{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport missingno as mso\nimport plotly.graph_objects as go\nimport plotly.offline as po\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\nimport plotly.express as px\nimport random\nimport plotly.figure_factory as ff\nfrom plotly.subplots import make_subplots\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-10-05T20:16:09.044827Z","iopub.execute_input":"2022-10-05T20:16:09.045221Z","iopub.status.idle":"2022-10-05T20:16:14.107108Z","shell.execute_reply.started":"2022-10-05T20:16:09.045132Z","shell.execute_reply":"2022-10-05T20:16:14.105954Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/geopandas/_compat.py:115: UserWarning:\n\nThe Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n\n","output_type":"stream"}]},{"cell_type":"code","source":"train_data=pd.read_feather(\"/kaggle/input/amexfeather/train_data.ftr\")\ntrain_labels=pd.read_csv(\"/kaggle/input/amex-default-prediction/train_labels.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:16:26.190512Z","iopub.execute_input":"2022-10-05T20:16:26.190887Z","iopub.status.idle":"2022-10-05T20:16:49.826043Z","shell.execute_reply.started":"2022-10-05T20:16:26.190855Z","shell.execute_reply":"2022-10-05T20:16:49.824998Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"# --- Reading Dataset ---\ntrain_data.head().style.background_gradient(cmap='Greens').set_properties(**{'font-family': 'Segoe UI'}).hide_index()","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:16:52.960211Z","iopub.execute_input":"2022-10-05T20:16:52.960963Z","iopub.status.idle":"2022-10-05T20:16:53.627377Z","shell.execute_reply.started":"2022-10-05T20:16:52.960925Z","shell.execute_reply":"2022-10-05T20:16:53.62641Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/pandas/io/formats/style.py:2813: RuntimeWarning:\n\nAll-NaN slice encountered\n\n/opt/conda/lib/python3.7/site-packages/pandas/io/formats/style.py:2814: RuntimeWarning:\n\nAll-NaN slice encountered\n\n","output_type":"stream"},{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7fedcad31f90>","text/html":"<style type=\"text/css\">\n#T_56d1c_row0_col0, #T_56d1c_row0_col1, #T_56d1c_row0_col53, #T_56d1c_row0_col54, #T_56d1c_row0_col60, #T_56d1c_row0_col62, #T_56d1c_row0_col105, #T_56d1c_row0_col145, 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#T_56d1c_row1_col143, #T_56d1c_row1_col146, #T_56d1c_row1_col148, #T_56d1c_row1_col149, #T_56d1c_row1_col150, #T_56d1c_row1_col172, #T_56d1c_row1_col175, #T_56d1c_row1_col178, #T_56d1c_row1_col179, #T_56d1c_row1_col180, #T_56d1c_row1_col181, #T_56d1c_row1_col182, #T_56d1c_row1_col186, #T_56d1c_row2_col10, #T_56d1c_row2_col11, #T_56d1c_row2_col20, #T_56d1c_row2_col31, #T_56d1c_row2_col57, #T_56d1c_row2_col74, #T_56d1c_row2_col78, #T_56d1c_row2_col81, #T_56d1c_row2_col87, #T_56d1c_row2_col104, #T_56d1c_row2_col108, #T_56d1c_row2_col111, #T_56d1c_row2_col139, #T_56d1c_row2_col143, #T_56d1c_row2_col146, #T_56d1c_row2_col148, #T_56d1c_row2_col149, #T_56d1c_row2_col150, #T_56d1c_row2_col172, #T_56d1c_row2_col175, #T_56d1c_row2_col178, #T_56d1c_row2_col179, #T_56d1c_row2_col180, #T_56d1c_row2_col181, #T_56d1c_row2_col182, #T_56d1c_row2_col186, #T_56d1c_row3_col10, #T_56d1c_row3_col11, #T_56d1c_row3_col20, #T_56d1c_row3_col31, #T_56d1c_row3_col57, #T_56d1c_row3_col74, #T_56d1c_row3_col78, #T_56d1c_row3_col81, #T_56d1c_row3_col87, #T_56d1c_row3_col104, #T_56d1c_row3_col108, #T_56d1c_row3_col111, #T_56d1c_row3_col139, #T_56d1c_row3_col143, #T_56d1c_row3_col146, #T_56d1c_row3_col148, #T_56d1c_row3_col149, #T_56d1c_row3_col150, #T_56d1c_row3_col172, #T_56d1c_row3_col175, #T_56d1c_row3_col178, #T_56d1c_row3_col179, #T_56d1c_row3_col180, #T_56d1c_row3_col181, #T_56d1c_row3_col182, #T_56d1c_row3_col186, #T_56d1c_row4_col10, #T_56d1c_row4_col11, #T_56d1c_row4_col19, #T_56d1c_row4_col20, #T_56d1c_row4_col31, #T_56d1c_row4_col57, #T_56d1c_row4_col74, #T_56d1c_row4_col78, #T_56d1c_row4_col81, #T_56d1c_row4_col87, #T_56d1c_row4_col104, #T_56d1c_row4_col108, #T_56d1c_row4_col111, #T_56d1c_row4_col139, #T_56d1c_row4_col143, #T_56d1c_row4_col146, #T_56d1c_row4_col148, #T_56d1c_row4_col149, #T_56d1c_row4_col150, #T_56d1c_row4_col172, #T_56d1c_row4_col175, #T_56d1c_row4_col178, #T_56d1c_row4_col179, #T_56d1c_row4_col180, #T_56d1c_row4_col181, #T_56d1c_row4_col182, #T_56d1c_row4_col186 {\n  background-color: #000000;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col16, #T_56d1c_row1_col16, #T_56d1c_row3_col96 {\n  background-color: #88ce87;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col18, #T_56d1c_row3_col9, #T_56d1c_row3_col130 {\n  background-color: #91d28e;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col26, #T_56d1c_row1_col67, #T_56d1c_row1_col70 {\n  background-color: #016e2d;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col27, #T_56d1c_row0_col189, #T_56d1c_row1_col65, #T_56d1c_row1_col142, #T_56d1c_row3_col3, #T_56d1c_row4_col3, #T_56d1c_row4_col43, #T_56d1c_row4_col47 {\n  background-color: #f6fcf4;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col28, #T_56d1c_row0_col58, #T_56d1c_row1_col147, #T_56d1c_row1_col153, #T_56d1c_row2_col73 {\n  background-color: #0e7936;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col29, #T_56d1c_row2_col52, #T_56d1c_row4_col100, #T_56d1c_row4_col142 {\n  background-color: #006729;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col36, #T_56d1c_row1_col189 {\n  background-color: #005f26;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col39, #T_56d1c_row1_col69, #T_56d1c_row2_col2, #T_56d1c_row2_col22 {\n  background-color: #268e47;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col42, #T_56d1c_row2_col71, #T_56d1c_row4_col147 {\n  background-color: #006529;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col43 {\n  background-color: #eff9ec;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col45, #T_56d1c_row2_col76 {\n  background-color: #79c67a;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col46, #T_56d1c_row3_col50 {\n  background-color: #e3f4de;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col48, #T_56d1c_row0_col93, #T_56d1c_row4_col50 {\n  background-color: #b5e1ae;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col52, #T_56d1c_row3_col55, #T_56d1c_row4_col48 {\n  background-color: #68be70;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col55, #T_56d1c_row4_col94 {\n  background-color: #319a50;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col61 {\n  background-color: #8dd08a;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col63, #T_56d1c_row1_col14, #T_56d1c_row3_col85 {\n  background-color: #c4e8bd;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col64, #T_56d1c_row1_col159 {\n  background-color: #005522;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col65, #T_56d1c_row0_col147, #T_56d1c_row1_col3, #T_56d1c_row3_col23, #T_56d1c_row3_col51, #T_56d1c_row3_col65 {\n  background-color: #f1faee;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col66, #T_56d1c_row1_col171 {\n  background-color: #d9f0d3;\n  color: #000000;\n  font-family: Segoe 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UI;\n}\n#T_56d1c_row0_col89, #T_56d1c_row1_col152, #T_56d1c_row3_col26 {\n  background-color: #2d954d;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col91 {\n  background-color: #bae3b3;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col94, #T_56d1c_row0_col121, #T_56d1c_row0_col124, #T_56d1c_row1_col38, #T_56d1c_row1_col124, #T_56d1c_row2_col124 {\n  background-color: #228a44;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col98, #T_56d1c_row3_col44, #T_56d1c_row3_col107 {\n  background-color: #a5db9f;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col99, #T_56d1c_row2_col23, #T_56d1c_row3_col135, #T_56d1c_row4_col66 {\n  background-color: #248c46;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col101, #T_56d1c_row1_col77, #T_56d1c_row1_col121, #T_56d1c_row2_col90, #T_56d1c_row2_col122 {\n  background-color: #339c52;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col103 {\n  background-color: #c6e8bf;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col106, #T_56d1c_row2_col118 {\n  background-color: #238b45;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col114, #T_56d1c_row1_col73, #T_56d1c_row2_col140 {\n  background-color: #84cc83;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col115, #T_56d1c_row1_col37 {\n  background-color: #2f984f;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col116, #T_56d1c_row3_col153 {\n  background-color: #005321;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col117 {\n  background-color: #40aa5d;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col118, #T_56d1c_row4_col38 {\n  background-color: #f4fbf1;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col120, #T_56d1c_row2_col72, #T_56d1c_row4_col101 {\n  background-color: #f4fbf2;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col122, #T_56d1c_row1_col160, #T_56d1c_row2_col88 {\n  background-color: #005723;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col123, #T_56d1c_row1_col123, #T_56d1c_row3_col123 {\n  background-color: #e1f3dc;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col125 {\n  background-color: #8bcf89;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col126, #T_56d1c_row1_col17, #T_56d1c_row2_col63 {\n  background-color: #005120;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col127, #T_56d1c_row2_col98 {\n  background-color: #c9eac2;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col128, #T_56d1c_row1_col30, #T_56d1c_row2_col85 {\n  background-color: #e9f7e5;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col129, #T_56d1c_row1_col56 {\n  background-color: #006027;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col131, #T_56d1c_row1_col15, #T_56d1c_row3_col24, #T_56d1c_row3_col68, #T_56d1c_row3_col166 {\n  background-color: #7cc87c;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col133, #T_56d1c_row0_col168, #T_56d1c_row1_col68, #T_56d1c_row3_col37 {\n  background-color: #e5f5e0;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col137, #T_56d1c_row2_col25, #T_56d1c_row2_col156, #T_56d1c_row3_col45, #T_56d1c_row3_col137, #T_56d1c_row3_col141, #T_56d1c_row4_col82 {\n  background-color: #dbf1d6;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col140, #T_56d1c_row3_col114, #T_56d1c_row4_col92 {\n  background-color: #c1e6ba;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col154 {\n  background-color: #006428;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col163, #T_56d1c_row2_col163 {\n  background-color: #5eb96b;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col164, #T_56d1c_row3_col93 {\n  background-color: #83cb82;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col166, #T_56d1c_row3_col64, #T_56d1c_row3_col110, #T_56d1c_row4_col59 {\n  background-color: #006227;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col171, #T_56d1c_row2_col117, #T_56d1c_row4_col102 {\n  background-color: #63bc6e;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col174 {\n  background-color: #afdfa8;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col184 {\n  background-color: #e4f5df;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row0_col185 {\n  background-color: #98d594;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col6, #T_56d1c_row4_col21 {\n  background-color: #48ae60;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col8, #T_56d1c_row1_col46, #T_56d1c_row3_col125, #T_56d1c_row4_col7 {\n  background-color: #f5fbf3;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col12, #T_56d1c_row2_col37, #T_56d1c_row4_col91 {\n  background-color: #c2e7bb;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col13, #T_56d1c_row1_col39, #T_56d1c_row3_col17 {\n  background-color: #29914a;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col19, #T_56d1c_row3_col118 {\n  background-color: #278f48;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col22, #T_56d1c_row3_col84, #T_56d1c_row4_col18 {\n  background-color: #097532;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col24, #T_56d1c_row4_col84, #T_56d1c_row4_col114 {\n  background-color: #d2edcc;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col26, #T_56d1c_row2_col97, #T_56d1c_row3_col170, #T_56d1c_row4_col170 {\n  background-color: #00682a;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col27, #T_56d1c_row1_col109 {\n  background-color: #e8f6e4;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col29, #T_56d1c_row1_col129, #T_56d1c_row2_col160 {\n  background-color: #53b466;\n  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#b1e0ab;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col122, #T_56d1c_row2_col75, #T_56d1c_row4_col125 {\n  background-color: #a4da9e;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col127 {\n  background-color: #004c1e;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col130 {\n  background-color: #d8f0d2;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col131 {\n  background-color: #5ab769;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col132 {\n  background-color: #d7efd1;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col133, #T_56d1c_row3_col133, #T_56d1c_row3_col151 {\n  background-color: #a0d99b;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col134, #T_56d1c_row4_col109 {\n  background-color: #d4eece;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row1_col135, #T_56d1c_row4_col46 {\n  background-color: #a9dca3;\n  color: #000000;\n  font-family: Segoe 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#b4e1ad;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col21 {\n  background-color: #def2d9;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col24, #T_56d1c_row2_col64, #T_56d1c_row4_col4 {\n  background-color: #45ad5f;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col30 {\n  background-color: #00471c;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col32, #T_56d1c_row2_col100 {\n  background-color: #127c39;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col36 {\n  background-color: #3da65a;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col38 {\n  background-color: #94d390;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col40, #T_56d1c_row2_col138, #T_56d1c_row4_col130 {\n  background-color: #4eb264;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col41, #T_56d1c_row3_col165, #T_56d1c_row4_col184 {\n  background-color: #8ed08b;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col42, #T_56d1c_row4_col153 {\n  background-color: #208843;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col44 {\n  background-color: #95d391;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col45, #T_56d1c_row3_col101 {\n  background-color: #0b7734;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col49, #T_56d1c_row3_col128, #T_56d1c_row4_col70 {\n  background-color: #c0e6b9;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col51 {\n  background-color: #eff9eb;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col61, #T_56d1c_row3_col27 {\n  background-color: #a8dca2;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col67, #T_56d1c_row4_col30 {\n  background-color: #38a156;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col83, #T_56d1c_row3_col91 {\n  background-color: #39a257;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col84, #T_56d1c_row3_col189 {\n  background-color: #004e1f;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col89 {\n  background-color: #dff3da;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col94 {\n  background-color: #8ace88;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col95 {\n  background-color: #86cc85;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col96, #T_56d1c_row2_col136, #T_56d1c_row3_col119, #T_56d1c_row4_col137 {\n  background-color: #aedea7;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col109 {\n  background-color: #087432;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col110 {\n  background-color: #00451c;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col134 {\n  background-color: #43ac5e;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col135, #T_56d1c_row4_col77 {\n  background-color: #2a924a;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col151, #T_56d1c_row3_col144 {\n  background-color: #73c476;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col152 {\n  background-color: #9fd899;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col154, #T_56d1c_row4_col162 {\n  background-color: #a3da9d;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col173 {\n  background-color: #65bd6f;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col174 {\n  background-color: #d5efcf;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col187, #T_56d1c_row3_col106, #T_56d1c_row3_col126, #T_56d1c_row3_col132 {\n  background-color: #b2e0ac;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row2_col188 {\n  background-color: #077331;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col13 {\n  background-color: #99d595;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col15 {\n  background-color: #005020;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col22, #T_56d1c_row3_col34 {\n  background-color: #f2faf0;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col32, #T_56d1c_row3_col49 {\n  background-color: #cfecc9;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col33 {\n  background-color: #005622;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col35 {\n  background-color: #05712f;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col40 {\n  background-color: #a7dba0;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col48, #T_56d1c_row4_col34 {\n  background-color: #17813d;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col56, #T_56d1c_row3_col173, #T_56d1c_row4_col75 {\n  background-color: #2f974e;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col59, #T_56d1c_row3_col171, #T_56d1c_row4_col103 {\n  background-color: #349d53;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col69, #T_56d1c_row3_col115 {\n  background-color: #eaf7e6;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col71 {\n  background-color: #369f54;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col76 {\n  background-color: #d3eecd;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col80, #T_56d1c_row4_col90 {\n  background-color: #137d39;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col86 {\n  background-color: #4db163;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col88 {\n  background-color: #aadda4;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col102, #T_56d1c_row3_col176 {\n  background-color: #50b264;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col113 {\n  background-color: #1e8741;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col116 {\n  background-color: #c8e9c1;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col120, #T_56d1c_row3_col160 {\n  background-color: #258d47;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col134, #T_56d1c_row3_col152, #T_56d1c_row4_col120 {\n  background-color: #a2d99c;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col138 {\n  background-color: #2e964d;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col140 {\n  background-color: #107a37;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col142, #T_56d1c_row4_col8, #T_56d1c_row4_col28 {\n  background-color: #78c679;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col156 {\n  background-color: #bee5b8;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col159 {\n  background-color: #4aaf61;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col177 {\n  background-color: #e7f6e2;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col187 {\n  background-color: #006d2c;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row3_col188 {\n  background-color: #18823d;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col6 {\n  background-color: #16803c;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col15 {\n  background-color: #218944;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col16 {\n  background-color: #6ec173;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col35 {\n  background-color: #bce4b5;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col63, #T_56d1c_row4_col129 {\n  background-color: #ebf7e7;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col72, #T_56d1c_row4_col115 {\n  background-color: #f0f9ed;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col79 {\n  background-color: #0d7836;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col80 {\n  background-color: #5db96b;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col83 {\n  background-color: #46ae60;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col89, #T_56d1c_row4_col127 {\n  background-color: #19833e;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col95 {\n  background-color: #e2f4dd;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col98 {\n  background-color: #56b567;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col113 {\n  background-color: #006b2b;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col121 {\n  background-color: #b6e2af;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col132 {\n  background-color: #7dc87e;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col164, #T_56d1c_row4_col176 {\n  background-color: #97d492;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col177 {\n  background-color: #9ed798;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col183 {\n  background-color: #acdea6;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_56d1c_row4_col187 {\n  background-color: #e5f5e1;\n  color: #000000;\n  font-family: Segoe UI;\n}\n</style>\n<table id=\"T_56d1c_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >customer_ID</th>\n      <th class=\"col_heading level0 col1\" >S_2</th>\n      <th class=\"col_heading level0 col2\" >P_2</th>\n      <th class=\"col_heading level0 col3\" >D_39</th>\n      <th class=\"col_heading level0 col4\" >B_1</th>\n      <th class=\"col_heading level0 col5\" >B_2</th>\n      <th class=\"col_heading level0 col6\" >R_1</th>\n      <th class=\"col_heading level0 col7\" >S_3</th>\n      <th class=\"col_heading level0 col8\" >D_41</th>\n      <th class=\"col_heading level0 col9\" >B_3</th>\n      <th class=\"col_heading level0 col10\" >D_42</th>\n      <th class=\"col_heading level0 col11\" >D_43</th>\n      <th class=\"col_heading level0 col12\" >D_44</th>\n      <th class=\"col_heading level0 col13\" >B_4</th>\n      <th class=\"col_heading level0 col14\" >D_45</th>\n      <th class=\"col_heading level0 col15\" >B_5</th>\n      <th class=\"col_heading level0 col16\" >R_2</th>\n      <th class=\"col_heading level0 col17\" >D_46</th>\n      <th class=\"col_heading level0 col18\" >D_47</th>\n      <th class=\"col_heading level0 col19\" >D_48</th>\n      <th class=\"col_heading level0 col20\" >D_49</th>\n      <th class=\"col_heading level0 col21\" >B_6</th>\n      <th class=\"col_heading level0 col22\" >B_7</th>\n      <th class=\"col_heading level0 col23\" >B_8</th>\n      <th class=\"col_heading level0 col24\" >D_50</th>\n      <th class=\"col_heading level0 col25\" >D_51</th>\n      <th class=\"col_heading level0 col26\" >B_9</th>\n      <th class=\"col_heading level0 col27\" >R_3</th>\n      <th class=\"col_heading level0 col28\" >D_52</th>\n      <th class=\"col_heading level0 col29\" >P_3</th>\n      <th class=\"col_heading level0 col30\" >B_10</th>\n      <th class=\"col_heading level0 col31\" >D_53</th>\n      <th class=\"col_heading level0 col32\" >S_5</th>\n      <th class=\"col_heading level0 col33\" >B_11</th>\n      <th class=\"col_heading level0 col34\" >S_6</th>\n      <th class=\"col_heading level0 col35\" >D_54</th>\n      <th class=\"col_heading level0 col36\" >R_4</th>\n      <th class=\"col_heading level0 col37\" >S_7</th>\n      <th class=\"col_heading level0 col38\" >B_12</th>\n      <th class=\"col_heading level0 col39\" >S_8</th>\n      <th class=\"col_heading level0 col40\" >D_55</th>\n      <th class=\"col_heading level0 col41\" >D_56</th>\n      <th class=\"col_heading level0 col42\" >B_13</th>\n      <th class=\"col_heading level0 col43\" >R_5</th>\n      <th class=\"col_heading level0 col44\" >D_58</th>\n      <th class=\"col_heading level0 col45\" >S_9</th>\n      <th class=\"col_heading level0 col46\" >B_14</th>\n      <th class=\"col_heading level0 col47\" >D_59</th>\n      <th class=\"col_heading level0 col48\" >D_60</th>\n      <th class=\"col_heading level0 col49\" >D_61</th>\n      <th class=\"col_heading level0 col50\" >B_15</th>\n      <th class=\"col_heading level0 col51\" >S_11</th>\n      <th class=\"col_heading level0 col52\" >D_62</th>\n      <th class=\"col_heading level0 col53\" >D_63</th>\n      <th class=\"col_heading level0 col54\" >D_64</th>\n      <th class=\"col_heading level0 col55\" >D_65</th>\n      <th class=\"col_heading level0 col56\" >B_16</th>\n      <th class=\"col_heading level0 col57\" >B_17</th>\n      <th class=\"col_heading level0 col58\" >B_18</th>\n      <th class=\"col_heading level0 col59\" >B_19</th>\n      <th class=\"col_heading level0 col60\" >D_66</th>\n      <th class=\"col_heading level0 col61\" >B_20</th>\n      <th class=\"col_heading level0 col62\" >D_68</th>\n      <th class=\"col_heading level0 col63\" >S_12</th>\n      <th class=\"col_heading level0 col64\" >R_6</th>\n      <th class=\"col_heading level0 col65\" >S_13</th>\n      <th class=\"col_heading level0 col66\" >B_21</th>\n      <th class=\"col_heading level0 col67\" >D_69</th>\n      <th class=\"col_heading level0 col68\" >B_22</th>\n      <th class=\"col_heading level0 col69\" >D_70</th>\n      <th class=\"col_heading level0 col70\" >D_71</th>\n      <th class=\"col_heading level0 col71\" >D_72</th>\n      <th class=\"col_heading level0 col72\" >S_15</th>\n      <th class=\"col_heading level0 col73\" >B_23</th>\n      <th class=\"col_heading level0 col74\" >D_73</th>\n      <th class=\"col_heading