26024.1517874469.23011\n","(0008, 0020) Study Date                          DA: '19010101'\n","(0008, 0030) Study Time                          TM: '000000.00'\n","(0008, 0050) Accession Number                    SH: ''\n","(0008, 0060) Modality                            CS: 'CR'\n","(0008, 0064) Conversion Type                     CS: 'WSD'\n","(0008, 0090) Referring Physician's Name          PN: ''\n","(0008, 103e) Series Description                  LO: 'view: PA'\n","(0010, 0010) Patient's Name                      PN: '00313ee0-9eaa-42f4-b0ab-c148ed3241cd'\n","(0010, 0020) Patient ID                          LO: '00313ee0-9eaa-42f4-b0ab-c148ed3241cd'\n","(0010, 0030) Patient's Birth Date                DA: ''\n","(0010, 0040) Patient's Sex                       CS: 'F'\n","(0010, 1010) Patient's Age                       AS: '48'\n","(0018, 0015) Body Part Examined                  CS: 'CHEST'\n","(0018, 5101) View Position                       CS: 'PA'\n","(0020, 000d) Study Instance UID                  UI: 1.2.276.0.7230010.3.1.2.8323329.26024.1517874469.23010\n","(0020, 000e) Series Instance UID                 UI: 1.2.276.0.7230010.3.1.3.8323329.26024.1517874469.23009\n","(0020, 0010) Study ID                            SH: ''\n","(0020, 0011) Series Number                       IS: \"1\"\n","(0020, 0013) Instance Number                     IS: \"1\"\n","(0020, 0020) Patient Orientation                 CS: ''\n","(0028, 0002) Samples per Pixel                   US: 1\n","(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'\n","(0028, 0010) Rows                                US: 1024\n","(0028, 0011) Columns                             US: 1024\n","(0028, 0030) Pixel Spacing                       DS: [0.19431099999999998, 0.19431099999999998]\n","(0028, 0100) Bits Allocated                      US: 8\n","(0028, 0101) Bits Stored                         US: 8\n","(0028, 0102) High Bit                            US: 7\n","(0028, 0103) Pixel Representation                US: 0\n","(0028, 2110) Lossy Image Compression             CS: '01'\n","(0028, 2114) Lossy Image Compression Method      CS: 'ISO_10918_1'\n","(7fe0, 0010) Pixel Data                          OB: Array of 111474 elements\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vr0qKQuxzbey","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947282317,"user_tz":-330,"elapsed":22911,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["def load_image(imagename):\n","  image1 = pydicom.dcmread(imagename)\n","  print(type(image1))\n","  return image1"],"execution_count":38,"outputs":[]},{"cell_type":"code","metadata":{"id":"Re8VWGo4zbiM","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947282318,"user_tz":-330,"elapsed":22898,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["#import numpy as np\n","#data = np.load('resized_train_images.npy', allow_pickle=True)"],"execution_count":39,"outputs":[]},{"cell_type":"code","metadata":{"id":"m07wSp7wdKIk","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947282319,"user_tz":-330,"elapsed":22879,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["import matplotlib.pyplot as plt \n","%matplotlib inline \n","import seaborn as sns \n","import csv\n","from glob import glob\n","import sys\n","#csv.field_size_limit(sys.maxsize)\n","import ctypes as ct"],"execution_count":40,"outputs":[]},{"cell_type":"code","metadata":{"id":"Smo76VAkJw3a","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947282320,"user_tz":-330,"elapsed":22872,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":["#b = pd.read_csv(Capstone_Project_Path +'new.csv')"],"execution_count":41,"outputs":[]},{"cell_type":"code","metadata":{"id":"wNmfsVXwOsq8","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":71},"executionInfo":{"status":"ok","timestamp":1593947541567,"user_tz":-330,"elapsed":282108,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"5c59fd45-3e2b-4503-c914-da8f8b7e63a7"},"source":["path = \"/content/drive/My Drive/Colab Notebooks/Capstone Project:- Pneumonia /data from Akshay/new.csv\"\n","b = pd.read_csv(path)"],"execution_count":42,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (0,1,2,4,6,7,8,11,12,15,19,21,22,25,28,30,31,33) have mixed types.Specify dtype option on import or set low_memory=False.\n","  interactivity=interactivity, compiler=compiler, result=result)\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"InJ_NWxBV1vY","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":479},"executionInfo":{"status":"ok","timestamp":1593947541571,"user_tz":-330,"elapsed":282101,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"371da3a7-7731-443e-ee76-48f2eb54b023"},"source":["b.head()"],"execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>AccessionNumber</th>\n","      <th>BitsAllocated</th>\n","      <th>BitsStored</th>\n","      <th>BodyPartExamined</th>\n","      <th>Columns</th>\n","      <th>ConversionType</th>\n","      <th>HighBit</th>\n","      <th>InstanceNumber</th>\n","      <th>LossyImageCompression</th>\n","      <th>LossyImageCompressionMethod</th>\n","      <th>Modality</th>\n","      <th>PatientAge</th>\n","      <th>PatientBirthDate</th>\n","      <th>PatientID</th>\n","      <th>PatientName</th>\n","      <th>PatientOrientation</th>\n","      <th>PatientSex</th>\n","      <th>PhotometricInterpretation</th>\n","      <th>PixelData</th>\n","      <th>PixelRepresentation</th>\n","      <th>PixelSpacing</th>\n","      <th>ReferringPhysicianName</th>\n","      <th>Rows</th>\n","      <th>SOPClassUID</th>\n","      <th>SOPInstanceUID</th>\n","      <th>SamplesPerPixel</th>\n","      <th>SeriesDescription</th>\n","      <th>SeriesInstanceUID</th>\n","      <th>SeriesNumber</th>\n","      <th>SpecificCharacterSet</th>\n","      <th>StudyDate</th>\n","      <th>StudyID</th>\n","      <th>StudyInstanceUID</th>\n","      <th>StudyTime</th>\n","      <th>ViewPosition</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>CHEST</td>\n","      <td>1024</td>\n","      <td>WSD</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>ISO_10918_1</td>\n","      <td>CR</td>\n","      <td>51</td>\n","      <td>NaN</td>\n","      <td>0004cfab-14fd-4e49-80ba-63a80b6bddd6</td>\n","      <td>0004cfab-14fd-4e49-80ba-63a80b6bddd6</td>\n","      <td>NaN</td>\n","      <td>F</td>\n","      <td>MONOCHROME2</td>\n","      <td>b'\\xfe\\xff\\x00\\xe0\\x00\\x00\\x00\\x00\\xfe\\xff\\x00...</td>\n","      <td>0</td>\n","      <td>[0.14300000000000002, 0.14300000000000002]</td>\n","      <td>NaN</td>\n","      <td>1024</td>\n","      <td>1.2.840.10008.5.1.4.1.1.7</td>\n","      <td>1.2.276.0.7230010.3.1.4.8323329.28530.15178744...</td>\n","      <td>1</td>\n","      <td>view: PA</td>\n","      <td>1.2.276.0.7230010.3.1.3.8323329.28530.15178744...</td>\n","      <td>1</td>\n","      <td>ISO_IR 100</td>\n","      <td>19010101</td>\n","      <td>NaN</td>\n","      <td>1.2.276.0.7230010.3.1.2.8323329.28530.15178744...</td>\n","      <td>0</td>\n","      <td>PA</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>CHEST</td>\n","      <td>1024</td>\n","      <td>WSD</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>ISO_10918_1</td>\n","      <td>CR</td>\n","      <td>19</td>\n","      <td>NaN</td>\n","      <td>000924cf-0f8d-42bd-9158-1af53881a557</td>\n","      <td>000924cf-0f8d-42bd-9158-1af53881a557</td>\n","      <td>NaN</td>\n","      <td>F</td>\n","      <td>MONOCHROME2</td>\n","      <td>b'\\xfe\\xff\\x00\\xe0\\x00\\x00\\x00\\x00\\xfe\\xff\\x00...</td>\n","      <td>0</td>\n","      <td>[0.139, 0.139]</td>\n","      <td>NaN</td>\n","      <td>1024</td>\n","      <td>1.2.840.10008.5.1.4.1.1.7</td>\n","      <td>1.2.276.0.7230010.3.1.4.8323329.20023.15178744...</td>\n","      <td>1</td>\n","      <td>view: AP</td>\n","      <td>1.2.276.0.7230010.3.1.3.8323329.20023.15178744...</td>\n","      <td>1</td>\n","      <td>ISO_IR 100</td>\n","      <td>19010101</td>\n","      <td>NaN</td>\n","      <td>1.2.276.0.7230010.3.1.2.8323329.20023.15178744...</td>\n","      <td>0</td>\n","      <td>AP</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>CHEST</td>\n","      <td>1024</td>\n","      <td>WSD</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>ISO_10918_1</td>\n","      <td>CR</td>\n","      <td>25</td>\n","      <td>NaN</td>\n","      <td>000db696-cf54-4385-b10b-6b16fbb3f985</td>\n","      <td>000db696-cf54-4385-b10b-6b16fbb3f985</td>\n","      <td>NaN</td>\n","      <td>F</td>\n","      <td>MONOCHROME2</td>\n","      <td>b'\\xfe\\xff\\x00\\xe0\\x00\\x00\\x00\\x00\\xfe\\xff\\x00...</td>\n","      <td>0</td>\n","      <td>[0.168, 0.168]</td>\n","      <td>NaN</td>\n","      <td>1024</td>\n","      <td>1.2.840.10008.5.1.4.1.1.7</td>\n","      <td>1.2.276.0.7230010.3.1.4.8323329.4475.151787430...</td>\n","      <td>1</td>\n","      <td>view: AP</td>\n","      <td>1.2.276.0.7230010.3.1.3.8323329.4475.151787430...</td>\n","      <td>1</td>\n","      <td>ISO_IR 100</td>\n","      <td>19010101</td>\n","      <td>NaN</td>\n","      <td>1.2.276.0.7230010.3.1.2.8323329.4475.151787430...</td>\n","      <td>0</td>\n","      <td>AP</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>CHEST</td>\n","      <td>1024</td>\n","      <td>WSD</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>ISO_10918_1</td>\n","      <td>CR</td>\n","      <td>40</td>\n","      <td>NaN</td>\n","      <td>000fe35a-2649-43d4-b027-e67796d412e0</td>\n","      <td>000fe35a-2649-43d4-b027-e67796d412e0</td>\n","      <td>NaN</td>\n","      <td>M</td>\n","      <td>MONOCHROME2</td>\n","      <td>b'\\xfe\\xff\\x00\\xe0\\x00\\x00\\x00\\x00\\xfe\\xff\\x00...</td>\n","      <td>0</td>\n","      <td>[0.171, 0.171]</td>\n","      <td>NaN</td>\n","      <td>1024</td>\n","      <td>1.2.840.10008.5.1.4.1.1.7</td>\n","      <td>1.2.276.0.7230010.3.1.4.8323329.25090.15178744...