lung-cancer-analysis-accuracy-96-4.ipynb

{"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":"markdown","source":"# <p style=\"padding:10px;background-color:#0f4c5c;margin:0;color:white;font-family:newtimeroman;font-size:150%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Lung Cancer Analysis & Prediction</p>\n\n<p style=\"text-align:center; \">\n<img src=\"https://st.depositphotos.com/1909187/4810/i/450/depositphotos_48100511-stock-photo-lung-cancer-tumor-detail.jpg\" style='width: 300px; height: 300px;'>\n</p>\n\n\n<p style=\"text-align:justify; \">\n    \nLung cancer is a type of cancer that begins in the lungs. Lungs are two spongy organs in our chest that take in oxygen when we inhale and release carbon dioxide when we exhale.\nLung cancer is the leading cause of cancer deaths worldwide.<br>\n<br>\nPeople who smoke have the greatest risk of lung cancer, though lung cancer can also occur in people who have never smoked. The risk of lung cancer increases with the length of time and number of cigarettes someone's smoked. If someone quits smoking, even after smoking for many years, he/she can significantly reduce the chances of developing lung cancer.\n</p> \n\n\n<a id='top'></a>\n<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n<p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:130%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Table Of Contents</p>   \n    \n\n    \n|No  | Contents |No  | Contents  |\n|:---| :---     |:---| :----     |\n|1   | [<font color=\"#0f4c5c\"> Importing Libraries</font>](#1)                   |12   | [<font color=\"#0f4c5c\"> Correlation Heatmap</font>](#12)   |     \n|2   | [<font color=\"#0f4c5c\"> About Dataset</font>](#2)                         |13  | [<font color=\"#0f4c5c\"> Preprocessing For Classification</font>](#13)|      \n|3   | [<font color=\"#0f4c5c\"> Basic Exploration</font>](#3)                     |14  | [<font color=\"#0f4c5c\"> Logistic Regression Model</font>](#14)   |    \n|4   | [<font color=\"#0f4c5c\"> Dataset Summary</font>](#4)                       |15  | [<font color=\"#0f4c5c\"> Gaussian Naive Bayes Model</font>](#15)    |       \n|5   | [<font color=\"#0f4c5c\"> Digging Deeper</font>](#5)      |16  | [<font color=\"#0f4c5c\"> Bernoulli Naive Bayes Model</font>](#16)  |     \n|6   | [<font color=\"#0f4c5c\"> Custom Palette For Visualization</font>](#6)              |17  | [<font color=\"#0f4c5c\"> Support Vector Machine Model</font>](#17)     |     \n|7   | [<font color=\"#0f4c5c\"> Positive Lung Cancer Cases</font>](#7)              |18  | [<font color=\"#0f4c5c\"> Random Forest Model</font>](#18)     |\n|8   | [<font color=\"#0f4c5c\"> Positive Cases' Age Distribution</font>](#8)              |19  | [<font color=\"#0f4c5c\"> K Nearest Neighbors Model</font>](#19)     |\n|9   | [<font color=\"#0f4c5c\"> Positive Cases' Gender Distribution</font>](#9)              |20  | [<font color=\"#0f4c5c\"> Extreme Gradient Boosting Model</font>](#20)     |\n|10   | [<font color=\"#0f4c5c\"> Gender-wise