level0 col75\" >P_4</th>\n      <th class=\"col_heading level0 col76\" >D_74</th>\n      <th class=\"col_heading level0 col77\" >D_75</th>\n      <th class=\"col_heading level0 col78\" >D_76</th>\n      <th class=\"col_heading level0 col79\" >B_24</th>\n      <th class=\"col_heading level0 col80\" >R_7</th>\n      <th class=\"col_heading level0 col81\" >D_77</th>\n      <th class=\"col_heading level0 col82\" >B_25</th>\n      <th class=\"col_heading level0 col83\" >B_26</th>\n      <th class=\"col_heading level0 col84\" >D_78</th>\n      <th class=\"col_heading level0 col85\" >D_79</th>\n      <th class=\"col_heading level0 col86\" >R_8</th>\n      <th class=\"col_heading level0 col87\" >R_9</th>\n      <th class=\"col_heading level0 col88\" >S_16</th>\n      <th class=\"col_heading level0 col89\" >D_80</th>\n      <th class=\"col_heading level0 col90\" >R_10</th>\n      <th class=\"col_heading level0 col91\" >R_11</th>\n      <th class=\"col_heading level0 col92\" >B_27</th>\n      <th class=\"col_heading level0 col93\" >D_81</th>\n      <th class=\"col_heading level0 col94\" >D_82</th>\n      <th class=\"col_heading level0 col95\" >S_17</th>\n      <th class=\"col_heading level0 col96\" >R_12</th>\n      <th class=\"col_heading level0 col97\" >B_28</th>\n      <th class=\"col_heading level0 col98\" >R_13</th>\n      <th class=\"col_heading level0 col99\" >D_83</th>\n      <th class=\"col_heading level0 col100\" >R_14</th>\n      <th class=\"col_heading level0 col101\" >R_15</th>\n      <th class=\"col_heading level0 col102\" >D_84</th>\n      <th class=\"col_heading level0 col103\" >R_16</th>\n      <th class=\"col_heading level0 col104\" >B_29</th>\n      <th class=\"col_heading level0 col105\" >B_30</th>\n      <th class=\"col_heading level0 col106\" >S_18</th>\n      <th class=\"col_heading level0 col107\" >D_86</th>\n      <th class=\"col_heading level0 col108\" >D_87</th>\n      <th class=\"col_heading level0 col109\" >R_17</th>\n      <th class=\"col_heading level0 col110\" >R_18</th>\n      <th class=\"col_heading level0 col111\" >D_88</th>\n      <th class=\"col_heading level0 col112\" >B_31</th>\n      <th class=\"col_heading level0 col113\" >S_19</th>\n      <th class=\"col_heading level0 col114\" >R_19</th>\n      <th class=\"col_heading level0 col115\" >B_32</th>\n      <th class=\"col_heading level0 col116\" >S_20</th>\n      <th class=\"col_heading level0 col117\" >R_20</th>\n      <th class=\"col_heading level0 col118\" >R_21</th>\n      <th class=\"col_heading level0 col119\" >B_33</th>\n      <th class=\"col_heading level0 col120\" >D_89</th>\n      <th class=\"col_heading level0 col121\" >R_22</th>\n      <th class=\"col_heading level0 col122\" >R_23</th>\n      <th class=\"col_heading level0 col123\" >D_91</th>\n      <th class=\"col_heading level0 col124\" >D_92</th>\n      <th class=\"col_heading level0 col125\" >D_93</th>\n      <th class=\"col_heading level0 col126\" >D_94</th>\n      <th class=\"col_heading level0 col127\" >R_24</th>\n      <th class=\"col_heading level0 col128\" >R_25</th>\n      <th class=\"col_heading level0 col129\" >D_96</th>\n      <th class=\"col_heading level0 col130\" >S_22</th>\n      <th class=\"col_heading level0 col131\" >S_23</th>\n      <th class=\"col_heading level0 col132\" >S_24</th>\n      <th class=\"col_heading level0 col133\" >S_25</th>\n      <th class=\"col_heading level0 col134\" >S_26</th>\n      <th class=\"col_heading level0 col135\" >D_102</th>\n      <th class=\"col_heading level0 col136\" >D_103</th>\n      <th class=\"col_heading level0 col137\" >D_104</th>\n      <th class=\"col_heading level0 col138\" >D_105</th>\n      <th class=\"col_heading level0 col139\" >D_106</th>\n      <th class=\"col_heading level0 col140\" >D_107</th>\n      <th class=\"col_heading level0 col141\" >B_36</th>\n      <th class=\"col_heading level0 col142\" >B_37</th>\n      <th class=\"col_heading level0 col143\" >R_26</th>\n      <th class=\"col_heading level0 col144\" >R_27</th>\n      <th class=\"col_heading level0 col145\" >B_38</th>\n      <th class=\"col_heading level0 col146\" >D_108</th>\n      <th class=\"col_heading level0 col147\" >D_109</th>\n      <th class=\"col_heading level0 col148\" >D_110</th>\n      <th class=\"col_heading level0 col149\" >D_111</th>\n      <th class=\"col_heading level0 col150\" >B_39</th>\n      <th class=\"col_heading level0 col151\" >D_112</th>\n      <th class=\"col_heading level0 col152\" >B_40</th>\n      <th class=\"col_heading level0 col153\" >S_27</th>\n      <th class=\"col_heading level0 col154\" >D_113</th>\n      <th class=\"col_heading level0 col155\" >D_114</th>\n      <th class=\"col_heading level0 col156\" >D_115</th>\n      <th class=\"col_heading level0 col157\" >D_116</th>\n      <th class=\"col_heading level0 col158\" >D_117</th>\n      <th class=\"col_heading level0 col159\" >D_118</th>\n      <th class=\"col_heading level0 col160\" >D_119</th>\n      <th class=\"col_heading level0 col161\" >D_120</th>\n      <th class=\"col_heading level0 col162\" >D_121</th>\n      <th class=\"col_heading level0 col163\" >D_122</th>\n      <th class=\"col_heading level0 col164\" >D_123</th>\n      <th class=\"col_heading level0 col165\" >D_124</th>\n      <th class=\"col_heading level0 col166\" >D_125</th>\n      <th class=\"col_heading level0 col167\" >D_126</th>\n      <th class=\"col_heading level0 col168\" >D_127</th>\n      <th class=\"col_heading level0 col169\" >D_128</th>\n      <th class=\"col_heading level0 col170\" >D_129</th>\n      <th class=\"col_heading level0 col171\" >B_41</th>\n      <th class=\"col_heading level0 col172\" >B_42</th>\n      <th class=\"col_heading level0 col173\" >D_130</th>\n      <th class=\"col_heading level0 col174\" >D_131</th>\n      <th class=\"col_heading level0 col175\" >D_132</th>\n      <th class=\"col_heading level0 col176\" >D_133</th>\n      <th class=\"col_heading level0 col177\" >R_28</th>\n      <th class=\"col_heading level0 col178\" >D_134</th>\n      <th class=\"col_heading level0 col179\" >D_135</th>\n      <th class=\"col_heading level0 col180\" >D_136</th>\n      <th class=\"col_heading level0 col181\" >D_137</th>\n      <th class=\"col_heading level0 col182\" >D_138</th>\n      <th class=\"col_heading level0 col183\" >D_139</th>\n      <th class=\"col_heading level0 col184\" >D_140</th>\n      <th class=\"col_heading level0 col185\" >D_141</th>\n      <th class=\"col_heading level0 col186\" >D_142</th>\n      <th class=\"col_heading level0 col187\" >D_143</th>\n      <th class=\"col_heading level0 col188\" >D_144</th>\n      <th class=\"col_heading level0 col189\" >D_145</th>\n      <th class=\"col_heading level0 col190\" >target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_56d1c_row0_col0\" class=\"data row0 col0\" >0000099d6bd597052cdcda90ffabf56573fe9d7c79be5fbac11a8ed792feb62a</td>\n      <td id=\"T_56d1c_row0_col1\" class=\"data row0 col1\" >2017-03-09 00:00:00</td>\n      <td id=\"T_56d1c_row0_col2\" class=\"data row0 col2\" >0.938477</td>\n      <td id=\"T_56d1c_row0_col3\" class=\"data row0 col3\" >0.001734</td>\n      <td id=\"T_56d1c_row0_col4\" class=\"data row0 col4\" >0.008728</td>\n      <td id=\"T_56d1c_row0_col5\" class=\"data row0 col5\" >1.006836</td>\n      <td id=\"T_56d1c_row0_col6\" class=\"data row0 col6\" >0.009224</td>\n      <td id=\"T_56d1c_row0_col7\" class=\"data row0 col7\" >0.124023</td>\n      <td id=\"T_56d1c_row0_col8\" class=\"data row0 col8\" >0.008774</td>\n      <td id=\"T_56d1c_row0_col9\" class=\"data row0 col9\" >0.004707</td>\n      <td id=\"T_56d1c_row0_col10\" class=\"data row0 col10\" >nan</td>\n      <td id=\"T_56d1c_row0_col11\" class=\"data row0 col11\" >nan</td>\n      <td id=\"T_56d1c_row0_col12\" class=\"data row0 col12\" >0.000630</td>\n      <td id=\"T_56d1c_row0_col13\" class=\"data row0 col13\" >0.080994</td>\n      <td id=\"T_56d1c_row0_col14\" class=\"data row0 col14\" >0.708984</td>\n      <td id=\"T_56d1c_row0_col15\" class=\"data row0 col15\" >0.170654</td>\n      <td id=\"T_56d1c_row0_col16\" class=\"data row0 col16\" >0.006203</td>\n      <td id=\"T_56d1c_row0_col17\" class=\"data row0 col17\" >0.358643</td>\n      <td id=\"T_56d1c_row0_col18\" class=\"data row0 col18\" >0.525391</td>\n      <td id=\"T_56d1c_row0_col19\" class=\"data row0 col19\" >0.255615</td>\n      <td id=\"T_56d1c_row0_col20\" class=\"data row0 col20\" >nan</td>\n      <td id=\"T_56d1c_row0_col21\" class=\"data row0 col21\" >0.063904</td>\n      <td id=\"T_56d1c_row0_col22\" class=\"data row0 col22\" >0.059418</td>\n      <td id=\"T_56d1c_row0_col23\" class=\"data row0 col23\" >0.006466</td>\n      <td id=\"T_56d1c_row0_col24\" class=\"data row0 col24\" >0.148682</td>\n      <td id=\"T_56d1c_row0_col25\" class=\"data row0 col25\" >1.335938</td>\n      <td id=\"T_56d1c_row0_col26\" class=\"data row0 col26\" >0.008209</td>\n      <td id=\"T_56d1c_row0_col27\" class=\"data row0 col27\" >0.001423</td>\n      <td id=\"T_56d1c_row0_col28\" class=\"data row0 col28\" >0.207275</td>\n      <td id=\"T_56d1c_row0_col29\" class=\"data row0 col29\" >0.736328</td>\n      <td id=\"T_56d1c_row0_col30\" class=\"data row0 col30\" >0.096191</td>\n      <td id=\"T_56d1c_row0_col31\" class=\"data row0 col31\" >nan</td>\n      <td id=\"T_56d1c_row0_col32\" class=\"data row0 col32\" >0.023376</td>\n      <td id=\"T_56d1c_row0_col33\" class=\"data row0 col33\" >0.002768</td>\n      <td id=\"T_56d1c_row0_col34\" class=\"data row0 col34\" >0.008324</td>\n      <td id=\"T_56d1c_row0_col35\" class=\"data row0 col35\" >1.001953</td>\n      <td id=\"T_56d1c_row0_col36\" class=\"data row0 col36\" >0.008301</td>\n      <td id=\"T_56d1c_row0_col37\" class=\"data row0 col37\" >0.161377</td>\n      <td id=\"T_56d1c_row0_col38\" class=\"data row0 col38\" >0.148315</td>\n      <td id=\"T_56d1c_row0_col39\" class=\"data row0 col39\" >0.922852</td>\n      <td id=\"T_56d1c_row0_col40\" class=\"data row0 col40\" >0.354492</td>\n      <td id=\"T_56d1c_row0_col41\" class=\"data row0 col41\" >0.151978</td>\n      <td id=\"T_56d1c_row0_col42\" class=\"data row0 col42\" >0.118103</td>\n      <td id=\"T_56d1c_row0_col43\" class=\"data row0 col43\" >0.001882</td>\n      <td id=\"T_56d1c_row0_col44\" class=\"data row0 col44\" >0.158569</td>\n      <td id=\"T_56d1c_row0_col45\" class=\"data row0 col45\" >0.065735</td>\n      <td id=\"T_56d1c_row0_col46\" class=\"data row0 col46\" >0.018387</td>\n      <td id=\"T_56d1c_row0_col47\" class=\"data row0 col47\" >0.063660</td>\n      <td id=\"T_56d1c_row0_col48\" class=\"data row0 col48\" >0.199585</td>\n      <td id=\"T_56d1c_row0_col49\" class=\"data row0 col49\" >0.308350</td>\n      <td id=\"T_56d1c_row0_col50\" class=\"data row0 col50\" >0.016357</td>\n      <td id=\"T_56d1c_row0_col51\" class=\"data row0 col51\" >0.401611</td>\n      <td id=\"T_56d1c_row0_col52\" class=\"data row0 col52\" >0.091064</td>\n      <td id=\"T_56d1c_row0_col53\" class=\"data row0 col53\" >CR</td>\n      <td id=\"T_56d1c_row0_col54\" class=\"data row0 col54\" >O</td>\n      <td id=\"T_56d1c_row0_col55\" class=\"data row0 col55\" >0.007126</td>\n      <td id=\"T_56d1c_row0_col56\" class=\"data row0 col56\" >0.007664</td>\n      <td id=\"T_56d1c_row0_col57\" class=\"data row0 col57\" >nan</td>\n      <td id=\"T_56d1c_row0_col58\" class=\"data row0 col58\" >0.652832</td>\n      <td id=\"T_56d1c_row0_col59\" class=\"data row0 col59\" >0.008522</td>\n      <td id=\"T_56d1c_row0_col60\" class=\"data row0 col60\" >nan</td>\n      <td id=\"T_56d1c_row0_col61\" class=\"data row0 col61\" >0.004730</td>\n      <td id=\"T_56d1c_row0_col62\" class=\"data row0 col62\" >6.000000</td>\n      <td id=\"T_56d1c_row0_col63\" class=\"data row0 col63\" >0.271973</td>\n      <td id=\"T_56d1c_row0_col64\" class=\"data row0 col64\" >0.008362</td>\n      <td id=\"T_56d1c_row0_col65\" class=\"data row0 col65\" >0.515137</td>\n      <td id=\"T_56d1c_row0_col66\" class=\"data row0 col66\" >0.002644</td>\n      <td id=\"T_56d1c_row0_col67\" class=\"data row0 col67\" >0.009010</td>\n      <td id=\"T_56d1c_row0_col68\" class=\"data row0 col68\" >0.004807</td>\n      <td id=\"T_56d1c_row0_col69\" class=\"data row0 col69\" >0.008339</td>\n      <td id=\"T_56d1c_row0_col70\" class=\"data row0 col70\" >0.119385</td>\n      <td id=\"T_56d1c_row0_col71\" class=\"data row0 col71\" >0.004803</td>\n      <td id=\"T_56d1c_row0_col72\" class=\"data row0 col72\" >0.108276</td>\n      <td id=\"T_56d1c_row0_col73\" class=\"data row0 col73\" >0.050873</td>\n      <td id=\"T_56d1c_row0_col74\" class=\"data row0 col74\" >nan</td>\n      <td id=\"T_56d1c_row0_col75\" class=\"data row0 col75\" >0.007553</td>\n      <td id=\"T_56d1c_row0_col76\" class=\"data row0 col76\" >0.080444</td>\n      <td id=\"T_56d1c_row0_col77\" class=\"data row0 col77\" >0.069092</td>\n      <td id=\"T_56d1c_row0_col78\" class=\"data row0 col78\" >nan</td>\n      <td id=\"T_56d1c_row0_col79\" class=\"data row0 col79\" >0.004326</td>\n      <td id=\"T_56d1c_row0_col80\" class=\"data row0 col80\" >0.007561</td>\n      <td id=\"T_56d1c_row0_col81\" class=\"data row0 col81\" >nan</td>\n      <td id=\"T_56d1c_row0_col82\" class=\"data row0 col82\" >0.007729</td>\n      <td id=\"T_56d1c_row0_col83\" class=\"data row0 col83\" >0.000272</td>\n      <td id=\"T_56d1c_row0_col84\" class=\"data row0 col84\" >0.001575</td>\n      <td id=\"T_56d1c_row0_col85\" class=\"data row0 col85\" >0.004238</td>\n      <td id=\"T_56d1c_row0_col86\" class=\"data row0 col86\" >0.001434</td>\n      <td id=\"T_56d1c_row0_col87\" class=\"data row0 col87\" >nan</td>\n      <td id=\"T_56d1c_row0_col88\" class=\"data row0 col88\" >0.002272</td>\n      <td id=\"T_56d1c_row0_col89\" class=\"data row0 col89\" >0.004059</td>\n      <td id=\"T_56d1c_row0_col90\" class=\"data row0 col90\" >0.007122</td>\n      <td id=\"T_56d1c_row0_col91\" class=\"data row0 col91\" >0.002457</td>\n      <td id=\"T_56d1c_row0_col92\" class=\"data row0 col92\" >0.002310</td>\n      <td id=\"T_56d1c_row0_col93\" class=\"data row0 col93\" >0.003532</td>\n      <td id=\"T_56d1c_row0_col94\" class=\"data row0 col94\" >0.506836</td>\n      <td id=\"T_56d1c_row0_col95\" class=\"data row0 col95\" >0.008034</td>\n      <td id=\"T_56d1c_row0_col96\" class=\"data row0 col96\" >1.009766</td>\n      <td id=\"T_56d1c_row0_col97\" class=\"data row0 col97\" >0.084656</td>\n      <td id=\"T_56d1c_row0_col98\" class=\"data row0 col98\" >0.003820</td>\n      <td id=\"T_56d1c_row0_col99\" class=\"data row0 col99\" >0.007042</td>\n      <td id=\"T_56d1c_row0_col100\" class=\"data row0 col100\" >0.000438</td>\n      <td id=\"T_56d1c_row0_col101\" class=\"data row0 col101\" >0.006451</td>\n      <td id=\"T_56d1c_row0_col102\" class=\"data row0 col102\" >0.000830</td>\n      <td id=\"T_56d1c_row0_col103\" class=\"data row0 col103\" >0.005054</td>\n      <td id=\"T_56d1c_row0_col104\" class=\"data row0 col104\" >nan</td>\n      <td id=\"T_56d1c_row0_col105\" class=\"data row0 col105\" >0.000000</td>\n      <td id=\"T_56d1c_row0_col106\" class=\"data row0 col106\" >0.005722</td>\n      <td id=\"T_56d1c_row0_col107\" class=\"data row0 col107\" >0.007084</td>\n      <td id=\"T_56d1c_row0_col108\" class=\"data row0 col108\" >nan</td>\n      <td id=\"T_56d1c_row0_col109\" class=\"data row0 col109\" >0.000198</td>\n      <td id=\"T_56d1c_row0_col110\" class=\"data row0 col110\" >0.008911</td>\n      <td id=\"T_56d1c_row0_col111\" class=\"data row0 col111\" >nan</td>\n      <td id=\"T_56d1c_row0_col112\" class=\"data row0 col112\" >1.000000</td>\n      <td id=\"T_56d1c_row0_col113\" class=\"data row0 col113\" >0.002537</td>\n      <td id=\"T_56d1c_row0_col114\" class=\"data row0 col114\" >0.005177</td>\n      <td id=\"T_56d1c_row0_col115\" class=\"data row0 col115\" >0.006626</td>\n      <td id=\"T_56d1c_row0_col116\" class=\"data row0 col116\" >0.009705</td>\n      <td id=\"T_56d1c_row0_col117\" class=\"data row0 col117\" >0.007782</td>\n      <td id=\"T_56d1c_row0_col118\" class=\"data row0 col118\" >0.002449</td>\n      <td id=\"T_56d1c_row0_col119\" class=\"data row0 col119\" >1.000977</td>\n      <td id=\"T_56d1c_row0_col120\" class=\"data row0 col120\" >0.002665</td>\n      <td id=\"T_56d1c_row0_col121\" class=\"data row0 col121\" >0.007481</td>\n      <td id=\"T_56d1c_row0_col122\" class=\"data row0 col122\" >0.006893</td>\n      <td id=\"T_56d1c_row0_col123\" class=\"data row0 col123\" >1.503906</td>\n      <td id=\"T_56d1c_row0_col124\" class=\"data row0 col124\" >1.005859</td>\n      <td id=\"T_56d1c_row0_col125\" class=\"data row0 col125\" >0.003569</td>\n      <td id=\"T_56d1c_row0_col126\" class=\"data row0 col126\" >0.008873</td>\n      <td id=\"T_56d1c_row0_col127\" class=\"data row0 col127\" >0.003948</td>\n      <td id=\"T_56d1c_row0_col128\" class=\"data row0 col128\" >0.003647</td>\n      <td id=\"T_56d1c_row0_col129\" class=\"data row0 col129\" >0.004951</td>\n      <td id=\"T_56d1c_row0_col130\" class=\"data row0 col130\" >0.894043</td>\n      <td id=\"T_56d1c_row0_col131\" class=\"data row0 col131\" >0.135620</td>\n      <td id=\"T_56d1c_row0_col132\" class=\"data row0 col132\" >0.911133</td>\n      <td id=\"T_56d1c_row0_col133\" class=\"data row0 col133\" >0.974609</td>\n      <td id=\"T_56d1c_row0_col134\" class=\"data row0 col134\" >0.001244</td>\n      <td id=\"T_56d1c_row0_col135\" class=\"data row0 col135\" >0.766602</td>\n      <td id=\"T_56d1c_row0_col136\" class=\"data row0 col136\" >1.008789</td>\n      <td id=\"T_56d1c_row0_col137\" class=\"data row0 col137\" >1.004883</td>\n      <td id=\"T_56d1c_row0_col138\" class=\"data row0 col138\" >0.893555</td>\n      <td id=\"T_56d1c_row0_col139\" class=\"data row0 col139\" >nan</td>\n      <td id=\"T_56d1c_row0_col140\" class=\"data row0 col140\" >0.669922</td>\n      <td id=\"T_56d1c_row0_col141\" class=\"data row0 col141\" >0.009972</td>\n      <td id=\"T_56d1c_row0_col142\" class=\"data row0 col142\" >0.004570</td>\n      <td id=\"T_56d1c_row0_col143\" class=\"data row0 col143\" >nan</td>\n      <td id=\"T_56d1c_row0_col144\" class=\"data row0 col144\" >1.008789</td>\n      <td id=\"T_56d1c_row0_col145\" class=\"data row0 col145\" >2.000000</td>\n      <td id=\"T_56d1c_row0_col146\" class=\"data row0 col146\" >nan</td>\n      <td id=\"T_56d1c_row0_col147\" class=\"data row0 col147\" >0.004326</td>\n      <td id=\"T_56d1c_row0_col148\" class=\"data row0 col148\" >nan</td>\n      <td id=\"T_56d1c_row0_col149\" class=\"data row0 col149\" >nan</td>\n      <td id=\"T_56d1c_row0_col150\" class=\"data row0 col150\" >nan</td>\n      <td id=\"T_56d1c_row0_col151\" class=\"data row0 col151\" >1.007812</td>\n      <td id=\"T_56d1c_row0_col152\" class=\"data row0 col152\" >0.210083</td>\n      <td id=\"T_56d1c_row0_col153\" class=\"data row0 col153\" >0.676758</td>\n      <td id=\"T_56d1c_row0_col154\" class=\"data row0 col154\" >0.007874</td>\n      <td id=\"T_56d1c_row0_col155\" class=\"data row0 col155\" >1.000000</td>\n      <td id=\"T_56d1c_row0_col156\" class=\"data row0 col156\" >0.238281</td>\n      <td id=\"T_56d1c_row0_col157\" class=\"data row0 col157\" >0.000000</td>\n      <td id=\"T_56d1c_row0_col158\" class=\"data row0 col158\" >4.000000</td>\n      <td id=\"T_56d1c_row0_col159\" class=\"data row0 col159\" >0.232178</td>\n      <td id=\"T_56d1c_row0_col160\" class=\"data row0 col160\" >0.236206</td>\n      <td id=\"T_56d1c_row0_col161\" class=\"data row0 col161\" >0.000000</td>\n      <td id=\"T_56d1c_row0_col162\" class=\"data row0 col162\" >0.702148</td>\n      <td id=\"T_56d1c_row0_col163\" class=\"data row0 col163\" >0.434326</td>\n      <td id=\"T_56d1c_row0_col164\" class=\"data row0 col164\" >0.003057</td>\n      <td id=\"T_56d1c_row0_col165\" class=\"data row0 col165\" >0.686523</td>\n      <td id=\"T_56d1c_row0_col166\" class=\"data row0 col166\" >0.008743</td>\n      <td id=\"T_56d1c_row0_col167\" class=\"data row0 col167\" >1.000000</td>\n      <td id=\"T_56d1c_row0_col168\" class=\"data row0 col168\" >1.002930</td>\n      <td id=\"T_56d1c_row0_col169\" class=\"data row0 col169\" >1.007812</td>\n      <td id=\"T_56d1c_row0_col170\" class=\"data row0 col170\" >1.000000</td>\n      <td id=\"T_56d1c_row0_col171\" class=\"data row0 col171\" >0.006805</td>\n      <td id=\"T_56d1c_row0_col172\" class=\"data row0 col172\" >nan</td>\n      <td id=\"T_56d1c_row0_col173\" class=\"data row0 col173\" >0.002052</td>\n      <td id=\"T_56d1c_row0_col174\" class=\"data row0 col174\" >0.005970</td>\n      <td id=\"T_56d1c_row0_col175\" class=\"data row0 col175\" >nan</td>\n      <td id=\"T_56d1c_row0_col176\" class=\"data row0 col176\" >0.004345</td>\n      <td id=\"T_56d1c_row0_col177\" class=\"data row0 col177\" >0.001534</td>\n      <td id=\"T_56d1c_row0_col178\" class=\"data row0 col178\" >nan</td>\n      <td id=\"T_56d1c_row0_col179\" class=\"data row0 col179\" >nan</td>\n      <td id=\"T_56d1c_row0_col180\" class=\"data row0 col180\" >nan</td>\n      <td id=\"T_56d1c_row0_col181\" class=\"data row0 col181\" >nan</td>\n      <td id=\"T_56d1c_row0_col182\" class=\"data row0 col182\" >nan</td>\n      <td id=\"T_56d1c_row0_col183\" class=\"data row0 col183\" >0.002426</td>\n      <td id=\"T_56d1c_row0_col184\" class=\"data row0 col184\" >0.003706</td>\n      <td id=\"T_56d1c_row0_col185\" class=\"data row0 col185\" >0.003819</td>\n      <td id=\"T_56d1c_row0_col186\" class=\"data row0 col186\" >nan</td>\n      <td id=\"T_56d1c_row0_col187\" class=\"data row0 col187\" >0.000569</td>\n      <td id=\"T_56d1c_row0_col188\" class=\"data row0 col188\" >0.000610</td>\n      <td id=\"T_56d1c_row0_col189\" class=\"data row0 col189\" >0.002674</td>\n      <td id=\"T_56d1c_row0_col190\" class=\"data row0 col190\" >0</td>\n    </tr>\n    <tr>\n      <td id=\"T_56d1c_row1_col0\" class=\"data row1 col0\" >0000099d6bd597052cdcda90ffabf56573fe9d7c79be5fbac11a8ed792feb62a</td>\n      <td id=\"T_56d1c_row1_col1\" class=\"data row1 col1\" >2017-04-07 00:00:00</td>\n      <td id=\"T_56d1c_row1_col2\" class=\"data row1 col2\" >0.936523</td>\n      <td id=\"T_56d1c_row1_col3\" class=\"data row1 col3\" >0.005775</td>\n      <td id=\"T_56d1c_row1_col4\" class=\"data row1 col4\" >0.004925</td>\n      <td id=\"T_56d1c_row1_col5\" class=\"data row1 col5\" >1.000977</td>\n      <td id=\"T_56d1c_row1_col6\" class=\"data row1 col6\" >0.006153</td>\n      <td id=\"T_56d1c_row1_col7\" class=\"data row1 col7\" >0.126709</td>\n      <td id=\"T_56d1c_row1_col8\" class=\"data row1 col8\" >0.000798</td>\n      <td id=\"T_56d1c_row1_col9\" class=\"data row1 col9\" >0.002714</td>\n      <td id=\"T_56d1c_row1_col10\" class=\"data row1 col10\" >nan</td>\n      <td id=\"T_56d1c_row1_col11\" class=\"data row1 col11\" >nan</td>\n      <td id=\"T_56d1c_row1_col12\" class=\"data row1 col12\" >0.002525</td>\n      <td id=\"T_56d1c_row1_col13\" class=\"data row1 col13\" >0.069397</td>\n      <td id=\"T_56d1c_row1_col14\" class=\"data row1 col14\" >0.712891</td>\n      <td id=\"T_56d1c_row1_col15\" class=\"data row1 col15\" >0.113220</td>\n      <td id=\"T_56d1c_row1_col16\" class=\"data row1 col16\" >0.006207</td>\n      <td id=\"T_56d1c_row1_col17\" class=\"data row1 col17\" >0.353516</td>\n      <td id=\"T_56d1c_row1_col18\" class=\"data row1 col18\" >0.521484</td>\n      <td id=\"T_56d1c_row1_col19\" class=\"data row1 col19\" >0.223389</td>\n      <td id=\"T_56d1c_row1_col20\" class=\"data row1 col20\" >nan</td>\n      <td id=\"T_56d1c_row1_col21\" class=\"data row1 col21\" >0.065247</td>\n      <td id=\"T_56d1c_row1_col22\" class=\"data row1 col22\" >0.057739</td>\n      <td id=\"T_56d1c_row1_col23\" class=\"data row1 col23\" >0.001614</td>\n      <td id=\"T_56d1c_row1_col24\" class=\"data row1 col24\" >0.149780</td>\n      <td id=\"T_56d1c_row1_col25\" class=\"data row1 col25\" >1.339844</td>\n      <td id=\"T_56d1c_row1_col26\" class=\"data row1 col26\" >0.008369</td>\n      <td id=\"T_56d1c_row1_col27\" class=\"data row1 col27\" >0.001984</td>\n      <td id=\"T_56d1c_row1_col28\" class=\"data row1 col28\" >0.202759</td>\n      <td id=\"T_56d1c_row1_col29\" class=\"data row1 col29\" >0.720703</td>\n      <td id=\"T_56d1c_row1_col30\" class=\"data row1 col30\" >0.099792</td>\n      <td id=\"T_56d1c_row1_col31\" class=\"data row1 col31\" >nan</td>\n      <td id=\"T_56d1c_row1_col32\" class=\"data row1 col32\" >0.030594</td>\n      <td id=\"T_56d1c_row1_col33\" class=\"data row1 col33\" >0.002748</td>\n      <td id=\"T_56d1c_row1_col34\" class=\"data row1 col34\" >0.002481</td>\n      <td id=\"T_56d1c_row1_col35\" class=\"data row1 col35\" >1.008789</td>\n      <td id=\"T_56d1c_row1_col36\" class=\"data row1 col36\" >0.005135</td>\n      <td id=\"T_56d1c_row1_col37\" class=\"data row1 col37\" >0.140991</td>\n      <td id=\"T_56d1c_row1_col38\" class=\"data row1 col38\" >0.143555</td>\n      <td id=\"T_56d1c_row1_col39\" class=\"data row1 col39\" >0.919434</td>\n      <td id=\"T_56d1c_row1_col40\" class=\"data row1 col40\" >0.326660</td>\n      <td id=\"T_56d1c_row1_col41\" class=\"data row1 col41\" >0.156250</td>\n      <td id=\"T_56d1c_row1_col42\" class=\"data row1 col42\" >0.118713</td>\n      <td id=\"T_56d1c_row1_col43\" class=\"data row1 col43\" >0.001610</td>\n      <td id=\"T_56d1c_row1_col44\" class=\"data row1 col44\" >0.148438</td>\n      <td id=\"T_56d1c_row1_col45\" class=\"data row1 col45\" >0.093933</td>\n      <td id=\"T_56d1c_row1_col46\" class=\"data row1 col46\" >0.013039</td>\n      <td id=\"T_56d1c_row1_col47\" class=\"data row1 col47\" >0.065491</td>\n      <td id=\"T_56d1c_row1_col48\" class=\"data row1 col48\" >0.151367</td>\n      <td id=\"T_56d1c_row1_col49\" class=\"data row1 col49\" >0.265137</td>\n      <td id=\"T_56d1c_row1_col50\" class=\"data row1 col50\" >0.017685</td>\n      <td id=\"T_56d1c_row1_col51\" class=\"data row1 col51\" >0.406250</td>\n      <td id=\"T_56d1c_row1_col52\" class=\"data row1 col52\" >0.086792</td>\n      <td id=\"T_56d1c_row1_col53\" class=\"data row1 col53\" >CR</td>\n      <td id=\"T_56d1c_row1_col54\" class=\"data row1 col54\" >O</td>\n      <td id=\"T_56d1c_row1_col55\" class=\"data row1 col55\" >0.002413</td>\n      <td id=\"T_56d1c_row1_col56\" class=\"data row1 col56\" >0.007149</td>\n      <td id=\"T_56d1c_row1_col57\" class=\"data row1 col57\" >nan</td>\n      <td id=\"T_56d1c_row1_col58\" class=\"data row1 col58\" >0.646973</td>\n      <td id=\"T_56d1c_row1_col59\" class=\"data row1 col59\" >0.002237</td>\n      <td id=\"T_56d1c_row1_col60\" class=\"data row1 col60\" >nan</td>\n      <td id=\"T_56d1c_row1_col61\" class=\"data row1 col61\" >0.003880</td>\n      <td id=\"T_56d1c_row1_col62\" class=\"data row1 col62\" >6.000000</td>\n      <td id=\"T_56d1c_row1_col63\" class=\"data row1 col63\" >0.188965</td>\n      <td id=\"T_56d1c_row1_col64\" class=\"data row1 col64\" >0.004028</td>\n      <td id=\"T_56d1c_row1_col65\" class=\"data row1 col65\" >0.509277</td>\n      <td id=\"T_56d1c_row1_col66\" class=\"data row1 col66\" >0.004192</td>\n      <td id=\"T_56d1c_row1_col67\" class=\"data row1 col67\" >0.007843</td>\n      <td id=\"T_56d1c_row1_col68\" class=\"data row1 col68\" >0.001283</td>\n      <td id=\"T_56d1c_row1_col69\" class=\"data row1 col69\" >0.006523</td>\n      <td id=\"T_56d1c_row1_col70\" class=\"data row1 col70\" >0.140625</td>\n      <td id=\"T_56d1c_row1_col71\" class=\"data row1 col71\" >0.000094</td>\n      <td id=\"T_56d1c_row1_col72\" class=\"data row1 col72\" >0.101013</td>\n      <td id=\"T_56d1c_row1_col73\" class=\"data row1 col73\" >0.040466</td>\n      <td id=\"T_56d1c_row1_col74\" class=\"data row1 col74\" >nan</td>\n      <td id=\"T_56d1c_row1_col75\" class=\"data row1 col75\" >0.004833</td>\n      <td id=\"T_56d1c_row1_col76\" class=\"data row1 col76\" >0.081421</td>\n      <td id=\"T_56d1c_row1_col77\" class=\"data row1 col77\" >0.074158</td>\n      <td id=\"T_56d1c_row1_col78\" class=\"data row1 col78\" >nan</td>\n      <td id=\"T_56d1c_row1_col79\" class=\"data row1 col79\" >0.004204</td>\n      <td id=\"T_56d1c_row1_col80\" class=\"data row1 col80\" >0.005302</td>\n      <td id=\"T_56d1c_row1_col81\" class=\"data row1 col81\" >nan</td>\n      <td id=\"T_56d1c_row1_col82\" class=\"data row1 col82\" >0.001864</td>\n      <td id=\"T_56d1c_row1_col83\" class=\"data row1 col83\" >0.000978</td>\n      <td id=\"T_56d1c_row1_col84\" class=\"data row1 col84\" >0.009895</td>\n      <td id=\"T_56d1c_row1_col85\" class=\"data row1 col85\" >0.007599</td>\n      <td id=\"T_56d1c_row1_col86\" class=\"data row1 col86\" >0.000509</td>\n      <td id=\"T_56d1c_row1_col87\" class=\"data row1 col87\" >nan</td>\n      <td id=\"T_56d1c_row1_col88\" class=\"data row1 col88\" >0.009811</td>\n      <td id=\"T_56d1c_row1_col89\" class=\"data row1 col89\" >0.000126</td>\n      <td id=\"T_56d1c_row1_col90\" class=\"data row1 col90\" >0.005966</td>\n      <td id=\"T_56d1c_row1_col91\" class=\"data row1 col91\" >0.000395</td>\n      <td id=\"T_56d1c_row1_col92\" class=\"data row1 col92\" >0.001327</td>\n      <td id=\"T_56d1c_row1_col93\" class=\"data row1 col93\" >0.007774</td>\n      <td id=\"T_56d1c_row1_col94\" class=\"data row1 col94\" >0.500977</td>\n      <td id=\"T_56d1c_row1_col95\" class=\"data row1 col95\" >0.000761</td>\n      <td id=\"T_56d1c_row1_col96\" class=\"data row1 col96\" >1.009766</td>\n      <td id=\"T_56d1c_row1_col97\" class=\"data row1 col97\" >0.081848</td>\n      <td id=\"T_56d1c_row1_col98\" class=\"data row1 col98\" >0.000347</td>\n      <td id=\"T_56d1c_row1_col99\" class=\"data row1 col99\" >0.007790</td>\n      <td id=\"T_56d1c_row1_col100\" class=\"data row1 col100\" >0.004311</td>\n      <td id=\"T_56d1c_row1_col101\" class=\"data row1 col101\" >0.002333</td>\n      <td id=\"T_56d1c_row1_col102\" class=\"data row1 col102\" >0.009468</td>\n      <td id=\"T_56d1c_row1_col103\" class=\"data row1 col103\" >0.003754</td>\n      <td id=\"T_56d1c_row1_col104\" class=\"data row1 col104\" >nan</td>\n      <td id=\"T_56d1c_row1_col105\" class=\"data row1 col105\" >0.000000</td>\n      <td id=\"T_56d1c_row1_col106\" class=\"data row1 col106\" >0.007584</td>\n      <td id=\"T_56d1c_row1_col107\" class=\"data row1 col107\" >0.006676</td>\n      <td id=\"T_56d1c_row1_col108\" class=\"data row1 col108\" >nan</td>\n      <td id=\"T_56d1c_row1_col109\" class=\"data row1 col109\" >0.001143</td>\n      <td id=\"T_56d1c_row1_col110\" class=\"data row1 col110\" >0.005905</td>\n      <td id=\"T_56d1c_row1_col111\" class=\"data row1 col111\" >nan</td>\n      <td id=\"T_56d1c_row1_col112\" class=\"data row1 col112\" >1.000000</td>\n      <td id=\"T_56d1c_row1_col113\" class=\"data row1 col113\" >0.008430</td>\n      <td id=\"T_56d1c_row1_col114\" class=\"data row1 col114\" >0.008980</td>\n      <td id=\"T_56d1c_row1_col115\" class=\"data row1 col115\" >0.001854</td>\n      <td id=\"T_56d1c_row1_col116\" class=\"data row1 col116\" >0.009926</td>\n      <td id=\"T_56d1c_row1_col117\" class=\"data row1 col117\" >0.005989</td>\n      <td id=\"T_56d1c_row1_col118\" class=\"data row1 col118\" >0.002247</td>\n      <td id=\"T_56d1c_row1_col119\" class=\"data row1 col119\" >1.006836</td>\n      <td id=\"T_56d1c_row1_col120\" class=\"data row1 col120\" >0.002508</td>\n      <td id=\"T_56d1c_row1_col121\" class=\"data row1 col121\" >0.006828</td>\n      <td id=\"T_56d1c_row1_col122\" class=\"data row1 col122\" >0.002836</td>\n      <td id=\"T_56d1c_row1_col123\" class=\"data row1 col123\" >1.503906</td>\n      <td id=\"T_56d1c_row1_col124\" class=\"data row1 col124\" >1.005859</td>\n      <td id=\"T_56d1c_row1_col125\" class=\"data row1 col125\" >0.000571</td>\n      <td id=\"T_56d1c_row1_col126\" class=\"data row1 col126\" >0.000391</td>\n      <td id=\"T_56d1c_row1_col127\" class=\"data row1 col127\" >0.008354</td>\n      <td id=\"T_56d1c_row1_col128\" class=\"data row1 col128\" >0.008850</td>\n      <td id=\"T_56d1c_row1_col129\" class=\"data row1 col129\" >0.003180</td>\n      <td id=\"T_56d1c_row1_col130\" class=\"data row1 col130\" >0.902344</td>\n      <td id=\"T_56d1c_row1_col131\" class=\"data row1 col131\" >0.136353</td>\n      <td id=\"T_56d1c_row1_col132\" class=\"data row1 col132\" >0.919922</td>\n      <td id=\"T_56d1c_row1_col133\" class=\"data row1 col133\" >0.975586</td>\n      <td id=\"T_56d1c_row1_col134\" class=\"data row1 col134\" >0.004562</td>\n      <td id=\"T_56d1c_row1_col135\" class=\"data row1 col135\" >0.786133</td>\n      <td id=\"T_56d1c_row1_col136\" class=\"data row1 col136\" >1.000000</td>\n      <td id=\"T_56d1c_row1_col137\" class=\"data row1 col137\" >1.003906</td>\n      <td id=\"T_56d1c_row1_col138\" class=\"data row1 col138\" >0.906738</td>\n      <td id=\"T_56d1c_row1_col139\" class=\"data row1 col139\" >nan</td>\n      <td id=\"T_56d1c_row1_col140\" class=\"data row1 col140\" >0.668457</td>\n      <td id=\"T_56d1c_row1_col141\" class=\"data row1 col141\" >0.003922</td>\n      <td id=\"T_56d1c_row1_col142\" class=\"data row1 col142\" >0.004654</td>\n      <td id=\"T_56d1c_row1_col143\" class=\"data row1 col143\" >nan</td>\n      <td id=\"T_56d1c_row1_col144\" class=\"data row1 col144\" >1.002930</td>\n      <td id=\"T_56d1c_row1_col145\" class=\"data row1 col145\" >2.000000</td>\n      <td id=\"T_56d1c_row1_col146\" class=\"data row1 col146\" >nan</td>\n      <td id=\"T_56d1c_row1_col147\" class=\"data row1 col147\" >0.008705</td>\n      <td id=\"T_56d1c_row1_col148\" class=\"data row1 col148\" >nan</td>\n      <td id=\"T_56d1c_row1_col149\" class=\"data row1 col149\" >nan</td>\n      <td id=\"T_56d1c_row1_col150\" class=\"data row1 col150\" >nan</td>\n      <td id=\"T_56d1c_row1_col151\" class=\"data row1 col151\" >1.007812</td>\n      <td id=\"T_56d1c_row1_col152\" class=\"data row1 col152\" >0.184082</td>\n      <td id=\"T_56d1c_row1_col153\" class=\"data row1 col153\" >0.822266</td>\n      <td id=\"T_56d1c_row1_col154\" class=\"data row1 col154\" >0.003445</td>\n      <td id=\"T_56d1c_row1_col155\" class=\"data row1 col155\" >1.000000</td>\n      <td id=\"T_56d1c_row1_col156\" class=\"data row1 col156\" >0.247192</td>\n      <td id=\"T_56d1c_row1_col157\" class=\"data row1 col157\" >0.000000</td>\n      <td id=\"T_56d1c_row1_col158\" class=\"data row1 col158\" >4.000000</td>\n      <td id=\"T_56d1c_row1_col159\" class=\"data row1 col159\" >0.243530</td>\n      <td id=\"T_56d1c_row1_col160\" class=\"data row1 col160\" >0.241943</td>\n      <td id=\"T_56d1c_row1_col161\" class=\"data row1 col161\" >0.000000</td>\n      <td id=\"T_56d1c_row1_col162\" class=\"data row1 col162\" >0.707031</td>\n      <td id=\"T_56d1c_row1_col163\" class=\"data row1 col163\" >0.430420</td>\n      <td id=\"T_56d1c_row1_col164\" class=\"data row1 col164\" >0.001306</td>\n      <td id=\"T_56d1c_row1_col165\" class=\"data row1 col165\" >0.686523</td>\n      <td id=\"T_56d1c_row1_col166\" class=\"data row1 col166\" >0.000755</td>\n      <td id=\"T_56d1c_row1_col167\" class=\"data row1 col167\" >1.000000</td>\n      <td id=\"T_56d1c_row1_col168\" class=\"data row1 col168\" >1.008789</td>\n      <td id=\"T_56d1c_row1_col169\" class=\"data row1 col169\" >1.003906</td>\n      <td id=\"T_56d1c_row1_col170\" class=\"data row1 col170\" >1.008789</td>\n      <td id=\"T_56d1c_row1_col171\" class=\"data row1 col171\" >0.004406</td>\n      <td id=\"T_56d1c_row1_col172\" class=\"data row1 col172\" >nan</td>\n      <td id=\"T_56d1c_row1_col173\" class=\"data row1 col173\" >0.001034</td>\n      <td id=\"T_56d1c_row1_col174\" class=\"data row1 col174\" >0.004837</td>\n      <td id=\"T_56d1c_row1_col175\" class=\"data row1 col175\" >nan</td>\n      <td id=\"T_56d1c_row1_col176\" class=\"data row1 col176\" >0.007496</td>\n      <td id=\"T_56d1c_row1_col177\" class=\"data row1 col177\" >0.004932</td>\n      <td id=\"T_56d1c_row1_col178\" class=\"data row1 col178\" >nan</td>\n      <td id=\"T_56d1c_row1_col179\" class=\"data row1 col179\" >nan</td>\n      <td id=\"T_56d1c_row1_col180\" class=\"data row1 col180\" >nan</td>\n      <td id=\"T_56d1c_row1_col181\" class=\"data row1 col181\" >nan</td>\n      <td id=\"T_56d1c_row1_col182\" class=\"data row1 col182\" >nan</td>\n      <td id=\"T_56d1c_row1_col183\" class=\"data row1 col183\" >0.003956</td>\n      <td id=\"T_56d1c_row1_col184\" class=\"data row1 col184\" >0.003166</td>\n      <td id=\"T_56d1c_row1_col185\" class=\"data row1 col185\" >0.005032</td>\n      <td id=\"T_56d1c_row1_col186\" class=\"data row1 col186\" >nan</td>\n      <td id=\"T_56d1c_row1_col187\" class=\"data row1 col187\" >0.009575</td>\n      <td id=\"T_56d1c_row1_col188\" class=\"data row1 col188\" >0.005493</td>\n      <td id=\"T_56d1c_row1_col189\" class=\"data row1 col189\" >0.009216</td>\n      <td id=\"T_56d1c_row1_col190\" class=\"data row1 col190\" >0</td>\n    </tr>\n    <tr>\n      <td id=\"T_56d1c_row2_col0\" class=\"data row2 col0\" >0000099d6bd597052cdcda90ffabf56573fe9d7c79be5fbac11a8ed792feb62a</td>\n      <td id=\"T_56d1c_row2_col1\" class=\"data row2 col1\" >2017-05-28 00:00:00</td>\n      <td id=\"T_56d1c_row2_col2\" class=\"data row2 col2\" >0.954102</td>\n      <td id=\"T_56d1c_row2_col3\" class=\"data row2 col3\" >0.091492</td>\n      <td id=\"T_56d1c_row2_col4\" class=\"data row2 col4\" >0.021652</td>\n      <td id=\"T_56d1c_row2_col5\" class=\"data row2 col5\" >1.009766</td>\n      <td id=\"T_56d1c_row2_col6\" class=\"data row2 col6\" >0.006817</td>\n      <td id=\"T_56d1c_row2_col7\" class=\"data row2 col7\" >0.123962</td>\n      <td id=\"T_56d1c_row2_col8\" class=\"data row2 col8\" >0.007599</td>\n      <td id=\"T_56d1c_row2_col9\" class=\"data row2 col9\" >0.009422</td>\n      <td id=\"T_56d1c_row2_col10\" class=\"data row2 col10\" >nan</td>\n      <td id=\"T_56d1c_row2_col11\" class=\"data row2 col11\" >nan</td>\n      <td id=\"T_56d1c_row2_col12\" class=\"data row2 col12\" >0.007607</td>\n      <td id=\"T_56d1c_row2_col13\" class=\"data row2 col13\" >0.068848</td>\n      <td id=\"T_56d1c_row2_col14\" class=\"data row2 col14\" >0.720703</td>\n      <td id=\"T_56d1c_row2_col15\" class=\"data row2 col15\" >0.060486</td>\n      <td id=\"T_56d1c_row2_col16\" class=\"data row2 col16\" >0.003260</td>\n      <td id=\"T_56d1c_row2_col17\" class=\"data row2 col17\" >0.334717</td>\n      <td id=\"T_56d1c_row2_col18\" class=\"data row2 col18\" >0.524414</td>\n      <td id=\"T_56d1c_row2_col19\" class=\"data row2 col19\" >0.189453</td>\n      <td id=\"T_56d1c_row2_col20\" class=\"data row2 col20\" >nan</td>\n      <td id=\"T_56d1c_row2_col21\" class=\"data row2 col21\" >0.066956</td>\n      <td id=\"T_56d1c_row2_col22\" class=\"data row2 col22\" >0.056641</td>\n      <td id=\"T_56d1c_row2_col23\" class=\"data row2 col23\" >0.005127</td>\n      <td id=\"T_56d1c_row2_col24\" class=\"data row2 col24\" >0.151978</td>\n      <td id=\"T_56d1c_row2_col25\" class=\"data row2 col25\" >1.336914</td>\n      <td id=\"T_56d1c_row2_col26\" class=\"data row2 col26\" >0.009354</td>\n      <td id=\"T_56d1c_row2_col27\" class=\"data row2 col27\" >0.007427</td>\n      <td id=\"T_56d1c_row2_col28\" class=\"data row2 col28\" >0.206665</td>\n      <td id=\"T_56d1c_row2_col29\" class=\"data row2 col29\" >0.738281</td>\n      <td id=\"T_56d1c_row2_col30\" class=\"data row2 col30\" >0.134033</td>\n      <td id=\"T_56d1c_row2_col31\" class=\"data row2 col31\" >nan</td>\n      <td id=\"T_56d1c_row2_col32\" class=\"data row2 col32\" >0.048370</td>\n      <td id=\"T_56d1c_row2_col33\" class=\"data row2 col33\" >0.010078</td>\n      <td id=\"T_56d1c_row2_col34\" class=\"data row2 col34\" >0.000530</td>\n      <td id=\"T_56d1c_row2_col35\" class=\"data row2 col35\" >1.008789</td>\n      <td id=\"T_56d1c_row2_col36\" class=\"data row2 col36\" >0.006962</td>\n      <td id=\"T_56d1c_row2_col37\" class=\"data row2 col37\" >0.112244</td>\n      <td id=\"T_56d1c_row2_col38\" class=\"data row2 col38\" >0.136963</td>\n      <td id=\"T_56d1c_row2_col39\" class=\"data row2 col39\" >1.001953</td>\n      <td id=\"T_56d1c_row2_col40\" class=\"data row2 col40\" >0.304199</td>\n      <td id=\"T_56d1c_row2_col41\" class=\"data row2 col41\" >0.153809</td>\n      <td id=\"T_56d1c_row2_col42\" class=\"data row2 col42\" >0.114563</td>\n      <td id=\"T_56d1c_row2_col43\" class=\"data row2 col43\" >0.006329</td>\n      <td id=\"T_56d1c_row2_col44\" class=\"data row2 col44\" >0.139526</td>\n      <td id=\"T_56d1c_row2_col45\" class=\"data row2 col45\" >0.084778</td>\n      <td id=\"T_56d1c_row2_col46\" class=\"data row2 col46\" >0.056641</td>\n      <td id=\"T_56d1c_row2_col47\" class=\"data row2 col47\" >0.070618</td>\n      <td id=\"T_56d1c_row2_col48\" class=\"data row2 col48\" >0.305908</td>\n      <td id=\"T_56d1c_row2_col49\" class=\"data row2 col49\" >0.212158</td>\n      <td id=\"T_56d1c_row2_col50\" class=\"data row2 col50\" >0.063965</td>\n      <td id=\"T_56d1c_row2_col51\" class=\"data row2 col51\" >0.406738</td>\n      <td id=\"T_56d1c_row2_col52\" class=\"data row2 col52\" >0.093994</td>\n      <td id=\"T_56d1c_row2_col53\" class=\"data row2 col53\" >CR</td>\n      <td id=\"T_56d1c_row2_col54\" class=\"data row2 col54\" >O</td>\n      <td id=\"T_56d1c_row2_col55\" class=\"data row2 col55\" >0.001878</td>\n      <td id=\"T_56d1c_row2_col56\" class=\"data row2 col56\" >0.003637</td>\n      <td id=\"T_56d1c_row2_col57\" class=\"data row2 col57\" >nan</td>\n      <td id=\"T_56d1c_row2_col58\" class=\"data row2 col58\" >0.645996</td>\n      <td id=\"T_56d1c_row2_col59\" class=\"data row2 col59\" >0.000408</td>\n      <td id=\"T_56d1c_row2_col60\" class=\"data row2 col60\" >nan</td>\n      <td id=\"T_56d1c_row2_col61\" class=\"data row2 col61\" >0.004578</td>\n      <td id=\"T_56d1c_row2_col62\" class=\"data row2 col62\" >6.000000</td>\n      <td id=\"T_56d1c_row2_col63\" class=\"data row2 col63\" >0.495361</td>\n      <td id=\"T_56d1c_row2_col64\" class=\"data row2 col64\" >0.006840</td>\n      <td id=\"T_56d1c_row2_col65\" class=\"data row2 col65\" >0.679199</td>\n      <td id=\"T_56d1c_row2_col66\" class=\"data row2 col66\" >0.001336</td>\n      <td id=\"T_56d1c_row2_col67\" class=\"data row2 col67\" >0.006023</td>\n      <td id=\"T_56d1c_row2_col68\" class=\"data row2 col68\" >0.009392</td>\n      <td id=\"T_56d1c_row2_col69\" class=\"data row2 col69\" >0.002615</td>\n      <td id=\"T_56d1c_row2_col70\" class=\"data row2 col70\" >0.075867</td>\n      <td id=\"T_56d1c_row2_col71\" class=\"data row2 col71\" >0.007153</td>\n      <td id=\"T_56d1c_row2_col72\" class=\"data row2 col72\" >0.103210</td>\n      <td id=\"T_56d1c_row2_col73\" class=\"data row2 col73\" >0.047455</td>\n      <td id=\"T_56d1c_row2_col74\" class=\"data row2 col74\" >nan</td>\n      <td id=\"T_56d1c_row2_col75\" class=\"data row2 col75\" >0.006561</td>\n      <td id=\"T_56d1c_row2_col76\" class=\"data row2 col76\" >0.078918</td>\n      <td id=\"T_56d1c_row2_col77\" class=\"data row2 col77\" >0.076538</td>\n      <td id=\"T_56d1c_row2_col78\" class=\"data row2 col78\" >nan</td>\n      <td id=\"T_56d1c_row2_col79\" class=\"data row2 col79\" >0.001782</td>\n      <td id=\"T_56d1c_row2_col80\" class=\"data row2 col80\" >0.001422</td>\n      <td id=\"T_56d1c_row2_col81\" class=\"data row2 col81\" >nan</td>\n      <td id=\"T_56d1c_row2_col82\" class=\"data row2 col82\" >0.005417</td>\n      <td id=\"T_56d1c_row2_col83\" class=\"data row2 col83\" >0.006149</td>\n      <td id=\"T_56d1c_row2_col84\" class=\"data row2 col84\" >0.009628</td>\n      <td id=\"T_56d1c_row2_col85\" class=\"data row2 col85\" >0.003094</td>\n      <td id=\"T_56d1c_row2_col86\" class=\"data row2 col86\" >0.008293</td>\n      <td id=\"T_56d1c_row2_col87\" class=\"data row2 col87\" >nan</td>\n      <td id=\"T_56d1c_row2_col88\" class=\"data row2 col88\" >0.009361</td>\n      <td id=\"T_56d1c_row2_col89\" class=\"data row2 col89\" >0.000954</td>\n      <td id=\"T_56d1c_row2_col90\" class=\"data row2 col90\" >0.005447</td>\n      <td id=\"T_56d1c_row2_col91\" class=\"data row2 col91\" >0.007347</td>\n      <td id=\"T_56d1c_row2_col92\" class=\"data row2 col92\" >0.007626</td>\n      <td id=\"T_56d1c_row2_col93\" class=\"data row2 col93\" >0.008812</td>\n      <td id=\"T_56d1c_row2_col94\" class=\"data row2 col94\" >0.504395</td>\n      <td id=\"T_56d1c_row2_col95\" class=\"data row2 col95\" >0.004055</td>\n      <td id=\"T_56d1c_row2_col96\" class=\"data row2 col96\" >1.003906</td>\n      <td id=\"T_56d1c_row2_col97\" class=\"data row2 col97\" >0.081970</td>\n      <td id=\"T_56d1c_row2_col98\" class=\"data row2 col98\" >0.002710</td>\n      <td id=\"T_56d1c_row2_col99\" class=\"data row2 col99\" >0.004093</td>\n      <td id=\"T_56d1c_row2_col100\" class=\"data row2 col100\" >0.007141</td>\n      <td id=\"T_56d1c_row2_col101\" class=\"data row2 col101\" >0.008362</td>\n      <td id=\"T_56d1c_row2_col102\" class=\"data row2 col102\" >0.002325</td>\n      <td id=\"T_56d1c_row2_col103\" class=\"data row2 col103\" >0.007381</td>\n      <td id=\"T_56d1c_row2_col104\" class=\"data row2 col104\" >nan</td>\n      <td id=\"T_56d1c_row2_col105\" class=\"data row2 col105\" >0.000000</td>\n      <td id=\"T_56d1c_row2_col106\" class=\"data row2 col106\" >0.005901</td>\n      <td id=\"T_56d1c_row2_col107\" class=\"data row2 col107\" >0.001185</td>\n      <td id=\"T_56d1c_row2_col108\" class=\"data row2 col108\" >nan</td>\n      <td id=\"T_56d1c_row2_col109\" class=\"data row2 col109\" >0.008011</td>\n      <td id=\"T_56d1c_row2_col110\" class=\"data row2 col110\" >0.008881</td>\n      <td id=\"T_56d1c_row2_col111\" class=\"data row2 col111\" >nan</td>\n      <td id=\"T_56d1c_row2_col112\" class=\"data row2 col112\" >1.000000</td>\n      <td id=\"T_56d1c_row2_col113\" class=\"data row2 col113\" >0.007328</td>\n      <td id=\"T_56d1c_row2_col114\" class=\"data row2 col114\" >0.002016</td>\n      <td id=\"T_56d1c_row2_col115\" class=\"data row2 col115\" >0.008690</td>\n      <td id=\"T_56d1c_row2_col116\" class=\"data row2 col116\" >0.008446</td>\n      <td id=\"T_56d1c_row2_col117\" class=\"data row2 col117\" >0.007290</td>\n      <td id=\"T_56d1c_row2_col118\" class=\"data row2 col118\" >0.007793</td>\n      <td id=\"T_56d1c_row2_col119\" class=\"data row2 col119\" >1.000977</td>\n      <td id=\"T_56d1c_row2_col120\" class=\"data row2 col120\" >0.009636</td>\n      <td id=\"T_56d1c_row2_col121\" class=\"data row2 col121\" >0.009819</td>\n      <td id=\"T_56d1c_row2_col122\" class=\"data row2 col122\" >0.005081</td>\n      <td id=\"T_56d1c_row2_col123\" class=\"data row2 col123\" >1.502930</td>\n      <td id=\"T_56d1c_row2_col124\" class=\"data row2 col124\" >1.005859</td>\n      <td id=\"T_56d1c_row2_col125\" class=\"data row2 col125\" >0.007427</td>\n      <td id=\"T_56d1c_row2_col126\" class=\"data row2 col126\" >0.009232</td>\n      <td id=\"T_56d1c_row2_col127\" class=\"data row2 col127\" >0.002472</td>\n      <td id=\"T_56d1c_row2_col128\" class=\"data row2 col128\" >0.009766</td>\n      <td id=\"T_56d1c_row2_col129\" class=\"data row2 col129\" >0.005432</td>\n      <td id=\"T_56d1c_row2_col130\" class=\"data row2 col130\" >0.939453</td>\n      <td id=\"T_56d1c_row2_col131\" class=\"data row2 col131\" >0.134888</td>\n      <td id=\"T_56d1c_row2_col132\" class=\"data row2 col132\" >0.958496</td>\n      <td id=\"T_56d1c_row2_col133\" class=\"data row2 col133\" >0.974121</td>\n      <td id=\"T_56d1c_row2_col134\" class=\"data row2 col134\" >0.011734</td>\n      <td id=\"T_56d1c_row2_col135\" class=\"data row2 col135\" >0.806641</td>\n      <td id=\"T_56d1c_row2_col136\" class=\"data row2 col136\" >1.002930</td>\n      <td id=\"T_56d1c_row2_col137\" class=\"data row2 col137\" >1.009766</td>\n      <td id=\"T_56d1c_row2_col138\" class=\"data row2 col138\" >0.928711</td>\n      <td id=\"T_56d1c_row2_col139\" class=\"data row2 col139\" >nan</td>\n      <td id=\"T_56d1c_row2_col140\" class=\"data row2 col140\" >0.670898</td>\n      <td id=\"T_56d1c_row2_col141\" class=\"data row2 col141\" >0.001264</td>\n      <td id=\"T_56d1c_row2_col142\" class=\"data row2 col142\" >0.019180</td>\n      <td id=\"T_56d1c_row2_col143\" class=\"data row2 col143\" >nan</td>\n      <td id=\"T_56d1c_row2_col144\" class=\"data row2 col144\" >1.000977</td>\n      <td id=\"T_56d1c_row2_col145\" class=\"data row2 col145\" >2.000000</td>\n      <td id=\"T_56d1c_row2_col146\" class=\"data row2 col146\" >nan</td>\n      <td id=\"T_56d1c_row2_col147\" class=\"data row2 col147\" >0.004093</td>\n      <td id=\"T_56d1c_row2_col148\" class=\"data row2 col148\" >nan</td>\n      <td id=\"T_56d1c_row2_col149\" class=\"data row2 col149\" >nan</td>\n      <td id=\"T_56d1c_row2_col150\" class=\"data row2 col150\" >nan</td>\n      <td id=\"T_56d1c_row2_col151\" class=\"data row2 col151\" >1.003906</td>\n      <td id=\"T_56d1c_row2_col152\" class=\"data row2 col152\" >0.154785</td>\n      <td id=\"T_56d1c_row2_col153\" class=\"data row2 col153\" >0.853516</td>\n      <td id=\"T_56d1c_row2_col154\" class=\"data row2 col154\" >0.003269</td>\n      <td id=\"T_56d1c_row2_col155\" class=\"data row2 col155\" >1.000000</td>\n      <td id=\"T_56d1c_row2_col156\" class=\"data row2 col156\" >0.239868</td>\n      <td id=\"T_56d1c_row2_col157\" class=\"data row2 col157\" >0.000000</td>\n      <td id=\"T_56d1c_row2_col158\" class=\"data row2 col158\" >4.000000</td>\n      <td id=\"T_56d1c_row2_col159\" class=\"data row2 col159\" >0.240723</td>\n      <td id=\"T_56d1c_row2_col160\" class=\"data row2 col160\" >0.239746</td>\n      <td id=\"T_56d1c_row2_col161\" class=\"data row2 col161\" >0.000000</td>\n      <td id=\"T_56d1c_row2_col162\" class=\"data row2 col162\" >0.705078</td>\n      <td id=\"T_56d1c_row2_col163\" class=\"data row2 col163\" >0.434326</td>\n      <td id=\"T_56d1c_row2_col164\" class=\"data row2 col164\" >0.003956</td>\n      <td id=\"T_56d1c_row2_col165\" class=\"data row2 col165\" >0.689941</td>\n      <td id=\"T_56d1c_row2_col166\" class=\"data row2 col166\" >0.009621</td>\n      <td id=\"T_56d1c_row2_col167\" class=\"data row2 col167\" >1.000000</td>\n      <td id=\"T_56d1c_row2_col168\" class=\"data row2 col168\" >1.009766</td>\n      <td id=\"T_56d1c_row2_col169\" class=\"data row2 col169\" >1.007812</td>\n      <td id=\"T_56d1c_row2_col170\" class=\"data row2 col170\" >1.006836</td>\n      <td id=\"T_56d1c_row2_col171\" class=\"data row2 col171\" >0.003222</td>\n      <td id=\"T_56d1c_row2_col172\" class=\"data row2 col172\" >nan</td>\n      <td id=\"T_56d1c_row2_col173\" class=\"data row2 col173\" >0.005680</td>\n      <td id=\"T_56d1c_row2_col174\" class=\"data row2 col174\" >0.005497</td>\n      <td id=\"T_56d1c_row2_col175\" class=\"data row2 col175\" >nan</td>\n      <td id=\"T_56d1c_row2_col176\" class=\"data row2 col176\" >0.009224</td>\n      <td id=\"T_56d1c_row2_col177\" class=\"data row2 col177\" >0.009125</td>\n      <td id=\"T_56d1c_row2_col178\" class=\"data row2 col178\" >nan</td>\n      <td id=\"T_56d1c_row2_col179\" class=\"data row2 col179\" >nan</td>\n      <td id=\"T_56d1c_row2_col180\" class=\"data row2 col180\" >nan</td>\n      <td id=\"T_56d1c_row2_col181\" class=\"data row2 col181\" >nan</td>\n      <td id=\"T_56d1c_row2_col182\" class=\"data row2 col182\" >nan</td>\n      <td id=\"T_56d1c_row2_col183\" class=\"data row2 col183\" >0.003269</td>\n      <td id=\"T_56d1c_row2_col184\" class=\"data row2 col184\" >0.007328</td>\n      <td id=\"T_56d1c_row2_col185\" class=\"data row2 col185\" >0.000427</td>\n      <td id=\"T_56d1c_row2_col186\" class=\"data row2 col186\" >nan</td>\n      <td id=\"T_56d1c_row2_col187\" class=\"data row2 col187\" >0.003429</td>\n      <td id=\"T_56d1c_row2_col188\" class=\"data row2 col188\" >0.006985</td>\n      <td id=\"T_56d1c_row2_col189\" class=\"data row2 col189\" >0.002604</td>\n      <td id=\"T_56d1c_row2_col190\" class=\"data row2 col190\" >0</td>\n    </tr>\n    <tr>\n      <td id=\"T_56d1c_row3_col0\" class=\"data row3 col0\" >0000099d6bd597052cdcda90ffabf56573fe9d7c79be5fbac11a8ed792feb62a</td>\n      <td id=\"T_56d1c_row3_col1\" class=\"data row3 col1\" >2017-06-13 00:00:00</td>\n      <td id=\"T_56d1c_row3_col2\" class=\"data row3 col2\" >0.960449</td>\n      <td id=\"T_56d1c_row3_col3\" class=\"data row3 col3\" >0.002455</td>\n      <td id=\"T_56d1c_row3_col4\" class=\"data row3 col4\" >0.013687</td>\n      <td id=\"T_56d1c_row3_col5\" class=\"data row3 col5\" >1.002930</td>\n      <td id=\"T_56d1c_row3_col6\" class=\"data row3 col6\" >0.001372</td>\n      <td id=\"T_56d1c_row3_col7\" class=\"data row3 col7\" >0.117188</td>\n      <td id=\"T_56d1c_row3_col8\" class=\"data row3 col8\" >0.000685</td>\n      <td id=\"T_56d1c_row3_col9\" class=\"data row3 col9\" >0.005531</td>\n      <td id=\"T_56d1c_row3_col10\" class=\"data row3 col10\" >nan</td>\n      <td id=\"T_56d1c_row3_col11\" class=\"data row3 col11\" >nan</td>\n      <td id=\"T_56d1c_row3_col12\" class=\"data row3 col12\" >0.006405</td>\n      <td id=\"T_56d1c_row3_col13\" class=\"data row3 col13\" >0.055634</td>\n      <td id=\"T_56d1c_row3_col14\" class=\"data row3 col14\" >0.724121</td>\n      <td id=\"T_56d1c_row3_col15\" class=\"data row3 col15\" >0.166748</td>\n      <td id=\"T_56d1c_row3_col16\" class=\"data row3 col16\" >0.009918</td>\n      <td id=\"T_56d1c_row3_col17\" class=\"data row3 col17\" >0.323242</td>\n      <td id=\"T_56d1c_row3_col18\" class=\"data row3 col18\" >0.530762</td>\n      <td id=\"T_56d1c_row3_col19\" class=\"data row3 col19\" >0.135620</td>\n      <td id=\"T_56d1c_row3_col20\" class=\"data row3 col20\" >nan</td>\n      <td id=\"T_56d1c_row3_col21\" class=\"data row3 col21\" >0.083740</td>\n      <td id=\"T_56d1c_row3_col22\" class=\"data row3 col22\" >0.049255</td>\n      <td id=\"T_56d1c_row3_col23\" class=\"data row3 col23\" >0.001417</td>\n      <td id=\"T_56d1c_row3_col24\" class=\"data row3 col24\" >0.151245</td>\n      <td id=\"T_56d1c_row3_col25\" class=\"data row3 col25\" >1.339844</td>\n      <td id=\"T_56d1c_row3_col26\" class=\"data row3 col26\" >0.006783</td>\n      <td id=\"T_56d1c_row3_col27\" class=\"data row3 col27\" >0.003515</td>\n      <td id=\"T_56d1c_row3_col28\" class=\"data row3 col28\" >0.208252</td>\n      <td id=\"T_56d1c_row3_col29\" class=\"data row3 col29\" >0.741699</td>\n      <td id=\"T_56d1c_row3_col30\" class=\"data row3 col30\" >0.134399</td>\n      <td id=\"T_56d1c_row3_col31\" class=\"data row3 col31\" >nan</td>\n      <td id=\"T_56d1c_row3_col32\" class=\"data row3 col32\" >0.030060</td>\n      <td id=\"T_56d1c_row3_col33\" class=\"data row3 col33\" >0.009666</td>\n      <td id=\"T_56d1c_row3_col34\" class=\"data row3 col34\" >0.000783</td>\n      <td id=\"T_56d1c_row3_col35\" class=\"data row3 col35\" >1.007812</td>\n      <td id=\"T_56d1c_row3_col36\" class=\"data row3 col36\" >0.008705</td>\n      <td id=\"T_56d1c_row3_col37\" class=\"data row3 col37\" >0.102844</td>\n      <td id=\"T_56d1c_row3_col38\" class=\"data row3 col38\" >0.129028</td>\n      <td id=\"T_56d1c_row3_col39\" class=\"data row3 col39\" >0.704102</td>\n      <td id=\"T_56d1c_row3_col40\" class=\"data row3 col40\" >0.275146</td>\n      <td id=\"T_56d1c_row3_col41\" class=\"data row3 col41\" >0.155762</td>\n      <td id=\"T_56d1c_row3_col42\" class=\"data row3 col42\" >0.120728</td>\n      <td id=\"T_56d1c_row3_col43\" class=\"data row3 col43\" >0.004978</td>\n      <td id=\"T_56d1c_row3_col44\" class=\"data row3 col44\" >0.138062</td>\n      <td id=\"T_56d1c_row3_col45\" class=\"data row3 col45\" >0.048370</td>\n      <td id=\"T_56d1c_row3_col46\" class=\"data row3 col46\" >0.012497</td>\n      <td id=\"T_56d1c_row3_col47\" class=\"data row3 col47\" >0.065918</td>\n      <td id=\"T_56d1c_row3_col48\" class=\"data row3 col48\" >0.273438</td>\n      <td id=\"T_56d1c_row3_col49\" class=\"data row3 col49\" >0.204346</td>\n      <td id=\"T_56d1c_row3_col50\" class=\"data row3 col50\" >0.022736</td>\n      <td id=\"T_56d1c_row3_col51\" class=\"data row3 col51\" >0.405273</td>\n      <td id=\"T_56d1c_row3_col52\" class=\"data row3 col52\" >0.094849</td>\n      <td id=\"T_56d1c_row3_col53\" class=\"data row3 col53\" >CR</td>\n      <td id=\"T_56d1c_row3_col54\" class=\"data row3 col54\" >O</td>\n      <td id=\"T_56d1c_row3_col55\" class=\"data row3 col55\" >0.005898</td>\n      <td id=\"T_56d1c_row3_col56\" class=\"data row3 col56\" >0.005894</td>\n      <td id=\"T_56d1c_row3_col57\" class=\"data row3 col57\" >nan</td>\n      <td id=\"T_56d1c_row3_col58\" class=\"data row3 col58\" >0.654297</td>\n      <td id=\"T_56d1c_row3_col59\" class=\"data row3 col59\" >0.005898</td>\n      <td id=\"T_56d1c_row3_col60\" class=\"data row3 col60\" >nan</td>\n      <td id=\"T_56d1c_row3_col61\" class=\"data row3 col61\" >0.005207</td>\n      <td id=\"T_56d1c_row3_col62\" class=\"data row3 col62\" >6.000000</td>\n      <td id=\"T_56d1c_row3_col63\" class=\"data row3 col63\" >0.508789</td>\n      <td id=\"T_56d1c_row3_col64\" class=\"data row3 col64\" >0.008186</td>\n      <td id=\"T_56d1c_row3_col65\" class=\"data row3 col65\" >0.515137</td>\n      <td id=\"T_56d1c_row3_col66\" class=\"data row3 col66\" >0.008713</td>\n      <td id=\"T_56d1c_row3_col67\" class=\"data row3 col67\" >0.005272</td>\n      <td id=\"T_56d1c_row3_col68\" class=\"data row3 col68\" >0.004555</td>\n      <td id=\"T_56d1c_row3_col69\" class=\"data row3 col69\" >0.002052</td>\n      <td id=\"T_56d1c_row3_col70\" class=\"data row3 col70\" >0.150269</td>\n      <td id=\"T_56d1c_row3_col71\" class=\"data row3 col71\" >0.005363</td>\n      <td id=\"T_56d1c_row3_col72\" class=\"data row3 col72\" >0.206421</td>\n      <td id=\"T_56d1c_row3_col73\" class=\"data row3 col73\" >0.031708</td>\n      <td id=\"T_56d1c_row3_col74\" class=\"data row3 col74\" >nan</td>\n      <td id=\"T_56d1c_row3_col75\" class=\"data row3 col75\" >0.009560</td>\n      <td id=\"T_56d1c_row3_col76\" class=\"data row3 col76\" >0.077515</td>\n      <td id=\"T_56d1c_row3_col77\" class=\"data row3 col77\" >0.071533</td>\n      <td id=\"T_56d1c_row3_col78\" class=\"data row3 col78\" >nan</td>\n      <td id=\"T_56d1c_row3_col79\" class=\"data row3 col79\" >0.005596</td>\n      <td id=\"T_56d1c_row3_col80\" class=\"data row3 col80\" >0.006363</td>\n      <td id=\"T_56d1c_row3_col81\" class=\"data row3 col81\" >nan</td>\n      <td id=\"T_56d1c_row3_col82\" class=\"data row3 col82\" >0.000646</td>\n      <td id=\"T_56d1c_row3_col83\" class=\"data row3 col83\" >0.009193</td>\n      <td id=\"T_56d1c_row3_col84\" class=\"data row3 col84\" >0.008568</td>\n      <td id=\"T_56d1c_row3_col85\" class=\"data row3 col85\" >0.003895</td>\n      <td id=\"T_56d1c_row3_col86\" class=\"data row3 col86\" >0.005154</td>\n      <td id=\"T_56d1c_row3_col87\" class=\"data row3 col87\" >nan</td>\n      <td id=\"T_56d1c_row3_col88\" class=\"data row3 col88\" >0.004875</td>\n      <td id=\"T_56d1c_row3_col89\" class=\"data row3 col89\" >0.005665</td>\n      <td id=\"T_56d1c_row3_col90\" class=\"data row3 col90\" >0.001888</td>\n      <td id=\"T_56d1c_row3_col91\" class=\"data row3 col91\" >0.004963</td>\n      <td id=\"T_56d1c_row3_col92\" class=\"data row3 col92\" >0.000034</td>\n      <td id=\"T_56d1c_row3_col93\" class=\"data row3 col93\" >0.004650</td>\n      <td id=\"T_56d1c_row3_col94\" class=\"data row3 col94\" >0.508789</td>\n      <td id=\"T_56d1c_row3_col95\" class=\"data row3 col95\" >0.006969</td>\n      <td id=\"T_56d1c_row3_col96\" class=\"data row3 col96\" >1.004883</td>\n      <td id=\"T_56d1c_row3_col97\" class=\"data row3 col97\" >0.060638</td>\n      <td id=\"T_56d1c_row3_col98\" class=\"data row3 col98\" >0.009979</td>\n      <td id=\"T_56d1c_row3_col99\" class=\"data row3 col99\" >0.008820</td>\n      <td id=\"T_56d1c_row3_col100\" class=\"data row3 col100\" >0.008690</td>\n      <td id=\"T_56d1c_row3_col101\" class=\"data row3 col101\" >0.007362</td>\n      <td id=\"T_56d1c_row3_col102\" class=\"data row3 col102\" >0.005924</td>\n      <td id=\"T_56d1c_row3_col103\" class=\"data row3 col103\" >0.008804</td>\n      <td id=\"T_56d1c_row3_col104\" class=\"data row3 col104\" >nan</td>\n      <td id=\"T_56d1c_row3_col105\" class=\"data row3 col105\" >0.000000</td>\n      <td id=\"T_56d1c_row3_col106\" class=\"data row3 col106\" >0.002520</td>\n      <td id=\"T_56d1c_row3_col107\" class=\"data row3 col107\" >0.003325</td>\n      <td id=\"T_56d1c_row3_col108\" class=\"data row3 col108\" >nan</td>\n      <td id=\"T_56d1c_row3_col109\" class=\"data row3 col109\" >0.009453</td>\n      <td id=\"T_56d1c_row3_col110\" class=\"data row3 col110\" >0.008347</td>\n      <td id=\"T_56d1c_row3_col111\" class=\"data row3 col111\" >nan</td>\n      <td id=\"T_56d1c_row3_col112\" class=\"data row3 col112\" >1.000000</td>\n      <td id=\"T_56d1c_row3_col113\" class=\"data row3 col113\" >0.007053</td>\n      <td id=\"T_56d1c_row3_col114\" class=\"data row3 col114\" >0.003910</td>\n      <td id=\"T_56d1c_row3_col115\" class=\"data row3 col115\" >0.002478</td>\n      <td id=\"T_56d1c_row3_col116\" class=\"data row3 col116\" >0.006615</td>\n      <td id=\"T_56d1c_row3_col117\" class=\"data row3 col117\" >0.009979</td>\n      <td id=\"T_56d1c_row3_col118\" class=\"data row3 col118\" >0.007687</td>\n      <td id=\"T_56d1c_row3_col119\" class=\"data row3 col119\" >1.002930</td>\n      <td id=\"T_56d1c_row3_col120\" class=\"data row3 col120\" >0.007790</td>\n      <td id=\"T_56d1c_row3_col121\" class=\"data row3 col121\" >0.000458</td>\n      <td id=\"T_56d1c_row3_col122\" class=\"data row3 col122\" >0.007320</td>\n      <td id=\"T_56d1c_row3_col123\" class=\"data row3 col123\" >1.503906</td>\n      <td id=\"T_56d1c_row3_col124\" class=\"data row3 col124\" >1.006836</td>\n      <td id=\"T_56d1c_row3_col125\" class=\"data row3 col125\" >0.000664</td>\n      <td id=\"T_56d1c_row3_col126\" class=\"data row3 col126\" >0.003201</td>\n      <td id=\"T_56d1c_row3_col127\" class=\"data row3 col127\" >0.008507</td>\n      <td id=\"T_56d1c_row3_col128\" class=\"data row3 col128\" >0.004856</td>\n      <td id=\"T_56d1c_row3_col129\" class=\"data row3 col129\" >0.000063</td>\n      <td id=\"T_56d1c_row3_col130\" class=\"data row3 col130\" >0.913086</td>\n      <td id=\"T_56d1c_row3_col131\" class=\"data row3 col131\" >0.140015</td>\n      <td id=\"T_56d1c_row3_col132\" class=\"data row3 col132\" >0.926270</td>\n      <td id=\"T_56d1c_row3_col133\" class=\"data row3 col133\" >0.975586</td>\n      <td id=\"T_56d1c_row3_col134\" class=\"data row3 col134\" >0.007572</td>\n      <td id=\"T_56d1c_row3_col135\" class=\"data row3 col135\" >0.808105</td>\n      <td id=\"T_56d1c_row3_col136\" class=\"data row3 col136\" >1.001953</td>\n      <td id=\"T_56d1c_row3_col137\" class=\"data row3 col137\" >1.004883</td>\n      <td id=\"T_56d1c_row3_col138\" class=\"data row3 col138\" >0.935547</td>\n      <td id=\"T_56d1c_row3_col139\" class=\"data row3 col139\" >nan</td>\n      <td id=\"T_56d1c_row3_col140\" class=\"data row3 col140\" >0.672852</td>\n      <td id=\"T_56d1c_row3_col141\" class=\"data row3 col141\" >0.002729</td>\n      <td id=\"T_56d1c_row3_col142\" class=\"data row3 col142\" >0.011719</td>\n      <td id=\"T_56d1c_row3_col143\" class=\"data row3 col143\" >nan</td>\n      <td id=\"T_56d1c_row3_col144\" class=\"data row3 col144\" >1.004883</td>\n      <td id=\"T_56d1c_row3_col145\" class=\"data row3 col145\" >2.000000</td>\n      <td id=\"T_56d1c_row3_col146\" class=\"data row3 col146\" >nan</td>\n      <td id=\"T_56d1c_row3_col147\" class=\"data row3 col147\" >0.009705</td>\n      <td id=\"T_56d1c_row3_col148\" class=\"data row3 col148\" >nan</td>\n      <td id=\"T_56d1c_row3_col149\" class=\"data row3 col149\" >nan</td>\n      <td id=\"T_56d1c_row3_col150\" class=\"data row3 col150\" >nan</td>\n      <td id=\"T_56d1c_row3_col151\" class=\"data row3 col151\" >1.002930</td>\n      <td id=\"T_56d1c_row3_col152\" class=\"data row3 col152\" >0.153931</td>\n      <td id=\"T_56d1c_row3_col153\" class=\"data row3 col153\" >0.844727</td>\n      <td id=\"T_56d1c_row3_col154\" class=\"data row3 col154\" >0.000053</td>\n      <td id=\"T_56d1c_row3_col155\" class=\"data row3 col155\" >1.000000</td>\n      <td id=\"T_56d1c_row3_col156\" class=\"data row3 col156\" >0.240967</td>\n      <td id=\"T_56d1c_row3_col157\" class=\"data row3 col157\" >0.000000</td>\n      <td id=\"T_56d1c_row3_col158\" class=\"data row3 col158\" >4.000000</td>\n      <td id=\"T_56d1c_row3_col159\" class=\"data row3 col159\" >0.239380</td>\n      <td id=\"T_56d1c_row3_col160\" class=\"data row3 col160\" >0.240723</td>\n      <td id=\"T_56d1c_row3_col161\" class=\"data row3 col161\" >0.000000</td>\n      <td id=\"T_56d1c_row3_col162\" class=\"data row3 col162\" >0.711426</td>\n      <td id=\"T_56d1c_row3_col163\" class=\"data row3 col163\" >0.437012</td>\n      <td id=\"T_56d1c_row3_col164\" class=\"data row3 col164\" >0.005135</td>\n      <td id=\"T_56d1c_row3_col165\" class=\"data row3 col165\" >0.687988</td>\n      <td id=\"T_56d1c_row3_col166\" class=\"data row3 col166\" >0.004650</td>\n      <td id=\"T_56d1c_row3_col167\" class=\"data row3 col167\" >1.000000</td>\n      <td id=\"T_56d1c_row3_col168\" class=\"data row3 col168\" >1.001953</td>\n      <td id=\"T_56d1c_row3_col169\" class=\"data row3 col169\" >1.003906</td>\n      <td id=\"T_56d1c_row3_col170\" class=\"data row3 col170\" >1.007812</td>\n      <td id=\"T_56d1c_row3_col171\" class=\"data row3 col171\" >0.007702</td>\n      <td id=\"T_56d1c_row3_col172\" class=\"data row3 col172\" >nan</td>\n      <td id=\"T_56d1c_row3_col173\" class=\"data row3 col173\" >0.007107</td>\n      <td id=\"T_56d1c_row3_col174\" class=\"data row3 col174\" >0.008263</td>\n      <td id=\"T_56d1c_row3_col175\" class=\"data row3 col175\" >nan</td>\n      <td id=\"T_56d1c_row3_col176\" class=\"data row3 col176\" >0.007206</td>\n      <td id=\"T_56d1c_row3_col177\" class=\"data row3 col177\" >0.002409</td>\n      <td id=\"T_56d1c_row3_col178\" class=\"data row3 col178\" >nan</td>\n      <td id=\"T_56d1c_row3_col179\" class=\"data row3 col179\" >nan</td>\n      <td id=\"T_56d1c_row3_col180\" class=\"data row3 col180\" >nan</td>\n      <td id=\"T_56d1c_row3_col181\" class=\"data row3 col181\" >nan</td>\n      <td id=\"T_56d1c_row3_col182\" class=\"data row3 col182\" >nan</td>\n      <td id=\"T_56d1c_row3_col183\" class=\"data row3 col183\" >0.006119</td>\n      <td id=\"T_56d1c_row3_col184\" class=\"data row3 col184\" >0.004517</td>\n      <td id=\"T_56d1c_row3_col185\" class=\"data row3 col185\" >0.003201</td>\n      <td id=\"T_56d1c_row3_col186\" class=\"data row3 col186\" >nan</td>\n      <td id=\"T_56d1c_row3_col187\" class=\"data row3 col187\" >0.008423</td>\n      <td id=\"T_56d1c_row3_col188\" class=\"data row3 col188\" >0.006527</td>\n      <td id=\"T_56d1c_row3_col189\" class=\"data row3 col189\" >0.009598</td>\n      <td id=\"T_56d1c_row3_col190\" class=\"data row3 col190\" >0</td>\n    </tr>\n    <tr>\n      <td id=\"T_56d1c_row4_col0\" class=\"data row4 col0\" >0000099d6bd597052cdcda90ffabf56573fe9d7c79be5fbac11a8ed792feb62a</td>\n      <td id=\"T_56d1c_row4_col1\" class=\"data row4 col1\" >2017-07-16 00:00:00</td>\n      <td id=\"T_56d1c_row4_col2\" class=\"data row4 col2\" >0.947266</td>\n      <td id=\"T_56d1c_row4_col3\" class=\"data row4 col3\" >0.002483</td>\n      <td id=\"T_56d1c_row4_col4\" class=\"data row4 col4\" >0.015190</td>\n      <td id=\"T_56d1c_row4_col5\" class=\"data row4 col5\" >1.000977</td>\n      <td id=\"T_56d1c_row4_col6\" class=\"data row4 col6\" >0.007607</td>\n      <td id=\"T_56d1c_row4_col7\" class=\"data row4 col7\" >0.117310</td>\n      <td id=\"T_56d1c_row4_col8\" class=\"data row4 col8\" >0.004654</td>\n      <td id=\"T_56d1c_row4_col9\" class=\"data row4 col9\" >0.009308</td>\n      <td id=\"T_56d1c_row4_col10\" class=\"data row4 col10\" >nan</td>\n      <td id=\"T_56d1c_row4_col11\" class=\"data row4 col11\" >nan</td>\n      <td id=\"T_56d1c_row4_col12\" class=\"data row4 col12\" >0.007732</td>\n      <td id=\"T_56d1c_row4_col13\" class=\"data row4 col13\" >0.038849</td>\n      <td id=\"T_56d1c_row4_col14\" class=\"data row4 col14\" >0.720703</td>\n      <td id=\"T_56d1c_row4_col15\" class=\"data row4 col15\" >0.143677</td>\n      <td id=\"T_56d1c_row4_col16\" class=\"data row4 col16\" >0.006668</td>\n      <td id=\"T_56d1c_row4_col17\" class=\"data row4 col17\" >0.230957</td>\n      <td id=\"T_56d1c_row4_col18\" class=\"data row4 col18\" >0.529297</td>\n      <td id=\"T_56d1c_row4_col19\" class=\"data row4 col19\" >nan</td>\n      <td id=\"T_56d1c_row4_col20\" class=\"data row4 col20\" >nan</td>\n      <td id=\"T_56d1c_row4_col21\" class=\"data row4 col21\" >0.075928</td>\n      <td id=\"T_56d1c_row4_col22\" class=\"data row4 col22\" >0.048920</td>\n      <td id=\"T_56d1c_row4_col23\" class=\"data row4 col23\" >0.001199</td>\n      <td id=\"T_56d1c_row4_col24\" class=\"data row4 col24\" >0.154053</td>\n      <td id=\"T_56d1c_row4_col25\" class=\"data row4 col25\" >1.341797</td>\n      <td id=\"T_56d1c_row4_col26\" class=\"data row4 col26\" >0.000519</td>\n      <td id=\"T_56d1c_row4_col27\" class=\"data row4 col27\" >0.001362</td>\n      <td id=\"T_56d1c_row4_col28\" class=\"data row4 col28\" >0.205444</td>\n      <td id=\"T_56d1c_row4_col29\" class=\"data row4 col29\" >0.691895</td>\n      <td id=\"T_56d1c_row4_col30\" class=\"data row4 col30\" >0.121521</td>\n      <td id=\"T_56d1c_row4_col31\" class=\"data row4 col31\" >nan</td>\n      <td id=\"T_56d1c_row4_col32\" class=\"data row4 col32\" >0.054230</td>\n      <td id=\"T_56d1c_row4_col33\" class=\"data row4 col33\" >0.009483</td>\n      <td id=\"T_56d1c_row4_col34\" class=\"data row4 col34\" >0.006699</td>\n      <td id=\"T_56d1c_row4_col35\" class=\"data row4 col35\" >1.003906</td>\n      <td id=\"T_56d1c_row4_col36\" class=\"data row4 col36\" >0.003845</td>\n      <td id=\"T_56d1c_row4_col37\" class=\"data row4 col37\" >0.094299</td>\n      <td id=\"T_56d1c_row4_col38\" class=\"data row4 col38\" >0.129517</td>\n      <td id=\"T_56d1c_row4_col39\" class=\"data row4 col39\" >0.916992</td>\n      <td id=\"T_56d1c_row4_col40\" class=\"data row4 col40\" >0.231079</td>\n      <td id=\"T_56d1c_row4_col41\" class=\"data row4 col41\" >0.154907</td>\n      <td id=\"T_56d1c_row4_col42\" class=\"data row4 col42\" >0.095154</td>\n      <td id=\"T_56d1c_row4_col43\" class=\"data row4 col43\" >0.001654</td>\n      <td id=\"T_56d1c_row4_col44\" class=\"data row4 col44\" >0.126465</td>\n      <td id=\"T_56d1c_row4_col45\" class=\"data row4 col45\" >0.039246</td>\n      <td id=\"T_56d1c_row4_col46\" class=\"data row4 col46\" >0.027893</td>\n      <td id=\"T_56d1c_row4_col47\" class=\"data row4 col47\" >0.063721</td>\n      <td id=\"T_56d1c_row4_col48\" class=\"data row4 col48\" >0.233154</td>\n      <td id=\"T_56d1c_row4_col49\" class=\"data row4 col49\" >0.175659</td>\n      <td id=\"T_56d1c_row4_col50\" class=\"data row4 col50\" >0.031174</td>\n      <td id=\"T_56d1c_row4_col51\" class=\"data row4 col51\" >0.487549</td>\n      <td id=\"T_56d1c_row4_col52\" class=\"data row4 col52\" >0.093933</td>\n      <td id=\"T_56d1c_row4_col53\" class=\"data row4 col53\" >CR</td>\n      <td id=\"T_56d1c_row4_col54\" class=\"data row4 col54\" >O</td>\n      <td id=\"T_56d1c_row4_col55\" class=\"data row4 col55\" >0.009476</td>\n      <td id=\"T_56d1c_row4_col56\" class=\"data row4 col56\" >0.001715</td>\n      <td id=\"T_56d1c_row4_col57\" class=\"data row4 col57\" >nan</td>\n      <td id=\"T_56d1c_row4_col58\" class=\"data row4 col58\" >0.649902</td>\n      <td id=\"T_56d1c_row4_col59\" class=\"data row4 col59\" >0.007774</td>\n      <td id=\"T_56d1c_row4_col60\" class=\"data row4 col60\" >nan</td>\n      <td id=\"T_56d1c_row4_col61\" class=\"data row4 col61\" >0.005852</td>\n      <td id=\"T_56d1c_row4_col62\" class=\"data row4 col62\" >6.000000</td>\n      <td id=\"T_56d1c_row4_col63\" class=\"data row4 col63\" >0.216553</td>\n      <td id=\"T_56d1c_row4_col64\" class=\"data row4 col64\" >0.008606</td>\n      <td id=\"T_56d1c_row4_col65\" class=\"data row4 col65\" >0.507812</td>\n      <td id=\"T_56d1c_row4_col66\" class=\"data row4 col66\" >0.006821</td>\n      <td id=\"T_56d1c_row4_col67\" class=\"data row4 col67\" >0.000152</td>\n      <td id=\"T_56d1c_row4_col68\" class=\"data row4 col68\" >0.000104</td>\n      <td id=\"T_56d1c_row4_col69\" class=\"data row4 col69\" >0.001419</td>\n      <td id=\"T_56d1c_row4_col70\" class=\"data row4 col70\" >0.096436</td>\n      <td id=\"T_56d1c_row4_col71\" class=\"data row4 col71\" >0.007973</td>\n      <td id=\"T_56d1c_row4_col72\" class=\"data row4 col72\" >0.106018</td>\n      <td id=\"T_56d1c_row4_col73\" class=\"data row4 col73\" >0.032745</td>\n      <td id=\"T_56d1c_row4_col74\" class=\"data row4 col74\" >nan</td>\n      <td id=\"T_56d1c_row4_col75\" class=\"data row4 col75\" >0.008156</td>\n      <td id=\"T_56d1c_row4_col76\" class=\"data row4 col76\" >0.076538</td>\n      <td id=\"T_56d1c_row4_col77\" class=\"data row4 col77\" >0.074463</td>\n      <td id=\"T_56d1c_row4_col78\" class=\"data row4 col78\" >nan</td>\n      <td id=\"T_56d1c_row4_col79\" class=\"data row4 col79\" >0.004932</td>\n      <td id=\"T_56d1c_row4_col80\" class=\"data row4 col80\" >0.004829</td>\n      <td id=\"T_56d1c_row4_col81\" class=\"data row4 col81\" >nan</td>\n      <td id=\"T_56d1c_row4_col82\" class=\"data row4 col82\" >0.001833</td>\n      <td id=\"T_56d1c_row4_col83\" class=\"data row4 col83\" >0.005737</td>\n      <td id=\"T_56d1c_row4_col84\" class=\"data row4 col84\" >0.003288</td>\n      <td id=\"T_56d1c_row4_col85\" class=\"data row4 col85\" >0.002607</td>\n      <td id=\"T_56d1c_row4_col86\" class=\"data row4 col86\" >0.007339</td>\n      <td id=\"T_56d1c_row4_col87\" class=\"data row4 col87\" >nan</td>\n      <td id=\"T_56d1c_row4_col88\" class=\"data row4 col88\" >0.007446</td>\n      <td id=\"T_56d1c_row4_col89\" class=\"data row4 col89\" >0.004463</td>\n      <td id=\"T_56d1c_row4_col90\" class=\"data row4 col90\" >0.006111</td>\n      <td id=\"T_56d1c_row4_col91\" class=\"data row4 col91\" >0.002247</td>\n      <td id=\"T_56d1c_row4_col92\" class=\"data row4 col92\" >0.002110</td>\n      <td id=\"T_56d1c_row4_col93\" class=\"data row4 col93\" >0.001141</td>\n      <td id=\"T_56d1c_row4_col94\" class=\"data row4 col94\" >0.506348</td>\n      <td id=\"T_56d1c_row4_col95\" class=\"data row4 col95\" >0.001770</td>\n      <td id=\"T_56d1c_row4_col96\" class=\"data row4 col96\" >1.000977</td>\n      <td id=\"T_56d1c_row4_col97\" class=\"data row4 col97\" >0.062500</td>\n      <td id=\"T_56d1c_row4_col98\" class=\"data row4 col98\" >0.005859</td>\n      <td id=\"T_56d1c_row4_col99\" class=\"data row4 col99\" >0.001844</td>\n      <td id=\"T_56d1c_row4_col100\" class=\"data row4 col100\" >0.007812</td>\n      <td id=\"T_56d1c_row4_col101\" class=\"data row4 col101\" >0.002470</td>\n      <td id=\"T_56d1c_row4_col102\" class=\"data row4 col102\" >0.005516</td>\n      <td id=\"T_56d1c_row4_col103\" class=\"data row4 col103\" >0.007168</td>\n      <td id=\"T_56d1c_row4_col104\" class=\"data row4 col104\" >nan</td>\n      <td id=\"T_56d1c_row4_col105\" class=\"data row4 col105\" >0.000000</td>\n      <td id=\"T_56d1c_row4_col106\" class=\"data row4 col106\" >0.000155</td>\n      <td id=\"T_56d1c_row4_col107\" class=\"data row4 col107\" >0.001504</td>\n      <td id=\"T_56d1c_row4_col108\" class=\"data row4 col108\" >nan</td>\n      <td id=\"T_56d1c_row4_col109\" class=\"data row4 col109\" >0.002018</td>\n      <td id=\"T_56d1c_row4_col110\" class=\"data row4 col110\" >0.002678</td>\n      <td id=\"T_56d1c_row4_col111\" class=\"data row4 col111\" >nan</td>\n      <td id=\"T_56d1c_row4_col112\" class=\"data row4 col112\" >1.000000</td>\n      <td id=\"T_56d1c_row4_col113\" class=\"data row4 col113\" >0.007729</td>\n      <td id=\"T_56d1c_row4_col114\" class=\"data row4 col114\" >0.003431</td>\n      <td id=\"T_56d1c_row4_col115\" class=\"data row4 col115\" >0.002199</td>\n      <td id=\"T_56d1c_row4_col116\" class=\"data row4 col116\" >0.005512</td>\n      <td id=\"T_56d1c_row4_col117\" class=\"data row4 col117\" >0.004105</td>\n      <td id=\"T_56d1c_row4_col118\" class=\"data row4 col118\" >0.009659</td>\n      <td id=\"T_56d1c_row4_col119\" class=\"data row4 col119\" >1.006836</td>\n      <td id=\"T_56d1c_row4_col120\" class=\"data row4 col120\" >0.005157</td>\n      <td id=\"T_56d1c_row4_col121\" class=\"data row4 col121\" >0.003342</td>\n      <td id=\"T_56d1c_row4_col122\" class=\"data row4 col122\" >0.000264</td>\n      <td id=\"T_56d1c_row4_col123\" class=\"data row4 col123\" >1.509766</td>\n      <td id=\"T_56d1c_row4_col124\" class=\"data row4 col124\" >1.002930</td>\n      <td id=\"T_56d1c_row4_col125\" class=\"data row4 col125\" >0.003078</td>\n      <td id=\"T_56d1c_row4_col126\" class=\"data row4 col126\" >0.003845</td>\n      <td id=\"T_56d1c_row4_col127\" class=\"data row4 col127\" >0.007191</td>\n      <td id=\"T_56d1c_row4_col128\" class=\"data row4 col128\" >0.002983</td>\n      <td id=\"T_56d1c_row4_col129\" class=\"data row4 col129\" >0.000535</td>\n      <td id=\"T_56d1c_row4_col130\" class=\"data row4 col130\" >0.920898</td>\n      <td id=\"T_56d1c_row4_col131\" class=\"data row4 col131\" >0.131592</td>\n      <td id=\"T_56d1c_row4_col132\" class=\"data row4 col132\" >0.933594</td>\n      <td id=\"T_56d1c_row4_col133\" class=\"data row4 col133\" >0.978027</td>\n      <td id=\"T_56d1c_row4_col134\" class=\"data row4 col134\" >0.018204</td>\n      <td id=\"T_56d1c_row4_col135\" class=\"data row4 col135\" >0.822266</td>\n      <td id=\"T_56d1c_row4_col136\" class=\"data row4 col136\" >1.005859</td>\n      <td id=\"T_56d1c_row4_col137\" class=\"data row4 col137\" >1.005859</td>\n      <td id=\"T_56d1c_row4_col138\" class=\"data row4 col138\" >0.953125</td>\n      <td id=\"T_56d1c_row4_col139\" class=\"data row4 col139\" >nan</td>\n      <td id=\"T_56d1c_row4_col140\" class=\"data row4 col140\" >0.673828</td>\n      <td id=\"T_56d1c_row4_col141\" class=\"data row4 col141\" >0.009995</td>\n      <td id=\"T_56d1c_row4_col142\" class=\"data row4 col142\" >0.017593</td>\n      <td id=\"T_56d1c_row4_col143\" class=\"data row4 col143\" >nan</td>\n      <td id=\"T_56d1c_row4_col144\" class=\"data row4 col144\" >1.002930</td>\n      <td id=\"T_56d1c_row4_col145\" class=\"data row4 col145\" >2.000000</td>\n      <td id=\"T_56d1c_row4_col146\" class=\"data row4 col146\" >nan</td>\n      <td id=\"T_56d1c_row4_col147\" class=\"data row4 col147\" >0.009117</td>\n      <td id=\"T_56d1c_row4_col148\" class=\"data row4 col148\" >nan</td>\n      <td id=\"T_56d1c_row4_col149\" class=\"data row4 col149\" >nan</td>\n      <td id=\"T_56d1c_row4_col150\" class=\"data row4 col150\" >nan</td>\n      <td id=\"T_56d1c_row4_col151\" class=\"data row4 col151\" >1.000000</td>\n      <td id=\"T_56d1c_row4_col152\" class=\"data row4 col152\" >0.120728</td>\n      <td id=\"T_56d1c_row4_col153\" class=\"data row4 col153\" >0.811035</td>\n      <td id=\"T_56d1c_row4_col154\" class=\"data row4 col154\" >0.008720</td>\n      <td id=\"T_56d1c_row4_col155\" class=\"data row4 col155\" >1.000000</td>\n      <td id=\"T_56d1c_row4_col156\" class=\"data row4 col156\" >0.247925</td>\n      <td id=\"T_56d1c_row4_col157\" class=\"data row4 col157\" >0.000000</td>\n      <td id=\"T_56d1c_row4_col158\" class=\"data row4 col158\" >4.000000</td>\n      <td id=\"T_56d1c_row4_col159\" class=\"data row4 col159\" >0.244141</td>\n      <td id=\"T_56d1c_row4_col160\" class=\"data row4 col160\" >0.242310</td>\n      <td id=\"T_56d1c_row4_col161\" class=\"data row4 col161\" >0.000000</td>\n      <td id=\"T_56d1c_row4_col162\" class=\"data row4 col162\" >0.705566</td>\n      <td id=\"T_56d1c_row4_col163\" class=\"data row4 col163\" >0.437500</td>\n      <td id=\"T_56d1c_row4_col164\" class=\"data row4 col164\" >0.002850</td>\n      <td id=\"T_56d1c_row4_col165\" class=\"data row4 col165\" >0.688965</td>\n      <td id=\"T_56d1c_row4_col166\" class=\"data row4 col166\" >0.000097</td>\n      <td id=\"T_56d1c_row4_col167\" class=\"data row4 col167\" >1.000000</td>\n      <td id=\"T_56d1c_row4_col168\" class=\"data row4 col168\" >1.009766</td>\n      <td id=\"T_56d1c_row4_col169\" class=\"data row4 col169\" >1.004883</td>\n      <td id=\"T_56d1c_row4_col170\" class=\"data row4 col170\" >1.007812</td>\n      <td id=\"T_56d1c_row4_col171\" class=\"data row4 col171\" >0.009827</td>\n      <td id=\"T_56d1c_row4_col172\" class=\"data row4 col172\" >nan</td>\n      <td id=\"T_56d1c_row4_col173\" class=\"data row4 col173\" >0.009682</td>\n      <td id=\"T_56d1c_row4_col174\" class=\"data row4 col174\" >0.004848</td>\n      <td id=\"T_56d1c_row4_col175\" class=\"data row4 col175\" >nan</td>\n      <td id=\"T_56d1c_row4_col176\" class=\"data row4 col176\" >0.006313</td>\n      <td id=\"T_56d1c_row4_col177\" class=\"data row4 col177\" >0.004463</td>\n      <td id=\"T_56d1c_row4_col178\" class=\"data row4 col178\" >nan</td>\n      <td id=\"T_56d1c_row4_col179\" class=\"data row4 col179\" >nan</td>\n      <td id=\"T_56d1c_row4_col180\" class=\"data row4 col180\" >nan</td>\n      <td id=\"T_56d1c_row4_col181\" class=\"data row4 col181\" >nan</td>\n      <td id=\"T_56d1c_row4_col182\" class=\"data row4 col182\" >nan</td>\n      <td id=\"T_56d1c_row4_col183\" class=\"data row4 col183\" >0.003672</td>\n      <td id=\"T_56d1c_row4_col184\" class=\"data row4 col184\" >0.004944</td>\n      <td id=\"T_56d1c_row4_col185\" class=\"data row4 col185\" >0.008888</td>\n      <td id=\"T_56d1c_row4_col186\" class=\"data row4 col186\" >nan</td>\n      <td id=\"T_56d1c_row4_col187\" class=\"data row4 col187\" >0.001670</td>\n      <td id=\"T_56d1c_row4_col188\" class=\"data row4 col188\" >0.008125</td>\n      <td id=\"T_56d1c_row4_col189\" class=\"data row4 col189\" >0.009827</td>\n      <td id=\"T_56d1c_row4_col190\" class=\"data row4 col190\" >0</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"code","source":"# --- Reading Dataset ---\ntrain_labels.head().style.background_gradient(cmap='Greens').set_properties(**{'font-family': 'Segoe UI'}).hide_index()","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:13.520547Z","iopub.execute_input":"2022-10-05T20:17:13.520932Z","iopub.status.idle":"2022-10-05T20:17:13.53808Z","shell.execute_reply.started":"2022-10-05T20:17:13.520898Z","shell.execute_reply":"2022-10-05T20:17:13.537083Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7fec4e694950>","text/html":"<style type=\"text/css\">\n#T_3ef3d_row0_col0, #T_3ef3d_row1_col0, #T_3ef3d_row2_col0, #T_3ef3d_row3_col0, #T_3ef3d_row4_col0 {\n  font-family: Segoe UI;\n}\n#T_3ef3d_row0_col1, #T_3ef3d_row1_col1, #T_3ef3d_row2_col1, #T_3ef3d_row3_col1, #T_3ef3d_row4_col1 {\n  background-color: #f7fcf5;\n  color: #000000;\n  font-family: Segoe UI;\n}\n</style>\n<table id=\"T_3ef3d_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >customer_ID</th>\n      <th class=\"col_heading level0 col1\" >target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3ef3d_row0_col0\" class=\"data row0 col0\" >0000099d6bd597052cdcda90ffabf56573fe9d7c79be5fbac11a8ed792feb62a</td>\n      <td id=\"T_3ef3d_row0_col1\" class=\"data row0 col1\" >0</td>\n    </tr>\n    <tr>\n      <td id=\"T_3ef3d_row1_col0\" class=\"data row1 col0\" >00000fd6641609c6ece5454664794f0340ad84dddce9a267a310b5ae68e9d8e5</td>\n      <td id=\"T_3ef3d_row1_col1\" class=\"data row1 col1\" >0</td>\n    </tr>\n    <tr>\n      <td id=\"T_3ef3d_row2_col0\" class=\"data row2 col0\" >00001b22f846c82c51f6e3958ccd81970162bae8b007e80662ef27519fcc18c1</td>\n      <td id=\"T_3ef3d_row2_col1\" class=\"data row2 col1\" >0</td>\n    </tr>\n    <tr>\n      <td id=\"T_3ef3d_row3_col0\" class=\"data row3 col0\" >000041bdba6ecadd89a52d11886e8eaaec9325906c9723355abb5ca523658edc</td>\n      <td id=\"T_3ef3d_row3_col1\" class=\"data row3 col1\" >0</td>\n    </tr>\n    <tr>\n      <td id=\"T_3ef3d_row4_col0\" class=\"data row4 col0\" >00007889e4fcd2614b6cbe7f8f3d2e5c728eca32d9eb8ad51ca8b8c4a24cefed</td>\n      <td id=\"T_3ef3d_row4_col1\" class=\"data row4 col1\" >0</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"code","source":"# Shape of the train label data set\n\nprint('\\033[1m'\"Shape of the train label data file\\n\"'\\033[0m',train_labels.shape)","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:19.555408Z","iopub.execute_input":"2022-10-05T20:17:19.555944Z","iopub.status.idle":"2022-10-05T20:17:19.565529Z","shell.execute_reply.started":"2022-10-05T20:17:19.555912Z","shell.execute_reply":"2022-10-05T20:17:19.564295Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"\u001b[1mShape of the train label data file\n\u001b[0m (458913, 2)\n","output_type":"stream"}]},{"cell_type":"code","source":"## Data Type\nprint('\\033[1m'\"Data types of each column in train label data file\\n\"'\\033[0m',train_labels.dtypes)\n","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:19.859988Z","iopub.execute_input":"2022-10-05T20:17:19.86101Z","iopub.status.idle":"2022-10-05T20:17:19.870859Z","shell.execute_reply.started":"2022-10-05T20:17:19.860965Z","shell.execute_reply":"2022-10-05T20:17:19.867749Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"\u001b[1mData types of each column in train label data file\n\u001b[0m customer_ID    object\ntarget          int64\ndtype: object\n","output_type":"stream"}]},{"cell_type":"code","source":"#Missing value in the table \n\nprint('\\033[1m'\"Missing value present in each column of train label data file\\n\"'\\033[0m',train_labels.isna().any())\n","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:20.185502Z","iopub.execute_input":"2022-10-05T20:17:20.186095Z","iopub.status.idle":"2022-10-05T20:17:20.233088Z","shell.execute_reply.started":"2022-10-05T20:17:20.186025Z","shell.execute_reply":"2022-10-05T20:17:20.231062Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"\u001b[1mMissing value present in each column of train label data file\n\u001b[0m customer_ID    False\ntarget         False\ndtype: bool\n","output_type":"stream"}]},{"cell_type":"code","source":"# Check for the duplicated values \n\nprint('\\033[1m'\"Duplicate value present in each column of train label data file\\n\"'\\033[0m',train_labels.customer_ID.duplicated().any())\n\n","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:20.236387Z","iopub.execute_input":"2022-10-05T20:17:20.239248Z","iopub.status.idle":"2022-10-05T20:17:20.331671Z","shell.execute_reply.started":"2022-10-05T20:17:20.239211Z","shell.execute_reply":"2022-10-05T20:17:20.3303Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"\u001b[1mDuplicate value present in each column of train label data file\n\u001b[0m False\n","output_type":"stream"}]},{"cell_type":"code","source":"# No of unique customers \n\nprint('\\033[1m'\"No of Unique customers in the  train label data file\\n\"'\\033[0m',train_labels.customer_ID.nunique())\n","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:20.333657Z","iopub.execute_input":"2022-10-05T20:17:20.333999Z","iopub.status.idle":"2022-10-05T20:17:20.495406Z","shell.execute_reply.started":"2022-10-05T20:17:20.333971Z","shell.execute_reply":"2022-10-05T20:17:20.494447Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"\u001b[1mNo of Unique customers in the  train label data file\n\u001b[0m 458913\n","output_type":"stream"}]},{"cell_type":"code","source":"# Count of the Target\n\nprint('\\033[1m'\"Value count of the target column in the train label data file\\n\"'\\033[0m',train_labels.target.value_counts())\n","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:24.39515Z","iopub.execute_input":"2022-10-05T20:17:24.395518Z","iopub.status.idle":"2022-10-05T20:17:24.408183Z","shell.execute_reply.started":"2022-10-05T20:17:24.395488Z","shell.execute_reply":"2022-10-05T20:17:24.407109Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"\u001b[1mValue count of the target column in the train label data file\n\u001b[0m 0    340085\n1    118828\nName: target, dtype: int64\n","output_type":"stream"}]},{"cell_type":"code","source":"target=train_labels.target.value_counts(normalize=True)\ntarget.rename(index={1:'Default',0:'Paid'},inplace=True)\ncolors = ['#17becf', '#E1396C']\ndata = go.Pie(\nvalues= target,\nlabels= target.index,\nmarker=dict(colors=colors),\ntextinfo='label+percent'\n)\nlayout = go.Layout(\ntitle=dict(text = \"Target Distribution\",x=0.46,y=0.95,font_size=20)\n)\nfig = go.Figure(data=data,layout=layout)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:24.720235Z","iopub.execute_input":"2022-10-05T20:17:24.720586Z","iopub.status.idle":"2022-10-05T20:17:24.802204Z","shell.execute_reply.started":"2022-10-05T20:17:24.720556Z","shell.execute_reply":"2022-10-05T20:17:24.801289Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<div>                            <div id=\"3597ddb9-28ec-4ede-9488-a25d773e3bba\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"3597ddb9-28ec-4ede-9488-a25d773e3bba\")) {                    Plotly.newPlot(                        \"3597ddb9-28ec-4ede-9488-a25d773e3bba\",                        [{\"labels\":[\"Paid\",\"Default\"],\"marker\":{\"colors\":[\"#17becf\",\"#E1396C\"]},\"textinfo\":\"label+percent\",\"values\":[0.7410663894899469,0.2589336105100531],\"type\":\"pie\"}],                        {\"title\":{\"font\":{\"size\":20},\"text\":\"Target 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</div>"},"metadata":{}}]},{"cell_type":"code","source":"del train_labels","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:25.054803Z","iopub.execute_input":"2022-10-05T20:17:25.055431Z","iopub.status.idle":"2022-10-05T20:17:25.060631Z","shell.execute_reply.started":"2022-10-05T20:17:25.055391Z","shell.execute_reply":"2022-10-05T20:17:25.059425Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"# Shape of the train data set\n\nprint('\\033[1m'\"Shape of the train  data file\\n\"'\\033[0m',train_data.shape)","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:32.901284Z","iopub.execute_input":"2022-10-05T20:17:32.901671Z","iopub.status.idle":"2022-10-05T20:17:32.907234Z","shell.execute_reply.started":"2022-10-05T20:17:32.901641Z","shell.execute_reply":"2022-10-05T20:17:32.906226Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"\u001b[1mShape of the train  data file\n\u001b[0m (5531451, 191)\n","output_type":"stream"}]},{"cell_type":"code","source":"## Data Type\nprint('\\033[1m'\"Data types of each column in train label data file\\n\"'\\033[0m',train_data.dtypes)","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:33.170227Z","iopub.execute_input":"2022-10-05T20:17:33.170595Z","iopub.status.idle":"2022-10-05T20:17:33.179581Z","shell.execute_reply.started":"2022-10-05T20:17:33.170557Z","shell.execute_reply":"2022-10-05T20:17:33.178274Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"\u001b[1mData types of each column in train label data file\n\u001b[0m customer_ID            object\nS_2            datetime64[ns]\nP_2                   float16\nD_39                  float16\nB_1                   float16\n                    ...      \nD_142                 float16\nD_143                 float16\nD_144                 float16\nD_145                 float16\ntarget                  int64\nLength: 191, dtype: object\n","output_type":"stream"}]},{"cell_type":"code","source":"train_data.info(max_cols=200, show_counts=True)\n","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:33.740143Z","iopub.execute_input":"2022-10-05T20:17:33.741233Z","iopub.status.idle":"2022-10-05T20:17:38.487129Z","shell.execute_reply.started":"2022-10-05T20:17:33.741188Z","shell.execute_reply":"2022-10-05T20:17:38.486084Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 5531451 entries, 0 to 5531450\nData columns (total 191 columns):\n #    Column       Non-Null Count    Dtype         \n---   ------       --------------    -----         \n 0    customer_ID  5531451 non-null  object        \n 1    S_2          5531451 non-null  datetime64[ns]\n 2    P_2          5485466 non-null  float16       \n 3    D_39         5531451 non-null  float16       \n 4    B_1          5531451 non-null  float16       \n 5    B_2          5529435 non-null  float16       \n 6    R_1          5531451 non-null  float16       \n 7    S_3          4510907 non-null  float16       \n 8    D_41         5529435 non-null  float16       \n 9    B_3          5529435 non-null  float16       \n 10   D_42         791314 non-null   float16       \n 11   D_43         3873055 non-null  float16       \n 12   D_44         5257132 non-null  float16       \n 13   B_4          5531451 non-null  float16       \n 14   D_45         5529434 non-null  float16       \n 15   B_5          5531451 non-null  float16       \n 16   R_2          5531451 non-null  float16       \n 17   D_46         4319752 non-null  float16       \n 18   D_47         5531451 non-null  float16       \n 19   D_48         4812726 non-null  float16       \n 20   D_49         545534 non-null   float16       \n 21   B_6          5531218 non-null  float16       \n 22   B_7          5531451 non-null  float16       \n 23   B_8          5509183 non-null  float16       \n 24   D_50         2389049 non-null  float16       \n 25   D_51         5531451 non-null  float16       \n 26   B_9          5531451 non-null  float16       \n 27   R_3          5531451 non-null  float16       \n 28   D_52         5501888 non-null  float16       \n 29   P_3          5229959 non-null  float16       \n 30   B_10         5531451 non-null  float16       \n 31   D_53         1446866 non-null  float16       \n 32   S_5          5531451 non-null  float16       \n 33   B_11         5531451 non-null  float16       \n 34   S_6          5531451 non-null  float16       \n 35   D_54         5529435 non-null  float16       \n 36   R_4          5531451 non-null  float16       \n 37   S_7          4510907 non-null  float16       \n 38   B_12         5531451 non-null  float16       \n 39   S_8          5531451 non-null  float16       \n 40   D_55         5346648 non-null  float16       \n 41   D_56         2540508 non-null  float16       \n 42   B_13         5481932 non-null  float16       \n 43   R_5          5531451 non-null  float16       \n 44   D_58         5531451 non-null  float16       \n 45   S_9          2597808 non-null  float16       \n 46   B_14         5531451 non-null  float16       \n 47   D_59         5424726 non-null  float16       \n 48   D_60         5531451 non-null  float16       \n 49   D_61         4933399 non-null  float16       \n 50   B_15         5524528 non-null  float16       \n 51   S_11         5531451 non-null  float16       \n 52   D_62         4773290 non-null  float16       \n 53   D_63         5531451 non-null  category      \n 54   D_64         5531451 non-null  category      \n 55   D_65         5531451 non-null  float16       \n 56   B_16         5529435 non-null  float16       \n 57   B_17         2393853 non-null  float16       \n 58   B_18         5531451 non-null  float16       \n 59   B_19         5529435 non-null  float16       \n 60   D_66         623354 non-null   category      \n 61   B_20         5529435 non-null  float16       \n 62   D_68         5314948 non-null  category      \n 63   S_12         5531451 non-null  float16       \n 64   R_6          5531451 non-null  float16       \n 65   S_13         5531451 non-null  float16       \n 66   B_21         5531451 non-null  float16       \n 67   D_69         5336978 non-null  float16       \n 68   B_22         5529435 non-null  float16       \n 69   D_70         5436534 non-null  float16       \n 70   D_71         5531451 non-null  float16       \n 71   D_72         5507743 non-null  float16       \n 72   S_15         5531451 non-null  float16       \n 73   B_23         5531451 non-null  float16       \n 74   D_73         55856 non-null    float16       \n 75   P_4          5531451 non-null  float16       \n 76   D_74         5509678 non-null  float16       \n 77   D_75         5531451 non-null  float16       \n 78   D_76         622497 non-null   float16       \n 79   B_24         5531451 non-null  float16       \n 80   R_7          5531450 non-null  float16       \n 81   D_77         3017539 non-null  float16       \n 82   B_25         5524528 non-null  float16       \n 83   B_26         5529435 non-null  float16       \n 84   D_78         5257132 non-null  float16       \n 85   D_79         5455512 non-null  float16       \n 86   R_8          5531451 non-null  float16       \n 87   R_9          312533 non-null   float16       \n 88   S_16         5531451 non-null  float16       \n 89   D_80         5509678 non-null  float16       \n 90   R_10         5531451 non-null  float16       \n 91   R_11         5531451 non-null  float16       \n 92   B_27         5529435 non-null  float16       \n 93   D_81         5505764 non-null  float16       \n 94   D_82         1472837 non-null  float16       \n 95   S_17         5531451 non-null  float16       \n 96   R_12         5531395 non-null  float16       \n 97   B_28         5531451 non-null  float16       \n 98   R_13         5531451 non-null  float16       \n 99   D_83         5336978 non-null  float16       \n 100  R_14         5531450 non-null  float16       \n 101  R_15         5531451 non-null  float16       \n 102  D_84         5501888 non-null  float16       \n 103  R_16         5531451 non-null  float16       \n 104  B_29         381416 non-null   float16       \n 105  B_30         5529435 non-null  category      \n 106  S_18         5531451 non-null  float16       \n 107  D_86         5531451 non-null  float16       \n 108  D_87         3865 non-null     float16       \n 109  R_17         5531451 non-null  float16       \n 110  R_18         5531451 non-null  float16       \n 111  D_88         6004 non-null     float16       \n 112  B_31         5531451 non-null  float16       \n 113  S_19         5531451 non-null  float16       \n 114  R_19         5531451 non-null  float16       \n 115  B_32         5531451 non-null  float16       \n 116  S_20         5531451 non-null  float16       \n 117  R_20         5531376 non-null  float16       \n 118  R_21         5531451 non-null  float16       \n 119  B_33         5529435 non-null  float16       \n 120  D_89         5501888 non-null  float16       \n 121  R_22         5531451 non-null  float16       \n 122  R_23         5531451 non-null  float16       \n 123  D_91         5374235 non-null  float16       \n 124  D_92         5531451 non-null  float16       \n 125  D_93         5531451 non-null  float16       \n 126  D_94         5531451 non-null  float16       \n 127  R_24         5531451 non-null  float16       \n 128  R_25         5531451 non-null  float16       \n 129  D_96         5531451 non-null  float16       \n 130  S_22         5512427 non-null  float16       \n 131  S_23         5531006 non-null  float16       \n 132  S_24         5512858 non-null  float16       \n 133  S_25         5518604 non-null  float16       \n 134  S_26         5530817 non-null  float16       \n 135  D_102        5490796 non-null  float16       \n 136  D_103        5429903 non-null  float16       \n 137  D_104        5429903 non-null  float16       \n 138  D_105        2510020 non-null  float16       \n 139  D_106        541349 non-null   float16       \n 140  D_107        5429903 non-null  float16       \n 141  B_36         5531451 non-null  float16       \n 142  B_37         5531395 non-null  float16       \n 143  R_26         609305 non-null   float16       \n 144  R_27         5402748 non-null  float16       \n 145  B_38         5529435 non-null  category      \n 146  D_108        28938 non-null    float16       \n 147  D_109        5529854 non-null  float16       \n 148  D_110        31334 non-null    float16       \n 149  D_111        31334 non-null    float16       \n 150  B_39         33632 non-null    float16       \n 151  D_112        5528801 non-null  float16       \n 152  B_40         5531398 non-null  float16       \n 153  S_27         4130516 non-null  float16       \n 154  D_113        5354735 non-null  float16       \n 155  D_114        5354735 non-null  category      \n 156  D_115        5354735 non-null  float16       \n 157  D_116        5354735 non-null  category      \n 158  D_117        5354735 non-null  category      \n 159  D_118        5354735 non-null  float16       \n 160  D_119        5354735 non-null  float16       \n 161  D_120        5354735 non-null  category      \n 162  D_121        5354735 non-null  float16       \n 163  D_122        5354735 non-null  float16       \n 164  D_123        5354735 non-null  float16       \n 165  D_124        5354735 non-null  float16       \n 166  D_125        5354735 non-null  float16       \n 167  D_126        5414635 non-null  category      \n 168  D_127        5531451 non-null  float16       \n 169  D_128        5429903 non-null  float16       \n 170  D_129        5429903 non-null  float16       \n 171  B_41         5530761 non-null  float16       \n 172  B_42         71478 non-null    float16       \n 173  D_130        5429903 non-null  float16       \n 174  D_131        5429903 non-null  float16       \n 175  D_132        542577 non-null   float16       \n 176  D_133        5488735 non-null  float16       \n 177  R_28         5531451 non-null  float16       \n 178  D_134        194699 non-null   float16       \n 179  D_135        194699 non-null   float16       \n 180  D_136        194699 non-null   float16       \n 181  D_137        194699 non-null   float16       \n 182  D_138        194699 non-null   float16       \n 183  D_139        5429903 non-null  float16       \n 184  D_140        5490819 non-null  float16       \n 185  D_141        5429903 non-null  float16       \n 186  D_142        944408 non-null   float16       \n 187  D_143        5429903 non-null  float16       \n 188  D_144        5490724 non-null  float16       \n 189  D_145        5429903 non-null  float16       \n 190  target       5531451 non-null  int64         \ndtypes: category(11), datetime64[ns](1), float16(177), int64(1), object(1)\nmemory usage: 2.0+ GB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Following are the categorical variables 'B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_64', 'D_66', 'D_68'. We are going to analyze it w.r.t to Target","metadata":{}},{"cell_type":"code","source":"train_data[\"B_30\"].value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:43.84012Z","iopub.execute_input":"2022-10-05T20:17:43.840477Z","iopub.status.idle":"2022-10-05T20:17:43.891607Z","shell.execute_reply.started":"2022-10-05T20:17:43.840448Z","shell.execute_reply":"2022-10-05T20:17:43.890488Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"0.0    4710663\n1.0     763955\n2.0      54817\nName: B_30, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# --- Setting Colors, Labels, Order ---\nblack_grad = ['#100C07', '#3E3B39', '#6D6A6A', '#9B9A9C', '#CAC9CD']\ncyan_grad = ['#142459', '#176BA0', '#19AADE', '#1AC9E6', '#87EAFA']\ncolors=cyan_grad\nlabels=train_data['B_30'].dropna().unique()\norder=train_data['B_30'].value_counts().index\n\n# --- Size for Both Figures ---\nplt.figure(figsize=(16, 8))\nplt.suptitle('B_30 Distribution', fontweight='heavy', fontsize='16', fontfamily='sans-serif', \n             color=black_grad[0])\n\n# --- Histogram ---\ncountplt = plt.subplot(1, 2, 1)\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='B_30', data=train_data, palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nfor rect in ax.patches:\n    ax.text (rect.get_x()+rect.get_width()/2, rect.get_height()+100,rect.get_height(), horizontalalignment='center',\n             fontsize=12, bbox=dict(facecolor='none', edgecolor=black_grad[0], linewidth=0.15, boxstyle='round'))\n\nplt.xlabel('B_30 Distribution', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\ncountplt\n\n# --- Pie Chart ---\nplt.subplot(1, 2, 2)\nplt.title('Pie Chart', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nplt.pie(train_data['B_30'].value_counts(), colors=colors, labels=order, pctdistance=0.67, autopct='%.2f%%',\n        wedgeprops=dict(alpha=0.8, edgecolor=black_grad[1]), textprops={'fontsize':12})\ncentre=plt.Circle((0, 0), 0.45, fc='white', edgecolor=black_grad[1])\nplt.gcf().gca().add_artist(centre)\n\n# --- Count Categorical Labels w/out Dropping Null Walues ---\nprint('\\033[36m*' * 29)\nprint('\\033[1m'+'.: B_30 Content Total :.'+'\\033[0m')\nprint('\\033[36m*' * 29+'\\033[0m')\ntrain_data.B_38.value_counts(dropna=False)","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:44.000105Z","iopub.execute_input":"2022-10-05T20:17:44.000425Z","iopub.status.idle":"2022-10-05T20:17:44.710906Z","shell.execute_reply.started":"2022-10-05T20:17:44.000397Z","shell.execute_reply":"2022-10-05T20:17:44.709939Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\n\u001b[1m.: B_30 Content Total :.