</td>\n","      <td>1</td>\n","      <td>view: AP</td>\n","      <td>1.2.276.0.7230010.3.1.3.8323329.25090.15178744...</td>\n","      <td>1</td>\n","      <td>ISO_IR 100</td>\n","      <td>19010101</td>\n","      <td>NaN</td>\n","      <td>1.2.276.0.7230010.3.1.2.8323329.25090.15178744...</td>\n","      <td>0</td>\n","      <td>AP</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>CHEST</td>\n","      <td>1024</td>\n","      <td>WSD</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>ISO_10918_1</td>\n","      <td>CR</td>\n","      <td>57</td>\n","      <td>NaN</td>\n","      <td>001031d9-f904-4a23-b3e5-2c088acd19c6</td>\n","      <td>001031d9-f904-4a23-b3e5-2c088acd19c6</td>\n","      <td>NaN</td>\n","      <td>M</td>\n","      <td>MONOCHROME2</td>\n","      <td>b'\\xfe\\xff\\x00\\xe0\\x00\\x00\\x00\\x00\\xfe\\xff\\x00...</td>\n","      <td>0</td>\n","      <td>[0.139, 0.139]</td>\n","      <td>NaN</td>\n","      <td>1024</td>\n","      <td>1.2.840.10008.5.1.4.1.1.7</td>\n","      <td>1.2.276.0.7230010.3.1.4.8323329.9271.151787434...</td>\n","      <td>1</td>\n","      <td>view: PA</td>\n","      <td>1.2.276.0.7230010.3.1.3.8323329.9271.151787434...</td>\n","      <td>1</td>\n","      <td>ISO_IR 100</td>\n","      <td>19010101</td>\n","      <td>NaN</td>\n","      <td>1.2.276.0.7230010.3.1.2.8323329.9271.151787434...</td>\n","      <td>0</td>\n","      <td>PA</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  AccessionNumber BitsAllocated  ... StudyTime ViewPosition\n","0             NaN             8  ...         0           PA\n","1             NaN             8  ...         0           AP\n","2             NaN             8  ...         0           AP\n","3             NaN             8  ...         0           AP\n","4             NaN             8  ...         0           PA\n","\n","[5 rows x 35 columns]"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"id":"YHxmWfZzWtwI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947541574,"user_tz":-330,"elapsed":282091,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"02aefaa9-e3a5-48b2-9302-a9e778e08412"},"source":["b.shape"],"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(26687, 35)"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"code","metadata":{"id":"fwfa9j9UU5yN","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947541576,"user_tz":-330,"elapsed":282076,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":[" Entiredata = class_1_train_label_integrating.merge(b, left_on='patientId', right_on='PatientID', how='inner')"],"execution_count":45,"outputs":[]},{"cell_type":"code","metadata":{"id":"g2zEMGAdV7O6","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":530},"executionInfo":{"status":"ok","timestamp":1593947541577,"user_tz":-330,"elapsed":282067,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"df686c26-8b36-4c6f-b135-bbd70ed92082"},"source":["Entiredata.head()"],"execution_count":46,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>patientId</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>Target</th>\n","      <th>class</th>\n","      <th>AccessionNumber</th>\n","      <th>BitsAllocated</th>\n","      <th>BitsStored</th>\n","      <th>BodyPartExamined</th>\n","      <th>Columns</th>\n","      <th>ConversionType</th>\n","      <th>HighBit</th>\n","      <th>InstanceNumber</th>\n","      <th>LossyImageCompression</th>\n","      <th>LossyImageCompressionMethod</th>\n","      <th>Modality</th>\n","      <th>PatientAge</th>\n","      <th>PatientBirthDate</th>\n","      <th>PatientID</th>\n","      <th>PatientName</th>\n","      <th>PatientOrientation</th>\n","      <th>PatientSex</th>\n","      <th>PhotometricInterpretation</th>\n","      <th>PixelData</th>\n","      <th>PixelRepresentation</th>\n","      <th>PixelSpacing</th>\n","      <th>ReferringPhysicianName</th>\n","      <th>Rows</th>\n","      <th>SOPClassUID</th>\n","      <th>SOPInstanceUID</th>\n","      <th>SamplesPerPixel</th>\n","      <th>SeriesDescription</th>\n","      <th>SeriesInstanceUID</th>\n","      <th>SeriesNumber</th>\n","      <th>SpecificCharacterSet</th>\n","      <th>StudyDate</th>\n","      <th>StudyID</th>\n","      <th>StudyInstanceUID</th>\n","      <th>StudyTime</th>\n","      <th>ViewPosition</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0004cfab-14fd-4e49-80ba-63a80b6bddd6</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>No Lung Opacity / Not Normal</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>CHEST</td>\n","      <td>1024</td>\n","      <td>WSD</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>ISO_10918_1</td>\n","      <td>CR</td>\n","      <td>51</td>\n","      <td>NaN</td>\n","      <td>0004cfab-14fd-4e49-80ba-63a80b6bddd6</td>\n","      <td>0004cfab-14fd-4e49-80ba-63a80b6bddd6</td>\n","      <td>NaN</td>\n","      <td>F</td>\n","      <td>MONOCHROME2</td>\n","      <td>b'\\xfe\\xff\\x00\\xe0\\x00\\x00\\x00\\x00\\xfe\\xff\\x00...</td>\n","      <td>0</td>\n","      <td>[0.14300000000000002, 0.14300000000000002]</td>\n","      <td>NaN</td>\n","      <td>1024</td>\n","      <td>1.2.840.10008.5.1.4.1.1.7</td>\n","      <td>1.2.276.0.7230010.3.1.4.8323329.28530.15178744...</td>\n","      <td>1</td>\n","      <td>view: PA</td>\n","      <td>1.2.276.0.7230010.3.1.3.8323329.28530.15178744...</td>\n","      <td>1</td>\n","      <td>ISO_IR 100</td>\n","      <td>19010101</td>\n","      <td>NaN</td>\n","      <td>1.2.276.0.7230010.3.1.2.8323329.28530.15178744...</td>\n","      <td>0</td>\n","      <td>PA</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>00313ee0-9eaa-42f4-b0ab-c148ed3241cd</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>No Lung Opacity / Not Normal</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>CHEST</td>\n","      <td>1024</td>\n","      <td>WSD</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>ISO_10918_1</td>\n","      <td>CR</td>\n","      <td>48</td>\n","      <td>NaN</td>\n","      <td>00313ee0-9eaa-42f4-b0ab-c148ed3241cd</td>\n","      <td>00313ee0-9eaa-42f4-b0ab-c148ed3241cd</td>\n","      <td>NaN</td>\n","      <td>F</td>\n","      <td>MONOCHROME2</td>\n","      <td>b'\\xfe\\xff\\x00\\xe0\\x00\\x00\\x00\\x00\\xfe\\xff\\x00...</td>\n","      <td>0</td>\n","      <td>[0.19431099999999998, 0.19431099999999998]</td>\n","      <td>NaN</td>\n","      <td>1024</td>\n","      <td>1.2.840.10008.5.1.4.1.1.7</td>\n","      <td>1.2.276.0.7230010.3.1.4.8323329.26024.15178744...</td>\n","      <td>1</td>\n","      <td>view: PA</td>\n","      <td>1.2.276.0.7230010.3.1.3.8323329.26024.15178744...</td>\n","      <td>1</td>\n","      <td>ISO_IR 100</td>\n","      <td>19010101</td>\n","      <td>NaN</td>\n","      <td>1.2.276.0.7230010.3.1.2.8323329.26024.15178744...</td>\n","      <td>0</td>\n","      <td>PA</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>00322d4d-1c29-4943-afc9-b6754be640eb</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>No Lung Opacity / Not Normal</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>CHEST</td>\n","      <td>1024</td>\n","      <td>WSD</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>ISO_10918_1</td>\n","      <td>CR</td>\n","      <td>19</td>\n","      <td>NaN</td>\n","      <td>00322d4d-1c29-4943-afc9-b6754be640eb</td>\n","      <td>00322d4d-1c29-4943-afc9-b6754be640eb</td>\n","      <td>NaN</td>\n","      <td>M</td>\n","      <td>MONOCHROME2</td>\n","      <td>b'\\xfe\\xff\\x00\\xe0\\x00\\x00\\x00\\x00\\xfe\\xff\\x00...</td>\n","      <td>0</td>\n","      <td>[0.168, 0.168]</td>\n","      <td>NaN</td>\n","      <td>1024</td>\n","      <td>1.2.840.10008.5.1.4.1.1.7</td>\n","      <td>1.2.276.0.7230010.3.1.4.8323329.11252.15178743...</td>\n","      <td>1</td>\n","      <td>view: AP</td>\n","      <td>1.2.276.0.7230010.3.1.3.8323329.11252.15178743...</td>\n","      <td>1</td>\n","      <td>ISO_IR 100</td>\n","      <td>19010101</td>\n","      <td>NaN</td>\n","      <td>1.2.276.0.7230010.3.1.2.8323329.11252.15178743...</td>\n","      <td>0</td>\n","      <td>AP</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>003d8fa0-6bf1-40ed-b54c-ac657f8495c5</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>Normal</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>CHEST</td>\n","      <td>1024</td>\n","      <td>WSD</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>ISO_10918_1</td>\n","      <td>CR</td>\n","      <td>28</td>\n","      <td>NaN</td>\n","      <td>003d8fa0-6bf1-40ed-b54c-ac657f8495c5</td>\n","      <td>003d8fa0-6bf1-40ed-b54c-ac657f8495c5</td>\n","      <td>NaN</td>\n","      <td>M</td>\n","      <td>MONOCHROME2</td>\n","      <td>b'\\xfe\\xff\\x00\\xe0\\x00\\x00\\x00\\x00\\xfe\\xff\\x00...</td>\n","      <td>0</td>\n","      <td>[0.14300000000000002, 0.14300000000000002]</td>\n","      <td>NaN</td>\n","      <td>1024</td>\n","      <td>1.2.840.10008.5.1.4.1.1.7</td>\n","      <td>1.2.276.0.7230010.3.1.4.8323329.2293.151787429...</td>\n","      <td>1</td>\n","      <td>view: PA</td>\n","      <td>1.2.276.0.7230010.3.1.3.8323329.2293.151787429...</td>\n","      <td>1</td>\n","      <td>ISO_IR 100</td>\n","      <td>19010101</td>\n","      <td>NaN</td>\n","      <td>1.2.276.0.7230010.3.1.2.8323329.2293.151787429...</td>\n","      <td>0</td>\n","      <td>PA</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>00436515-870c-4b36-a041-de91049b9ab4</td>\n","      <td>264.0</td>\n","      <td>152.0</td>\n","      <td>213.0</td>\n","      <td>379.0</td>\n","      <td>1</td>\n","      <td>Lung Opacity</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>CHEST</td>\n","      <td>1024</td>\n","      <td>WSD</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>ISO_10918_1</td>\n","      <td>CR</td>\n","      <td>32</td>\n","      <td>NaN</td>\n","      <td>00436515-870c-4b36-a041-de91049b9ab4</td>\n","      <td>00436515-870c-4b36-a041-de91049b9ab4</td>\n","      <td>NaN</td>\n","      <td>F</td>\n","      <td>MONOCHROME2</td>\n","      <td>b'\\xfe\\xff\\x00\\xe0\\x00\\x00\\x00\\x00\\xfe\\xff\\x00...