Positive Cases' Reasons</font>](#10)              |21  | [<font color=\"#0f4c5c\"> Neural Network Architecture</font>](#21)     |\n|11   | [<font color=\"#0f4c5c\"> Gender-wise Positive Cases' Symptoms</font>](#11)              |22  | [<font color=\"#0f4c5c\"> Thank You</font>](#22)     |    \n  ","metadata":{}},{"cell_type":"markdown","source":"<a id=\"1\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Importing Libraries</p>","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.486719Z","iopub.execute_input":"2022-11-30T14:44:34.487338Z","iopub.status.idle":"2022-11-30T14:44:34.494066Z","shell.execute_reply.started":"2022-11-30T14:44:34.487279Z","shell.execute_reply":"2022-11-30T14:44:34.492553Z"},"trusted":true},"execution_count":61,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"2\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">About Dataset</p>","metadata":{}},{"cell_type":"markdown","source":"* **GENDER :** M [Male] , F [Female]\n* **AGE :** Age of patients\n* **SMOKING :** 2 [Yes] , 1 [No] \n* **YELLOW_FINGERS :** 2 [Yes] , 1 [No]  \n* **ANXIETY :** 2 [Yes] , 1 [No] \n* **PEER_PRESSURE :** 2 [Yes] , 1 [No] \n* **CHRONIC DISEASE :** 2 [Yes] , 1 [No] \n* **FATIGUE :** 2 [Yes] , 1 [No] \n* **ALLERGY :** 2 [Yes] , 1 [No] \n* **WHEEZING :** 2 [Yes] , 1 [No] \n* **ALCOHOL CONSUMING :** 2 [Yes] , 1 [No] \n* **COUGHING :** 2 [Yes] , 1 [No] \n* **SHORTNESS OF BREATH :** 2 [Yes] , 1 [No] \n* **SWALLOWING DIFFICULTY :** 2 [Yes] , 1 [No] \n* **CHEST PAIN :** 2 [Yes] , 1 [No] \n* **LUNG_CANCER :** YES [Positive] , NO [Negative] ","metadata":{}},{"cell_type":"code","source":"data = pd.read_csv(\"../input/lung-cancer/survey lung cancer.csv\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.495892Z","iopub.execute_input":"2022-11-30T14:44:34.496299Z","iopub.status.idle":"2022-11-30T14:44:34.516019Z","shell.execute_reply.started":"2022-11-30T14:44:34.496259Z","shell.execute_reply":"2022-11-30T14:44:34.514493Z"},"trusted":true},"execution_count":62,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"3\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Basic Exploration</p>\n<br>\n\n**Let's have a glimpse of the dataset.**","metadata":{}},{"cell_type":"code","source":"print(f\"Shape of The Dataset : {data.shape}\")\nprint(f\"\\nGlimpse of The Dataset :\")\ndata.head().style.set_properties(**{\"background-color\": \"#2a9d8f\",\"color\":\"white\",\"border\": \"1.5px  solid black\"})","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.519070Z","iopub.execute_input":"2022-11-30T14:44:34.519669Z","iopub.status.idle":"2022-11-30T14:44:34.557609Z","shell.execute_reply.started":"2022-11-30T14:44:34.519616Z","shell.execute_reply":"2022-11-30T14:44:34.556179Z"},"trusted":true},"execution_count":63,"outputs":[{"name":"stdout","text":"Shape of The Dataset : (309, 16)\n\nGlimpse of The Dataset :\n","output_type":"stream"},{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f4e40541290>","text/html":"<style