\u001b[0m\n\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[0m\n","output_type":"stream"},{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"2.0    1953232\n3.0    1255315\n1.0    1160047\n5.0     444856\n4.0     294917\n7.0     259028\n6.0     162040\nNaN       2016\nName: B_38, dtype: int64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# --- Setting Colors, Labels, Order ---\nblack_grad = ['#100C07', '#3E3B39', '#6D6A6A', '#9B9A9C', '#CAC9CD']\ncyan_grad = ['#142459', '#176BA0', '#19AADE', '#1AC9E6', '#87EAFA']\ncolors=cyan_grad\nlabels=train_data['B_18'].dropna().unique()\norder=train_data['B_18'].value_counts().index\n\n# --- Size for Both Figures ---\nplt.figure(figsize=(16, 8))\nplt.suptitle('B_18 Distribution', fontweight='heavy', fontsize='16', fontfamily='sans-serif', \n             color=black_grad[0])\n\n# --- Histogram ---\ncountplt = plt.subplot(1, 2, 1)\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='B_18', data=train_data, palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nfor rect in ax.patches:\n    ax.text (rect.get_x()+rect.get_width()/2, rect.get_height()+100,rect.get_height(), horizontalalignment='center',\n             fontsize=12, bbox=dict(facecolor='none', edgecolor=black_grad[0], linewidth=0.15, boxstyle='round'))\n\nplt.xlabel('B_18 Distribution', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\ncountplt\n\n# --- Pie Chart ---\nplt.subplot(1, 2, 2)\nplt.title('Pie Chart', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nplt.pie(train_data['B_18'].value_counts(), colors=colors, labels=order, pctdistance=0.67, autopct='%.2f%%',\n        wedgeprops=dict(alpha=0.8, edgecolor=black_grad[1]), textprops={'fontsize':12})\ncentre=plt.Circle((0, 0), 0.45, fc='white', edgecolor=black_grad[1])\nplt.gcf().gca().add_artist(centre)\n\n# --- Count Categorical Labels w/out Dropping Null Walues ---\nprint('\\033[36m*' * 29)\nprint('\\033[1m'+'.: B_18 Content Total :.'+'\\033[0m')\nprint('\\033[36m*' * 29+'\\033[0m')\ntrain_data.B_38.value_counts(dropna=False)","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:25:27.930431Z","iopub.execute_input":"2022-10-05T20:25:27.930851Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\n\u001b[1m.: B_18 Content Total :.\u001b[0m\n\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[0m\n","output_type":"stream"},{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"2.0    1953232\n3.0    1255315\n1.0    1160047\n5.0     444856\n4.0     294917\n7.0     259028\n6.0     162040\nNaN       2016\nName: B_38, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# --- Setting Colors, Labels, Order ---\nblack_grad = ['#100C07', '#3E3B39', '#6D6A6A', '#9B9A9C', '#CAC9CD']\ncyan_grad = ['#142459', '#176BA0', '#19AADE', '#1AC9E6', '#87EAFA']\ncolors=cyan_grad\nlabels=train_data['B_38'].dropna().unique()\norder=train_data['B_38'].value_counts().index\n\n# --- Size for Both Figures ---\nplt.figure(figsize=(16, 8))\nplt.suptitle('B_38 Distribution', fontweight='heavy', fontsize='16', fontfamily='sans-serif', \n             color=black_grad[0])\n\n# --- Histogram ---\ncountplt = plt.subplot(1, 2, 1)\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='B_38', data=train_data, palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nfor rect in ax.patches:\n    ax.text (rect.get_x()+rect.get_width()/2, rect.get_height()+100,rect.get_height(), horizontalalignment='center',\n             fontsize=12, bbox=dict(facecolor='none', edgecolor=black_grad[0], linewidth=0.15, boxstyle='round'))\n\nplt.xlabel('B_38 Distribution', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\ncountplt\n\n# --- Pie Chart ---\nplt.subplot(1, 2, 2)\nplt.title('Pie Chart', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nplt.pie(train_data['B_38'].value_counts(), colors=colors, labels=order, pctdistance=0.67, autopct='%.2f%%',\n        wedgeprops=dict(alpha=0.8, edgecolor=black_grad[1]), textprops={'fontsize':12})\ncentre=plt.Circle((0, 0), 0.45, fc='white', edgecolor=black_grad[1])\nplt.gcf().gca().add_artist(centre)\n\n# --- Count Categorical Labels w/out Dropping Null Walues ---\nprint('\\033[36m*' * 29)\nprint('\\033[1m'+'.: B_38 Content Total :.'+'\\033[0m')\nprint('\\033[36m*' * 29+'\\033[0m')\ntrain_data.B_38.value_counts(dropna=False)","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:44.713633Z","iopub.execute_input":"2022-10-05T20:17:44.714023Z","iopub.status.idle":"2022-10-05T20:17:45.429827Z","shell.execute_reply.started":"2022-10-05T20:17:44.713984Z","shell.execute_reply":"2022-10-05T20:17:45.428914Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\n\u001b[1m.: B_38 Content Total :.\u001b[0m\n\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[0m\n","output_type":"stream"},{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"2.0    1953232\n3.0    1255315\n1.0    1160047\n5.0     444856\n4.0     294917\n7.0     259028\n6.0     162040\nNaN       2016\nName: B_38, dtype: int64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# --- Setting Colors, Labels, Order ---\npurple_grad = ['#491D8B', '#6929C4', '#8A3FFC', '#A56EFF', '#BE95FF']\ncolors=purple_grad\nlabels=train_data['D_114'].dropna().unique()\norder=train_data['D_114'].value_counts().index\n\n# --- Size for Both Figures ---\nplt.figure(figsize=(18, 8))\nplt.suptitle('D_114 Distribution', fontweight='heavy', fontsize='16', fontfamily='sans-serif', \n             color=black_grad[0])\n\n# --- Histogram ---\ncountplt = plt.subplot(1, 2, 1)\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='D_114', data=train_data, palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nfor rect in ax.patches:\n    ax.text (rect.get_x()+rect.get_width()/2, rect.get_height()+20,rect.get_height(), horizontalalignment='center', \n             fontsize=12, bbox=dict(facecolor='none', edgecolor=black_grad[0], linewidth=0.15, boxstyle='round'))\nplt.tight_layout(rect=[0, 0.04, 1, 0.965])\nplt.xlabel('D_114 Distribution', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\ncountplt\n\n# --- Pie Chart ---\nplt.subplot(1, 2, 2)\nplt.title('Pie Chart', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nplt.pie(train_data['D_114'].value_counts(), colors=colors, labels=order, pctdistance=0.67, autopct='%.2f%%', \n        wedgeprops=dict(alpha=0.8, edgecolor=black_grad[1]), textprops={'fontsize':12})\ncentre=plt.Circle((0, 0), 0.45, fc='white', edgecolor=black_grad[1])\nplt.gcf().gca().add_artist(centre);\n\n# --- Count Categorical Labels w/out Dropping Null Walues ---\nprint('\\033[36m*' * 30)\nprint('\\033[1m'+'.: D_114 Total :.'+'\\033[0m')\nprint('\\033[36m*' * 30+'\\033[0m')\ntrain_data.D_114.value_counts(dropna=False)","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:45.431515Z","iopub.execute_input":"2022-10-05T20:17:45.432235Z","iopub.status.idle":"2022-10-05T20:17:46.108752Z","shell.execute_reply.started":"2022-10-05T20:17:45.432194Z","shell.execute_reply":"2022-10-05T20:17:46.107415Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\n\u001b[1m.: D_114 Total :.\u001b[0m\n\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[0m\n","output_type":"stream"},{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"1.0    3316478\n0.0    2038257\nNaN     176716\nName: D_114, dtype: int64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x576 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# --- Setting Colors, Labels, Order ---\npurple_grad = ['#491D8B', '#6929C4', '#8A3FFC', '#A56EFF', '#BE95FF']\ncolors=purple_grad\nlabels=train_data['D_117'].dropna().unique()\norder=train_data['D_117'].value_counts().index\n\n# --- Size for Both Figures ---\nplt.figure(figsize=(18, 8))\nplt.suptitle('D_117 Distribution', fontweight='heavy', fontsize='16', fontfamily='sans-serif', \n             color=black_grad[0])\n\n# --- Histogram ---\ncountplt = plt.subplot(1, 2, 1)\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='D_117', data=train_data, palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nfor rect in ax.patches:\n    ax.text (rect.get_x()+rect.get_width()/2, rect.get_height()+20,rect.get_height(), horizontalalignment='center', \n             fontsize=12, bbox=dict(facecolor='none', edgecolor=black_grad[0], linewidth=0.15, boxstyle='round'))\nplt.tight_layout(rect=[0, 0.04, 1, 0.965])\nplt.xlabel('D_117 Distribution', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\ncountplt\n\n# --- Pie Chart ---\nplt.subplot(1, 2, 2)\nplt.title('Pie Chart', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nplt.pie(train_data['D_117'].value_counts(), colors=colors, labels=order, pctdistance=0.67, autopct='%.2f%%', \n        wedgeprops=dict(alpha=0.8, edgecolor=black_grad[1]), textprops={'fontsize':12})\ncentre=plt.Circle((0, 0), 0.45, fc='white', edgecolor=black_grad[1])\nplt.gcf().gca().add_artist(centre);\n\n# --- Count Categorical Labels w/out Dropping Null Walues ---\nprint('\\033[36m*' * 30)\nprint('\\033[1m'+'.: D_117 Total :.'+'\\033[0m')\nprint('\\033[36m*' * 30+'\\033[0m')\ntrain_data.D_117.value_counts(dropna=False)","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:46.922594Z","iopub.execute_input":"2022-10-05T20:17:46.922985Z","iopub.status.idle":"2022-10-05T20:17:47.720009Z","shell.execute_reply.started":"2022-10-05T20:17:46.92294Z","shell.execute_reply":"2022-10-05T20:17:47.719016Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\n\u001b[1m.: D_117 Total :.\u001b[0m\n\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[0m\n","output_type":"stream"},{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"-1.0    1456084\n3.0     1166400\n4.0     1138666\n2.0      666808\n5.0      459290\n6.0      344520\nNaN      176716\n1.0      122967\nName: D_117, dtype: int64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x576 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# --- Setting Colors, Labels, Order ---\npurple_grad = ['#491D8B', '#6929C4', '#8A3FFC', '#A56EFF', '#BE95FF']\ncolors=purple_grad\nlabels=train_data['D_64'].dropna().unique()\norder=train_data['D_64'].value_counts().index\n\n# --- Size for Both Figures ---\nplt.figure(figsize=(18, 8))\nplt.suptitle('D_64 Distribution', fontweight='heavy', fontsize='16', fontfamily='sans-serif', \n             color=black_grad[0])\n\n# --- Histogram ---\ncountplt = plt.subplot(1, 2, 1)\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='D_64', data=train_data, palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nfor rect in ax.patches:\n    ax.text (rect.get_x()+rect.get_width()/2, rect.get_height()+20,rect.get_height(), horizontalalignment='center', \n             fontsize=12, bbox=dict(facecolor='none', edgecolor=black_grad[0], linewidth=0.15, boxstyle='round'))\nplt.tight_layout(rect=[0, 0.04, 1, 0.965])\nplt.xlabel('D_64 Distribution', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\ncountplt\n\n# --- Pie Chart ---\nplt.subplot(1, 2, 2)\nplt.title('Pie Chart', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nplt.pie(train_data['D_64'].value_counts(), colors=colors, labels=order, pctdistance=0.67, autopct='%.2f%%', \n        wedgeprops=dict(alpha=0.8, edgecolor=black_grad[1]), textprops={'fontsize':12})\ncentre=plt.Circle((0, 0), 0.45, fc='white', edgecolor=black_grad[1])\nplt.gcf().gca().add_artist(centre);\n\n# --- Count Categorical Labels w/out Dropping Null Walues ---\nprint('\\033[36m*' * 30)\nprint('\\033[1m'+'.: D_64 Total :.'+'\\033[0m')\nprint('\\033[36m*' * 30+'\\033[0m')\ntrain_data.D_64.value_counts(dropna=False)","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:49.846919Z","iopub.execute_input":"2022-10-05T20:17:49.847226Z","iopub.status.idle":"2022-10-05T20:17:50.5545Z","shell.execute_reply.started":"2022-10-05T20:17:49.847199Z","shell.execute_reply":"2022-10-05T20:17:50.5536Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\n\u001b[1m.: D_64 Total :.\u001b[0m\n\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[36m*\u001b[0m\n","output_type":"stream"},{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"O     2913244\nU     1523448\nR      840112\n       217442\n-1      37205\nName: D_64, dtype: int64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x576 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.figure(figsize=(12,10))\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='B_30', data=train_data,hue='target', palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nplt.tight_layout(rect=[0, 0.04, 1, 0.965])\nplt.xlabel('B_30 Distribution w.r.t target', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:51.815899Z","iopub.execute_input":"2022-10-05T20:17:51.816526Z","iopub.status.idle":"2022-10-05T20:17:52.633132Z","shell.execute_reply.started":"2022-10-05T20:17:51.81648Z","shell.execute_reply":"2022-10-05T20:17:52.632237Z"},"trusted":true},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x720 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.figure(figsize=(12,10))\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='B_38', data=train_data,hue='target', palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nplt.tight_layout(rect=[0, 0.04, 1, 0.965])\nplt.xlabel('B_38 Distribution w.r.t target', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:52.634449Z","iopub.execute_input":"2022-10-05T20:17:52.634892Z","iopub.status.idle":"2022-10-05T20:17:53.651879Z","shell.execute_reply.started":"2022-10-05T20:17:52.634855Z","shell.execute_reply":"2022-10-05T20:17:53.650953Z"},"trusted":true},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x720 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.figure(figsize=(12,10))\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='D_114', data=train_data,hue='target', palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nplt.tight_layout(rect=[0, 0.04, 1, 0.965])\nplt.xlabel('D_114 Distribution w.r.t target', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:53.653501Z","iopub.execute_input":"2022-10-05T20:17:53.65389Z","iopub.status.idle":"2022-10-05T20:17:54.419852Z","shell.execute_reply.started":"2022-10-05T20:17:53.653849Z","shell.execute_reply":"2022-10-05T20:17:54.418841Z"},"trusted":true},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x720 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.figure(figsize=(12,10))\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='D_117', data=train_data,hue='target', palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nplt.tight_layout(rect=[0, 0.04, 1, 0.965])\nplt.xlabel('D_117 Distribution w.r.t target', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:55.190544Z","iopub.execute_input":"2022-10-05T20:17:55.190893Z","iopub.status.idle":"2022-10-05T20:17:55.949009Z","shell.execute_reply.started":"2022-10-05T20:17:55.190856Z","shell.execute_reply":"2022-10-05T20:17:55.947978Z"},"trusted":true},"execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x720 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA1gAAAKSCAYAAADPm3rOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAy00lEQVR4nO3de5xdVX0//M+SIEGM5uIVQk2sGEVQ5KI4TA2WVoEq+LTaH+hT8dLyq7fWO4htbbW0UP1ptVYtLVZoVWqxLehjtTwIY8d5RMHiDaSAoCSAIJlgKI4IrOePsyPjMJkksE7OnOT9fr3mNWevvfbe3zM7OeHDWntNqbUGAACA++8Bgy4AAABgeyFgAQAANCJgAQAANCJgAQAANCJgAQAANCJgAQAANCJgATAUlixaeOiSRQtr93XooOsBgNkIWAAM1JJFCy/sQtO1M9qnB6qXJvlRkou6rx9txfmv7c7x0YZlA8CsFgy6AADYEpMbpr6W5OBB17E1lixa+MDJDVN3DLoOALYdAQuAodBNC7yg23zW5IapC5csWvjIJO9OcliSZUluTXJFkr9JMp7kmmmnOG7JooXHJcnkhqnSnXM0yR+mF9wWJrk2yZlJ/mJyw9RPuz4PTfLBJEcnua17/YtJXpLke5MbplZ0/a5N8pgk/5jkpiTHJbkuyVOXLFr4riRHJtkjyW5Jbk5yXpITJzdM3dAd/8dJ3t7V+pwk7+vO95kkr0jy20nenOSBST6R5PWTG6buvC8/SwD6R8ACYJh9MMmvJ/mfJN9KL2SNpBeyzk9vOuFT0wslP0xy9cYDu8B2Xnr/Fq5PL1w9PsmfJtknybFd179N8sLu9fVJ3phkpzlq+s3u+3+nF8iS5Ij0wtV13fVWpRfQnpjkabOc4+zuWrt2135SkpVJbkjy6CSvSfKNrjYA5hHPYAEwXzxm2jNXNfeMVs3l8d33V05umDqgG016ZJK/mtwwdcPkhqmD0wslSfL/TG6YOrhrS5I/SS/sXJfksZMbplYlObXbd8ySRQv3XbJo4S/mnnD1gckNU49PLxz9dDN1HTS5YWrfJId22y9KsnRyw9S+kxumnpjk+I39umvM9MrJDVNPSPKlbnvvJL+aZK8k3+vaDttMDQAMgIAFwHxxR+5ZxOKiJJdvwTGf7r5/dMmihVcvWbTws0n+d3qjP5tzUPf9c5Mbpia71x+ftv/A9EaONvpEkkxumLoxc4e/CyY3TH2j63tX1/aUJF9dsmjhbV14nD7ytPss59j4vq7tvk9Obpj60uSGqbtzT8B65Bw1ADAgpggCMF/cMG10aeYzV5vytvRGeZ6T3rS+0fSm470wyX6N66tb2O8H0ze657zOSFKS3JLksiQPTm96YDLLdMPJDVMbV0nc+IzV9FUTN9ZRtrAeALYhI1gADLNDkoxNbpj6vckNU7+ce6bePWXJooXLute3d993m3HsV7vvhy9ZtHBJ9/pF0/ZfnN5zXRsDzQuSZMmihY9K8qw5apoZxJ6ee8LQvpMbpp6W3kIaAGyHjGABMMxOSe85puvSW0Fw46jQmiTrutff6dp/fcmihZck+cbkhqmXpbdi33lJ9kzy3SWLFt6Ue57pOmtyw9Q3k2TJooVnpzci9oYlixY+N8mjsnX/fn5j2utvLlm08OYkj9i6twnAsDCCBcAw+6ckX0myKMm+STYkOSfJEZMbpjaOJP1Bki+n94zX/l2/TG6YujC9kaj/SO/fw5Xprfz3h+mt8LfR76T3bNb/JFmS3vLpn+v2/XhzBU5umDovyQm5Z1XA7yR55X14rwAMgVLrlk4pB4Adz5JFC/dMcvPkhqmpbvthSb6d3ijUWZMbpo6d63gAdiymCALA3H4jyR910wvvSPKM9Eaybkty8iALA2D+MUUQAOb2zfSmDh6U5NlJptJbsv3pkxumvjXIwgCYf0wRBAAAaMQIFgAAQCMCFgAAQCMWueg87GEPqytWrBh0GQAAwBC45JJLflhrffjMdgGrs2LFilx88cWDLgMAABgCpZTvzdZuiiAAAEAjAhYAAEAjAhYAAEAjnsGaw09/+tOsWbMmU1NTgy5lkxYuXJjly5dn5513HnQpAACwwxOw5rBmzZosWrQoK1asSCll0OXcS601t9xyS9asWZOVK1cOuhwAANjhmSI4h6mpqSxbtmxehqskKaVk2bJl83qEDQAAdiQC1mbM13C10XyvDwAAdiQC1n2wfv36fPCDH+z7df7t3/4tl112Wd+vAwAAtCFg3QdbG7Bqrbn77ru3+joCFgAADBcB6z448cQTc/XVV2e//fbL61//+hx22GHZf//9s+++++acc85Jklx77bVZtWpVXvKSl2SfffbJddddl3e+851ZtWpVRkdHc+yxx+bd7353kuTqq6/O4YcfngMOOCC/9Eu/lO985zuZmJjIueeemze/+c3Zb7/9cvXVVw/yLQMAAFvAKoL3wSmnnJJvfetbufTSS3PnnXfm9ttvz0Me8pD88Ic/zMEHH5yjjjoqSXLllVfmjDPOyMEHH5yvfvWr+dSnPpWvf/3r+elPf5r9998/BxxwQJLk+OOPz4c//OHstddeueiii/KqV70qX/jCF3LUUUfluc99bl7wghcM8u0CAABbSMC6n2qtOemkk/LFL34xD3jAA7J27dr84Ac/SJI85jGPycEHH5wk+dKXvpSjjz46CxcuzMKFC/O85z0vSXLbbbdlYmIiL3zhC392zp/85Cfb/o0AAAD3m4B1P33sYx/LzTffnEsuuSQ777xzVqxY8bNl03fbbbfNHn/33Xdn8eLFufTSS/tcKQAA0G+ewboPFi1alA0bNiRJbr311jziEY/IzjvvnAsuuCDf+973Zj3mkEMOyac//elMTU3ltttuy2c+85kkyUMe8pCsXLky//zP/5ykNyL29a9//V7XAQAA5j8B6z5YtmxZDjnkkOyzzz659NJLc/HFF2fffffNmWeemSc84QmzHnPQQQflqKOOypOf/OQcccQR2XffffPQhz40SW8U7PTTT89TnvKUPOlJT/rZQhnHHHNM3vWud+WpT32qRS4AAGAIlFrroGuYFw488MB68cUX/1zb5Zdfnic+8YnNrnHbbbflwQ9+cG6//fY885nPzGmnnZb999//fp+3dZ0AAMDcSimX1FoPnNnuGaxt6Pjjj89ll12WqampHHfccU3CFQAAMH8IWNvQxz/+8UGXAAAA9JFnsAAAABoRsAAAABoRsAAAABoRsAAAABoRsOa5z33uc1m1alUe97jH5ZRTThl0OQAAwBysIrgVTj351EyuW9/sfEuWLs4Jbzthk/vvuuuuvPrVr855552X5cuX/+yXFe+9997NagAAANoRsLbC5Lr1WX/Fo9qdcNWNc+7+yle+ksc97nF57GMfmyQ55phjcs455whYAAAwT5kiOI+tXbs2e+6558+2ly9fnrVr1w6wIgAAYC5GsADYZlpPtd4WNjedGwCmE7DmsT322CPXXXfdz7bXrFmTPfbYY4AVAdw/zadabwubmc4NANOZIjiPHXTQQbnyyitzzTXX5I477shZZ52Vo446atBlAQAAm2AEax5bsGBBPvCBD+Q5z3lO7rrrrrz85S/Pk570pEGXBQAAbIKAtRWWLF3cdKrIkqWLN9vnyCOPzJFHHtnsmgAAQP8IWFvBQ84AAMBcPIMFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIA1z7385S/PIx7xiOyzzz6DLgUAANgMy7Rvhb/4s3dn/br1zc63eOnivOWkN83Z56UvfWle85rX5CUveUmz6wIAAP0hYG2F9evWZ9frn9XufLlgs32e+cxn5tprr212TQAAoH9MEQQAAGhEwAIAAGhEwAIAAGhEwAIAAGhEwJrnjj322DzjGc/IFVdckeXLl+f0008fdEkAAMAmWEVwKyxeuniLVv7bmvNtzic+8Ylm1wMAAPpLwNoKm/udVQAAwI7NFEEAAIBGBCwAAIBGBKzNqLUOuoQ5zff6AABgRyJgzWHhwoW55ZZb5m2IqbXmlltuycKFCwddCgAAEItczGn58uVZs2ZNbr755kGXskkLFy7M8uXLB10GAAAQAWtOO++8c1auXDnoMgAAgCFhiiAAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjCwZdAMDWOPXkUzO5bv2gy9gqS5YuzglvO2HQZQAA24CABQyVyXXrs/6KRw26jK2z6sZBVwAAbCOmCAIAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADQiYAEAADTS14BVSnl9KeXbpZRvlVI+UUpZWEpZWUq5qJRyVSnln0opD+z67tJtX9XtXzHtPG/t2q8opTxnWvvhXdtVpZQTp7XPeg0AAIB+6lvAKqXskeT3khxYa90nyU5JjklyapL31lofl2QyySu6Q16RZLJrf2/XL6WUvbvjnpTk8CQfLKXsVErZKclfJzkiyd5Jju36Zo5rAAAA9E2/pwguSLJrKWVBkgcluSHJLyc5u9t/RpLnd6+P7rbT7T+slFK69rNqrT+ptV6T5KokT+u+rqq1frfWekeSs5Ic3R2zqWsAAAD0Td8CVq11bZJ3J/l+esHq1iSXJFlfa72z67YmyR7d6z2SXNcde2fXf9n09hnHbKp92RzXAAAA6JsF/TpxKWVJeqNPK5OsT/LP6U3xmzdKKccnOT5Jli9fnuuvv37AFQGbs3TZkuyy4kGDLmOr7LZsic+XjvsHwPaubwErya8kuabWenOSlFL+JckhSRaXUhZ0I0zLk6zt+q9NsmeSNd2UwocmuWVa+0bTj5mt/ZY5rvFzaq2nJTktSQ488MC6++673793DPTdulsms/7aXQZdxlZZvMtkfL70uH8AbO/6+QzW95McXEp5UPdc1GFJLktyQZIXdH2OS3JO9/rcbjvd/i/UWmvXfky3yuDKJHsl+UqSrybZq1sx8IHpLYRxbnfMpq4BAADQN/18Buui9Baa+FqSb3bXOi3JCUneUEq5Kr3npU7vDjk9ybKu/Q1JTuzO8+0kn0wvnH0uyatrrXd1o1OvSfL5JJcn+WTXN3NcAwAAoG/6OUUwtda3J3n7jObvprcC4My+U0leuInznJzk5FnaP5vks7O0z3oNAACAfur3Mu0AAAA7DAELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgEQELAACgkb4GrFLK4lLK2aWU75RSLi+lPKOUsrSUcl4p5cru+5KubymlvL+UclUp5RullP2nnee4rv+VpZTjprUfUEr5ZnfM+0sppWuf9RoAAAD91O8RrPcl+Vyt9QlJnpLk8iQnJjm/1rpXkvO77SQ5Isle3dfxST6U9MJSkrcneXqSpyV5+7TA9KEkvzPtuMO79k1dAwAAoG/6FrBKKQ9N8swkpydJrfWOWuv6JEcnOaPrdkaS53evj05yZu35cpLFpZRHJ3lOkvNqretqrZNJzktyeLfvIbXWL9daa5IzZ5xrtmsAAAD0TT9HsFYmuTnJ35dS/quU8nellN2SPLLWekPX58Ykj+xe75HkumnHr+na5mpfM0t75rgGAABA3yzo87n3T/LaWutFpZT3ZcZUvVprLaXUPtYw5zVKKcenNx0xy5cvz/XXX9/PUoAGli5bkl1WPGjQZWyV3ZYt8fnScf8A2N71M2CtSbKm1npRt312egHrB6WUR9dab+im+d3U7V+bZM9pxy/v2tYmOXRG+4Vd+/JZ+meOa/ycWutpSU5LkgMPPLDuvvvu9+V9AtvQulsms/7aXQZdxlZZvMtkfL70uH8AbO/6NkWw1npjkutKKau6psOSXJbk3CQbVwI8Lsk53etzk7ykW03w4CS3dtP8Pp/k2aWUJd3iFs9O8vlu349KKQd3qwe+ZMa5ZrsGAABA3/RzBCtJXpvkY6WUByb5bpKXpRfqPllKeUWS7yX5za7vZ5McmeSqJLd3fVNrXVdKeWeSr3b93lFrXde9flWSjybZNcm/d19JcsomrgEAANA3fQ1YtdZLkxw4y67DZulbk7x6E+f5SJKPzNJ+cZJ9Zmm/ZbZrAAAA9FO/fw8WAADADkPAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaETAAgAAaGTBpnasHh35hbkOHBuf+H77cgAAAIbXJgNWkmuT1E3sq5s5FgAAYIczV0j6YjYdsAAAAJhhkwFrbHzi0G1YBwAAwNDboml+q0dHXphkvyS7dk11bHzijf0qCgAAYBhtNmCtHh358yQnpDddsHTNNYmABQAAMM2WLNP+4iTnda//Isl3uu8AAABMsyUB65FJPt29vjDJe5I8r18FAQAADKsteQbrtiQ/TvLTJG9L7zmsFX2sCQAAYChtScC6JMnyJP+W5De7tnP7VRAAAMCw2mzAGhufeHaSrB4d2TXJ+V3zP/SzKAAAgGG02WewVo+OfGH16MihY+MTPx4bn/jbJBfHIhcAAAD3siWLXBya5OHTtp+U5NV9qQYAAGCIbXKK4OrRkeOSHNdtvn316Mgru9ePT7Kh34UBAAAMm7mewVqR3uhVTbJ397XRu/pXEgAAwHCaa4rgXyZ5bJKS5PeSrEwvdD10bHzihL5XBgAAMGQ2OYI1Nj5xa5JbV4+OrExyU5Jf6HZdty0KAwAAGDZbsshFSXJRksu6r0tXj448pq9VAQAADKEtCVj/J8k+Sb7dfe2b5N39LAoAAGAYbUnA+qUkp4yNTzx5bHziyUlOSbK6v2UBAAAMny0JWAuSrJ+2fWuSnfpSDQAAwBCb6/dg/VGSs5N8Nck7V4+OHNLtek6SC/tfGgAAwHCZ6/dg/XGS7yR5c5L/SPK8rv0HXRsAAADTbG6KYB0bn/hGklVJfi3JkUlWjY1PfLPvlQEAAAyZuUawkuQDq0dHTp3ZuHp0pI6NT/xin2oCAAAYSpsLWA/vvmaqfagFAABgqG0uYL02yWe2RSEAAADDbnMB6+ax8YnvbZNKAAAAhtxci1x8L8n/bKtCAAAAht0mR7DGxidWbstCAAAAht3mlmkHAABgCwlYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjSwYdAEAwHA49eRTM7lu/aDL2CpLli7OCW87YdBlADsQAQsA2CKT69Zn/RWPGnQZW2fVjYOuANjBmCIIAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQiIAFAADQSN8DVillp1LKf5VSPtNtryylXFRKuaqU8k+llAd27bt021d1+1dMO8dbu/YrSinPmdZ+eNd2VSnlxGnts14DAACgn7bFCNbvJ7l82vapSd5ba31ckskkr+jaX5Fksmt/b9cvpZS9kxyT5ElJDk/ywS607ZTkr5MckWTvJMd2fee6BgAAQN/0NWCVUpYn+bUkf9dtlyS/nOTsrssZSZ7fvT662063/7Cu/9FJzqq1/qTWek2Sq5I8rfu6qtb63VrrHUnOSnL0Zq4BAADQNwv6fP6/TPKWJIu67WVJ1tda7+y21yTZo3u9R5LrkqTWemcp5dau/x5JvjztnNOPuW5G+9M3c42fU0o5PsnxSbJ8+fJcf/31W/8OgW1q6bIl2WXFgwZdxlbZbdkSny8d92+4uX8Am9e3gFVKeW6Sm2qtl5RSDu3Xde6PWutpSU5LkgMPPLDuvvvuA64I2Jx1t0xm/bW7DLqMrbJ4l8n4fOlx/4ab+wewef0cwTokyVGllCOTLEzykCTvS7K4lLKgG2FanmRt139tkj2TrCmlLEjy0CS3TGvfaPoxs7XfMsc1IKeefGom160fdBlbZcnSxTnhbScMugwAADajbwGr1vrWJG9Nkm4E60211heXUv45yQvSe2bquCTndIec223/f93+L9Raaynl3CQfL6W8J8nuSfZK8pUkJclepZSV6QWoY5K8qDvmgk1cAzK5bn3WX/GoQZexdVbdOOgKAADYAoP4PVgnJHlDKeWq9J6XOr1rPz3Jsq79DUlOTJJa67eTfDLJZUk+l+TVtda7utGp1yT5fHqrFH6y6zvXNQAAAPqm34tcJElqrRcmubB7/d30VgCc2WcqyQs3cfzJSU6epf2zST47S/us1wAAAOinQYxgAQAAbJcELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYELAAAgEYWDLoAgO3dj2+fyklv+oNBl7FVFi9dnLec9KZBlwEAQ0fAAuizWmt2vf5Zgy5jq6zPBYMuAQCGkimCAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjQhYAAAAjfhFw/fBqSefmsl16wddxlZZsnRxTnjbCYMuAwAAtmsC1n0wuW591l/xqEGXsXVW3TjoCgAAYLtniiAAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjAhYAAEAjCwZdAABAv/z49qmc9KY/GHQZW2zx0sV5y0lvGnQZwP0gYAEA261aa3a9/lmDLmOLrc8Fgy4BuJ9MEQQAAGhEwAIAAGhEwAIAAGhEwAIAAGhEwAIAAGhEwAIAAGhEwAIAAGhEwAIAAGhEwAIAAGhEwAIAAGikbwGrlLJnKeWCUsplpZRvl1J+v2tfWko5r5RyZfd9SddeSinvL6VcVUr5Rill/2nnOq7rf2Up5bhp7QeUUr7ZHfP+UkqZ6xoAAAD91M8RrDuTvLHWuneSg5O8upSyd5ITk5xfa90ryfnddpIckWSv7uv4JB9KemEpyduTPD3J05K8fVpg+lCS35l23OFd+6auAQAA0Dd9C1i11htqrV/rXm9IcnmSPZIcneSMrtsZSZ7fvT46yZm158tJFpdSHp3kOUnOq7Wuq7VOJjkvyeHdvofUWr9ca61JzpxxrtmuAQAA0Dfb5BmsUsqKJE9NclGSR9Zab+h23Zjkkd3rPZJcN+2wNV3bXO1rZmnPHNcAAADomwX9vkAp5cFJPpXkdbXWH3WPSSVJaq21lFL7ef25rlFKOT696YhZvnx5rr/++i0659JlS7LLige1K3Ib2G3Zki1+f9s792+4DeP9W7DsYXnIojsHXcZWWbi0P3/mhvH++ft3j2G8f8P2969ff/eAbaevAauUsnN64epjtdZ/6Zp/UEp5dK31hm6a301d+9oke047fHnXtjbJoTPaL+zal8/Sf65r/Jxa62lJTkuSAw88sO6+++5b9L7W3TKZ9dfuskV954vFu0xmS9/f9s79G27DeP8eeOcPkx/1/f9nNfXj2p8/c8N4/3a564Z84D0fHHQZW2zx0sV5y0lv6su5h/H+Ddvfv3793QO2nb594nQr+p2e5PJa63um7To3yXFJTum+nzOt/TWllLPSW9Di1i4gfT7Jn01b2OLZSd5aa11XSvlRKeXg9KYeviTJX23mGgCwVWqt2fX6Zw26jC22PhcMugSAHVo//5fOIUl+K8k3SymXdm0npRd6PllKeUWS7yX5zW7fZ5McmeSqJLcneVmSdEHqnUm+2vV7R611Xff6VUk+mmTXJP/efWWOawAAAPRN3wJWrXU8SdnE7sNm6V+TvHoT5/pIko/M0n5xkn1mab9ltmsAAAD00zZZRRAAAGBHIGABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0ImABAAA0smDQBQAA0H+nnnxqJtetH3QZW2XJ0sU54W0nDLoM2CoCFgDADmBy3fqsv+JRgy5j66y6cdAVwFYzRRAAAKARI1gwBH58+1ROetMfDLqMrbJ46eK85aQ3DboMAIBtSsCCIVBrza7XP2vQZWyV9blg0CUAAGxzpggCAAA0ImABAAA0YoogAADz0rA9g+z5YxIBCwCAeWrYnkH2/DGJKYIAAADNCFgAAACNCFgAAACNCFgAAACNWORiBzFsq/AkVuIBAGD4CFg7iGFbhSexEg8AAMPHFEEAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGBCwAAIBGttuAVUo5vJRyRSnlqlLKiYOuBwAA2P5tlwGrlLJTkr9OckSSvZMcW0rZe7BVAQAA27vtMmAleVqSq2qt36213pHkrCRHD7gmAABgO7e9Bqw9klw3bXtN1wYAANA3pdY66BqaK6W8IMnhtdbf7rZ/K8nTa62vmdHv+CTHd5urklyxTQvdth6W5IeDLoL7zP0bbu7fcHP/hpv7N9zcv+G2vd+/x9RaHz6zccEgKtkG1ibZc9r28q7t59RaT0ty2rYqapBKKRfXWg8cdB3cN+7fcHP/hpv7N9zcv+Hm/g23HfX+ba9TBL+aZK9SyspSygOTHJPk3AHXBAAAbOe2yxGsWuudpZTXJPl8kp2SfKTW+u0BlwUAAGzntsuAlSS11s8m+eyg65hHdoipkNsx92+4uX/Dzf0bbu7fcHP/htsOef+2y0UuAAAABmF7fQYLAABgmxOwtjOllMWllLNLKd8ppVxeSnnGjP2llPL+UspVpZRvlFL2H1St3Fsp5dpSyjdLKZeWUi6eZb/7N0+VUhaWUr5SSvl6KeXbpZQ/maXPLqWUf+ru30WllBUDKJVNKKXsVEr5r1LKZ2bZ597NU6WUPUspF5RSLuv+7v3+LH18ds5jpZSPlFJuKqV8axP73b95rJRyeCnliu7+nDjL/h3u81PA2v68L8nnaq1PSPKUJJfP2H9Ekr26r+OTfGjblscWeFatdb9NLGvq/s1fP0nyy7XWpyTZL8nhpZSDZ/R5RZLJWuvjkrw3yanbtkQ24/dz78/Mjdy7+evOJG+ste6d5OAkry6l7D2jj8/O+e2jSQ6fY7/7N0+VUnZK8tfp3aO9kxw7y9+/He7zU8DajpRSHprkmUlOT5Ja6x211vUzuh2d5Mza8+Uki0spj962lXI/uH/zVHdPbus2d+6+Zj7kenSSM7rXZyc5rJRStlGJzKGUsjzJryX5u010ce/mqVrrDbXWr3WvN6QXkveY0c1n5zxWa/1iknVzdHH/5q+nJbmq1vrdWusdSc5K735Nt8N9fgpY25eVSW5O8vfdNJe/K6XsNqPPHkmum7a9Jvf+h4jBqUn+o5RySSnl+Fn2u3/zWDfF7NIkNyU5r9Z60YwuP7t/tdY7k9yaZNk2LZJN+cskb0ly9yb2u3dDoJt69NQkm/y71/HZOVzcv/lrS+7NDvf5KWBtXxYk2T/Jh2qtT03yP0nuNReWeW201rp/ekPtry6lPHPQBbHlaq131Vr3S7I8ydNKKfsMuCS2QCnluUluqrVeMuhauO9KKQ9O8qkkr6u1/mjQ9QA7LgFr+7ImyZpp/9f87PQC13Rrk+w5bXt518Y8UGtd232/Kcm/pjf0Pp37NwS6qbkX5N7PFPzs/pVSFiR5aJJbtmlxzOaQJEeVUq5Nb3rLL5dS/nFGH/duHiul7JxeuPpYrfVfZunis3O4uX/z15bcmx3u81PA2o7UWm9Mcl0pZVXXdFiSy2Z0OzfJS7oVeQ5Ocmut9YZtWSezK6XsVkpZtPF1kmcnmbmikvs3T5VSHl5KWdy93jXJryb5zoxu5yY5rnv9giRfqH4Z4cDVWt9aa11ea12R5Jj07sv/PaObezdPdc9ynJ7k8lrrezbRzWfncHP/5q+vJtmrlLKylPLA9D5Dz53RZ4f7/Fww6AJo7rVJPtb9If9ukpeVUn43SWqtH07y2SRHJrkqye1JXjaoQrmXRyb51+65zwVJPl5r/Zz7NzQeneSMbkWlByT5ZK31M6WUdyS5uNZ6bnr/EfgPpZSr0nug+5jBlcvmuHdD45Akv5Xkm90zkElyUpJfSHx2DoNSyieSHJrkYaWUNUnent5CQe7fPFdrvbOU8pokn0+yU5KP1Fq/vaN/fpbtPEACAABsM6YIAgAANCJgAQAANCJgAQAANCJgAQAANCJgAQAANGKZdoAhsnp0ZOPSr3cluS29ZYvPSvKXY+MTd85x3C8k+cckByR5UJIzxsYnXjpt/0uTvCXJE5KUJM8aG5+4cNq+v5/ltD93jmnn+mh6v/OkJvlxkhuTfDHJO8bGJ66Z0edn19lE3YuTvC7JtWPjEx/dVL+u74VJVidZ2TVdk2RsbHzi0LmO28S5ViR5aZJLx8Yn/q1rOzS9XyA96/uej1aPjuyX5PlJLtzUz3m29zoIq0dHXpdk8dj4xB8PqgaAFoxgAQyn30ryZ0mWJnlXeiFrLrskWZPe75OZzYOSnJfk6ln2jSU5dtrXJV37VzZzzb9J8oYk30jvP+C/0v3HfJJ8qDvXzF+GPtPi9H4nzkvn6rR6dGRBknd057x5M+fcEiu66z5/Wttl3fk/1OD8fdH9HKbbL733cegch63Ivd/r/b3uffG6rg6Aoeb3YAEMkY0jWGPjE6XbfniS/04viBwyNj4xsZnjX5reaNSmRp8uTG8UaNaRpdWjI49I8v0k/5Nkz7Hxidtn6fPR9EanXrZx1Gn16Mg/Jnlxkr8dG584fvoIVnoB7t3d/qXp/SLKc5L8eXqjUNONpRe2rkkv6F2V5PCu7XW59wjWV5JckeQ3urpfMzY+cf600aixsfGJQ7vgd013/j/u9k13RpKPZtoI1urRkd2SvLM798PSuw9/OjY+8anuPdcka5N8Kr1frHl3kjeNjU98bJaf2WlJfie9IPSlJJNJJsfGJ35h9ejIMUk+keTPx8YnTtrEz/p9SZ6T5Mdj4xP7d/temnuPPP7J9BGiaT+Hme/1tUnOT7IqycL0wvkHxsYn3tsdd22SxyT5k67uTyZ5W5IPpxfUvted97Vz/Ly+neSt3f3YeL6f2fhnHGDYGMECGGJj4xM3J/nPbvOQbXDJV6U3Gvbh2cLVHP6l+z5bjU9Jb6TrsiS/neS96QW4m5P8Xtfn8vRGj94x7bgDktzeHfvfm7ju09ILOe9I8otJzl49OrJ0M7Vell4QSHpTGzc1avWeJK9PcmlXwyOSfHL16MjItD57pBd+35PkUUk+vInRnvO7789Msn+SByfZc/XoyMrcM/r0/85R828keX+Sv5zWNpZe4El6Ie/YJGfPOG5T77Um+fckb0xyQpJbkrxn9ejI02ccf1iSP0xvZPSk9EZWv5bkr5L8XzP6/p/0fl6fT3Jyej+Xc7tw+9okP+z6bRwpBRhKAhbA8Nv4Wd7XKQmrR0d2SfLKJHck+cBWHj5XjWuT/CjJE9Mb0fppkveNjU/8T5JPd31uGhufOGtsfOIL04777tj4xMvHxic+MjY+sampht8aG59469j4xKnp/Yf94iQjm+ibJBkbn7gpycbrXNNd96JZum4MEC8fG5/4m/RC1AOSHD2tz4Ykr+iuf2N6welRs5zrC+n9bFZ3X19Lcv207akkc41Ovm1sfOJDY+MTZ057H9ck2Vj3t7r38a0tfK8PSvKM9ALae5Mc1PXZb8Z1X9z9/M9LckTX9sax8YnT8vNhL0l+vfv+O+kFrL266zxrbHzi0+mF6nQ1bG7KK8C8ZZELgCHWTRHcOCo05/TABl6c3ijNmWPjEzds5bEbw8i9ahwbn7h59ejI3kmOSrJ3kj9I8kfdwhxzhcbrtrKG6TYuCLLx38GZo1qtwuq6aYuP/GTGNX+m+xl8I71Qc3d6o0mPTPKb6S08cv7Y+MTU6tGRnZPslOSnY+MTd007xfeTZJb9W/I+ZuvzuiS/mt5UzY3Pyx2XZNcZdX9/lmPvnuM6Nb0phNNHP6+cow6AoSNgAQyh1aMjxyZZnuR30xuV+dRcz1+tHh15cHrPAW0cvdlr9ejIbyf52tj4xNdWj47sn97UtEd3+39t9ejI45KcNTY+cVvX9rru+3u2sMynrx4deWB6IxvPT28K2J/NUtvjk7w5ycXpPVd1eJLHJXlIes8iJcnjV4+O/FZ6z+2s28LrJ8k+q0dH/jzJ+vSeUVqfXsjbNb0gsN/q0ZH/lV54mG7jNQ5cPTryotyzsMd0/5Lkfyf5u9WjI/+e3s/n7vRCyX1xfnrTJX8lvVDziNwzxW/j9MC/7Wp9WXrPhM00c//G93Fo92fmP8fGJ9bMOGau9/rgJI9N8uwtqP/f0/sz9K7VoyOfzD1/Xjb61/R+Xr+d3s/u0en9mdw44rcuyYrVoyOvTXLl2PjE57bgmgDzjimCAMPpH9Ib6bk1veXVj9lM/4el9x/fL+u2R7rto7rto7rtx3fbb+q2H5Ykq0dHfiXJvkm+MDY+8fUtrPF301t8Yb8kZyY5aGx84tpZ+k2lNz3wlCSnpTcC84ax8Yk1Y+MTP0pvAYwHd+d4+RZee6OvpPcc1AlJvpvkBWPjE+vGxifWpvfzuyO9VRhnvqdvpbewxMokH0vyvFnO/cb0ps8d0H2/Jcn/2txCI3M4f9rr/0zvGarZ9m2N87pjD07y8e77TLO91/elN4o2mt4CIlsSGv8svT+XB6V37/+ja98Y4N6Y3nNYT04vOL4yyTdzT4jeOI3y/endc4ChZBVBgO3I6tGRJekFlOmmpo1CQV90K0y+KL3AtjjJn6YX2H91bHzivgZEgKFjiiDA9uW/MmO56/SW3X7pti+FHcwD0vtztiq9hUr+O8mLhCtgRyNgAWxfXpwZCxGktxod9NXY+MSNufcqgwA7HFMEAQAAGrHIBQAAQCMCFgAAQCMCFgAAQCMCFgAAQCMCFgAAQCP/Pw5qL+elD6L8AAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.figure(figsize=(12,10))\nplt.title('Histogram', fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='D_120', data=train_data,hue='target', palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nplt.tight_layout(rect=[0, 