</td>\n","      <td>0</td>\n","      <td>[0.139, 0.139]</td>\n","      <td>NaN</td>\n","      <td>1024</td>\n","      <td>1.2.840.10008.5.1.4.1.1.7</td>\n","      <td>1.2.276.0.7230010.3.1.4.8323329.6379.151787432...</td>\n","      <td>1</td>\n","      <td>view: AP</td>\n","      <td>1.2.276.0.7230010.3.1.3.8323329.6379.151787432...</td>\n","      <td>1</td>\n","      <td>ISO_IR 100</td>\n","      <td>19010101</td>\n","      <td>NaN</td>\n","      <td>1.2.276.0.7230010.3.1.2.8323329.6379.151787432...</td>\n","      <td>0</td>\n","      <td>AP</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                              patientId      x  ...  StudyTime  ViewPosition\n","0  0004cfab-14fd-4e49-80ba-63a80b6bddd6    NaN  ...          0            PA\n","1  00313ee0-9eaa-42f4-b0ab-c148ed3241cd    NaN  ...          0            PA\n","2  00322d4d-1c29-4943-afc9-b6754be640eb    NaN  ...          0            AP\n","3  003d8fa0-6bf1-40ed-b54c-ac657f8495c5    NaN  ...          0            PA\n","4  00436515-870c-4b36-a041-de91049b9ab4  264.0  ...          0            AP\n","\n","[5 rows x 42 columns]"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"code","metadata":{"id":"dunVBIn_XZDR","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":748},"executionInfo":{"status":"ok","timestamp":1593947541580,"user_tz":-330,"elapsed":282055,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"ed2b7ff2-2a02-494e-e44b-2efa7beaf7d0"},"source":["Entiredata[\"PatientAge\"] = Entiredata[\"PatientAge\"].astype(str).astype(int)\n","print(Entiredata.dtypes)"],"execution_count":47,"outputs":[{"output_type":"stream","text":["patientId                       object\n","x                              float64\n","y                              float64\n","width                          float64\n","height                         float64\n","Target                           int64\n","class                           object\n","AccessionNumber                 object\n","BitsAllocated                   object\n","BitsStored                      object\n","BodyPartExamined                object\n","Columns                         object\n","ConversionType                  object\n","HighBit                         object\n","InstanceNumber                  object\n","LossyImageCompression           object\n","LossyImageCompressionMethod     object\n","Modality                        object\n","PatientAge                       int64\n","PatientBirthDate                object\n","PatientID                       object\n","PatientName                     object\n","PatientOrientation              object\n","PatientSex                      object\n","PhotometricInterpretation       object\n","PixelData                       object\n","PixelRepresentation             object\n","PixelSpacing                    object\n","ReferringPhysicianName          object\n","Rows                            object\n","SOPClassUID                     object\n","SOPInstanceUID                  object\n","SamplesPerPixel                 object\n","SeriesDescription               object\n","SeriesInstanceUID               object\n","SeriesNumber                    object\n","SpecificCharacterSet            object\n","StudyDate                       object\n","StudyID                         object\n","StudyInstanceUID                object\n","StudyTime                       object\n","ViewPosition                    object\n","dtype: object\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wjfk-9GnXZIm","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"status":"ok","timestamp":1593947541582,"user_tz":-330,"elapsed":282039,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"574be094-5712-41c5-cb0b-adc01ca4d42b"},"source":["sns.countplot(x=\"Modality\", data=Entiredata,hue=\"Target\")"],"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f3d5dce8f28>"]},"metadata":{"tags":[]},"execution_count":48},{"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":"99__Wm7tcGe2","colab_type":"text"},"source":["All Patients are checked with Computed radiography modality"]},{"cell_type":"code","metadata":{"id":"SpiDUhjSWy5e","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947541584,"user_tz":-330,"elapsed":282028,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"a9320a30-a87a-43e5-815c-4d5763dc1d14"},"source":["print('BodyPartExamined` is: {}'.format(b['BodyPartExamined'].unique()[0]))"],"execution_count":49,"outputs":[{"output_type":"stream","text":["BodyPartExamined` is: CHEST\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"d80UUSCamj-N","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"status":"ok","timestamp":1593947541586,"user_tz":-330,"elapsed":282016,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"1a0887bc-0843-4309-f39f-ed89e7b7ce84"},"source":["sns.boxplot(x=\"Target\", y='PatientAge', data=Entiredata)"],"execution_count":50,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f3d5dc69cf8>"]},"metadata":{"tags":[]},"execution_count":50},{"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":"rYPH_ROHomF3","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947541589,"user_tz":-330,"elapsed":282002,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"134bb823-774a-4015-c812-713788d33666"},"source":["print('Minimum `PatientAge` in the training dataset: {}'.format(Entiredata['PatientAge'].min()))"],"execution_count":51,"outputs":[{"output_type":"stream","text":["Minimum `PatientAge` in the training dataset: 1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"2uxzeCr3ox55","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593947541591,"user_tz":-330,"elapsed":281989,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"a912cc70-3d0f-49ed-e96c-320a27399708"},"source":["print('`PatientAge` in higher side for box plot: {}'.format(Entiredata['PatientAge'].quantile(0.75) + (Entiredata['PatientAge'].quantile(0.75) - Entiredata['PatientAge'].quantile(0.25))))"],"execution_count":52,"outputs":[{"output_type":"stream","text":["`PatientAge` in higher side for box plot: 85.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yAJgfhPXnHn2","colab_type":"text"},"source":["There are some unique cases where patient age are above 140 but they belong to Normal or No lung opacity/Not normal class."]},{"cell_type":"code","metadata":{"id":"_W6MY8Yxr-9p","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"status":"ok","timestamp":1593947543945,"user_tz":-330,"elapsed":284331,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"ddceacc5-eb69-4cf5-f411-e9dd8f2b0871"},"source":["sns.countplot(x=\"ViewPosition\", data=Entiredata,hue=\"Target\")"],"execution_count":53,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f3d5dbdb048>"]},"metadata":{"tags":[]},"execution_count":53},{"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":"OLeobO4cw_wq","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":162},"executionInfo":{"status":"ok","timestamp":1593947543946,"user_tz":-330,"elapsed":284318,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"c36a883a-813a-4b80-c57f-6af5a831206b"},"source":["Entiredata.groupby(['ViewPosition']).count() "],"execution_count":54,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>patientId</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>Target</th>\n","      <th>class</th>\n","      <th>AccessionNumber</th>\n","      <th>BitsAllocated</th>\n","      <th>BitsStored</th>\n","      <th>BodyPartExamined</th>\n","      <th>Columns</th>\n","      <th>ConversionType</th>\n","      <th>HighBit</th>\n","      <th>InstanceNumber</th>\n","      <th>LossyImageCompression</th>\n","      <th>LossyImageCompressionMethod</th>\n","      <th>Modality</th>\n","      <th>PatientAge</th>\n","      <th>PatientBirthDate</th>\n","      <th>PatientID</th>\n","      <th>PatientName</th>\n","      <th>PatientOrientation</th>\n","      <th>PatientSex</th>\n","      <th>PhotometricInterpretation</th>\n","      <th>PixelData</th>\n","      <th>PixelRepresentation</th>\n","      <th>PixelSpacing</th>\n","      <th>ReferringPhysicianName</th>\n","      <th>Rows</th>\n","      <th>SOPClassUID</th>\n","      <th>SOPInstanceUID</th>\n","      <th>SamplesPerPixel</th>\n","      <th>SeriesDescription</th>\n","      <th>SeriesInstanceUID</th>\n","      <th>SeriesNumber</th>\n","      <th>SpecificCharacterSet</th>\n","      <th>StudyDate</th>\n","      <th>StudyID</th>\n","      <th>StudyInstanceUID</th>\n","      <th>StudyTime</th>\n","    </tr>\n","    <tr>\n","      <th>ViewPosition</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>AP</th>\n","      <td>21817</td>\n","      <td>14308</td>\n","      <td>14308</td>\n","      <td>14308</td>\n","      <td>14308</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>0</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>0</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>0</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>0</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","      <td>0</td>\n","      <td>21817</td>\n","      <td>21817</td>\n","    </tr>\n","    <tr>\n","      <th>PA</th>\n","      <td>15812</td>\n","      <td>2649</td>\n","      <td>2649</td>\n","      <td>2649</td>\n","      <td>2649</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>0</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>0</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>0</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>0</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","      <td>0</td>\n","      <td>15812</td>\n","      <td>15812</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              patientId      x      y  ...  