type=\"text/css\">\n#T_14ac5_row0_col0, #T_14ac5_row0_col1, #T_14ac5_row0_col2, #T_14ac5_row0_col3, #T_14ac5_row0_col4, #T_14ac5_row0_col5, #T_14ac5_row0_col6, #T_14ac5_row0_col7, #T_14ac5_row0_col8, #T_14ac5_row0_col9, #T_14ac5_row0_col10, #T_14ac5_row0_col11, #T_14ac5_row0_col12, #T_14ac5_row0_col13, #T_14ac5_row0_col14, #T_14ac5_row0_col15, #T_14ac5_row1_col0, #T_14ac5_row1_col1, #T_14ac5_row1_col2, #T_14ac5_row1_col3, #T_14ac5_row1_col4, #T_14ac5_row1_col5, #T_14ac5_row1_col6, #T_14ac5_row1_col7, #T_14ac5_row1_col8, #T_14ac5_row1_col9, #T_14ac5_row1_col10, #T_14ac5_row1_col11, #T_14ac5_row1_col12, #T_14ac5_row1_col13, #T_14ac5_row1_col14, #T_14ac5_row1_col15, #T_14ac5_row2_col0, #T_14ac5_row2_col1, #T_14ac5_row2_col2, #T_14ac5_row2_col3, #T_14ac5_row2_col4, #T_14ac5_row2_col5, #T_14ac5_row2_col6, #T_14ac5_row2_col7, #T_14ac5_row2_col8, #T_14ac5_row2_col9, #T_14ac5_row2_col10, #T_14ac5_row2_col11, #T_14ac5_row2_col12, #T_14ac5_row2_col13, #T_14ac5_row2_col14, #T_14ac5_row2_col15, #T_14ac5_row3_col0, #T_14ac5_row3_col1, #T_14ac5_row3_col2, #T_14ac5_row3_col3, #T_14ac5_row3_col4, #T_14ac5_row3_col5, #T_14ac5_row3_col6, #T_14ac5_row3_col7, #T_14ac5_row3_col8, #T_14ac5_row3_col9, #T_14ac5_row3_col10, #T_14ac5_row3_col11, #T_14ac5_row3_col12, #T_14ac5_row3_col13, #T_14ac5_row3_col14, #T_14ac5_row3_col15, #T_14ac5_row4_col0, #T_14ac5_row4_col1, #T_14ac5_row4_col2, #T_14ac5_row4_col3, #T_14ac5_row4_col4, #T_14ac5_row4_col5, #T_14ac5_row4_col6, #T_14ac5_row4_col7, #T_14ac5_row4_col8, #T_14ac5_row4_col9, #T_14ac5_row4_col10, #T_14ac5_row4_col11, #T_14ac5_row4_col12, #T_14ac5_row4_col13, #T_14ac5_row4_col14, #T_14ac5_row4_col15 {\n  background-color: #2a9d8f;\n  color: white;\n  border: 1.5px  solid black;\n}\n</style>\n<table id=\"T_14ac5_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >GENDER</th>\n      <th class=\"col_heading level0 col1\" >AGE</th>\n      <th class=\"col_heading level0 col2\" >SMOKING</th>\n      <th class=\"col_heading level0 col3\" >YELLOW_FINGERS</th>\n      <th class=\"col_heading level0 col4\" >ANXIETY</th>\n      <th class=\"col_heading level0 col5\" >PEER_PRESSURE</th>\n      <th class=\"col_heading level0 col6\" >CHRONIC DISEASE</th>\n      <th class=\"col_heading level0 col7\" >FATIGUE </th>\n      <th class=\"col_heading level0 col8\" >ALLERGY </th>\n      <th class=\"col_heading level0 col9\" >WHEEZING</th>\n      <th class=\"col_heading level0 col10\" >ALCOHOL CONSUMING</th>\n      <th class=\"col_heading level0 col11\" >COUGHING</th>\n      <th class=\"col_heading level0 col12\" >SHORTNESS OF BREATH</th>\n      <th class=\"col_heading level0 col13\" >SWALLOWING DIFFICULTY</th>\n      <th class=\"col_heading level0 col14\" >CHEST PAIN</th>\n      <th class=\"col_heading level0 col15\" >LUNG_CANCER</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_14ac5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n      <td id=\"T_14ac5_row0_col0\" class=\"data row0 col0\" >M</td>\n      <td id=\"T_14ac5_row0_col1\" class=\"data row0 col1\" >69</td>\n      <td id=\"T_14ac5_row0_col2\" class=\"data