0.04, 1, 0.965])\nplt.xlabel('D_120 Distribution w.r.t target', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:55.950368Z","iopub.execute_input":"2022-10-05T20:17:55.950936Z","iopub.status.idle":"2022-10-05T20:17:56.724919Z","shell.execute_reply.started":"2022-10-05T20:17:55.950897Z","shell.execute_reply":"2022-10-05T20:17:56.723953Z"},"trusted":true},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x720 with 1 Axes>","image/png":"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fontweight='bold', fontsize=14, fontfamily='sans-serif', color=black_grad[0])\nax = sns.countplot(x='D_64', data=train_data,hue='target', palette=colors, order=order, edgecolor=black_grad[2], alpha=0.85)\nplt.tight_layout(rect=[0, 0.04, 1, 0.965])\nplt.xlabel('D_64 Distribution w.r.t target', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.ylabel('Total', fontweight='bold', fontsize=11, fontfamily='sans-serif', color=black_grad[1])\nplt.grid(axis='y', alpha=0.4)\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data analysis on the Balance attributes","metadata":{}},{"cell_type":"code","source":"# Categorcal columns \n\ncategorical_cols=['B_30', 'B_38', 'D_63', 'D_64', 'D_66', 'D_68',\n          'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'target']","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:58.87524Z","iopub.execute_input":"2022-10-05T20:17:58.875558Z","iopub.status.idle":"2022-10-05T20:17:58.880269Z","shell.execute_reply.started":"2022-10-05T20:17:58.875531Z","shell.execute_reply":"2022-10-05T20:17:58.879041Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"\ncols=[col for col in train_data.columns if (col.startswith(('B','T'))) & (col not in categorical_cols[:-1])]\n\ntrain_balance_cols =train_data[cols]","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:58.88183Z","iopub.execute_input":"2022-10-05T20:17:58.882499Z","iopub.status.idle":"2022-10-05T20:17:59.576527Z","shell.execute_reply.started":"2022-10-05T20:17:58.882465Z","shell.execute_reply":"2022-10-05T20:17:59.575524Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"train_balance_cols.select_dtypes(exclude='object').describe().T.style.background_gradient(cmap='RdPu').set_properties(**{'font-family': 'Segoe UI'})","metadata":{"execution":{"iopub.status.busy":"2022-10-05T20:17:59.57798Z","iopub.execute_input":"2022-10-05T20:17:59.578692Z","iopub.status.idle":"2022-10-05T20:18:30.586074Z","shell.execute_reply.started":"2022-10-05T20:17:59.578647Z","shell.execute_reply":"2022-10-05T20:18:30.584973Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 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#T_687ad_row14_col4, #T_687ad_row14_col5, #T_687ad_row14_col6, #T_687ad_row15_col2, #T_687ad_row15_col4, #T_687ad_row15_col7, #T_687ad_row16_col2, #T_687ad_row16_col7, #T_687ad_row17_col2, #T_687ad_row17_col7, #T_687ad_row18_col2, #T_687ad_row18_col4, #T_687ad_row18_col5, #T_687ad_row18_col7, #T_687ad_row19_col2, #T_687ad_row19_col4, #T_687ad_row19_col5, #T_687ad_row19_col7, #T_687ad_row20_col4, #T_687ad_row20_col5, #T_687ad_row20_col6, #T_687ad_row21_col2, #T_687ad_row21_col4, #T_687ad_row21_col5, #T_687ad_row21_col6, #T_687ad_row21_col7, #T_687ad_row22_col2, #T_687ad_row22_col7, #T_687ad_row23_col2, #T_687ad_row23_col4, #T_687ad_row23_col5, #T_687ad_row23_col6, #T_687ad_row24_col2, #T_687ad_row24_col3, #T_687ad_row24_col4, #T_687ad_row24_col7, #T_687ad_row25_col4, #T_687ad_row25_col5, #T_687ad_row25_col6, #T_687ad_row26_col1, #T_687ad_row26_col2, #T_687ad_row26_col4, #T_687ad_row26_col5, #T_687ad_row26_col6, #T_687ad_row26_col7, #T_687ad_row27_col2, #T_687ad_row28_col1, #T_687ad_row28_col2, #T_687ad_row28_col4, #T_687ad_row28_col5, #T_687ad_row28_col6, #T_687ad_row28_col7, #T_687ad_row29_col2, #T_687ad_row29_col7, #T_687ad_row30_col2, #T_687ad_row30_col4, #T_687ad_row30_col5, #T_687ad_row30_col6, #T_687ad_row30_col7, #T_687ad_row31_col2, #T_687ad_row31_col4, #T_687ad_row31_col7, #T_687ad_row32_col1, #T_687ad_row32_col2, #T_687ad_row32_col4, #T_687ad_row32_col5, #T_687ad_row32_col6, #T_687ad_row32_col7, #T_687ad_row33_col2, #T_687ad_row33_col7, #T_687ad_row34_col0, #T_687ad_row34_col7, #T_687ad_row36_col2, #T_687ad_row36_col4, #T_687ad_row36_col5, #T_687ad_row36_col6, #T_687ad_row36_col7, #T_687ad_row37_col1, #T_687ad_row37_col2, #T_687ad_row37_col7 {\n  background-color: #fff7f3;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row0_col3, #T_687ad_row8_col6 {\n  background-color: #fbaeb9;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row0_col4, #T_687ad_row2_col5, #T_687ad_row4_col4, #T_687ad_row4_col5, #T_687ad_row7_col5, #T_687ad_row8_col7, #T_687ad_row10_col4, #T_687ad_row11_col4, #T_687ad_row12_col4, #T_687ad_row13_col4, #T_687ad_row13_col7, #T_687ad_row14_col7, #T_687ad_row23_col7, #T_687ad_row27_col7, #T_687ad_row33_col4, #T_687ad_row37_col0, #T_687ad_row37_col4 {\n  background-color: #fff6f2;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row0_col5, #T_687ad_row3_col4, #T_687ad_row4_col7, #T_687ad_row6_col4, #T_687ad_row9_col4, #T_687ad_row12_col5, #T_687ad_row13_col5, #T_687ad_row27_col4, #T_687ad_row33_col5 {\n  background-color: #fff3ef;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row0_col6 {\n  background-color: #fde1de;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row1_col4 {\n  background-color: #fde4e1;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row1_col5 {\n  background-color: #94017b;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row1_col6, #T_687ad_row7_col0, #T_687ad_row16_col6, #T_687ad_row29_col6 {\n  background-color: #4b006a;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row2_col6, #T_687ad_row34_col5 {\n  background-color: #fddcd8;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row3_col5 {\n  background-color: #fee9e6;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row3_col6 {\n  background-color: #fcc9c4;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row4_col6 {\n  background-color: #feefeb;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row5_col4, #T_687ad_row37_col5 {\n  background-color: #fff4f0;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row5_col5 {\n  background-color: #fee9e5;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row5_col6, #T_687ad_row27_col6 {\n  background-color: #fdd3cf;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row5_col7 {\n  background-color: #fccdc9;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row6_col3 {\n  background-color: #a8017d;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row6_col5, #T_687ad_row11_col7, #T_687ad_row27_col5 {\n  background-color: #feeae7;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row6_col6 {\n  background-color: #fcc1bf;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row8_col5 {\n  background-color: #fff3f0;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row9_col5 {\n  background-color: #fde4e0;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row9_col6 {\n  background-color: #fbbabd;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row9_col7 {\n  background-color: #bc1085;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row10_col5, #T_687ad_row11_col5, #T_687ad_row22_col4, #T_687ad_row24_col5, #T_687ad_row35_col4 {\n  background-color: #fff5f1;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row10_col6 {\n  background-color: #fde6e2;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row11_col6 {\n  background-color: #feece9;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row12_col0 {\n  background-color: #4c006b;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row12_col6 {\n  background-color: #fee8e5;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row12_col7, #T_687ad_row20_col7 {\n  background-color: #feeeeb;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row13_col3 {\n  background-color: #fcc6c1;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row13_col6 {\n  background-color: #fee6e3;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row14_col3 {\n  background-color: #fde3e0;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row15_col5, #T_687ad_row18_col6 {\n  background-color: #fee7e4;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row15_col6 {\n  background-color: #ad017e;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row16_col0 {\n  background-color: #f988ad;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row16_col4 {\n  background-color: #f871a4;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row16_col5 {\n  background-color: #630171;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row17_col4 {\n  background-color: #fccfcb;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row17_col5 {\n  background-color: #d62d93;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row19_col6 {\n  background-color: #faa7b7;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row22_col5, #T_687ad_row35_col5 {\n  background-color: #feeeea;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row22_col6 {\n  background-color: #fcc7c3;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row24_col6, #T_687ad_row37_col6 {\n  background-color: #fde5e2;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row25_col7 {\n  background-color: #fcc2bf;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row28_col0 {\n  background-color: #feebe8;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row33_col3 {\n  background-color: #fbafba;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row33_col6 {\n  background-color: #fde2df;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row34_col3 {\n  background-color: #6b0173;\n  color: #f1f1f1;\n  font-family: Segoe UI;\n}\n#T_687ad_row34_col4 {\n  background-color: #feede9;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row34_col6 {\n  background-color: #fbbbbd;\n  color: #000000;\n  font-family: Segoe UI;\n}\n#T_687ad_row35_col6 {\n  background-color: #fcc8c3;\n  color: #000000;\n  font-family: Segoe UI;\n}\n</style>\n<table id=\"T_687ad_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >count</th>\n      <th class=\"col_heading level0 col1\" >mean</th>\n      <th class=\"col_heading level0 col2\" >std</th>\n      <th class=\"col_heading level0 col3\" >min</th>\n      <th class=\"col_heading level0 col4\" >25%</th>\n      <th class=\"col_heading level0 col5\" >50%</th>\n      <th class=\"col_heading level0 col6\" >75%</th>\n      <th class=\"col_heading level0 col7\" >max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_687ad_level0_row0\" class=\"row_heading level0 row0\" >B_1</th>\n      <td id=\"T_687ad_row0_col0\" class=\"data row0 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row0_col1\" class=\"data row0 col1\" >nan</td>\n      <td id=\"T_687ad_row0_col2\" class=\"data row0 col2\" >0.000000</td>\n      <td id=\"T_687ad_row0_col3\" class=\"data row0 col3\" >-7.589844</td>\n      <td id=\"T_687ad_row0_col4\" class=\"data row0 col4\" >0.008865</td>\n      <td id=\"T_687ad_row0_col5\" class=\"data row0 col5\" >0.031342</td>\n      <td id=\"T_687ad_row0_col6\" class=\"data row0 col6\" >0.125854</td>\n      <td id=\"T_687ad_row0_col7\" class=\"data row0 col7\" >1.324219</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row1\" class=\"row_heading level0 row1\" >B_2</th>\n      <td id=\"T_687ad_row1_col0\" class=\"data row1 col0\" >5529435.000000</td>\n      <td id=\"T_687ad_row1_col1\" class=\"data row1 col1\" >nan</td>\n      <td id=\"T_687ad_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n      <td id=\"T_687ad_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n      <td id=\"T_687ad_row1_col4\" class=\"data row1 col4\" >0.105347</td>\n      <td id=\"T_687ad_row1_col5\" class=\"data row1 col5\" >0.814453</td>\n      <td id=\"T_687ad_row1_col6\" class=\"data row1 col6\" >1.001953</td>\n      <td id=\"T_687ad_row1_col7\" class=\"data row1 col7\" >1.009766</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row2\" class=\"row_heading level0 row2\" >B_3</th>\n      <td id=\"T_687ad_row2_col0\" class=\"data row2 col0\" >5529435.000000</td>\n      <td id=\"T_687ad_row2_col1\" class=\"data row2 col1\" >nan</td>\n      <td id=\"T_687ad_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n      <td id=\"T_687ad_row2_col3\" class=\"data row2 col3\" >0.000000</td>\n      <td id=\"T_687ad_row2_col4\" class=\"data row2 col4\" >0.005226</td>\n      <td id=\"T_687ad_row2_col5\" class=\"data row2 col5\" >0.009781</td>\n      <td id=\"T_687ad_row2_col6\" class=\"data row2 col6\" >0.155029</td>\n      <td id=\"T_687ad_row2_col7\" class=\"data row2 col7\" >1.625000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row3\" class=\"row_heading level0 row3\" >B_4</th>\n      <td id=\"T_687ad_row3_col0\" class=\"data row3 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row3_col1\" class=\"data row3 col1\" >nan</td>\n      <td id=\"T_687ad_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n      <td id=\"T_687ad_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n      <td id=\"T_687ad_row3_col4\" class=\"data row3 col4\" >0.027496</td>\n      <td id=\"T_687ad_row3_col5\" class=\"data row3 col5\" >0.082214</td>\n      <td id=\"T_687ad_row3_col6\" class=\"data row3 col6\" >0.238892</td>\n      <td id=\"T_687ad_row3_col7\" class=\"data row3 col7\" >19.796875</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row4\" class=\"row_heading level0 row4\" >B_5</th>\n      <td id=\"T_687ad_row4_col0\" class=\"data row4 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row4_col1\" class=\"data row4 col1\" >nan</td>\n      <td id=\"T_687ad_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n      <td id=\"T_687ad_row4_col3\" class=\"data row4 col3\" >0.000000</td>\n      <td id=\"T_687ad_row4_col4\" class=\"data row4 col4\" >0.007282</td>\n      <td id=\"T_687ad_row4_col5\" class=\"data row4 col5\" >0.015373</td>\n      <td id=\"T_687ad_row4_col6\" class=\"data row4 col6\" >0.053711</td>\n      <td id=\"T_687ad_row4_col7\" class=\"data row4 col7\" >144.250000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row5\" class=\"row_heading level0 row5\" >B_6</th>\n      <td id=\"T_687ad_row5_col0\" class=\"data row5 col0\" >5531218.000000</td>\n      <td id=\"T_687ad_row5_col1\" class=\"data row5 col1\" >nan</td>\n      <td id=\"T_687ad_row5_col2\" class=\"data row5 col2\" >nan</td>\n      <td id=\"T_687ad_row5_col3\" class=\"data row5 col3\" >-0.005177</td>\n      <td id=\"T_687ad_row5_col4\" class=\"data row5 col4\" >0.020493</td>\n      <td id=\"T_687ad_row5_col5\" class=\"data row5 col5\" >0.083374</td>\n      <td id=\"T_687ad_row5_col6\" class=\"data row5 col6\" >0.191895</td>\n      <td id=\"T_687ad_row5_col7\" class=\"data row5 col7\" >1215.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row6\" class=\"row_heading level0 row6\" >B_7</th>\n      <td id=\"T_687ad_row6_col0\" class=\"data row6 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row6_col1\" class=\"data row6 col1\" >nan</td>\n      <td id=\"T_687ad_row6_col2\" class=\"data row6 col2\" >0.000000</td>\n      <td id=\"T_687ad_row6_col3\" class=\"data row6 col3\" >-2.652344</td>\n      <td id=\"T_687ad_row6_col4\" class=\"data row6 col4\" >0.028244</td>\n      <td id=\"T_687ad_row6_col5\" class=\"data row6 col5\" >0.075745</td>\n      <td id=\"T_687ad_row6_col6\" class=\"data row6 col6\" >0.270996</td>\n      <td id=\"T_687ad_row6_col7\" class=\"data row6 col7\" >1.252930</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row7\" class=\"row_heading level0 row7\" >B_8</th>\n      <td id=\"T_687ad_row7_col0\" class=\"data row7 col0\" >5509183.000000</td>\n      <td id=\"T_687ad_row7_col1\" class=\"data row7 col1\" >nan</td>\n      <td id=\"T_687ad_row7_col2\" class=\"data row7 col2\" >0.000000</td>\n      <td id=\"T_687ad_row7_col3\" class=\"data row7 col3\" >0.000000</td>\n      <td id=\"T_687ad_row7_col4\" class=\"data row7 col4\" >0.004505</td>\n      <td id=\"T_687ad_row7_col5\" class=\"data row7 col5\" >0.009018</td>\n      <td id=\"T_687ad_row7_col6\" class=\"data row7 col6\" >1.003906</td>\n      <td id=\"T_687ad_row7_col7\" class=\"data row7 col7\" >1.019531</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row8\" class=\"row_heading level0 row8\" >B_9</th>\n      <td id=\"T_687ad_row8_col0\" class=\"data row8 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row8_col1\" class=\"data row8 col1\" >nan</td>\n      <td id=\"T_687ad_row8_col2\" class=\"data row8 col2\" >0.000000</td>\n      <td id=\"T_687ad_row8_col3\" class=\"data row8 col3\" >0.000000</td>\n      <td id=\"T_687ad_row8_col4\" class=\"data row8 col4\" >0.005753</td>\n      <td id=\"T_687ad_row8_col5\" class=\"data row8 col5\" >0.025879</td>\n      <td id=\"T_687ad_row8_col6\" class=\"data row8 col6\" >0.334229</td>\n      <td id=\"T_687ad_row8_col7\" class=\"data row8 col7\" >27.421875</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row9\" class=\"row_heading level0 row9\" >B_10</th>\n      <td id=\"T_687ad_row9_col0\" class=\"data row9 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row9_col1\" class=\"data row9 col1\" >nan</td>\n      <td id=\"T_687ad_row9_col2\" class=\"data row9 col2\" >nan</td>\n      <td id=\"T_687ad_row9_col3\" class=\"data row9 col3\" >-0.002958</td>\n      <td id=\"T_687ad_row9_col4\" class=\"data row9 col4\" >0.028992</td>\n      <td id=\"T_687ad_row9_col5\" class=\"data row9 col5\" >0.110535</td>\n      <td id=\"T_687ad_row9_col6\" class=\"data row9 col6\" >0.295654</td>\n      <td id=\"T_687ad_row9_col7\" class=\"data row9 col7\" >4096.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row10\" class=\"row_heading level0 row10\" >B_11</th>\n      <td id=\"T_687ad_row10_col0\" class=\"data row10 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row10_col1\" class=\"data row10 col1\" >nan</td>\n      <td id=\"T_687ad_row10_col2\" class=\"data row10 col2\" >0.000000</td>\n      <td id=\"T_687ad_row10_col3\" class=\"data row10 col3\" >0.000000</td>\n      <td id=\"T_687ad_row10_col4\" class=\"data row10 col4\" >0.006603</td>\n      <td id=\"T_687ad_row10_col5\" class=\"data row10 col5\" >0.019455</td>\n      <td id=\"T_687ad_row10_col6\" class=\"data row10 col6\" >0.101990</td>\n      <td id=\"T_687ad_row10_col7\" class=\"data row10 col7\" >1.970703</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row11\" class=\"row_heading level0 row11\" >B_12</th>\n      <td id=\"T_687ad_row11_col0\" class=\"data row11 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row11_col1\" class=\"data row11 col1\" >nan</td>\n      <td id=\"T_687ad_row11_col2\" class=\"data row11 col2\" >nan</td>\n      <td id=\"T_687ad_row11_col3\" class=\"data row11 col3\" >0.000000</td>\n      <td id=\"T_687ad_row11_col4\" class=\"data row11 col4\" >0.010872</td>\n      <td id=\"T_687ad_row11_col5\" class=\"data row11 col5\" >0.019440</td>\n      <td id=\"T_687ad_row11_col6\" class=\"data row11 col6\" >0.069153</td>\n      <td id=\"T_687ad_row11_col7\" class=\"data row11 col7\" >420.750000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row12\" class=\"row_heading level0 row12\" >B_13</th>\n      <td id=\"T_687ad_row12_col0\" class=\"data row12 col0\" >5481932.000000</td>\n      <td id=\"T_687ad_row12_col1\" class=\"data row12 col1\" >nan</td>\n      <td id=\"T_687ad_row12_col2\" class=\"data row12 col2\" >nan</td>\n      <td id=\"T_687ad_row12_col3\" class=\"data row12 col3\" >0.000000</td>\n      <td id=\"T_687ad_row12_col4\" class=\"data row12 col4\" >0.009254</td>\n      <td id=\"T_687ad_row12_col5\" class=\"data row12 col5\" >0.029312</td>\n      <td id=\"T_687ad_row12_col6\" class=\"data row12 col6\" >0.089417</td>\n      <td id=\"T_687ad_row12_col7\" class=\"data row12 col7\" >276.250000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row13\" class=\"row_heading level0 row13\" >B_14</th>\n      <td id=\"T_687ad_row13_col0\" class=\"data row13 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row13_col1\" class=\"data row13 col1\" >nan</td>\n      <td id=\"T_687ad_row13_col2\" class=\"data row13 col2\" >0.000000</td>\n      <td id=\"T_687ad_row13_col3\" class=\"data row13 col3\" >-8.468750</td>\n      <td id=\"T_687ad_row13_col4\" class=\"data row13 col4\" >0.008148</td>\n      <td id=\"T_687ad_row13_col5\" class=\"data row13 col5\" >0.028488</td>\n      <td id=\"T_687ad_row13_col6\" class=\"data row13 col6\" >0.100342</td>\n      <td id=\"T_687ad_row13_col7\" class=\"data row13 col7\" >55.