StudyID  StudyInstanceUID  StudyTime\n","ViewPosition                           ...                                      \n","AP                21817  14308  14308  ...        0             21817      21817\n","PA                15812   2649   2649  ...        0             15812      15812\n","\n","[2 rows x 41 columns]"]},"metadata":{"tags":[]},"execution_count":54}]},{"cell_type":"code","metadata":{"id":"RF9jaQmypvQB","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":301},"executionInfo":{"status":"ok","timestamp":1593947543947,"user_tz":-330,"elapsed":284301,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"daa1f1ed-59f3-4780-aaef-e6e7a605638c"},"source":["# Bifurcation of PA & AP variable\n","fig = plt.figure(figsize = (10, 6))\n","ax = fig.add_subplot(121)\n","g = (Entiredata['ViewPosition'].value_counts()\n","    .plot(kind = 'pie', autopct = '%.0f%%', \n","          labels = ['AP', 'PA'], \n","          colors = ['green', 'red'], \n","          startangle = 90, \n","          title = 'Bifurcation of ViewPosition', fontsize = 12)\n","    .set_ylabel(''))\n"],"execution_count":55,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQwAAAEcCAYAAAAyWooIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd3hUZd7G8e+ZySSTSSMk9CqCR7CADcFFbIuddV0LRRFUFHEXXVddWdsrrm2tiIq7FrACsrYVsKEiLtKkKiAPRUoCCQmEZDK9nfePGdaIlAkJOVN+n+vKBZlMztwzmbnneU4bzTAMhBAiHhazAwghkocUhhAiblIYQoi4SWEIIeImhSGEiJsUhhAibmldGLqu/1PX9fvqfD9a1/Uduq67dF0vMjnbal3Xz2zi29R0XZ+s6/puXdcXH8LvX6Xr+ueHI1tjOVhGXddP13VdNWWmZKKl8n4Yuq5vBloBYSAIzAduUkqV7OO6NsAJ9FFKrWzCmOi6/hpQqpS6tylvdx85TgemArpSyr3Xz/oAXwKtlFKuvX62HHhVKfX8Ycj0NdAHCAE+4Bvgj0qpskZavgF0U0ptaIzlpbp0GGEMVErlAm2AHcBz+7leK8AOrD6UG9F1PePQ4iWUTsDmvcsCQCm1ECgFLq97ua7rxwI9iBbN4fKn2N/wKKAZ8MxhvC1xAKnwJI+LUsqn6/q7wPg9l+15ZwfeAJbHLq6ODcevAzYBNqVUKHb9r4G3lFKv6Lo+ArgBWAxcA7yo6/rDwENEX1TNgB+AAUopr67r/wZOB7KBlcBopdRqXddvBK4CDF3X/wzMUUoNjI2ORiqlvtB1PQv4B3BlLON04C6llD82bXmL6IvoLqKjqbuVUpP39Tjout4W+CfQD6gC/qGUelnX9euBFwCbrusu4Cml1P/t9euvx+7ra3Uuuwb4WCm1K/aYjFRK9Yvd1tFEC/okoBK4Tyk1Xdf1I2KPd3OlVETX9ZeBS5RSLWO/9yawVCk1vs7toJSq0nX9PWB07HqnAc8SLZJ1wK1Kqfmxn40A7gdaADuBe5VSb9fNqOv6N7FFr4yNNK4n+qbyllKqfWw53YEXgV7ANuBvSqmPYj97DXADnYH+wBpgqFJq474e+1SQDiMMAHRddwCDgIV7/0wptQ44JvZtM6XU2XEu9lTgJ6Kjk4eBJ4m+OE4DmgN/BSKx634CdANaAsuAt2O3/VLs/48rpXKVUgP3cTv3EB2W9wJ6Ar2ButOX1kAB0I7ok/4FXdcL95N5GtGSbEu02B7Rdf1spdSrwE3AgliOvcsC4E2gv67rHQB0XbcAQ4kWyS/oup4DzAamxO7zYGCirus9lFKbiE7/TohdvT/gir04Ac4A5u5jmcXAZcByXdebA7OACUAR8DQwS9f1othtTwAuUErlEf17rNh7eUqp/rH/9ozd53f2uj0bMAP4PHYfxgBv67qu17naYGAcUAhsIPo8SFnpUBgf6rpeDdQAA4AnGnHZ25VSz8VGIH6io5JblVLblFJhpdR8pZQfQCk1SSlVG/v+AaCnrusFcd7OVcCDSqkKpVQl0SfosDo/D8Z+HlRKfQy4AH3vhcRe6L8hOjrxKaVWAK8QHSUcVGzdz9d1bvscIIvoC3dvFxOd3kxWSoWUUsuB94ArYj+fC5yh63rr2Pfvxr4/AsgnOgrbY0Lsb7gSKAP+AlwErFdKvRlb/lRgLbCncCPAsbquZyulypRShzLV7APkAo8ppQJKqa+AmcCQOtf5QCm1OPYceJtoqaesdJiS/D42rLcClwBzY+9y5Y2w7LorT4uJrgP51XA0dtsPE32xtODnUUcx0SI7mLbAljrfb4ldtseuPdOmGA/RJ/q+llOllKrda1knx5Fhj9eBu4FHiBbHNKVUcB/X6wScGnuh75FBdJQC0cL4HdHRzjf8XEQ+4L9KqUid37tFKfVK3YXHplZ1H5M996WdUsqt6/og4A7gVV3XvwVuV0qtrcf9hOjjVbJXli1ER3J71H0e7e9xTxnpMMIAIPaO/z7ROX6/OH5lz4o/R53LWu91nbqbmHYSfbIfuY9lDSVaVr8lOnXoHLtc28dy9mU70RfgHh1jl9XXdqC5rut5ey1rWz2W8T7QXtf1s4A/sI/pSEwJMFcp1azOV65SanTs53OJrtM5M/b/eURHP/ucjuznvnTa67L/3Rel1GdKqQFEV3avBV6O8/7tfRsdYlOvX91GOkqHEQYQ3ceA6DtaIfDjwa6vlKrUdX0bcLWu6/8ChrPvMthz/Yiu65OAp3VdH0Z05Vlvousr8ohOWXYRLaBH9vr1HUCXA8SZCtyr6/p3RMvlfqIrOutFKVWi6/p84FFd1+8gurLweqJTnniX4Y6tPJ4MbFFKLdnPVWcCj8Uei2mxy3oBLqXUj0qp9bque4GrgUeVUk5d13cQXUcxMY4oHwPP6bo+lOhK4MuIbq2Zqet6K6LTiS8AL9EpWmQ/y9nz2O9rs+oioqOGv+q6/hTRQhsInBJHvpSUDiOMGbG1/k6i04Lh9ZjP3gDcSfSFfgzR/TgO5A6iW0a+I7YFguhj/AbRoew2omvS917x+irQQ9f1al3XP9zHch8ClgDfx5a/LHbZoRhCdISzHfgA+D+l1Bf1XMbrRN/d39jfFWLTnnOJrhTcTnTo/g+i6zz2mEt0OlVS53uN6P07IKXULqLrSW4n+vf5K3CxUmon0cf8L7HbrSI6ahm9n0U9ALwee+yvrPsDpVSAaEFcQHQEORG45hCmNikjpXfcEkI0rnQYYQghGokUhhAiblIYQoi4SWEIIeImhSGEiJsUhhAiblIYQoi4SWEIIeImhSGEiJsUhhAiblIYQoi4SWEIIeImhSGEiJsUhhAiblIYQoi4SWEIIeImhSGEiJsUhhAiblIYQoi4SWEIIeImhSGEiJsUhhAiblIYQoi4SWEIIeImhSGEiJsUhhAiblIYQoi4SWEIIeImhSGEiJsUhhAibhlmBxCHnQVoDXTY8+UJeo70Br3dgE42q625RbNkxL6ssa8MawRNi0RCGEaESCRCOBwgGKwmHK5E03aQlVVCbu42oBKoiH2tA3aadUfF4acZhmF2BtE48oE+wXCwX22g9njDMI6wWW1tHDZHc3fAHSh3lQc2VW/SNlZttG+q3pS5tWYrJc4SKtwVBMIBguEgwUiQYDjIh4M/5Kz3lhnaAw9oZGSA1QoOBxQXQ8uW0KJF9KtVqzDt2vlp0yZE27YGnTtnA348nvXY7cvIzV0GrI597TL10RGNQkYYyasj8BtXwHV2MBw822FzdFhVscrzxaYvclZXrM4ocZZQUlNCqbMUf9ifDWTHu+BwJAzBILjdP19YVQWlpXtf1Qo4fnFJ27aZHHPMifTocSInnODhhBOCdOuWTSTiw+dbRmHhR1gsc4Dvgcih3XVhFimM5GAFjgP6VXmrzsu0Zp4WMSKOBSULgrN/mp07b+s8bVnZMoKRYIHZQdm+Pfo1ezbULZP27TM57bQzOffcPpx3XoiiIg2PZwFFRTOAOURHIVIgCU4KI3FZgTNr/DUjs6xZl1S6K0NzNs/JmLN5Tva8rfPYULUBwG5yxviVlsL06TB9ejRzmzZw5pm/5bzzfsO554Zp1gwCgU8pKJgMzAaCpuYV+ySFkVg04AR3wD0CGFbqLLW+vOzlnKmrplq21243OVojKyuDqVNh6tToVKlDB7j00su49tpz0XUrweD75Oe/DnwNhM2MKn4mhZEYugTCgau9Ie8N3qC3cPLyyVlvfP9Gxtqda83O1XRKSmDCBI0JE/Lp2BEGDbqK6677PR07GkQi75Cb+wYwH5m2mEq2kpinRcSIXFnjq7nJarF2nfrDVF5b+Zp9YelCs3Px+dWf89vpSwzt7rs1s7PQtSsMGRLmuuu8FBbuJi/vYSyWNwGP2dHSkRRG0+vm9Dvvy7BkXPHx+o/Dk5ZPypn902xCkZDZuf4noQqjrjPOgLFj3fTvbwAv4XCMB0rMjpVOZErSdE6u9lWPs2rWsycsmpAxfuH4jF1e2TWhXubOhblzc+jSBW677Y9ce+3NBIOzadbsEcD8oVkakF3DD7/Tq33VCyvdlXPHzR13fpun2tjvm3OflEVD/PQTjBmTRdu2dsaNu4jy8i+orv4BOMfsaKlOCuPwObXaV/1tuav809s/u713u6fbOcYvHG9xB90H/00RH6cTxo+30K5dDjfddCzbt/+Hmpp5wAlmR0tVUhiN74QaX81XOz07vxr7xdi+HZ/p6Ji0YpIWjMhuBYdNJALvvAOdO+dwzz19qa7+lpqa94AjzI6WaqQwGk+h0+98s9pX/e39X99/Rvun2zv+tfRfUhRNKRiEF16w0KFDNs8+OxC3ew0u14tAC7OjpQopjIbTgMvdAfdP01ZNu6LjMx2zJyyaYPGH/WbnSl8uF/zf/9no0sXOm29ei9e7Gb//dqJ7z4oGkMJomHY1vprPNldvfu3ct85tNmrmqKzaQK3ZmcQeFRVw881Z9OzpYOXKcdTULAd6mB0rmUlhHBpLMBwc7Ql61IRFE87Un9dz5pfMNzuT2J/166FPnxzGjj0Gl2sJHs84INPsWMlICqP+jq7x1SxZXbn6iVNePiXn/q/vtwXCAbMziYMxDPjnPy10757NwoV34HSuAU42O1aykcKIX6Yn6HnAFXAtu3fOvT1PeumknDWVa8zOJOqrtBTOOcfBH//YBafzG9zuZ6jHuULSnRRGfI6q9df+OL9k/p3dX+ie/fzi5y0RQ46BSmpvvaXRrVs2X301Cqfze+AosyMlAymMgzvfHXAvvXP2nZ0HvDnAUer81VmnRLKqqIDf/S6bsWO74HYvIxIZZHakRCeFsX+aN+Qdu9u3+/3z3jov919L/yWPVap68UUL/frlUFY2idraV5AVovslL4J9y3b6nf/etHvTvT1f7Jn9bcm3ZucRh9uKFdCjh4Nvvx2C07mI6JnWxV6kMH6tvdPvXPr5xs8vPPmlk3NKnHL0dNpwOuHCCx2MH38MbvdqoI/ZkRKNFMYvneYOuL9/dN6j3a749xXZ3pDX7DyiqRlGdC/RIUOa43J9RTh8mdmREokURkwwHBxZ66/94op/X1H42LzH5Dwh6W7GDDj99GyczjcJBEaZHSdRSGGApdZf+88yV9mzp7x8SvYnGz4xO49IFCtWQO/e2eza9TQez31EjxtKa+leGFqtv3bS2l1rhx3/4vEOtUuZnUckmg0b4KSTHGzfPhaX6znS/DWTzndecwVc/9pQteHys18/21HjrzE7j0hUZWVwyikO1q0bgdM5DbCZHcks6VoYmivgem7T7k1Dz3z9zBxXwGV2HpHoqquhX78cFi++CKfzM9J0d/J0LAzNHXA/VVJTMqL/a/1znH6n2XlEsvB64YILHHzxRR+czhmk4Um0064w3AH3I9trt4/qN7lfTrWv2uw4ItmEQjB4cDZLlvTF6XyLNHsNpdWd9QQ943a4d9zym0m/cVR5q8yOI5JVMAgXX+xgw4aLcbkmkEZbT9KmMLxB7z2V7so7Tnv1NEelp9LsOCLZeb1wzjk5lJdfi8dzr9lxmkpaFIY36L1zl3fX3X1f7evY4d5hdhyRKqqroX9/BzU1YwkERpsdpymkfGGEI+HLawO1D/R9ta+jzFVmdhyRasrK4PTTHbhcTxGJXGF2nMMt1QvjeF/I9/r5b50v57EQh8/GjXDWWdl4PK+R4qf9S+XCKHIFXJ/dOOPG7OXly83OIlLd99/DiBEOXK6PgWKz4xwuqVoYGU6/86OXl77cfMqqKWmzBluY7L334OWXC3A6PyBFPwMlJQvDFXA9taJ8Ra87Z98pZ04STevOOzNZvfoE3O6HzI5yOKRiYQz0Br03XPrOpY6wETY7i0g34TD8/vc5eL23ABebHaexpVphdHQH3W9fMu2SbNkxS5imogIGDnTg8UwFupodpzGlUmHYnH7njIe+eSh7QekCs7OIdLdwIdx1Vza1tZ8CWWbHaSwpUxjugPvxJduXdP3HvH+k3QFBIkE9/7yVb75pg8fzoNlRGkuqFEY/f9h/46B3BzkMDLOzCPGzkSMdhEJjgBPMjtIYUqEwbLX+2jdumnmTY6dnp9lZhPil8nIYM8aO0zmdFDjxTtIXhj/kv3XFjhUt/73m32ZHEWLf3nhDY8mStng895gdpaGSvTDahY3wuOv/c32O2UGEOKDhwx1EIn8FjjE7SkMkdWHU+Gr+OX7heNv6qvVmRxHiwEpL4fbbs2JTk6RdMZ/MhXGuN+Q9+6FvHkr6eaFIEy+9ZGHVqk74fH8xO8qhStbCyKr1104a+dFIh3w6mUgq116bQyRyP1BkdpRDkZSF4Q16x84vmV84a/0ss6MIUT/r1sGUKVZcrnFmRzkUyVgYR0SMyF2jZo5ymB1EiENy7712NO064Aizo9RX0hVGja/m5UfmPWLbUrPF7ChCHJodO+Dpp23U1DxldpT6SrbCON0T9PR54tsnknYtsxAAPP54BoZxPnCi2VHqI6kKY7dv94Pj5o5zBCNBs6MI0TAuF9xzTxY1NS+QRB9TkEyFcYyGdurrK19PmgdXiAN66SULtbXHAeeaHSVeSVMYNb6a+5+c/2SmL+QzO4oQjSMUgjvuyKG6+jGzo8QrWQqjvc1q+90L372QkudJFGns3XchEDiKJDnbeFIUhivg+usry17R5LNQRcoJh+Hxx+3U1CTFgWnJUBjNrJp15JPzn0yZsxYJ8Qsvv2zBZjsfaG92lINJ+MLwh/x//Eh9ZJQ4S8yOIsTh4XTC5MkaHs9tZkc5mEQvDHsoErrzof8+JHt1itT29NNZaNooIKFP1ZDQhRExIsMWb1tsXVWxyuwoQhxeP/0Ec+ZAODzC7CgHksiFobkCrvvHzR2Xa3YQIZrEo4/m4HbfTQLvyJXIhXGCO+AunLtlrtk5hGga8+ZBTU0+0NvsKPuTsIXhC/kGv/X9W/JRhyK9TJpkx+UabnaM/UnUwtAC4cDV76x+R86mJdLLlCkZaNoQEvTDnBO1MI73Br35S8uWmp1DiKa1bh2UllqBM8yOsi8JeZi4L+Qb9PYPb8t0RKSnV1/N4d57ryU//6v6/Jqu65uBVkAYcAOfAH9SSrl0XR8BTAYGK6XeOdRoiTjC0ALhwDXTVk2T6YhIT9OmWbDZLgUO5U1zoFIql+h5Nk4G7o1dPhyoAq5pSLRELIxj/CF/s++2f2d2DiHMUVICa9eGacBh70qpbURHGMfqut6J6BTnRuA8XddbH+pyE64w/CH/4Ck/TJHRhUhvr76aR3X1tYf667qudwAuBJYTHVUsUUq9B/wIXHWoy024wvCFfNdMWz1N1l+I9DZjhkZm5gDq/xr9UNf1amAeMBd4hGhhTIn9fAoNmJYkWmH0CEaCRYtKF5mdQwhzbd0KVVUa0LOev/l7pVQzpVQnpdTNRNdlHAFMi/18CnCcruu9DiVWQhVGIBy4/J1V72QYGGZHEcJ8M2faCIcbevq+4UR3NV+h63o5sKjO5fWWUIVR66+98JMNn8h0RAiAjz/Oorr6skP9dV3X7cCVRFd29qrzNQYYqut6vXerSKTCsOZm5vZcWLrQ7BxCJIZvvoHc3J4c2uZVgN8DXuANpVT5ni9gEtF9sM6v7wITacetYys8FcFd3l12s4MIkRBqamDrVh/dup0ELDjY1ZVSnff6fho/r7uoe7mXQ/xs10QaYZz2zeZvEnL/eSFM8/nndsLhhNlNPGEKY7d394A5m+fImbWEqOurrzKprr7I7Bh7JExhWC3WvgtKDzrqEiJum202juvalTtaR3ds/DonhyEdOnDykUfymy5duKdVK1zaz+eqeaWwkFOPPJKLOnVCZf682mCp3c7Nbds2eX4AliwBu/04c2781xKlMPLsGfYitVOZnUOkkAdbtuQ4388ffFVrsTB61y7++9NPfLx5MzsyMni8RQsAKqxW3i0o4MtNmxhSU8PTxcUAhIB/tGjB3RUVZtyF6P4YVqsDKDQnwC8lSmEcv6FqgydshM3OIVLErLw88iIR+nq9/7tsYG0t/T0esg2DgkiEK2tqWJ6dDUCZzUYPn4/cSIS+bjcltujRCa8XFnK22037UMiU+wHAhg0e4BjzAvwsUQqj1+Jti2X/C9EoXBYLE4qK+Ftl5QGv9112Nl39fgA6BgKsy8rCabGwICeHroEAZRkZzMrL47qqqqaIvX8rVtiAY80NEZUQhVHtq+67eNvibLNziNQwvqiIy2pqaH2AUcG3Dgcf5udzy65dABRGItxUVcXw9u35OieHuyorebhFC+6srOSL3Fyubt+e0W3bUp5hwp4Iy5Y5cLlOaPob/rWE2A8jYkR6ryhfYXYMkQJ+zMpigcPBB1u27Pc6K+x2bm/dmgnbt3NEMPi/yy+ureXi2loguoI00zDo7vdzSadOzNqyhS9zcvhHcTHPlJcf9vvxC6tWgc93Mrnmn0A/IQrDYXO0X7drndkxRApYlJ3NNpuNs7p0AcBjsRAGLu3YkQ+2bmVNVhaj27blkR07frF+oy6fpvF0cTEvl5ayJTOTNqEQuZEIx/l8/Kt58ya8NzGrV4PD0a3pb/jXEqEw7BmWjKwqr8nzRJESBtXUcFFslAAwqbCQbTYbD1RUsC4zk5Ht2nFfRQVnu937XcaLzZvzh5oaWoXDaMCmzEx2Wq0scjjoUGdE0mS2bwfIAloAB14xc5glQmG0dPqdPgNDdtoSDZZtGGSHf97a5jAMMg2D5uEwTxQXU2W1ck/r1uz5qPS2wSCz6kxfNtpszMvJYfrWrQC0DIe5oaqKizp3pigU4pmysqa8Oz/bssVL9+5dkcKg1U7PThNqW6SDMbGVmgCP7tjBozt2HPD6RwaDvBcriz1G7t7NyN27D0u+uJWVQffuLcwNkRhbSVqVuUxqbSGSRVlZBtDS7BgJURilztJEGOkIkbi2bbMjhQFA69KaUjmkXYgDKS+34nK1NzuG6YVR66/tWOYqk8PahTiQigrw+9uZHcP0wgiEAx3KXU28I4wQyaaiAgyjjdkxTC8MA6PNDveB11wLkfYqKsBqla0kGZaMljtcUhhCHNCuXWCzNTM7humFkWXNKtjp2Wl2DCESWygEmmb669X0AAaGfAiJEAeTIC8T0wsjYkSCmVY5FYYQB6XVOZ+gSRKhMEJSGAnIYvpTQ9QVHWFIYcgII/HM/mk2XH01mHGyGLFvMiWJMgxDCiPBPDH/CWrtWsQYNSoxnqUiYZhfGBgyJUlAN3x1m5WHH9bIyzM7ioDoCEPWYYBhGAEpjMQzfc10Sj3lYePuuyNmZxHIOow9DKQwEtUVs4ZbueUWC2Z9iI/4WV4ehEL7PqdgEzK9MAApjAS1aNsillasCBuPPy4fGGO24mIIBk0+i08CFIZhGAGb1WZ2DLEfl30wxMqll1o5NiE+FiN9tWgBkYjpu0SbXhiapvllhJG4tjq38tHmTw1jwgRZl2Gm4mKwWEw/rNv0wrBq1soWDtMPwhMHMPSDYVrk5JM0zjrL7Cjpq7gYsrK2mx3D9MIosBf80K2om4kfXCkOxhPy8NzqSZoxcaKB+Vv20lOrVmFycraZHcP0XfksmmXjMS2O8QKmbPBv/0V77Dvt/6vOUHaIzQM3A9BMNaNwbSEWv4VgfpCKEyvwtYx+Gnje5jxaLGuBYTUo71OOt1V0Bbat1kbrBa0p+W1JAtRx47nts9sYfeswI2vwYI2pU82Ok37atPEDpq/DML0wgI3dirqZukdhxckVOLs6f3GZfaed4hXFlAwowV/op2BDAW3/25afLv0JgOIVxWy5YAv2Kjstl7Rky0XRz7ZosbQFlSdWplRZ7HHngnGWZ595xtDee08jEDA7Tnpp3TqEyZ9JAonxtN7QIb9Dwp0EOMOdQaAggL+5HzRwHuEkw5+B1W/F6rcSyg4Rzg7jae3B5opu5cndmksoO4Sv2Gdy+sPjucXPsdsSiBi33CIrQJtap04GIOswgN1AqNhRbFqA4pXFHPnekXT4vAPZO6IfIu9p6wEjOtIgAgUbC/AV+gjbw4TtYawBKxmeDBxlDgIFAbSgRvNVzdnZy/RR42E1fPbNVu6/30JhodlR0oemQefODmCt6VGMBDgKrspbte6iKRd1W1i6sMlv277Tjr/ADxbI25IXnV5csIVgbpDma5pT9H0RAJHMCKVnluIv8gOQXZ5N8cpiDKtB5UmV5P+Uj7+Zn2BukKIfijAsBpUnVhJolnpD9/U3/BA+cvoXaLfdJmd7bwodO8Lq1bvJzTXhk6B/KRFGGADrjiw80pQb9hX7MGwGhtXA2cWJt4WXnO055G/MJ39jPpsv2sz6wesp61tGu7ntsHqirxFvay8l55VQ+ttSDAyyqrJwHuGk9YLWlPctZ9exu2i1qJUp9+lwu+yjq6zceKOVTp3MjpIeevQAn2+92TEgQQojLzPv+67NuybUvNi+2467nZtgfhC06BQlZA+RvTP7l1c0oOWSllSeXInVb0UzNEI5IfxFfrKqs8wJf5h9v+N75pcvjhhPPim7jDeFHj0gO3uZ2TEgQQrDZrWt79Gih6epb9cSsODY7kALaxCBvE15ZFdk427jxlfkI2d7TnSFpgGOMgeZtZnR6UsdBRsL8Bf68Rf6CWeF0UIamTWZZO/IJpibup8xffkHQyxceIGVE04wO0rqO+EEDzk5K8yOAYmxWRVg49HFRzf5u5UW0Sj+vphMZyaGZhDID7C9/3aC+UGCeUFsLhvtv2iPNWAl6Aiyo/cOggU/l4DFZ6GZakbJuSWxC6KbaNt/2f5/+2ekqnJXOdPW/8cYPHGiofXtmxBvPCnr+OODwBqzY0CCrPQE2rgCrk15j+al5hg+Rdkz7NSOKTcyBg3W+PRTs+OkLrc7gMPRjgTYcStR3hnKDMNwHVV0lNk5RD34Qj6eWDlRM154wZCTBh8mHTsC+EiAsoDEKQxCkdC8fh37mR1D1NPdX92NtzDXMIYPT4ihaso54wzw+eabHWOPQy4MXde/1nV9t67rWXUue03X9YCu6y5d16t0XZ+t6/rR8SyvMLvw03OOOKfJV3yKhhvzzd8sPPGERnb2wa8s6mfAAA/Nm880O8Yeh8KGjc8AABHkSURBVFQYuq53Bk4HDOB3e/34caVULtAeqABei3Ox887qfFZCbVoV8Zm0YhIVYWfYuP12+fs1tgEDIsDXZsfY41BHGNcAC4mWwfB9XUEp5QGmAPGeqmlNYXahpXVu60OMJMw09JORVu66y0Kxebv4p5z27SE/3yBBtpBAwwrj7djXebqu/2qXRl3Xc4GrgOVxLjPiDrgXntVZTtKSjL7a/BVrdq8PGw89JDtzNZYzzgCvdwHRkXxCqHdh6LreD+gETFdKLQU2AkPrXOUOXdergQ1ALjAi3mUXOYreG3jUQFmPkaQu+88QK8OGWena1ewoqeHcc70UFc0wO0ZdhzLCGA58rpTas5lnCr+cljyplGqmlGqtlPqdUmpjPZb92fldz5dTOiUptUvxZek3EWP8MzLKaAwDBoRJoPUXUM89PXVdzwauBKy6ru/ZjTELaKbres9GyLPRarE6j2t5XPYPFT80wuJEUxv0wVWWyj9tQTv1VFi0yOw4yatTJygoSKj1F1D/EcbvgTDQA+gV++oO/Jfoeo0G09Bmnd/1/ISZs4n6qfJV8Zp6xzBefFG2mDTElVdGCIU+BBLqcaxvYQwHJiultiqlyvd8Ac8TXcHZ4GNT8rLyPrqs+2W1DV2OMM+oWTdpoS6dNS65xOwoyevaa93k579udoy9JcqxJHVle4PenfrzuqPEWWJ2FnGI7u53Nw/pNxnaEV00QnJS+Hrp0gV++KEWh6M5kFAPXsLsGl6HNxQJTR3Ra4SsOEtij8x7BFd2hmHccEPCvSMlvEGDIoTD/ybBygISc4QBcHJZbdnX7Z5ul2MkziZoUU9Djh3C2+dMROvQAVwus+Mkj3XraunWbSAw1+woe0vEEQbAUofNUdm/U3+zc4gGmLpqKtt8FWFj7NiEWnGX0I46Ctq2DQPzzI6yL4laGEZuZu6E0SePlp24ktyVs0ZYue3PFtq0MTtKchg8OIxhvEN0a2TCSdQpCUALT9CztfWTre21AdloksyWjVgY7vX1WrQRI+Qs4wezdauLDh3OAxLmkPa6EnWEAVDpD/nnDjp2kNk5RANd+sEgK1dcYaV7d7OjJLazz4b8/F3AArOj7E8iFwaF2YXPjek9RoYXSW5LzRZmbpltGM89l5DD7IRx++1u8vL+QQIdbLa3RJ6SAGS4A+7Kk18+udnanaZ/6JNogNzMXKrHbDesFw/UmJtwK//N1749rFvnJTu7FZCwb5IJPcIAQpqmvTryxJGpe77+NOEKuJi4+jXNmDgxgibHF/7Kn/4UJBx+iwQuC0j8EQbAUTW+mhVtn26b7QnKRpNkZsGC59aKSNaomy1Mn252nMSRkwPl5T5yc48jelqIhJXoIwyAdcCXt556q8x/k1yECGMXPWQxnn3WIDPT7DiJ47rrDEKhuSR4WUByjDAAjnb6ncs6PNMh2+l3mp1FNFDVH0vCzR552qI984zMTSwWKC1106bNuSToptS6kmGEAbDWMIyP7jztzoTbt17U37Vf/MnKuAc0CgrMjmK+YcMMHI71JEFZQPKMMACOcAfcazqN72Tf5d1ldhbRQD/dsDrcedqnaLffnr47c2VlwdatHlq2PI8E3RV8b8kywgDYFDEiU+45/Z6A2UFEw10+c5iVm26y0qGD2VHMc/PNEbKyFpAkZQHJNcIAaOsJejZ2ndDVXuYqMzuLaKD5w+ZG+nxXZmiDB6ffKCM/H0pKvOTn9wZWmR0nXsk0wgDYbhjGKw+c+YDf7CCi4S7/YIiFgRdb6dkYp4NNMmPHBoEZJFFZQPKNMCB6UNrmHi/0cGyp2WJ2FtFA7/xhmnGFs11E63d6+owyWreGDRu85OQcDWw1O059JNsIA6ASmPDQ2Q95zQ4iGm74RyO0yPHHWRgwwOwoTefBB33AqyRZWUByjjAAmnmCnpLeL/fOXV252uwsooEeO+cx/trqD4Z29NEakRQ/106PHvDdd24cjk5A0m3uS8YRBkC1zWK7453L33FnWBp8onJhsrFfjsVX3Mwwhg1LynevuFks8Pbbbmy2O0nCsoDkLQxsVttLHQo6rBjbb6zszJUCbp13j4Unn9Cw282OcvjcckuYI45Yi832L7OjHKpknZLs0cET9PzY55U+OfJJacmvYvSmcPH4lzTt0UeT9o1svzp3hlWrvOTk9CJ6fFRSSvY/TEmmNfPW6VdMl6lJCrjqsxut3H23haIis6M0vjfecJOR8XeSuCwg+QuDDEvGpDa5bb67r/99cs6MJDf7p9msq94YNh58MLWOTL72WoOePUvJynrC7CgNlexTkj3aeYKetb+Z9JvcFeUrzM4iGqBHix6sGvEd2nHHwU8/mR2n4Vq3jp5JKy/vNCDpn5xJP8KI2WbPsN/87yv+7bZZbGZnEQ2wpnINX2/7NmI880zyjzI0DV5/3YPV+jwpUBaQOoWBRbO81TKn5YIHznxApiZJ7soPh1qMc8620ru32VEaZuzYEH36bMDhuNfsKI0lVaYke7R2B93rzph8Rt7SsqVmZxEN8NrvJhvXhI81tFNOSc43tTPPhJkza8jJORYoNTtOY0nOP8b+lWdnZI/8z+D/eArthWZnEQ1w48xRWuiorhoXX2x2lPpr2xbef99LTs7lpFBZQOoVBhbNMr3AXvDKR0M+8li19DmeKdUEIgEeXjZeM1543sCaRH/HjAyYMcNNVtY/gC/MjtPYUq4wAHIzc/9yfKvjl0y4YIIcBp/Exs0dhzs3yzCuvz555s1PP+2na9fFOBx/NzvK4ZCShQGE87PyLxl2/LCKG068IcWPZkpto7++08Jjj2nk5Jgd5eCuuMLg2muryc+/HEjJ512qFgZAdV5W3oDx54/39O/U3+ws4hC99cNblAV2hY27/prYL8C+fWHyZC+5uRcBVWbHOVxSbSvJvpzj9Dtn9H21b/aayjVmZxGHoF/Hfnxz5adoRx4JO3aYHefXevSABQu85Of/AfjU7DiHUyqPMPb40mFzjJozfI6nbV5bs7OIQzBv6zy+37UmbDzySOLtzNWhA8yd68XhuJEULwtIj8Igw5LxZl5m3qNzhs9x52flmx1HHIJLPxhkZfBgK7pudpSfFRXBf//rITf3XjIy3jI7TlNIi8IAyLZlP9wmt820WUNneTKt8jF9yWZT9SY+3fpVxJgwITFGGTk5MGeOm+LiidjtT5sdp6mkwzqMuqxOv/PDleUrz77g7Qsc7qDb7DyiHvIz89k9phTLBRfCPBM/ysNmg9mzPZx44gfk5Q0D0uZFlDYjjJhwflb+pb1a9/pw3nXz3LI3aHJxBpz868e3MCZONG+Lid0OM2d66NXrv+TljSCNygLSb4Sxh+YKuJ4td5Vff/rk0x3lrnKz84g4WbDg/XOlkTlylMa77zbtjeflweefe+jR43Py868E0u5Ax3QbYexh5Gbm3to2r+3jS29c6uncrLPZeUScIkS4Z9GjmvHsswa2JjyVQXExLFzo5phjppKffxlpWBaQvoUBYDhsjnFF2UV3LblhibdHix5m5xFxenLBkzgzjYhx001NMzzu0AGWLPHQqdPz5OXdQIruxRmPdJ2S/EIoErraHXC/NODNAdnfbf/O7DgiDn84+g+8e8FktA4dwOk8fDd01FHRTaf5+Q9gtyf9KfYaKp1HGP+TYcl4q8BecOVXw7/ynNX5LLPjiDi8v/Z9trq3h4177zl87/YnnQSLFnkpLLxFyiJKCuNnM3Mzcy+cMWSG+/Iel5udRcTh8hnDrPzxTxbatWv8hY8YYTB3roe8vKHYbK82/g0kJ5mS/NqJroDrkzdWvlFw22e3ZQXCAbPziANYdM28yCkLtxra0KGNc9IMmw1eeMHHkCE7yc09D5ADkOqQwti3whp/zZRtzm2nXzLtkpwNVRvMziP2o31+e7aOWovWpy/80MAPs2rbFmbNctOly3zy868AaholZAqRKcm+7S7IKriwa/OuY5ePWu4deuxQadUEVeos5f1NnxjGc881bF3G6adHP5lM1x8nP/98pCz2SUYYB9er1l8744O1HxTfNPMmuzfkNTuP2Isjw4HzljLDeukfNL78sv4LuO22MH//u5ucnCuBzxo9YAqRwohPntPvnLzTs/P8gVMH5sh5NRLPk+c+yV+aX2xo3btrxPuc7tgR3nzTTa9eW8jPvwjYfDgzpgKZksSnNj8r/4qOBR3HLB652HP9Cden7Y47ieqOz+/A36rIYOjQg19Z02D06AirV3vp3ftR8vN7ImURFxlh1F/3Wn/tzC83fdlm9KzR2XIcSuK4+ZSbef6UB6I7c/n3c/7nI46AKVPc9OixKXY8yI9NGjLJyQij/n7My8o7dkCXAS9uGLPBe/fpd4eyrFlmZxLAxO8mUoUnbPz5z78eAWoajBkT4YcfvPTq9Xfy83shZVFvMsJomCNrfDUTPUFPv5s/vtnx4doPzc6T9s4/8nw+vmQ6WufOUBU7F+9xx8Grr7o56qj1FBQMAtaZGDGpSWE0jt/W+mtfXlWxqsWomaNyfqho4P4AokHWjVwZ7vr+12gPPWzlscd8DBoUxGb7GzbbP4HEOGNXkpLCaDwZwXBwVDASfOzt79/O+NuXf7Pv8u4yO1NaOqXtKSy6ei5aOOxD014hJ+d+YLfZuVKBFEbja17rr30UGHbfnPuyXvjuBUsoEjI7U1rItGZy40k3Rh4880G/zWpbkpuZexOya3ejksI4fI6p8dW85PQ7ez4490HHm9+/qfnD8smNh4NVs3JNz2uMR8951JuVkfVdM3uz24DlZudKRVIYh5cGnFXtq34AOPnJ+U9mvvDdC9ZqX7XJsVJDob2Q4b2GR27ve7s3LzNPFdgLbgVMPDtw6pPCaDrH1fhq7rNZbQMnLZ9kGb9wfObG3RvNzpSUerfrza2n3uq99OhLLYFw4JMCe8FTwLek2Ql5zSCF0fTae4KevwAjl25fylMLnsqbuW4mYUNW3h9Iji2Hq467yrj9tNtdbXLbeOwZ9mdsVtskoNLsbOlECsM8duDy3d7dd4WNcJfnFj+XOXn55IwSZ4nZuRLKsS2PZUzvMf6rjrvKCIQD8wqzC58AviCNz6tpJimMxNDT6XfelmHJuGybc5sxbfU0x4drP7QuK1tmdi5TdMjvwAXdLmD0yaNruzXvFrJarBPtGfZ/AqVmZ0t3UhiJxQr09QQ9lwUjwSvDkXCzD9Z+YHlvzXv2rzZ9RapuZcmyZtG/U38uOuqiwO/13/tb5rTUfCHfl4XZha8DMwDZLp0gpDASmx6OhH9X46+5Kjsju/vXm78OvLP6ndxZ62ex07PT7GwN0q15Ny7oeoFxafdLa/u072P3BD0qNzP33Uxr5ifAMmSPzIQkhZE8ioELq7xVQ3NsOWeUOEsCS7YvsS7etjhnRfkKVu5YSZW3yuyM+5RpzeSooqPo0aIHvz3it76B+sBwbmZuIBwJf1JgL/gA+BLZEzMpSGEkJztwPNCz1l/bOxAOnJqbmdvNHXRHvt/xfWhh6cKc5eXLrSvLV7K+aj0Ro2nWD+Zm5tK9uDvdW3TnmBbHhE5sc6KnR4selpY5Le2ugKs8FAn9WGgv/NxqsX5CdA9MefIlGSmM1KEBnYGeoUjohBpfzWk2q+14e4a9cEvNFm+lu9LY5dllqXBXZFR4KrJ2eXZZdvt2s9u7mypvFbt9sX+9uwkbYbKsWWRlZP3i3wJ7AYX2QprZm1GYXUihvZA2eW38PVv19OlFui0vK8/mCri2AqsLsgqWWC3W1UQPId9Amn60YKqRwkh9+YAONAcK9/zrCXpa+UP+VmEj3AJobtWshTarLS/LmpWraZoWjoRDYSMcihiRYNgIBwzDCIYj4VoDY7emabtsFlulw+Yoz7Bk7AQU0WLYgmzuTGlSGEKIuMkZt4QQcZPCEELETQpDCBE3KQwhRNykMIQQcZPCEELETQpDCBE3KQwhRNykMIQQcZPCEELETQpDCBE3KQwhRNykMIQQcZPCEELETQpDCBE3KQwhRNykMIQQcZPCEELETQpDCBE3KQwhRNykMIQQcZPCEELETQpDCBE3KQwhRNykMIQQcZPCEELETQpDCBE3KQwhRNykMIQQcZPCEELETQpDCBG3/wc6DwICxBERFQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"G7DynkUssxZv","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":264},"executionInfo":{"status":"ok","timestamp":1593947543948,"user_tz":-330,"elapsed":284285,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"ab82adef-8558-4bfd-818f-d666092eb66c"},"source":["ax = fig.add_subplot(122)\n","g = (Entiredata.loc[Entiredata['Target'] == 1, 'ViewPosition']\n","     .value_counts().sort_index(ascending = False)\n","    .plot(kind = 'pie', autopct = '%.0f%%', \n","          startangle = 90, counterclock = False, colors = ['green', 'red'], \n","          title = 'Bifurcation of ViewPosition, Pneumonia Proof', \n","          fontsize = 12)\n","    .set_ylabel(''))"],"execution_count":56,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAARsAAAD3CAYAAAAt3PBsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXhTZdrH8e9J2rRNmrTQlrIj64OAwDi4IW64Im7oCCgC4ujL4IjbuM24zrjPjI67jgqK+7jCCMKwCe4oKCggD4ssZW0ptGnSNm2W94+TasECLbQ5aXJ/rotLm+3cOTn5nft5kpxjRCIRhBCiqdmsLkAIkRwkbIQQMSFhI4SICQkbIURMSNgIIWJCwkYIEROHHDZKqeeUUnfW+nuCUmqHUsqnlMo51Mc/xNpWKKVOjvEyDaXUS0qp3Uqprw/i/qOUUrOborbGcqAalVInKKV0LGtKRM1lPSql7lNK7VRKbd/f7YwDfc9GKbUByAdCQDXwBfAHrXVBHbdNBbzAsVrrZQdX+sFRSr0MbNZa3xHL5dZRxwnAm4DSWvv3uu5YYB6Qr7X27XXdd8AkrfVTTVDTAuBYIAhUAp8Af9Rab2ukx48A3bXWaxvj8Rqw3MuBSUAFEAZ+Au7QWk+PZR3xSCl1D3A7EMB83VcCf9Jaf9nIy+kIaKCT1rpwf7etb2dzrtY6E2gD7ACe3Mft8oF0YEU9H3cPSqmUg7lfnOkEbNg7aAC01l8Bm4Hf1b5cKdUH6IUZUk3lmuhr2APIBv7VhMuKpS+jzysbM3jeVkq1sLimePGf6LrJAz4D3ldKGXvfSCllP4RldASKDxQ0AA16c2utK5VS7wKP1VxW01EArwDfRS8uiQ4hrgDWA6la62D09guA17TWL0b3TFcBXwNjgGeVUvcD92G+IbOBH4DTtdYVSql3gBOADGAZMEFrvUIp9X/AKCCilLoe+FhrfW60K7tSaz1XKZUGPAwMj9b4NnCr1joQHWq9hvkGvBWzi/uL1vqlutaDUqot8BwwCNgFPKy1fkEp9XvgaSBVKeUDHtFa373X3adEn+vLtS4bA3yktS6OrpMrtdaDosvqiRnuvwWKgDu11m8rpTpH13dLrXVYKfUCcL7WulX0fq8CS7TWj9VaDlrrXUqp94AJ0dsNBB7HDKHVwHVa6y+i110O3IW5se7E7Bper12jUuqT6EMvi3Y4v8fcIb2mtW4ffZzDgWeB/sAW4M9a6/9Gr3sZ8AOHASdi7oEv1Vqvq2vd70t0HUwGngC6KqXOwQzwSmAYsAkYq7VeHF1u2+h6PRHwAf/SWj9Rq6afu+Sa7aPW89mA+TqPBroCbwF/wXxNBwGLgIu11rujtz8PeBBoByzF3G5/rPVYT2FuA52AWdE6K+tY7m2Y75dWQAFwu9b6g3qsm2ql1BTgZiBHKfVPzG6wE3AScL5Sagv7fo2youtqCFAOvAA8AAwGPgTSotv7u1rry/dVR4PmbJRSTmAE8FUdT2g10Dv6Z7bWenA9H/YYzPY3H7gf+CfmG2sg0BK4BbNFBpgJdMdc2d8Cr0eX/Xz0//+utc7UWp9bx3JuxxxK9Af6AUcDtYdcrYEszA3i98DT+9lDvoUZsG0xQ/EBpdRgrfUk4A9E97Z1BA3Aq8CJSqkOAEopG3ApZgjtQSnlAuYAb0Sf80jgGaVUL631eswh62+iNz8R8EXf2GBuRAvreMxc4CLgO6VUS2AG5hs0B3gUmKGUyoku+wlgiNbajfl6LN378bTWJ0b/t1/0Of9nr+WlYm6Qs6PPYSLwulJK1brZSOCvQAtgLeZ20CDRrvhKzOBYE734PMzXKhv4L+abumadf4i5w2oHnApcr5Q6swGLvAg4HTOkz8XcNv+CGcw24NrosnpgdqzXR6/7CPhQKeWo9VjDgbOAzkBf4PJ9LHMd5s42C3N9vaaUanOgQqM72suBAq31zujFl2KuZzdmOO7vNXoyuswumNvVGGCc1nouZgBtjb72+6obqH/YTFVKlQClmCv4H/W8X31s1Vo/Ge18Apjd0HVa6y1a65DW+gutdQBAaz1Za10W/fseoF80detjFPA3rXWh1roI88UaXev66uj11VrrjzA3WrX3g0RD4njMrqhSa70UeBHzBTig6FzXglrLPhVIw3zT7+0czCHZS1rroNb6O+A94OLo9QuBk5RSraN/vxv9uzPgwXwz1Xgi+houA7YBNwJDgTVa61ejj/8msArzzQNmyPdRSmVorbdprQ9meHwskAk8pLWu0lrPB6YDl9S6zQda66+j28DrmDuEej9+9Hltjz7mMK11afS6z7TWH2mtQ5gh3y96+VFAntb6b9GafsLcW49swHKf1Frv0FpvAT4FFmmtv9NaVwIf8MtOYAQwQ2s9R2tdjbkzzcAM7xpPaK23aq13Yb7p63z+Wut3orcLR0N9DeZOc1+GR9dNAeYOfFit66ZprT/XWoejy6vzNYoOsUZidjplWusNwCPs+d6pl/oOoy6IDkXswPnAwujedb+zz/VUe6I5F3PO51ctdHTZ92O+0fL4pdvJxQzBA2kLbKz198boZTWKa4Z6UeWYL0Bdj7NLa12212MNqEcNNaZg7gUfwHzR3opuiHvrBBwT3WBqpGC+ccAMm/Mwu6xP+CXEKoFPoxtSjWu11i/WfvDoUKL2Oql5Lu201n6l1AjgJmCSUupzzAnGVQ14nmCur4K9atmI2VHUqL0d7Wu978tXNUPOOuz9uOnRDqgT0Hav9WrHDI362lHr/yvq+LvmOeyxjqPDvQL2//xrb5c/U0qNwdxJHBa9KBNz+9+Xt7XWl+3jutrvu/29RrlAKr9+79Suv14aOmcTwpxk+jfm2PTdA9ylZpLUidnygzlcqa32x2E7Md8oXdlzrwxm23c+cBqwAbOt2w3UTHgd6OfrWzE3spq9c8foZQ21FWiplHLXCpyOmOPc+nofczh0CnAhcPI+blcALNRan76P6xdidpmbo///GeZcUiV1DKHqULNOauuIOW+A1vp/wP+UUhmY82gvYLbxDbEV6KCUstXamDtizg9ZpQBYr7Xuvo/r/ZjbbI29t9mG2AocUfNHdIK2Aw3bXlBKdcJc/6diDtNDSqml/LL9N1Tt98v+XqOdmF1/J8z5tJrrGlQ/NDBsoivqPMyx9Y8Hur3Wuig68XRZNKDGYgbJvm5fM8n3qFJqNObe4mjM+Rk35jCrGHNDeGCvu+/AHFPuy5vAHUqpbzBX9F2Yk8INorUuUEp9ATyolLoJc8z+e8xhWn0fwx+daH8J2FgzaVmH6cBD0XXxVvSy/oBPa/2j1nqNUqoCuAx4UGvtVUrtwJxPeKYepXwEPKmUuhRzwvwizEnV6UqpfMwh0FzMPbWPX7rJvdWs+7o++l6Eube+RSn1COYQ9FzMocwBRT9QWKC1vqc+t6+nr4EypdStmPNSVcDhQIbW+hvMuak/KaXuAxyY8y0H623gNqXUqZjd53WY2/EXDXwcF+Z2WwSglBoH9DmEumrb52sUDbW3gfujnVVLzO7qnw1dSH3nbD6MzjZ7MYcyYxswfr8Kcxa8GHMC+UAr+SbMT6C+IfpJT7TOVzDbty2YCbv3JPUkoJdSqkQpNbWOx70PWAx8H338b6OXHYxLMFvZrZjj87ujk2UNMQVzb/HKvm4Q7ZzOwBwzb8Vstx/GnOOpsRBzCFhQ628D8/ntl9a6GHNe6E+Yr88twDnRSUQb5ka1FfN1OInoJ1h1uAeYEl33w2tfobWuwtxwh2DuJZ8BxjRgONYB+Lyet62XaId+DmZwr4/W9SJmtwzmMHUZZgc9G/jPrx+l3svSmDuDJ6PLORfzqyRVDXyclZhzJV9ihvsRNNJ6qcdrNBGz2/sJs3t+A5jc0OUc8Et9QlhFKdUec95h4AFvLOKehI0QIibkh5hCiJiQsBFCxISEjRAiJiRshBAxIWEjhIgJCRshRExI2AghYkLCRggRExI2QoiYkLARQsSEhI0QIiYkbIQQMSFhI4SICQkbIURMSNgIIWJCwkYIERMSNkKImJCwEULEhISNECImJGyEEDEhYSOEiAkJGyFETEjYCCFiokGn3xWiuVNKbQDygRDmWR5nAtdorX1KqcsxT4k8Umt90GfBFHWTsBF1MTBPRZsX/dcKyATs0X/5mKcCro7+q4r+txjzlLU72Pd5wePBuVrruUqpdsD/gDuA2zDPRb8LGMMhnHJX1E3CJnllAr2AXlRU9MPvH4Dd3o7U1BZkZLipqgpRUlJFcXGIHTsMSksNqqsNgkEjMmKEa8ZPs/yBYCCclpIWcdgdOOyOSL4rn3aedmkZKRkp5dXlO6vD1ZtSbCmrPWmeVTbDtgEziDYC2zA7C0tprbcopWYCfZRSnTDPZ34x8B+lVGut9XZrK0wsEjbJIRMYRGXlYHy+40hL60laWhYbN5azdKmd775zsWKFwaZNUFRk/quqsgOOOh/t/PO5ftb1rnW719V5tTPVScesjq07ZXVqfVj2YUd3adEl1COnR3m3lt3C7T3tHa5UV4o34F3uSfNMT7Wnzge+Aiqb6snvi1KqA3A28D5mN7NYa/2eUupHYBTwSKxrSmQSNonJCQyksvI0ysvPweXqwfLl5Xz0USaLF9tZsQLWr4dwOKspFl5eXc6qnatYtXNVzUV2wF3zh9vh5viOx//m1M6n9hnSbch13XO6p5cFylZEw2cesIimDZ+pSqkgUArMAB4Avgeejl7/Bmb4SNg0IiMSiVhdg2gcbQmHL6SkZBwuVx9WrapkxgwX8+bZ+eILqGy8926krIzuk/uzr86moaLhw6mdT60e0m1IRTR8ajqfd4EfGmVB/DxBfKXWem6ty44HFgLttdbbo0Oq9cCRWuuljbXsZCedTfOWRzg8nJKS8aSl9eDDD0O88YaT+fPB7697CBSHyqrKmLV2FrPWzkq9ec7NqdHwOfKMLmccMbrf6Jvshn1TVnrW4zbD9iZmN9LYxmJOii9VSu19uYRNI5HOpvlJAc5j9+6JpKcfx4wZQV56ycWcOVBdHZMCGruz2R+bYeOMrmdw9YCr/ad3Pd0eCAamZ6VnPY3ZiTR44927s1FKpWN+svYnzCFVjYuAu4B2WuvgIT4NgXQ2zUkWweCVVFbeytq16Tz6qJsPPgCfL83qwppSOBKu6Xpcuc5cRvcdfdHEoyeelevM9TnsjmfTUtImAVsOYREXABXAK1rrn9NaKTUZ+BtwFjD9kJ6EAKSzaQ664vPdhN0+lo8+ivDww06++cbSgmLZ2ezLUW2P4g8D/lA5ovcIoypUtahFRou/AfM5iG5HxIaETfw6kpKSh0lJGcSzz9p5/PFUthzKDrzxxEPY1HCmOhneezj3nnKv3+1w66z0rBuAT6yuS/yahE386Uxp6aNEImdy113pTJpkUF5udU17iKewqWE37FzW97LIg6c+WJ6RmvFDdnr2n4AvrK5L/EJ+iBk/cvH5nsXvX8njjw+lffsMnnwy7oImXoUiIaYsm2J0fKyj6+bZNx9T6C+cU1pZ+j+gu9W1CZOEjfWcVFbeSXn5Bt54YxxduqRz992p+P1W19UsBcNBXvzuRaPTY52cD3/+8GBflW+Zr8r3NNDS6tqSnYSNtU7F5/uJ2bNvo18/F+PHp1FYaHVNCaEyWMmDnz2Y0uXxLhlvLX/rivLq8o1VoaprkW3eMjJnY40svN4nCQQuYuxYJzNnWl1Pg8TjnM2B9MrrxWvDXvN3bdl1mSfNM5xD+7hcHARJ+dgbit//E+++ezHdujW7oGmuVhat5KgXjnI9+uWjR/mr/T9ifmlPxJB0NrGTg9f7b/z+IYwa5eTjj62u56A1x86mtqPbHc17w98r96R5/utJ84wHvFbXlAyks4mNgfj9mldfPYdu3Zp10CSCr7d8Tc+nejrf+/G9C3xVPg0MtLqmZCBh07QMKitvweudy4gROVxzTZp8lB0f/NV+rph2Rfpl71/WurSydG55dfkDQKrVdSUyCZum48brnc6aNXfRt28GM2Yc+B4i5qbpafR8umfGos2LrvUGvEuBrlbXlKgkbJpGN8rKvuf99wczYICLjRutrkfsx3bfdga/Mth194K7e/qr/IuB31pdUyKSsGl8J+P3f8stt3Rk3Lh0qqqsrkfU02NfPWYb9f6obH+VfyFwmtX1JBoJm8Z1AWVlMzjnHDfPPSfrthmapqcx5PUhLm/AOy0cCY+0up5EIm+IxlJdfQUlJW9w8slOFiywuhpxCD7d9CnHTz7eubti9+RAMHC91fUkCgmbxlBRcQu7dj3JMcdk8O23VlcjGsHywuX89vnfZuzw77jfX+X/B+ZhQ8UhkLA5NAZ+/z/ZseNuBgxwsnq11fWIRrSxdCNH/vtI57rd6yaUBcpeQ45seUgkbA6eQVnZc/z00x8YMMDJ5s1W1yOaQHFFMQMnDXQt2bbkAm/AOwvzNDniIEjYHKzy8nvZsmUUgwa5KC62uhrRhPzVfk5/9XTnnHVzBnoD3qmY58ESDSRhczACgWvYtesGTjrJhVd+VpMMguEgl7x3ScYPhT8M9FX5nrK6nuZIwqahwuGR+Hx/54QTnHLsmeRSHa5m6OtDXUX+ojGBYOA6q+tpbiRsGuYMfL7JnHxyBhs2WF2LsEBpoJRTppziLK8ufxA41+p6mhMJm/obgM/3PkOGZLB8udW1CAttLN3Ima+dmeGv8r+F/LSh3iRs6icPv38mo0e7+EIO2C/gm63fcNkHlzn9Vf7ZQEer62kOJGwOzI7XO5Vnn/UwdarVtYg4MnXVVO5ecHdWWaDsYyDL6nrinYTNgfj997N8eT9uu81hdSki/jzy5SP2N5e/2c4b8M5AjoezXxI2+3cOFRUTGTbMRShkdS0iTl094+q0JVuX/MZf5X/Y6lrimYTNvnWhvPxNzjtPPuIW+xWKhBjx7ghnVahqPHCC1fXEK/mtR91S8Xo/4q67MvjyS6trSQjZOhvPeg+OEgdlncrYcdyOn68zggZ53+Xh3uSGMASyA2w+3fz5h3uDm7xv84jYI2w/djsV+RUApJal0vrL1hScVhAXu8yi8iLGTB3jfPOiN9/NdGR2Rw6i/itx8DLFoYqKu1i8uAOPPy5fS28kQWeQ4t7FeLv8+j2Y/3U+toCNDUM3sO6idRT9tsi8Igy5S3PZOGQjhQMKabW41c/3yVuSR9GRRXG1BU9fPZ13Vrzj8Qa8z1tdSzyKo5cqbvQnGPwTo0fLD+4aka+DD38HP6G0Pee+UktTcW12UXhMIaH0ENgg0DIAgD1gJ5gRJJQRorx1Oak+c/41c1MmwYwglbmVMX8eB/LgZw+mu0L24cB4q2uJNxI2e0rF632biRPT2brV6lqSQnpxOkFXkJzvc+j6Xlc6zehE5qZMAELpIexVdlLKU3Buc1KVVYVRbdByeUt29t9pceW/dtvxt7Fq3BJs8z+O4Pc/DLSwuqZ4ImFTWyBwE99915YpU+RASTGSWpFKWmka4dQw6y5YR+GAQlp/1RpHqQMM2HHUDtp82oYWq1qw45gd5H6fS4kqIa0kjfZz29NufjscJdZ+K6FtZlvWXLU89EC/G7FdeBHGuefaeO21NLzepy0tLM5I2PyiC8HgnYwb57K6kGQStoeJ2CIU9ykGO1TkV1DeqhznNnMUW9G6goIzC9h82mYiREjblYa3s5fWX7Zm+3HbKe5TTP6ifMvqv+X4WygYr+k6dwlGt24wZ455xU03pVNZeT4w2LLi4ox8GlWjtPQlHnrIwfr1VleSVALZgV9fWFdfGYFWi1tRNKAIe8COETEIuoKE0kOklaQ1eZ17a53Zmk8umR3q5mhtNy66CGbP3vPDBJ8PJkxw8tJLz+PxKCDpv6glnY3pVPz+ATzyiHz61FTCYIQMjEj0X8iAMFS0qqDaWU3LFS0hDOlF6Th3OPG38e9x96x1WQRaBAi0CBBKC2EEDRylDjJ2ZFCdWR3Tp3LTwJvYPH413eYtxejaDWbPrvuG778Pa9e2JhweHdMC45QRiUSsrsFqBqWlKxk/vif/+Y/VtTQLkbIyuk/uz7rd6+p9n5zvc8hZnrPHZcV9iinuW4yjxEH+onzSStKodlVT3K8YXwffz7ezVdroMK8DBWcUEE4NA+Be7ybvu19//6Yptc5szcKRs0Pd09rYjdGjYdasA9/pmGNg3rzduFztgaQ+97KEDVzMjz9OpnfvTGRd1MvBhE1zd+OxN/L34++J2P77Ydi4+mo7paX1v/MHH5Rz5pn/ICPjniYrsBlI9rBJpaxsI8OGtWHePKtraTaSKWxaOVvxyaWzQz3S2tmNMWNg5syGP0jnzrB8eTlOZ1dge6MX2Uwk95xNKHQVS5e6JWhEXa475jq2Tlgb6fHJSvOTpoMJGoD16+GFF1IoK3uocStsXpK5s3Hh92/hhBOy+O47q2tpVhK9s2nlbMXCS/4XUhkdzG7mo48O/UFbtIDNmytxOrsBWw79AZuf5O1sQqFxLFyYIkEjapt49ES2TlgTUZ+tMruZxggagN274aWXDPz+GxvnAZufZO1sbHi9mxg6tB2ffWZ1Lc1OInY2uc5cPrlkdqhnRke7MXYszJjR+As57DBYudJPRkYboKzxFxDfkrWzOYutWz0SNALgmqOuYfuEdZGeX6wxu5mmCBqADRvMbxgHg79vmgXEt+QMm5KSO3jgAbfVZQhr5TpzWX7F4tATx/4V+yWjDGPECDslJU270Pvvd1FZ+ReS8Nv7yRg2vYH+8gW+5Hb1UVez/Q/rIr2++snsZqZPj82Cv/4atE4HLozNAuNH8oWN13sLjz2WSlWV1ZUIC7RMb8kPVywOPXXsvdhHXWYYw4fb2b07tkXce6+bkpI7Y7tQ6yVb2GSRmjqcZ59NuhZWwIQBEyi8en2k96L1Zjfz4YfWFDJ9OhhGV6CbNQVYI9nC5gI++SQoBzBPLi3TW/L9uG9CTw+8H/voMYZx8cWx72ZqC4Xg9ddtBAJJ9QPN5AqbXbuu5OWXM60uQ8TO+N+OZ8fV6yN9Fm8yu5lp06wuyfTKK2kEAr+n7gNqJKRkGk7k4HQeZVnrLGIqOz2bBSNnh/p6utmNMWMNpk6Nr8OHLFoEPl82Hs+RwBKry4mFZOpshjFnThC//8C3FM3aVUdeRdHVGyJ9v9uM0b07cXva5MmT0/D7x1ldRqwkT9js2nUVL78sh/xMYNnp2Xx3+aLQv0/4OyljxxnGsAvtFBdbXda+vfpqCjAKiK+uq4kkS9jkk5HRr9F+5yLizpVHXknR1Rsi/ZZuM+dmPvjA6pIObPVq2LLFBgyyupRYSJawOZ9Zs4JUxt95hsShyU7P5tuxX4WeP/GfpIz7vWFccEF8dzN7e/ddJ5WVQ60uIxaSI2x27bqAqVNlCJVgruh/BUVXb4j0/6HQ7Gbee8/qkhruo49SqKhIim8TJ8OnUTYyMk6UA2QlDo/Dw/xLZoWObNHLbvz+KoN33mm+cx6LFkFaWnsgH9hxoJs3Z8nQ2fSnqCjMlqQ8XlHCGdtvLMV/3BQ5csUu85Omd96xuqRDEwzC559XASdbXUpTS/ywCYdPYvbsVKvLEIfG4/CweOznoZdOeZyUq/7PMM45x05RkdVlNY6ZMzMpKzvD6jKaWuKHze7dQ5g/P93qMsTBG9N3DDv/uDFy5MoSjB494O23rS6pcS1YYBAKJXzYJPqcjUFGxrFykKzmKdORyfyRs0IDco6wM348xltvNd+5mf1ZtgzS0loBOUAz+iitYRK9s+lMRYWdggKr6xANNKbvGHb9sSAyYJUXo3t3jLfeStzfEIXDsHp1BXCE1aU0pUQPm94sXx7bc7OKQ5LpyOSr0Z+EXj71SVIm/BHj7LPtSfEr/cWL05CwacbC4V58+63T6jJE/Vx6xKXs+mNB5Og15Rg9emC88UbidjN7W7IkndLSo60uoykl9pxNSclR/PCDfBIV5zIdmcwZ8VH4mNx+Nq6+GuP11xNzbmZ/fvgBgsHfWl1GU0rszgb6snKl1TWI/bikzyXs+mNB5Ji1FRFDKYzXX0+ebqa25cvB7e5CAr8nE7mzsZGZ2Ykff7S6DlEHZ4qTuZd8FD4270izm3ntteTrZmorKYGyshA5OYcBP1ldTlNI2BQFOuDzBfF6ra5D7GVkn5GUTNwSOXZdVcTo0QPjtdeSs5vZ28qV1UAvq8toKokcNj1ZvVo+iYojzhQnn132cfiNM54nZeJ1GGecYWf7dqvLih8bNqQCbawuo6kkctjkU1CQ3K15HBneazi7r9kcGbghhKEUxiuvSDezt4KCdMLhfKvLaCqJPGeTx7ZtDquLSHbpKenMHjEjNCj/KDsTJ2JMmZLIO7hDs22bDZ/vMDweqytpEon7wldVtWL7dgkbC/2u1+8