row0 col2\" >1</td>\n      <td id=\"T_14ac5_row0_col3\" class=\"data row0 col3\" >2</td>\n      <td id=\"T_14ac5_row0_col4\" class=\"data row0 col4\" >2</td>\n      <td id=\"T_14ac5_row0_col5\" class=\"data row0 col5\" >1</td>\n      <td id=\"T_14ac5_row0_col6\" class=\"data row0 col6\" >1</td>\n      <td id=\"T_14ac5_row0_col7\" class=\"data row0 col7\" >2</td>\n      <td id=\"T_14ac5_row0_col8\" class=\"data row0 col8\" >1</td>\n      <td id=\"T_14ac5_row0_col9\" class=\"data row0 col9\" >2</td>\n      <td id=\"T_14ac5_row0_col10\" class=\"data row0 col10\" >2</td>\n      <td id=\"T_14ac5_row0_col11\" class=\"data row0 col11\" >2</td>\n      <td id=\"T_14ac5_row0_col12\" class=\"data row0 col12\" >2</td>\n      <td id=\"T_14ac5_row0_col13\" class=\"data row0 col13\" >2</td>\n      <td id=\"T_14ac5_row0_col14\" class=\"data row0 col14\" >2</td>\n      <td id=\"T_14ac5_row0_col15\" class=\"data row0 col15\" >YES</td>\n    </tr>\n    <tr>\n      <th id=\"T_14ac5_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n      <td id=\"T_14ac5_row1_col0\" class=\"data row1 col0\" >M</td>\n      <td id=\"T_14ac5_row1_col1\" class=\"data row1 col1\" >74</td>\n      <td id=\"T_14ac5_row1_col2\" class=\"data row1 col2\" >2</td>\n      <td id=\"T_14ac5_row1_col3\" class=\"data row1 col3\" >1</td>\n      <td id=\"T_14ac5_row1_col4\" class=\"data row1 col4\" >1</td>\n      <td id=\"T_14ac5_row1_col5\" class=\"data row1 col5\" >1</td>\n      <td id=\"T_14ac5_row1_col6\" class=\"data row1 col6\" >2</td>\n      <td id=\"T_14ac5_row1_col7\" class=\"data row1 col7\" >2</td>\n      <td id=\"T_14ac5_row1_col8\" class=\"data row1 col8\" >2</td>\n      <td id=\"T_14ac5_row1_col9\" class=\"data row1 col9\" >1</td>\n      <td id=\"T_14ac5_row1_col10\" class=\"data row1 col10\" >1</td>\n      <td id=\"T_14ac5_row1_col11\" class=\"data row1 col11\" >1</td>\n      <td id=\"T_14ac5_row1_col12\" class=\"data row1 col12\" >2</td>\n      <td id=\"T_14ac5_row1_col13\" class=\"data row1 col13\" >2</td>\n      <td id=\"T_14ac5_row1_col14\" class=\"data row1 col14\" >2</td>\n      <td id=\"T_14ac5_row1_col15\" class=\"data row1 col15\" >YES</td>\n    </tr>\n    <tr>\n      <th id=\"T_14ac5_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n      <td id=\"T_14ac5_row2_col0\" class=\"data row2 col0\" >F</td>\n      <td id=\"T_14ac5_row2_col1\" class=\"data row2 col1\" >59</td>\n      <td id=\"T_14ac5_row2_col2\" class=\"data row2 col2\" >1</td>\n      <td id=\"T_14ac5_row2_col3\" class=\"data row2 col3\" >1</td>\n      <td id=\"T_14ac5_row2_col4\" class=\"data row2 col4\" >1</td>\n      <td id=\"T_14ac5_row2_col5\" class=\"data row2 col5\" >2</td>\n      <td id=\"T_14ac5_row2_col6\" class=\"data row2 col6\" >1</td>\n      <td id=\"T_14ac5_row2_col7\" class=\"data row2 col7\" >2</td>\n      <td id=\"T_14ac5_row2_col8\" class=\"data row2 col8\" >1</td>\n      <td id=\"T_14ac5_row2_col9\" class=\"data row2 col9\" >2</td>\n      <td id=\"T_14ac5_row2_col10\" class=\"data row2 