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row14\" class=\"row_heading level0 row14\" >B_15</th>\n      <td id=\"T_687ad_row14_col0\" class=\"data row14 col0\" >5524528.000000</td>\n      <td id=\"T_687ad_row14_col1\" class=\"data row14 col1\" >nan</td>\n      <td id=\"T_687ad_row14_col2\" class=\"data row14 col2\" >0.000000</td>\n      <td id=\"T_687ad_row14_col3\" class=\"data row14 col3\" >-10.007812</td>\n      <td id=\"T_687ad_row14_col4\" class=\"data row14 col4\" >0.003139</td>\n      <td id=\"T_687ad_row14_col5\" class=\"data row14 col5\" >0.006233</td>\n      <td id=\"T_687ad_row14_col6\" class=\"data row14 col6\" >0.009293</td>\n      <td id=\"T_687ad_row14_col7\" class=\"data row14 col7\" >65.062500</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row15\" class=\"row_heading level0 row15\" >B_16</th>\n      <td id=\"T_687ad_row15_col0\" class=\"data row15 col0\" >5529435.000000</td>\n      <td id=\"T_687ad_row15_col1\" class=\"data row15 col1\" >nan</td>\n      <td id=\"T_687ad_row15_col2\" class=\"data row15 col2\" >0.000000</td>\n      <td id=\"T_687ad_row15_col3\" class=\"data row15 col3\" >0.000000</td>\n      <td id=\"T_687ad_row15_col4\" class=\"data row15 col4\" >0.006237</td>\n      <td id=\"T_687ad_row15_col5\" class=\"data row15 col5\" >0.091736</td>\n      <td id=\"T_687ad_row15_col6\" class=\"data row15 col6\" >0.756348</td>\n      <td id=\"T_687ad_row15_col7\" class=\"data row15 col7\" >1.009766</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row16\" class=\"row_heading level0 row16\" >B_17</th>\n      <td id=\"T_687ad_row16_col0\" class=\"data row16 col0\" >2393853.000000</td>\n      <td id=\"T_687ad_row16_col1\" class=\"data row16 col1\" >nan</td>\n      <td id=\"T_687ad_row16_col2\" class=\"data row16 col2\" >0.000000</td>\n      <td id=\"T_687ad_row16_col3\" class=\"data row16 col3\" >0.000000</td>\n      <td id=\"T_687ad_row16_col4\" class=\"data row16 col4\" >0.479980</td>\n      <td id=\"T_687ad_row16_col5\" class=\"data row16 col5\" >0.932129</td>\n      <td id=\"T_687ad_row16_col6\" class=\"data row16 col6\" >1.001953</td>\n      <td id=\"T_687ad_row16_col7\" class=\"data row16 col7\" >1.009766</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row17\" class=\"row_heading level0 row17\" >B_18</th>\n      <td id=\"T_687ad_row17_col0\" class=\"data row17 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row17_col1\" class=\"data row17 col1\" >nan</td>\n      <td id=\"T_687ad_row17_col2\" class=\"data row17 col2\" >0.000000</td>\n      <td id=\"T_687ad_row17_col3\" class=\"data row17 col3\" >0.000000</td>\n      <td id=\"T_687ad_row17_col4\" class=\"data row17 col4\" >0.206787</td>\n      <td id=\"T_687ad_row17_col5\" class=\"data row17 col5\" >0.646484</td>\n      <td id=\"T_687ad_row17_col6\" class=\"data row17 col6\" >1.002930</td>\n      <td id=\"T_687ad_row17_col7\" class=\"data row17 col7\" >1.009766</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row18\" class=\"row_heading level0 row18\" >B_19</th>\n      <td id=\"T_687ad_row18_col0\" class=\"data row18 col0\" >5529435.000000</td>\n      <td id=\"T_687ad_row18_col1\" class=\"data row18 col1\" >nan</td>\n      <td id=\"T_687ad_row18_col2\" class=\"data row18 col2\" >0.000000</td>\n      <td id=\"T_687ad_row18_col3\" class=\"data row18 col3\" >0.000000</td>\n      <td id=\"T_687ad_row18_col4\" class=\"data row18 col4\" >0.003456</td>\n      <td id=\"T_687ad_row18_col5\" class=\"data row18 col5\" >0.006908</td>\n      <td id=\"T_687ad_row18_col6\" class=\"data row18 col6\" >0.095459</td>\n      <td id=\"T_687ad_row18_col7\" class=\"data row18 col7\" >1.009766</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row19\" class=\"row_heading level0 row19\" >B_20</th>\n      <td id=\"T_687ad_row19_col0\" class=\"data row19 col0\" >5529435.000000</td>\n      <td id=\"T_687ad_row19_col1\" class=\"data row19 col1\" >nan</td>\n      <td id=\"T_687ad_row19_col2\" class=\"data row19 col2\" >0.000000</td>\n      <td id=\"T_687ad_row19_col3\" class=\"data row19 col3\" >0.000000</td>\n      <td id=\"T_687ad_row19_col4\" class=\"data row19 col4\" >0.004040</td>\n      <td id=\"T_687ad_row19_col5\" class=\"data row19 col5\" >0.008072</td>\n      <td id=\"T_687ad_row19_col6\" class=\"data row19 col6\" >0.357910</td>\n      <td id=\"T_687ad_row19_col7\" class=\"data row19 col7\" >1.009766</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row20\" class=\"row_heading level0 row20\" >B_21</th>\n      <td id=\"T_687ad_row20_col0\" class=\"data row20 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row20_col1\" class=\"data row20 col1\" >nan</td>\n      <td id=\"T_687ad_row20_col2\" class=\"data row20 col2\" >nan</td>\n      <td id=\"T_687ad_row20_col3\" class=\"data row20 col3\" >0.000000</td>\n      <td id=\"T_687ad_row20_col4\" class=\"data row20 col4\" >0.002550</td>\n      <td id=\"T_687ad_row20_col5\" class=\"data row20 col5\" >0.005100</td>\n      <td id=\"T_687ad_row20_col6\" class=\"data row20 col6\" >0.007652</td>\n      <td id=\"T_687ad_row20_col7\" class=\"data row20 col7\" >288.250000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row21\" class=\"row_heading level0 row21\" >B_22</th>\n      <td id=\"T_687ad_row21_col0\" class=\"data row21 col0\" >5529435.000000</td>\n      <td id=\"T_687ad_row21_col1\" class=\"data row21 col1\" >nan</td>\n      <td id=\"T_687ad_row21_col2\" class=\"data row21 col2\" >0.000000</td>\n      <td id=\"T_687ad_row21_col3\" class=\"data row21 col3\" >0.000000</td>\n      <td id=\"T_687ad_row21_col4\" class=\"data row21 col4\" >0.003080</td>\n      <td id=\"T_687ad_row21_col5\" class=\"data row21 col5\" >0.006161</td>\n      <td id=\"T_687ad_row21_col6\" class=\"data row21 col6\" >0.009239</td>\n      <td id=\"T_687ad_row21_col7\" class=\"data row21 col7\" >3.507812</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row22\" class=\"row_heading level0 row22\" >B_23</th>\n      <td id=\"T_687ad_row22_col0\" class=\"data row22 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row22_col1\" class=\"data row22 col1\" >nan</td>\n      <td id=\"T_687ad_row22_col2\" class=\"data row22 col2\" >0.000000</td>\n      <td id=\"T_687ad_row22_col3\" class=\"data row22 col3\" >0.000000</td>\n      <td id=\"T_687ad_row22_col4\" class=\"data row22 col4\" >0.017487</td>\n      <td id=\"T_687ad_row22_col5\" class=\"data row22 col5\" >0.059509</td>\n      <td id=\"T_687ad_row22_col6\" class=\"data row22 col6\" >0.247192</td>\n      <td id=\"T_687ad_row22_col7\" class=\"data row22 col7\" >1.693359</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row23\" class=\"row_heading level0 row23\" >B_24</th>\n      <td id=\"T_687ad_row23_col0\" class=\"data row23 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row23_col1\" class=\"data row23 col1\" >nan</td>\n      <td id=\"T_687ad_row23_col2\" class=\"data row23 col2\" >0.000000</td>\n      <td id=\"T_687ad_row23_col3\" class=\"data row23 col3\" >0.000000</td>\n      <td id=\"T_687ad_row23_col4\" class=\"data row23 col4\" >0.002588</td>\n      <td id=\"T_687ad_row23_col5\" class=\"data row23 col5\" >0.005177</td>\n      <td id=\"T_687ad_row23_col6\" class=\"data row23 col6\" >0.007759</td>\n      <td id=\"T_687ad_row23_col7\" class=\"data row23 col7\" >46.593750</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row24\" class=\"row_heading level0 row24\" >B_25</th>\n      <td id=\"T_687ad_row24_col0\" class=\"data row24 col0\" >5524528.000000</td>\n      <td id=\"T_687ad_row24_col1\" class=\"data row24 col1\" >nan</td>\n      <td id=\"T_687ad_row24_col2\" class=\"data row24 col2\" >0.000000</td>\n      <td id=\"T_687ad_row24_col3\" class=\"data row24 col3\" >-11.250000</td>\n      <td id=\"T_687ad_row24_col4\" class=\"data row24 col4\" >0.005966</td>\n      <td id=\"T_687ad_row24_col5\" class=\"data row24 col5\" >0.019852</td>\n      <td id=\"T_687ad_row24_col6\" class=\"data row24 col6\" >0.106140</td>\n      <td id=\"T_687ad_row24_col7\" class=\"data row24 col7\" >15.703125</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row25\" class=\"row_heading level0 row25\" >B_26</th>\n      <td id=\"T_687ad_row25_col0\" class=\"data row25 col0\" >5529435.000000</td>\n      <td id=\"T_687ad_row25_col1\" class=\"data row25 col1\" >nan</td>\n      <td id=\"T_687ad_row25_col2\" class=\"data row25 col2\" >nan</td>\n      <td id=\"T_687ad_row25_col3\" class=\"data row25 col3\" >0.000000</td>\n      <td id=\"T_687ad_row25_col4\" class=\"data row25 col4\" >0.002621</td>\n      <td id=\"T_687ad_row25_col5\" class=\"data row25 col5\" >0.005238</td>\n      <td id=\"T_687ad_row25_col6\" class=\"data row25 col6\" >0.007858</td>\n      <td id=\"T_687ad_row25_col7\" class=\"data row25 col7\" >1499.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row26\" class=\"row_heading level0 row26\" >B_27</th>\n      <td id=\"T_687ad_row26_col0\" class=\"data row26 col0\" >5529435.000000</td>\n      <td id=\"T_687ad_row26_col1\" class=\"data row26 col1\" >0.000000</td>\n      <td id=\"T_687ad_row26_col2\" class=\"data row26 col2\" >0.000000</td>\n      <td id=\"T_687ad_row26_col3\" class=\"data row26 col3\" >0.000000</td>\n      <td id=\"T_687ad_row26_col4\" class=\"data row26 col4\" >0.002502</td>\n      <td id=\"T_687ad_row26_col5\" class=\"data row26 col5\" >0.005005</td>\n      <td id=\"T_687ad_row26_col6\" class=\"data row26 col6\" >0.007507</td>\n      <td id=\"T_687ad_row26_col7\" class=\"data row26 col7\" >1.004883</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row27\" class=\"row_heading level0 row27\" >B_28</th>\n      <td id=\"T_687ad_row27_col0\" class=\"data row27 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row27_col1\" class=\"data row27 col1\" >nan</td>\n      <td id=\"T_687ad_row27_col2\" class=\"data row27 col2\" >0.000000</td>\n      <td id=\"T_687ad_row27_col3\" class=\"data row27 col3\" >-0.000022</td>\n      <td id=\"T_687ad_row27_col4\" class=\"data row27 col4\" >0.027451</td>\n      <td id=\"T_687ad_row27_col5\" class=\"data row27 col5\" >0.077087</td>\n      <td id=\"T_687ad_row27_col6\" class=\"data row27 col6\" >0.194458</td>\n      <td id=\"T_687ad_row27_col7\" class=\"data row27 col7\" >25.531250</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row28\" class=\"row_heading level0 row28\" >B_29</th>\n      <td id=\"T_687ad_row28_col0\" class=\"data row28 col0\" >381416.000000</td>\n      <td id=\"T_687ad_row28_col1\" class=\"data row28 col1\" >0.000000</td>\n      <td id=\"T_687ad_row28_col2\" class=\"data row28 col2\" >0.000000</td>\n      <td id=\"T_687ad_row28_col3\" class=\"data row28 col3\" >0.000000</td>\n      <td id=\"T_687ad_row28_col4\" class=\"data row28 col4\" >0.002558</td>\n      <td id=\"T_687ad_row28_col5\" class=\"data row28 col5\" >0.005089</td>\n      <td id=\"T_687ad_row28_col6\" class=\"data row28 col6\" >0.007633</td>\n      <td id=\"T_687ad_row28_col7\" class=\"data row28 col7\" >12.023438</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row29\" class=\"row_heading level0 row29\" >B_31</th>\n      <td id=\"T_687ad_row29_col0\" class=\"data row29 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row29_col1\" class=\"data row29 col1\" >nan</td>\n      <td id=\"T_687ad_row29_col2\" class=\"data row29 col2\" >0.000000</td>\n      <td id=\"T_687ad_row29_col3\" class=\"data row29 col3\" >0.000000</td>\n      <td id=\"T_687ad_row29_col4\" class=\"data row29 col4\" >1.000000</td>\n      <td id=\"T_687ad_row29_col5\" class=\"data row29 col5\" >1.000000</td>\n      <td id=\"T_687ad_row29_col6\" class=\"data row29 col6\" >1.000000</td>\n      <td id=\"T_687ad_row29_col7\" class=\"data row29 col7\" >1.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row30\" class=\"row_heading level0 row30\" >B_32</th>\n      <td id=\"T_687ad_row30_col0\" class=\"data row30 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row30_col1\" class=\"data row30 col1\" >nan</td>\n      <td id=\"T_687ad_row30_col2\" class=\"data row30 col2\" >0.000000</td>\n      <td id=\"T_687ad_row30_col3\" class=\"data row30 col3\" >0.000000</td>\n      <td id=\"T_687ad_row30_col4\" class=\"data row30 col4\" >0.002556</td>\n      <td id=\"T_687ad_row30_col5\" class=\"data row30 col5\" >0.005116</td>\n      <td id=\"T_687ad_row30_col6\" class=\"data row30 col6\" >0.007675</td>\n      <td id=\"T_687ad_row30_col7\" class=\"data row30 col7\" >1.009766</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row31\" class=\"row_heading level0 row31\" >B_33</th>\n      <td id=\"T_687ad_row31_col0\" class=\"data row31 col0\" >5529435.000000</td>\n      <td id=\"T_687ad_row31_col1\" class=\"data row31 col1\" >nan</td>\n      <td id=\"T_687ad_row31_col2\" class=\"data row31 col2\" >0.000000</td>\n      <td id=\"T_687ad_row31_col3\" class=\"data row31 col3\" >0.000000</td>\n      <td id=\"T_687ad_row31_col4\" class=\"data row31 col4\" >0.006363</td>\n      <td id=\"T_687ad_row31_col5\" class=\"data row31 col5\" >1.001953</td>\n      <td id=\"T_687ad_row31_col6\" class=\"data row31 col6\" >1.005859</td>\n      <td id=\"T_687ad_row31_col7\" class=\"data row31 col7\" >1.009766</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row32\" class=\"row_heading level0 row32\" >B_36</th>\n      <td id=\"T_687ad_row32_col0\" class=\"data row32 col0\" >5531451.000000</td>\n      <td id=\"T_687ad_row32_col1\" class=\"data row32 col1\" >0.000000</td>\n      <td id=\"T_687ad_row32_col2\" class=\"data row32 col2\" >0.000000</td>\n      <td id=\"T_687ad_row32_col3\" class=\"data row32 col3\" >0.000000</td>\n      <td id=\"T_687ad_row32_col4\" class=\"data row32 col4\" >0.002514</td>\n      <td id=\"T_687ad_row32_col5\" class=\"data row32 col5\" >0.005028</td>\n      <td id=\"T_687ad_row32_col6\" class=\"data row32 col6\" >0.007542</td>\n      <td id=\"T_687ad_row32_col7\" class=\"data row32 col7\" >1.001953</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row33\" class=\"row_heading level0 row33\" >B_37</th>\n      <td id=\"T_687ad_row33_col0\" class=\"data row33 col0\" >5531395.000000</td>\n      <td id=\"T_687ad_row33_col1\" class=\"data row33 col1\" >nan</td>\n      <td id=\"T_687ad_row33_col2\" class=\"data row33 col2\" >0.000000</td>\n      <td id=\"T_687ad_row33_col3\" class=\"data row33 col3\" >-7.605469</td>\n      <td id=\"T_687ad_row33_col4\" class=\"data row33 col4\" >0.008835</td>\n      <td id=\"T_687ad_row33_col5\" class=\"data row33 col5\" >0.031143</td>\n      <td id=\"T_687ad_row33_col6\" class=\"data row33 col6\" >0.123840</td>\n      <td id=\"T_687ad_row33_col7\" class=\"data row33 col7\" >1.328125</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row34\" class=\"row_heading level0 row34\" >B_39</th>\n      <td id=\"T_687ad_row34_col0\" class=\"data row34 col0\" >33632.000000</td>\n      <td id=\"T_687ad_row34_col1\" class=\"data row34 col1\" >0.254395</td>\n      <td id=\"T_687ad_row34_col2\" class=\"data row34 col2\" >0.297852</td>\n      <td id=\"T_687ad_row34_col3\" class=\"data row34 col3\" >-0.979492</td>\n      <td id=\"T_687ad_row34_col4\" class=\"data row34 col4\" >0.058380</td>\n      <td id=\"T_687ad_row34_col5\" class=\"data row34 col5\" >0.149170</td>\n      <td id=\"T_687ad_row34_col6\" class=\"data row34 col6\" >0.291504</td>\n      <td id=\"T_687ad_row34_col7\" class=\"data row34 col7\" >2.060547</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row35\" class=\"row_heading level0 row35\" >B_40</th>\n      <td id=\"T_687ad_row35_col0\" class=\"data row35 col0\" >5531398.000000</td>\n      <td id=\"T_687ad_row35_col1\" class=\"data row35 col1\" >nan</td>\n      <td id=\"T_687ad_row35_col2\" class=\"data row35 col2\" >nan</td>\n      <td id=\"T_687ad_row35_col3\" class=\"data row35 col3\" >0.000000</td>\n      <td id=\"T_687ad_row35_col4\" class=\"data row35 col4\" >0.017395</td>\n      <td id=\"T_687ad_row35_col5\" class=\"data row35 col5\" >0.058319</td>\n      <td id=\"T_687ad_row35_col6\" class=\"data row35 col6\" >0.245117</td>\n      <td id=\"T_687ad_row35_col7\" class=\"data row35 col7\" >5756.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row36\" class=\"row_heading level0 row36\" >B_41</th>\n      <td id=\"T_687ad_row36_col0\" class=\"data row36 col0\" >5530761.000000</td>\n      <td id=\"T_687ad_row36_col1\" class=\"data row36 col1\" >nan</td>\n      <td id=\"T_687ad_row36_col2\" class=\"data row36 col2\" >0.000000</td>\n      <td id=\"T_687ad_row36_col3\" class=\"data row36 col3\" >0.000000</td>\n      <td id=\"T_687ad_row36_col4\" class=\"data row36 col4\" >0.002554</td>\n      <td id=\"T_687ad_row36_col5\" class=\"data row36 col5\" >0.005108</td>\n      <td id=\"T_687ad_row36_col6\" class=\"data row36 col6\" >0.007660</td>\n      <td id=\"T_687ad_row36_col7\" class=\"data row36 col7\" >22.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_687ad_level0_row37\" class=\"row_heading level0 row37\" >B_42</th>\n      <td id=\"T_687ad_row37_col0\" class=\"data row37 col0\" >71478.000000</td>\n      <td id=\"T_687ad_row37_col1\" class=\"data row37 col1\" >0.000000</td>\n      <td id=\"T_687ad_row37_col2\" class=\"data row37 col2\" >0.000000</td>\n      <td id=\"T_687ad_row37_col3\" class=\"data row37 col3\" >0.000003</td>\n      <td id=\"T_687ad_row37_col4\" class=\"data row37 col4\" >0.007259</td>\n      <td id=\"T_687ad_row37_col5\" class=\"data row37 col5\" >0.021606</td>\n      <td id=\"T_687ad_row37_col6\" class=\"data row37 col6\" >0.105591</td>\n      <td id=\"T_687ad_row37_col7\" class=\"data row37 col7\" >9.054688</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"code","source":"# --- Plot Missing Values ---\nmso.bar(train_balance_cols, fontsize=9, color=[purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0],\n                               purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[1], purple_grad[1]], \n        figsize=(15, 8), sort='descending', labels=True)\n\n# --- Title & Subtitle Settings ---\nplt.suptitle('Missing Values in each Columns', fontweight='heavy', x=0.124, y=1.22, ha='left',fontsize='16', \n             fontfamily='sans-serif', color=black_grad[0])\nplt.title('Almost all columns have  missing value.\\n\\nThe total of missing values in each column is less than 25%, which means that imputation can still be done to fill in the missing values in the\\ntwo columns.', \n          fontsize='8', fontfamily='sans-serif', loc='left', color=black_grad[1], pad=5)\nplt.grid(axis='both', alpha=0);\n\n# --- Total Missing Values in each Columns ---\nprint('\\033[36m*' * 43)\nprint('\\033[1m'+'.: Total Missing Values in each Columns :.'+'\\033[0m')\nprint('\\033[36m*' * 43+'\\033[0m')\ntrain_balance_cols.isnull().sum()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#plot distribution for selected B variables with respect to target label\nnrows = 8\nncols = 5\nfig, axes = plt.subplots(figsize=(24,22)) \nb_columns=['B_1', 'B_2', 'B_3', 'B_4', 'B_5', 'B_6', 'B_7', 'B_8', 'B_9', 'B_10',\n       'B_11', 'B_12', 'B_13', 'B_14', 'B_15', 'B_16', 'B_17', 'B_18', 'B_19',\n       'B_20', 'B_21', 'B_22', 'B_23', 'B_24', 'B_25', 'B_26', 'B_27', 'B_28',\n       'B_29', 'B_31', 'B_32', 'B_33', 'B_36', 'B_37', 'B_39', 'B_40', 'B_41',\n       'B_42']\nfor i, col in enumerate(b_columns):\n    ax=fig.add_subplot(nrows, ncols, i+1)\n    sns.kdeplot(x=train_data[col],hue=train_data['target'], multiple=\"stack\",palette=[\"#FF3333\" ,\"#00CC00\"],ax=ax)\n    \nfig.tight_layout()  \nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del train_data[col]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr=train_balance_cols.corr()\nplt.figure(figsize=(24,22))\nax = sns.heatmap(corr,cmap=\"YlGnBu\", linewidths=.5, vmin=-1, vmax=1, center=0, annot=True, annot_kws={'fontsize':12,'fontweight':'bold'}, cbar=False,fmt='.2f' )\nplt.yticks(rotation=0)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-31T09:44:39.347562Z","iopub.execute_input":"2022-07-31T09:44:39.347903Z","iopub.status.idle":"2022-07-31T09:45:03.630565Z","shell.execute_reply.started":"2022-07-31T09:44:39.347873Z","shell.execute_reply":"2022-07-31T09:45:03.62951Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del train_balance_cols","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data analysis on the Spend attributes","metadata":{}},{"cell_type":"code","source":"cols=[col for col in train_data.columns if (col.startswith(('S','T'))) & (col not in categorical_cols[:-1])]\n\ntrain_spend_cols =train_data[cols]","metadata":{"execution":{"iopub.status.busy":"2022-07-30T07:05:20.223494Z","iopub.execute_input":"2022-07-30T07:05:20.223859Z","iopub.status.idle":"2022-07-30T07:05:20.627219Z","shell.execute_reply.started":"2022-07-30T07:05:20.223824Z","shell.execute_reply":"2022-07-30T07:05:20.626198Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_spend_cols.select_dtypes(exclude='object').describe().T.style.background_gradient(cmap='RdPu').set_properties(**{'font-family': 'Segoe UI'})","metadata":{"execution":{"iopub.status.busy":"2022-07-30T07:05:20.628862Z","iopub.execute_input":"2022-07-30T07:05:20.629249Z","iopub.status.idle":"2022-07-30T07:05:38.428201Z","shell.execute_reply.started":"2022-07-30T07:05:20.629211Z","shell.execute_reply":"2022-07-30T07:05:38.426867Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# --- Plot Missing Values ---\nmso.bar(train_spend_cols, fontsize=9, color=[purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0],\n                               purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[1], purple_grad[1]], \n        figsize=(15, 8), sort='descending', labels=True)\n\n# --- Title & Subtitle Settings ---\nplt.suptitle('Missing Values in each Columns', fontweight='heavy', x=0.124, y=1.22, ha='left',fontsize='16', \n             fontfamily='sans-serif', color=black_grad[0])\nplt.title('Almost all columns have no missing value except \\n\\nThe total of missing values in each column is less than 25%, which means that imputation can still be done to fill in the missing values in the\\nthe columns.', \n          fontsize='8', fontfamily='sans-serif', loc='left', color=black_grad[1], pad=5)\nplt.grid(axis='both', alpha=0);\n\n# --- Total Missing Values in each Columns ---\nprint('\\033[36m*' * 43)\nprint('\\033[1m'+'.: Total Missing Values in each Columns :.'+'\\033[0m')\nprint('\\033[36m*' * 43+'\\033[0m')\ntrain_spend_cols.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T07:05:38.42999Z","iopub.execute_input":"2022-07-30T07:05:38.430407Z","iopub.status.idle":"2022-07-30T07:05:42.383793Z","shell.execute_reply.started":"2022-07-30T07:05:38.430368Z","shell.execute_reply":"2022-07-30T07:05:42.38267Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#plot distribution for selected B variables with respect to target label\nnrows = 5\nncols = 5\nfig, axes = plt.subplots(figsize=(24,22)) \ns_columns=['S_2', 'S_3', 'S_5', 'S_6', 'S_7', 'S_8', 'S_9', 'S_11', 'S_12', 'S_13',\n       'S_15', 'S_16', 'S_17', 'S_18', 'S_19', 'S_20', 'S_22', 'S_23', 'S_24',\n       'S_25', 'S_26', 'S_27']\nfor i, col in enumerate(s_columns):\n    ax=fig.add_subplot(nrows, ncols, i+1)\n    sns.kdeplot(x=train_data[col],hue=train_data['target'], multiple=\"stack\",palette=[\"#FF3333\" ,\"#00CC00\"],ax=ax)\n    \nfig.tight_layout()  \nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T07:05:42.394794Z","iopub.execute_input":"2022-07-30T07:05:42.395225Z","iopub.status.idle":"2022-07-30T07:12:50.443086Z","shell.execute_reply.started":"2022-07-30T07:05:42.395187Z","shell.execute_reply":"2022-07-30T07:12:50.441858Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del train_data[col]","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr=train_spend_cols.corr()\nplt.figure(figsize=(24,22))\nax = sns.heatmap(corr,cmap=\"YlGnBu\", linewidths=.5, vmin=-1, vmax=1, center=0, annot=True, annot_kws={'fontsize':12,'fontweight':'bold'}, cbar=False,fmt='.2f' )\nplt.yticks(rotation=0)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T07:12:50.445053Z","iopub.execute_input":"2022-07-30T07:12:50.445418Z","iopub.status.idle":"2022-07-30T07:12:59.74556Z","shell.execute_reply.started":"2022-07-30T07:12:50.445359Z","shell.execute_reply":"2022-07-30T07:12:59.744627Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del train_spend_cols","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data analysis on the Deliquency attributes","metadata":{}},{"cell_type":"code","source":"cols=[col for col in train_data.columns if (col.startswith(('D','T'))) & (col not in categorical_cols[:-1])]\n\ntrain_deliquency_cols =train_data[cols]","metadata":{"execution":{"iopub.status.busy":"2022-07-30T07:12:59.746961Z","iopub.execute_input":"2022-07-30T07:12:59.747476Z","iopub.status.idle":"2022-07-30T07:13:01.315813Z","shell.execute_reply.started":"2022-07-30T07:12:59.747442Z","shell.execute_reply":"2022-07-30T07:13:01.314793Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_deliquency_cols.select_dtypes(exclude='object').describe().T.style.background_gradient(cmap='RdPu').set_properties(**{'font-family': 'Segoe UI'})","metadata":{"execution":{"iopub.status.busy":"2022-07-30T07:13:01.317279Z","iopub.execute_input":"2022-07-30T07:13:01.317637Z","iopub.status.idle":"2022-07-30T07:14:14.280534Z","shell.execute_reply.started":"2022-07-30T07:13:01.3176Z","shell.execute_reply":"2022-07-30T07:14:14.279276Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# --- Plot Missing Values ---\nmso.bar(train_deliquency_cols, fontsize=9, color=[purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0],\n                               purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[0], purple_grad[1], purple_grad[1]], \n        figsize=(24, 22), sort='descending', labels=True)\n\n# --- Title & Subtitle Settings ---\nplt.suptitle('Missing Values in each Columns', fontweight='heavy', x=0.124, y=1.22, ha='left',fontsize='16', \n             fontfamily='sans-serif', color=black_grad[0])\nplt.title('Almost all columns have no missing value except \\n\\nThe total of missing values in each column is less than 25%, which means that imputation can still be done to fill in the missing values in the\\nthe columns.', \n          fontsize='8', fontfamily='sans-serif', loc='left', color=black_grad[1], pad=5)\nplt.grid(axis='both', alpha=0);\n\n# --- Total Missing Values in each Columns ---\nprint('\\033[36m*' * 43)\nprint('\\033[1m'+'.: Total Missing Values in each Columns :.'+'\\033[0m')\nprint('\\033[36m*' * 43+'\\033[0m')\ntrain_deliquency_cols.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T07:14:14.281956Z","iopub.execute_input":"2022-07-30T07:14:14.282408Z","iopub.status.idle":"2022-07-30T07:14:31.995502Z","shell.execute_reply.started":"2022-07-30T07:14:14.282372Z","shell.execute_reply":"2022-07-30T07:14:31.994605Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#plot distribution for selected B variables with respect to target label\nnrows = 18\nncols = 5\nfig, axes = plt.subplots(figsize=(24,22)) \nd_columns=['D_39', 'D_41', 'D_42', 'D_43', 'D_44', 'D_45', 'D_46', 'D_47', 'D_48',\n       'D_49', 'D_50', 'D_51', 'D_52', 'D_53', 'D_54', 'D_55', 'D_56', 'D_58',\n       'D_59', 'D_60', 'D_61', 'D_62', 'D_65', 'D_69', 'D_70', 'D_71', 'D_72',\n       'D_73', 'D_74', 'D_75', 'D_76', 'D_77', 'D_78', 'D_79', 'D_80', 'D_81',\n       'D_82', 'D_83', 'D_84', 'D_86', 'D_88', 'D_89', 'D_91', 'D_92',\n       'D_93', 'D_94', 'D_96', 'D_102', 'D_103', 'D_104', 'D_105',\n       'D_107', 'D_108', 'D_109', 'D_110', 'D_111', 'D_112', 'D_113', 'D_115',\n       'D_118', 'D_119', 'D_121', 'D_122', 'D_123', 'D_124', 'D_125', 'D_127',\n       'D_128', 'D_129', 'D_130', 'D_131', 'D_132', 'D_133', 'D_134', 'D_135',\n       'D_136', 'D_137', 'D_138', 'D_139', 'D_140', 'D_141', 'D_142', 'D_143',\n       'D_144', 'D_145']\nfor i, col in enumerate(d_columns):\n    ax=fig.add_subplot(nrows, ncols, i+1)\n    sns.kdeplot(x=train_data[col],hue=train_data['target'], multiple=\"stack\",palette=[\"#FF3333\" ,\"#00CC00\"],ax=ax)\n    \nfig.tight_layout()  \nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T07:14:32.007849Z","iopub.execute_input":"2022-07-30T07:14:32.008483Z","iopub.status.idle":"2022-07-30T07:37:39.247282Z","shell.execute_reply.started":"2022-07-30T07:14:32.008448Z","shell.execute_reply":"2022-07-30T07:37:39.246425Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del train_data[col]","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del train_deliquency_cols","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}