ovWZLZFABhtGzJ8aUKdLN7M/27VBV1dHqMppK4nY2fn97du60uoqklJ6Szv9GTA+dkH+0nWuvxXj55cTdqTWm7dvBMGTOptkJhdokzCEImpELe15I6TVbIidsthnG4YdjvPyydDP1tWMHpKTkWV1GU0nczgbypLOJnfSUdGaN+DB0Yutj7Vx3HcbkyYm7I2sqO3ZAenq21WU0lcQNm5SUlhI2sTGs5zDePOvFiGPZD4ZxyuGwebN0MwcjEAC7PWHfkwn7xLDZUqiWr9k0JYfNwcwRH4ZPaTvQxvXXY0yaJN3MoQiFwDASNqgTd+OIRCJWl5DIzu5+Nt5rt4ZP2ZGBcfjhGJMmJeybJGbC4YQOGyNh35OlpdsZMCCftWutriThRMrKwGbAdddHjBdfTNg3hyXM96MNSLg3ZuIOowASdydhrZEjI8b33xsUFMgKbmzhMNhsBhI2zUpEwqZpGDNmyIptKuZQwwaErS6lsSXunI2EjWiOzLBJyN/0JW7YRCISNqJ5sdtrhlAJ+TFq4oZNOFyJUw4/LJoRjweqqipJwCEUJHbY7CInx+oqhKi/7GyorvZbXUZTSdywMYwiWra0ugoh6i87G4LBMqvLaCqJGzapqdvJzbW6CiHqr1UrCIUS9tfDiRs2mZkbaNMmIce+IkG1agU22zary2gqiRs2NttWDjuswuoyhKi3/HxIT99kdRlNJXHDBrbSqVPQ6iKEqPx27n8AAA7oSURBVLeuXQM4neutLqOpJHLYbKRz50R+fiLR9O9fCSTsic4S+c24hvz8DNLTra5DiPrp0SMVCZtmqQqfbys9e1pdhxAH5nZDZmYKUGB1KU0lkcMGQqHl9O5tdRVCHJhS4PdvJkG/PQyJHjbZ2Yvo21cmiUX869kTwuEVVpfRlBI7bOz2HxgwIGG//i0SSO/eIbKyllhdRlNK7LCBFfTpk8jH7BGJYuBAHykpy6wuoykl7mFBTXYCAR/t26fLmRZE3LLboaysioyMdkDCbqiJ3tmE8Pm+5uSTra5DiH3r3x8Cge0kcNBA4ocNtGgxjTPOqLS6DCH26eSTI9jtc6wuo6klftjYbPM566yEPPKZSBBnnlmG253wYZPoczYANioqvHTr5mLrVqtrEWJPhgFlZQFcrs5Awv7iG5Khs4Ew5eVfcMopVtchxK/16QPB4C4SPGggOcIGcnKmceaZ5VaXIcSvnH12GJttptVlxEIyDKMAulNSsoycnAzCCfttcNEc/fijl549hwHzrS6lqSVHZwNrgC3yEbiIK507Q8eOBvCJ1aXEQrKEDWRmvsDYsXLkPhE/hg8PEwy+ByTF7/eSZRgF0AGfbzU5OelUVVldixBJNYSCZOpsoICqKs0ZZ1hdhxBJN4SC5AobyM7+N5dfLr8CF9ZLsiEUJNcwCiCPiooC8vLS8EvmCIsYBmza5Kd9+zOBz60uJ1aSq7OBIgKBzxg1KqkSVsSZwYPB7S4EvrC6lFhKtrCB7OyHuPVWaWuEdW64wY/b/XcgqXZ6yRc2MI/c3N2cdJLVdYhk1LEjDB5sYLO9bnUpsZaMYRMhM/NB/vxn6W5E7N1wQxWh0CSgzOpSYi3ZJohrOCkv386RR7rR2upaRLLIzITt2ytxuXoCG60uJ9aSsbMBKMdme4y//EW+USxiZ/z4MNXVc0nCoIHk7WwAcqioKECpDAoS9rxgIl643VBQUEFW1tHAcqvLsUKydjYAxUQiT/LQQ9LdiKZ3yy1BDGM6SRo0kNydDYAHv7+AgQM9fP+91bWIRJWXB+vXV+JyHQ5ssLocqyRzZwPgxeG4gyeeiPtPpjanpHBVu3Yc1bUrx3fpwt9atfrV99ynut2oHj14x+P5+bIP3W4GdenC4M6d+Soj4+fLN6WmMrJDB0Ixqj+p3X13gHB4CkkcNCBhA6mp/+bII72cdprVlezXX/PzyQkG+eynn5i6cSPfZGTwRnb2z9eX2mw8l5ND90Dg58uCwCO5uby/cSN3FhZyX6tWP193X14efy4qwh7LJ5GMOnWCceNCuN13WV2K1SRsoAq3+1qeesqHYVhdyz5tTklhiM9HWiRCXijEIL+ftQ7Hz9c/kpvL6N27aRH6pVcpsdvJDwZpFQoxsLycgtRUAGZlZpIfDNKvUs5w0+TMOcHHgUKrS7GahI3pPVq33sDo0XE7gTW2pIQZbjcVhsGOlBQ+dbk4odw8rPL36eksT0/nktLSPe7TMhSixG5ne0oKnzuddKuqwmcYPNuyJTfKGUKb3kknwbnnVuB0PmR1KfEg2SeIazua0tIFdOuWEY+n6l3ncHBz69asSksjZBgMKy3lwR07CAMXd+zIXYWF9K+sZHT79pzn9XKx1wvAlxkZ/Cs3F0ckwu1FRUz1eOgWCNChupqnc3JwRCLcWlREDzmgWONKT4c1a/y0bz8KmGZ1OfFAOptffI3d/gIvvBB3Z2EIA1e2a8fpPh9L167lq7VrKbXb+UduLm9kZ6MCAfrvY0h0XEUFbxcU8NrmzRiRCMvT0rjQ6+XW1q15aPt2JhQXc0d+fmyfUDK4554q3O4FSND8LMXqAuJKZuZtnHrqhQwb5uSDD6yu5mclNhtbU1O5rKQERySCIxLhotJSHsvNpWN1Nd9kZPCJywVAqd3OyrQ0fkxP567CX6YJIsDfWrXizqIidtvthAyDdsEgeaEQOi3NomeWoPr1g4kTq3A6r7S6lHgiYbOnCtzukUyaNIeFCzPYtcvqegBoGQ7TvqqKN7OyuGL3bsptNj7IykIFAtxdWEig1sT2xLZtObOsjN9Fh1E13snKolcgwOGBAEEgYBisdTjYmpJCh2o5O3Gjsdvh9df9OBzXA9utLieeyDDq1z4nNXUKzzwTV98sfmrbNj51uTiua1dO79yZlEiEPxcV4QmHyQuFfv6XGomQGQ7jrnV+rF02G69kZ3N9cTFg7mHuLCxkbPv23JOfzx2FSf9BSeO58cYQ7dv/QErKZKtLiTcyQVw3Fz7fWi69tDUffmh1LaK5OOoo+PhjHy5XP+Anq8uJNxI2+zYQr3cu/ftnsH691bWIeNeyJaxcWU5+/mVA/Ez4xREZRu3bF6Sl/ZmZM/3U+pq/EL9iGPDOO+W4XJOQoNknCZv9SUt7gjZtZvHSS3E1fyPizO23BxkwYDWZmX+yupR4JsOoA3NSVvY9t99+GE8+KT8lEnsaPBj++99SXK7ewBary4lnEjb105Xy8qWcfnomXyTV2TfE/nTsCEuXVtCixXnAXKvLiXcyjKqfdTidI/nwwwo6dbK6FhEPsrNhwYJyMjLuRIKmXiRs6m8GTuftfPJJOTk5VtcirORwwKxZfvLyXiY9/RGry2kuJGwaIj39X7Rs+Rzz55fjdFpdjbCCzQZvv13B4YcvJDNzotXlNCcSNg2VmXkThx02lZkzy5HfFCWfyZMrOeWUpXg8F2H+RlbUk4RNw0XweMbQv/88pk0rJ0V+XpY0Hn20imHD1uLxnAHIkccaSMLm4ITweH7Hccd9yTvvVBA9Ap5IUIYBTz8d4MorN+LxnAT4rC6pOZKPvg9NOl7vdL799jiGDnVSHneHwhGHym6HV1+tZOjQVXg8pwAlVpfUXEnYHLoUyspe4aefzmPwYFe8HJZCNIK0NHj//XIGDVqMxzMEkL3JIZBh1KEL4naPolu351mypJwOHayuRzSGzEyYN6+cQYPm4/GcjgTNIZOwaRwRXK4bad36bpYsKefww62uRxyK3Fz47DM/ffu+h8dzASAHaG4EEjaNKT39n7Ro8Qe+/LKcwYOtrkYcjAEDYMWKcrp1exq3eyzIefwai4RNY0tJeZWsrHP5739LufPO6ng+F5XYyxVXhFmwwE+rVqNxuW7FPHSzaCQyQdx02uH1Tufrr3swfLiT3butrkfsi8MBzzxTyYgRRWRmngn8aHVJiUjCpmml4vP9C59vHOec42TJEqvrEXtr2xZmzPDTpctneDzDAe8B7yMOigyjmlY1mZnXkJc3loUL/UyYIF9vjycXXWTOzyj1dzyes5GgaVLS2cSOwuudxrJl7Rk71iXHNbZQXh688EI5gwcX43YPB76yuqRkIJ1N7Gg8nj4MGHAfP/xQwY03hrDJ6o+5iy+GNWsqOO2053G7eyBBEzPS2VijO6Wlb7Bhw+GMGuVixQqr60l8rVrBiy+Wc/LJO6PdzCKrS0o2smu1xhqyso6hV68/8fXXfu67r1rO4NBEUlLg2mvDrF1bwSmnPIfbrZCgsYR0NtZrR2npcwSDg7n55gymTDEIyzxyoxg6FJ55xk9W1lKyssYD0kJaSMImfhxLaemzFBV144YbMpk+3ep6mq9jj4XHH/fRs+cuPJ4JwEzkC3qWk7CJLwZwHl7vv9iwIY8bb8xk3jyra2o+BgyAe+/1M2hQJU7nzdhsrwJBq8sSJgmb+GQDhlNW9k8KCjw88ICbt9+G6mqr64o/Nhucdx7ccUcZSlXicDyEw/EMciS9uCNhE9/swNns3v0XDKMfjz+eyjPPpFBYaHVd1nO5YNy4CH/+czlO50ays/8KvI90MnFLwqaRKaUWAP2A1lrrQPSyl4FLMQ9VUAUsASZqrVc14KF7U1Z2MykpI5g2LcIjj2SweHHjFt8c9O0LY8dW83//FyIYXEh29r3AF8icTNyTj74bkVLqMOAEzA3/vL2u/rvWOhNoDxQCLzfw4Vfgdl9ORkY7Lrzwr8yfv4MtW3w8+GCQI4441NLjW+fOcMcdIdav9/HZZzuZMOEJMjP7kJ19FvA5EjTNgoRN4xqD+Y3Ul4Gxdd1Aa10OvAH0Ochl7MLheBi3uw1t257Ctdc+xeef72TTJh/33hukd++DfNg4064dTJwYYdkyL8uXl3HLLS9x2GFn4Xbnk5FxE7DO6hJFw8gwqhEppdYCj2J+aewroL3Wekd0GLVZa32HUioTeB7ooLU+oZEWbQBH4/ePIhIZRWmpg9mzU5gzJ52FC2Hr1kZaTBNq2RJOPhnOOivAkCHV5OQYBAKzyM5+EZgHyOx4Mydh00iUUoOAj4E2WuudSqlVwL+11v+Khs1IzE9IKoGvgRu01k2xd7YBRwAnUVx8Lk7ncZSURJg7187s2Rl8/jls2ABWv+4dO8JvfgMnnljNOedU0KlTGj7fYrKzp2K3zwW+R04Cl1AkbBqJUuoFoK3Wemj077uAC7XW/Wt3NhaUZgN6Ayexa9dQHI5jSEnJZO3acpYscbBsWQZr1sDq1bB+feN+vO50Qn4+dOkChx8ORxxRyW9+E6BXr3QikQoqK5fj8czD4ZgDfIMc6zehSdg0AqVUBrAd86PqmhOYpQHZQH/gBqwLm7q0wJwzOgK//wgqKvricHTF5cojEAhSWlrF7t0hCguhsNDOtm0Odu50EA6b32ux2cwTt9lsYLdHsNki5OcHaNeumrZtI+Tl2WnRIg273aCiopRAoACHYykez3eYR8H7Hthh6RoQMSfnjm0cF2AeGPsI9tw7v405aRxvdgOfAp/icpnfWTHZcDqzcDpzadMml169coBcIIdgMIdIxMBmC2GzhTGMMOYwJxL9727MAKn5Vwh4cbsjuN0xfnoiHknYNI6xwEta6021L1RKPQU8Acy1pKqGqwmN3cCaPa6Rc5qLQyTDKCFETMj3bIQQMSFhI4SICQkbIURMSNgIIWJCwkYIERMSNkKImJCwEULEhISNECImJGyEEDEhYSOEiAkJGyFETEjYCCFiQsJGCBETEjZCiJiQsBFCxISEjRAiJiRshBAxIWEjhIgJCRshRExI2AghYkLCRggRExI2QoiYkLARQsSEhI0QIiYkbIQQMSFhI4SIif8HE2mC+mDp9I8AAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"Nx8yKc9lui0S","colab_type":"text"},"source":["Posterior/Anterior (PA):- Meaning of PA is the X-ray is taken from back of the Patient side.\n","\n","Anterior/Posterior (AP):- Meaning of AP is the X-ray is taken from front (chest) side of the patient.\n","\n","Patients are mostly examined with AP position.\n","\n","Patients examined with AP position is having more lung opacity issue than PA.\n","\n","It is always recommended to have PA posture X-ray than AP because of several good reasons, some of the vital reasons are \n","1. It Reduces magnification of heart therefore preventing appearance of cardiomegaly. \n","\n","2. Reduces radiation dose to radiation sensitive organs such as thyroid,eyes,breasts.\n","\n","3. Visualised maximum areas of lung. etc.\n","\n","But due to health reasons, AP X-rays are taken \n","\n","84% of patients with AP view position variable have lung opacity.\n","\n","AP & PA X-rays Overall ViewPosition is 58% & 42% respectively\n","For target=1, AP having most of the view positions"]},{"cell_type":"code","metadata":{"id":"UFpm-KCg2ih8","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"status":"ok","timestamp":1593947543949,"user_tz":-330,"elapsed":284271,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"e02b9e1b-df9a-487d-e956-91ef28a581dd"},"source":["sns.countplot(x=\"PatientSex\", data=Entiredata,hue=\"Target\")"],"execution_count":57,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f3d626a5940>"]},"metadata":{"tags":[]},"execution_count":57},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZEAAAEGCAYAAACkQqisAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYaUlEQVR4nO3de7iWdZ3v8fdyLU4eIV0eBpy0Yn8bMq0s1HR3Mg3NxEzNSkXDcdyZ2jg709klacplo6Ob7WhOJgltlcypZGYsRa2mrvIQxWTq/naRh4BQGAERAYHF2n88P3BFa8HiZq3nWYv1fl3Xc637/t2n7+MffPz9fvd9P03t7e1IklTFDo0uQJLUfxkikqTKDBFJUmWGiCSpMkNEklRZS6MLqLf169e3t7V5R5okbY1Bg5r/C2jdtL3XQiQipgLHAYsy84DSdg3wEWAN8HvgrMxcVrZdCkwE2oALMvO+0j4OmAI0A9/IzKtL+/7ADGB3YDZwemau2VJdbW3tLFu2sie/qiRt91pbd3mus/beHM66DRi3Sdss4IDMPBD4HXApQESMAU4F3lKOuSkimiOiGbgROAYYA3yi7AvwVeD6zHwTsJRaAEmS6qjXQiQz/wNYsknb/Zm5rqw+DIwqy+OBGZn5amY+A8wFxpbP3Mx8uvQyZgDjI6IJ+ABwdzl+GnBCb30XSVLnGjmx/mngB2V5JDCvw7b5pa2r9t2BZR0CaUO7JKmOGjKxHhH/C1gH3N6I62+qrW0dS5cuZt26LU6pNExLy2BGjGiluXnA3QshqQ+r+79IEXEmtQn3IzNzw21SC4B9O+w2qrTRRfuLwPCIaCm9kY77b7WlSxczdOiO7LTT3jQ1NVU9Ta9pb2/nlVeWs3TpYvbYY59GlyNJG9V1OKvcaXUxcHxmdrxFaiZwakQMKXddjQYeBR4DRkfE/hExmNrk+8wSPj8CTirHTwDuqVrXunVr2GmnXftkgAA0NTWx00679umekqSBqddCJCLuBH5RW4z5ETER+CdgF2BWRMyJiJsBMvMJ4C7gSeCHwHmZ2VZ6GZ8F7gOeAu4q+wJ8AbgoIuZSmyO5dVvq7asBskFfr0/SwNQ00F4Fv3ZtW/umz4k8//xz7L336xtUUff1lzolbX9aW3eZDbxz03Znabvw0kvLuPDCzwCwZMmL7LDDDgwfPgKAW26ZxqBBg3rsWi+//DKzZv2QE088ucfOKUn1YIh0YbfdhnPbbXcAcOut/8ywYTvyyU+evsXj1q1bR0vL1v1nXbHiZb73ve8YItpmO+86lGFDeu5/cPqzVa+uZcXy1Y0uY7tniGyFmTO/x8yZ32Pt2rWMGjWKL33pKwwdOpSrrvoygwcP5ne/Sw488CBOPPEULr/8i6xevYojjngv3/nOncya9VMA7rhjOg899ABr167hPe95PxMn/g0333wDCxYs4MwzP8m73nUI5513YYO/qfqrYUMGcfDnpze6jD5h9jVnsAJDpLcZIlvhve99P8cf/1EAvv71m/i3f/s+J510KgCLFy/i5pun0tzczMUXf46TTz6Vo44ax/e/f/fG4x999GHmzZvHLbdMo729nUsuuYg5c37Fueeez9NP/35jz0eS+gtDZCs8/fTvueWWr7FixcusWrWKsWMP3bjt/e//IM3NzQD89rePM3nytQAcddQ4brxxClALkccee5izzvoUAKtWrWT+/D+w11571/mbSFLPMES2wuTJlzN58rWMHv3fuPfef+XXv569cdvQoUO3eHx7ezunnXYmJ5zwsT9pX7jwjz1eqyTVgz9KtRVWrnyFPfbYg3Xr1nH//T/ocr+3vOUAfvKThwB44IH7N7Yfcshh/Pu/z2TlytotxosXL2Lp0iXsuOOOG9skqT+xJ7IVzj77f3DOOWcyfPhwxow5oMt/+C+44O+44oovMX36VA455DB22mlnAMaOPZRnn32Gc889C4Bhw3bkssu+wsiRo3jrWw/i9NNP4dBDD3diXVK/4cOG9PxDfKtXr2bIkCE0NTXxwAP38cAD93H11ddt83l92FBb0tq6i3dnFbOvOYPFi19udBnbDR82rKPMp7juun8A2tl551249NLLGl2SJPUKQ6QXHHTQ25k27c5GlyFJvc6JdUlSZYaIJKkyQ0SSVJkhIkmqzIn1TvT0m1C78zbRhx/+OVOmXMv69es57rgTOP30M3vs+pLUWwyRTvT0m1C39DbRtrY2rrvuq1x//Y3suedenH32GRxxxHvYf/839FgNktQbHM7qA5566glGjdqXkSNHMWjQID74waP52c9+0uiyJGmLDJE+YPHiRey5514b11tb92Tx4kUNrEiSuscQkSRVZoj0Aa2te7Jo0Qsb1xcvXkRr654NrEiSuscQ6QPe/OYxzJs3jz/+cQFr167lgQfu5/DD39PosiRpi7w7qxOrXl3L7GvO6NHzbU5LSwsXXfR5LrrofNavb+PDHz6eN7zhjT12fUnqLYZIJ1YsX73ZW3J7w2GHHcFhhx1R12tK0rZyOEuSVJkhIkmqzBCRJFXWa3MiETEVOA5YlJkHlLbXAd8G9gOeBU7JzKUR0QRMAY4FVgJnZuavyjETgC+W016ZmdNK+8HAbcAw4F7gwswcWL/1K0kN1ps9kduAcZu0XQI8mJmjgQfLOsAxwOjyOQf4GmwMnUnAIcBYYFJEjCjHfA346w7HbXotSVIv67UQycz/AJZs0jwemFaWpwEndGifnpntmfkwMDwi9gE+BMzKzCWZuRSYBYwr23bNzIdL72N6h3NJkuqk3rf47pWZC8vy88CGF0aNBOZ12G9+adtc+/xO2nvE63YbRPPgoT11OtrWrGbJS5t/VmTy5Mv5+c9/xogRI/jWt+7qsWtLUm9q2HMimdkeEX1yDqN58FD+cMVbe+x8f3nZ48DmQ+TYYz/Cxz72ca688rIeu64k9bZ63531QhmKovzd8KraBcC+HfYbVdo21z6qk/Z+621vewe77rpro8uQpK1S757ITGACcHX5e0+H9s9GxAxqk+gvZebCiLgPmNxhMv1o4NLMXBIRyyPiUOAR4Azghnp+EUl9W/u6V2lt3aXRZfQJ3RlSr6o3b/G9E3gfsEdEzKd2l9XVwF0RMRF4Djil7H4vtdt751K7xfcsgBIWXwEeK/tdkZkbJus/w2u3+P6gfCQJgKaWIT06LN2fdWdIvapeC5HM/EQXm47sZN924LwuzjMVmNpJ+y+BA7alRknStvGJdUlSZb7FtxNta1aX7l/PnW9LJk36e+bMmc2yZcv46EePZeLEczjuOB99kdS3GSKdqE1A9c74YVcuv3xyXa8nST3B4SxJUmWGiCSpMkOkaG/vkw/Pb9TX65M0MBkiQEvLYF55ZXmf/Ye6vb2dV15ZTkvL4EaXIkl/wol1YMSIVpYuXcyKFcsaXUqXWloGM2JEa6PLkKQ/YYgAzc0t7LHHPo0uQ5L6HYezJEmVGSKSpMoMEUlSZc6JbKWddx3KsCGDGl1Gn7Dq1bWsWL7lV7pI2n4ZIltp2JBBHPz56Y0uo0+Yfc0ZrMAQkQYyh7MkSZUZIpKkygwRSVJlhogkqTJDRJJUmSEiSarMEJEkVWaISJIqM0QkSZUZIpKkygwRSVJlhogkqTJDRJJUWUPe4hsRfwucDbQDjwNnAfsAM4DdgdnA6Zm5JiKGANOBg4EXgY9n5rPlPJcCE4E24ILMvK/OX0WSBrS690QiYiRwAfDOzDwAaAZOBb4KXJ+ZbwKWUgsHyt+lpf36sh8RMaYc9xZgHHBTRDTX87tI0kDXqOGsFmBYRLQAOwILgQ8Ad5ft04ATyvL4sk7ZfmRENJX2GZn5amY+A8wFxtapfkkSDQiRzFwAXAv8gVp4vERt+GpZZq4ru80HRpblkcC8cuy6sv/uHds7OUaSVAeNGM4aQa0XsT/wF8BO1IajJEn9TCOGsz4IPJOZizNzLfBd4HBgeBneAhgFLCjLC4B9Acr23ahNsG9s7+QYSVIdNCJE/gAcGhE7lrmNI4EngR8BJ5V9JgD3lOWZZZ2y/aHMbC/tp0bEkIjYHxgNPFqn7yBJojFzIo9QmyD/FbXbe3cAvg58AbgoIuZSm/O4tRxyK7B7ab8IuKSc5wngLmoB9EPgvMxsq+NXkaQBryHPiWTmJGDSJs1P08ndVZm5Gji5i/NcBVzV4wVKkrrFJ9YlSZUZIpKkygwRSVJlhogkqTJDRJJUmSEiSarMEJEkVWaISJIqM0QkSZU15Il1bR/a171Ka+sujS6jT2hbs5olL61tdBlS3RkiqqypZQh/uOKtjS6jT/jLyx4HDBENPA5nSZIqM0QkSZUZIpKkygwRSVJlhogkqbJuhUhEPNidNknSwLLZW3wjYiiwI7BHRIwAmsqmXYGRvVybJKmP29JzIn8DfA74C2A2r4XIcuCferEuSVI/sNkQycwpwJSIOD8zb6hTTZKkfqJbT6xn5g0R8W5gv47HZOb0XqpLktQPdCtEIuJbwBuBOUBbaW4HDBFJGsC6++6sdwJjMrO9N4uRJPUv3X1O5LfA3r1ZiCSp/+luT2QP4MmIeBR4dUNjZh7fK1VJkvqF7obIl3uzCElS/9Tdu7N+0pMXjYjhwDeAA6hN0H8aSODb1O4AexY4JTOXRkQTMAU4FlgJnJmZvyrnmQB8sZz2ysyc1pN1SpI2r7uvPXk5IpaXz+qIaIuI5dtw3SnADzPzzcBBwFPAJcCDmTkaeLCsAxwDjC6fc4CvlZpeB0wCDgHGApPKU/WSpDrpbk9k42+glp7BeODQKheMiN2A9wBnlnOvAdZExHjgfWW3acCPgS+Ua00vd4Y9HBHDI2Kfsu+szFxSzjsLGAfcWaUuSdLW2+qfxy3/mH8/IibxWm9ha+wPLAa+GREHUXudyoXAXpm5sOzzPLBXWR4JzOtw/PzS1lW7JKlOuvuw4YkdVneg9tzI6m245juA8zPzkYiYwiZhlJntEeEzKZLUx3X3OZGPdPh8CHiZ2jBTFfOB+Zn5SFm/m1qovFCGqSh/F5XtC4B9Oxw/qrR11S5JqpPuzomc1VMXzMznI2JeRERmJnAk8GT5TACuLn/vKYfMBD4bETOoTaK/lJkLI+I+YHKHyfSjgUt7qk5J0pZ1dzhrFHADcHhp+ilwYWbOr3jd84HbI2Iw8DRwFrVe0V0RMRF4Djil7Hsvtdt751K7xfcsgMxcEhFfAR4r+12xYZJdklQf3Z1Y/yZwB3ByWT+ttB1V5aKZOYfavMqmjuxk33bgvC7OMxWYWqUGSdK2626ItGbmNzus3xYRn+uNgiRJ/Ud3Q+TFiDiN157B+ATwYu+UJEnqL7p7d9anqc1RPA8sBE6iPCwoSRq4utsTuQKYkJlLYeMrR66lFi6SpAGquz2RAzcECNTujALe3jslSZL6i+6GyA4dX25YeiJb/coUSdL2pbtB8I/ALyLiO2X9ZOCq3ilJktRfdKsnkpnTgROBF8rnxMz8Vm8WJknq+7o9JJWZG15NIkkS0P05EUmS/owhIkmqzBCRJFVmiEiSKjNEJEmVGSKSpMoMEUlSZYaIJKkyQ0SSVJkhIkmqzBCRJFVmiEiSKjNEJEmVGSKSpMoMEUlSZYaIJKkyQ0SSVJkhIkmqrNs/j9vTIqIZ+CWwIDOPi4j9gRnA7sBs4PTMXBMRQ4DpwMHAi8DHM/PZco5LgYlAG3BBZt5X/28iSQNXI3siFwJPdVj/KnB9Zr4JWEotHCh/l5b268t+RMQY4FTgLcA44KYSTJKkOmlIiETEKODDwDfKehPwAeDusss04ISyPL6sU7YfWfYfD8zIzFcz8xlgLjC2Pt9AkgSN64n8b+BiYH1Z3x1Ylpnryvp8YGRZHgnMAyjbXyr7b2zv5BhJUh3UPUQi4jhgUWbOrve1JUk9qxE9kcOB4yPiWWoT6R8ApgDDI2LDRP8oYEFZXgDsC1C270Ztgn1jeyfHSJLqoO4hkpmXZuaozNyP2sT4Q5n5KeBHwElltwnAPWV5ZlmnbH8oM9tL+6kRMaTc2TUaeLROX0OSRN96TuQLwEURMZfanMetpf1WYPfSfhFwCUBmPgHcBTwJ/BA4LzPb6l61JA1gDXtOBCAzfwz8uCw/TSd3V2XmauDkLo6/Criq9yqUJG1OX+qJSJL6GUNEklSZISJJqswQkSRVZohIkiozRCRJlRkikqTKDBFJUmWGiCSpMkNEklSZISJJqswQkSRVZohIkiozRCRJlRkikqTKDBFJUmWGiCSpMkNEklSZISJJqswQkSRVZohIkiozRCRJlRkikqTKDBFJUmWGiCSpMkNEklRZS70vGBH7AtOBvYB24OuZOSUiXgd8G9gPeBY4JTOXRkQTMAU4FlgJnJmZvyrnmgB8sZz6ysycVs/vIkkDXSN6IuuAv8vMMcChwHkRMQa4BHgwM0cDD5Z1gGOA0eVzDvA1gBI6k4BDgLHApIgYUc8vIkkDXd1DJDMXbuhJZObLwFPASGA8sKEnMQ04oSyPB6ZnZntmPgwMj4h9gA8BszJzSWYuBWYB4+r4VSRpwGvonEhE7Ae8HXgE2CszF5ZNz1Mb7oJawMzrcNj80tZVuySpThoWIhGxM/AvwOcyc3nHbZnZTm2+RJLUhzUkRCJiELUAuT0zv1uaXyjDVJS/i0r7AmDfDoePKm1dtUuS6qTuIVLutroVeCozr+uwaSYwoSxPAO7p0H5GRDRFxKHAS2XY6z7g6IgYUSbUjy5tkqQ6qfstvsDhwOnA4xExp7T9PXA1cFdETASeA04p2+6ldnvvXGq3+J4FkJlLIuIrwGNlvysyc0l9voIkCRoQIpn5M6Cpi81HdrJ/O3BeF+eaCkztueokSVvDJ9YlSZUZIpKkygwRSVJlhogkqTJDRJJUmSEiSarMEJEkVWaISJIqM0QkSZUZIpKkygwRSVJlhogkqTJDRJJUmSEiSarMEJEkVWaISJIqM0QkSZUZIpKkygwRSVJlhogkqTJDRJJUmSEiSarMEJEkVWaISJIqM0QkSZUZIpKkygwRSVJlLY0uYFtFxDhgCtAMfCMzr25wSZI0YPTrnkhENAM3AscAY4BPRMSYxlYlSQNHvw4RYCwwNzOfzsw1wAxgfINrkqQBo6m9vb3RNVQWEScB4zLz7LJ+OnBIZn52M4ctBp6rR32StB15PdC6aWO/nxOp4M/+I0iSqunvw1kLgH07rI8qbZKkOujvPZHHgNERsT+18DgV+GRjS5KkgaNf90Qycx3wWeA+4Cngrsx8orFVSdLA0a8n1iVJjdWveyKSpMYyRCRJlfX3iXU1SES0AY93aDohM59tUDkSEdEO3J6Zp5X1FmAh8EhmHtfQ4rZjhoiqWpWZb2t0EVIHrwAHRMSwzFwFHIW3/Pc6h7MkbU/uBT5clj8B3NnAWgYEQ0RVDYuIOeXzvUYXIxUzgFMjYihwIPBIg+vZ7jmcpaoczlKfk5m/iYj9qPVC7m1wOQOCISJpezMTuBZ4H7B7Y0vZ/jmcJWl7MxW4PDMf3+Ke2mb2RCRtVzJzPvB/Gl3HQOFrTyRJlTmcJUmqzBCRJFVmiEiSKjNEJEmVGSKSpMq8xVfqhg5vLW6h9iuaEzJzZRf7vg9Yk5k/L+vnAiszc3qF6+4HvDsz7yjrOwK3UHulRxOwDBiXmSu29txSTzBEpO7Z+JqXiLgdOBe4rot93wesAH4OkJk3b8N19wM+CdxR1i8EXsjMt5ZaAli7DeeXtokhIm29nwIHRsRHgC8Cg4EXgU8Bw6gFTFtEnAacDxwJrMjMayPijcCNQCuwEvjrzPx/EXEbsBx4J7A3cHFm3g1cDfxVRMwBpgH7AM9tKCQzc8Nyud4FpZ5HgM8A7wBuBcYCzcCjwMcz87e98N9FA5BzItJWKD90dAy1oa2fAYdm5tupvT324vLDXDcD12fm2zLzp5uc4uvA+Zl5MPA/gZs6bNsHOAI4jlp4AFwC/LSc63pqr/T4QkT8IiKujIjRpa6/Aj4OHF56TG3ApzLzMWrvkroS+Afg/xog6kn2RKTuGVZ6A1DridwKBPDtiNiH2v/9P7O5E0TEzsC7ge/URqEAGNJhl+9n5nrgyYjYq7NzZOaciHgDcDTwQeCxiDiMWm/n4LIOtR7RonLYFcBjwGpqPRWpxxgiUvf82avvI+IG4LrMnFkm07+8hXPsACzbzCv0X+2w3NTVScok+neB70bEeuBYYA0wLTMv7eSQ3YGdgUHAUGq/ACj1CIezpOp247WfX53Qof1lYJdNd87M5cAzEXEyQEQ0RcRBW7jGn5wrIg6PiBFleTAwhtocyYPASRGxZ9n2uoh4fTnsn4EvAbcDX92qbyhtgSEiVfdlakNTs4H/6tD+r8BHy68+/vdNjvkUMDEi/hN4Ahi/hWv8htok/X9GxN8CbwR+EhGPA78Gfgn8S2Y+SW2S//6I+A0wC9gnIs4A1pZbhK8G3hURH9iG7yz9Cd/iK0mqzJ6IJKkyQ0SSVJkhIkmqzBCRJFVmiEiSKjNEJEmVGSKSpMr+P/n0GO3yv1v/AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"STWrc03T3sBK","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":162},"executionInfo":{"status":"ok","timestamp":1593947543953,"user_tz":-330,"elapsed":284263,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}},"outputId":"8d12acc7-f9aa-4cde-bb84-b1a3a1dfe3a1"},"source":["Entiredata.groupby(['PatientSex']).count() "],"execution_count":58,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>patientId</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>Target</th>\n","      <th>class</th>\n","      <th>AccessionNumber</th>\n","      <th>BitsAllocated</th>\n","      <th>BitsStored</th>\n","      <th>BodyPartExamined</th>\n","      <th>Columns</th>\n","      <th>ConversionType</th>\n","      <th>HighBit</th>\n","      <th>InstanceNumber</th>\n","      <th>LossyImageCompression</th>\n","      <th>LossyImageCompressionMethod</th>\n","      <th>Modality</th>\n","      <th>PatientAge</th>\n","      <th>PatientBirthDate</th>\n","      <th>PatientID</th>\n","      <th>PatientName</th>\n","      <th>PatientOrientation</th>\n","      <th>PhotometricInterpretation</th>\n","      <th>PixelData</th>\n","      <th>PixelRepresentation</th>\n","      <th>PixelSpacing</th>\n","      <th>ReferringPhysicianName</th>\n","      <th>Rows</th>\n","      <th>SOPClassUID</th>\n","      <th>SOPInstanceUID</th>\n","      <th>SamplesPerPixel</th>\n","      <th>SeriesDescription</th>\n","      <th>SeriesInstanceUID</th>\n","      <th>SeriesNumber</th>\n","      <th>SpecificCharacterSet</th>\n","      <th>StudyDate</th>\n","      <th>StudyID</th>\n","      <th>StudyInstanceUID</th>\n","      <th>StudyTime</th>\n","      <th>ViewPosition</th>\n","    </tr>\n","    <tr>\n","      <th>PatientSex</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>F</th>\n","      <td>16131</td>\n","      <td>7115</td>\n","      <td>7115</td>\n","      <td>7115</td>\n","      <td>7115</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>0</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>0</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>0</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>0</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>0</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","      <td>16131</td>\n","    </tr>\n","    <tr>\n","      <th>M</th>\n","      <td>21498</td>\n","      <td>9842</td>\n","      <td>9842</td>\n","      <td>9842</td>\n","      <td>9842</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>0</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>0</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>0</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>0</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>0</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","      <td>21498</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            patientId     x     y  ...  StudyInstanceUID  StudyTime  ViewPosition\n","PatientSex                         ...                                           \n","F               16131  7115  7115  ...             16131      16131         16131\n","M               21498  9842  9842  ...             21498      21498         21498\n","\n","[2 rows x 41 columns]"]},"metadata":{"tags":[]},"execution_count":58}]},{"cell_type":"markdown","metadata":{"id":"0evrwXGM3VPX","colab_type":"text"},"source":["Out of total patients, Male patients are having more Lung opacity evidence than Female"]},{"cell_type":"code","metadata":{"id":"uuvxRMiijgma","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1593947543953,"user_tz":-330,"elapsed":284250,"user":{"displayName":"Asim Nadaf","photoUrl":"","userId":"09325430508312611957"}}},"source":[""],"execution_count":58,"outputs":[]}]}