col10\" >1</td>\n      <td id=\"T_14ac5_row2_col11\" class=\"data row2 col11\" >2</td>\n      <td id=\"T_14ac5_row2_col12\" class=\"data row2 col12\" >2</td>\n      <td id=\"T_14ac5_row2_col13\" class=\"data row2 col13\" >1</td>\n      <td id=\"T_14ac5_row2_col14\" class=\"data row2 col14\" >2</td>\n      <td id=\"T_14ac5_row2_col15\" class=\"data row2 col15\" >NO</td>\n    </tr>\n    <tr>\n      <th id=\"T_14ac5_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n      <td id=\"T_14ac5_row3_col0\" class=\"data row3 col0\" >M</td>\n      <td id=\"T_14ac5_row3_col1\" class=\"data row3 col1\" >63</td>\n      <td id=\"T_14ac5_row3_col2\" class=\"data row3 col2\" >2</td>\n      <td id=\"T_14ac5_row3_col3\" class=\"data row3 col3\" >2</td>\n      <td id=\"T_14ac5_row3_col4\" class=\"data row3 col4\" >2</td>\n      <td id=\"T_14ac5_row3_col5\" class=\"data row3 col5\" >1</td>\n      <td id=\"T_14ac5_row3_col6\" class=\"data row3 col6\" >1</td>\n      <td id=\"T_14ac5_row3_col7\" class=\"data row3 col7\" >1</td>\n      <td id=\"T_14ac5_row3_col8\" class=\"data row3 col8\" >1</td>\n      <td id=\"T_14ac5_row3_col9\" class=\"data row3 col9\" >1</td>\n      <td id=\"T_14ac5_row3_col10\" class=\"data row3 col10\" >2</td>\n      <td id=\"T_14ac5_row3_col11\" class=\"data row3 col11\" >1</td>\n      <td id=\"T_14ac5_row3_col12\" class=\"data row3 col12\" >1</td>\n      <td id=\"T_14ac5_row3_col13\" class=\"data row3 col13\" >2</td>\n      <td id=\"T_14ac5_row3_col14\" class=\"data row3 col14\" >2</td>\n      <td id=\"T_14ac5_row3_col15\" class=\"data row3 col15\" >NO</td>\n    </tr>\n    <tr>\n      <th id=\"T_14ac5_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n      <td id=\"T_14ac5_row4_col0\" class=\"data row4 col0\" >F</td>\n      <td id=\"T_14ac5_row4_col1\" class=\"data row4 col1\" >63</td>\n      <td id=\"T_14ac5_row4_col2\" class=\"data row4 col2\" >1</td>\n      <td id=\"T_14ac5_row4_col3\" class=\"data row4 col3\" >2</td>\n      <td id=\"T_14ac5_row4_col4\" class=\"data row4 col4\" >1</td>\n      <td id=\"T_14ac5_row4_col5\" class=\"data row4 col5\" >1</td>\n      <td id=\"T_14ac5_row4_col6\" class=\"data row4 col6\" >1</td>\n      <td id=\"T_14ac5_row4_col7\" class=\"data row4 col7\" >1</td>\n      <td id=\"T_14ac5_row4_col8\" class=\"data row4 col8\" >1</td>\n      <td id=\"T_14ac5_row4_col9\" class=\"data row4 col9\" >2</td>\n      <td id=\"T_14ac5_row4_col10\" class=\"data row4 col10\" >1</td>\n      <td id=\"T_14ac5_row4_col11\" class=\"data row4 col11\" >2</td>\n      <td id=\"T_14ac5_row4_col12\" class=\"data row4 col12\" >2</td>\n      <td id=\"T_14ac5_row4_col13\" class=\"data row4 col13\" >1</td>\n      <td id=\"T_14ac5_row4_col14\" class=\"data row4 col14\" >1</td>\n      <td id=\"T_14ac5_row4_col15\" class=\"data row4 col15\" >NO</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"code","source":"print(f\"Informations About The Dataset :\\n\")\nprint(data.info())","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.559252Z","iopub.execute_input":"2022-11-30T14:44:34.559708Z","iopub.status.idle":"2022-11-30T14:44:34.578648Z","shell.execute_reply.started":"2022-11-30T14:44:34.559671Z","shell.execute_reply":"2022-11-30T14:44:34.577306Z"},"trusted":true},"execution_count":64,"outputs":[{"name":"stdout","text":"Informations About The Dataset :\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 309 entries, 0 to 308\nData columns (total 16 columns):\n #   Column                 Non-Null Count  Dtype \n---  ------                 --------------  ----- \n 0   GENDER                 309 non-null    object\n 1   AGE                    309 non-null    int64 \n 2   SMOKING                309 non-null    int64 \n 3   YELLOW_FINGERS         309 non-null    int64 \n 4   ANXIETY                309 non-null    int64 \n 5   PEER_PRESSURE          309 non-null    int64 \n 6   CHRONIC DISEASE        309 non-null    int64 \n 7   FATIGUE                309 non-null    int64 \n 8   ALLERGY                309 non-null    int64 \n 9   WHEEZING               309 non-null    int64 \n 10  ALCOHOL CONSUMING      309 non-null    int64 \n 11  COUGHING               309 non-null    int64 \n 12  SHORTNESS OF BREATH    309 non-null    int64 \n 13  SWALLOWING DIFFICULTY  309 non-null    int64 \n 14  CHEST PAIN             309 non-null    int64 \n 15  LUNG_CANCER            309 non-null    object\ndtypes: int64(14), object(2)\nmemory usage: 38.8+ KB\nNone\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"4\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Dataset Summary</p>","metadata":{}},{"cell_type":"code","source":"print(f\"Summary of This Dataset :\")\ndata.describe().T.style.set_properties(**{\"background-color\": \"#2a9d8f\",\"color\":\"white\",\"border\": \"1.5px  solid black\"})","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.581963Z","iopub.execute_input":"2022-11-30T14:44:34.582905Z","iopub.status.idle":"2022-11-30T14:44:34.645536Z","shell.execute_reply.started":"2022-11-30T14:44:34.582851Z","shell.execute_reply":"2022-11-30T14:44:34.644058Z"},"trusted":true},"execution_count":65,"outputs":[{"name":"stdout","text":"Summary of This Dataset :\n","output_type":"stream"},{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f4e2b7121d0>","text/html":"<style type=\"text/css\">\n#T_8af1f_row0_col0, #T_8af1f_row0_col1, #T_8af1f_row0_col2, #T_8af1f_row0_col3, #T_8af1f_row0_col4, #T_8af1f_row0_col5, #T_8af1f_row0_col6, #T_8af1f_row0_col7, #T_8af1f_row1_col0, #T_8af1f_row1_col1, #T_8af1f_row1_col2, #T_8af1f_row1_col3, #T_8af1f_row1_col4, #T_8af1f_row1_col5, #T_8af1f_row1_col6, #T_8af1f_row1_col7, #T_8af1f_row2_col0, #T_8af1f_row2_col1, #T_8af1f_row2_col2, #T_8af1f_row2_col3, #T_8af1f_row2_col4, #T_8af1f_row2_col5, #T_8af1f_row2_col6, #T_8af1f_row2_col7, #T_8af1f_row3_col0, #T_8af1f_row3_col1, #T_8af1f_row3_col2, #T_8af1f_row3_col3, #T_8af1f_row3_col4, #T_8af1f_row3_col5, #T_8af1f_row3_col6, #T_8af1f_row3_col7, #T_8af1f_row4_col0, #T_8af1f_row4_col1, #T_8af1f_row4_col2, #T_8af1f_row4_col3, #T_8af1f_row4_col4, #T_8af1f_row4_col5, #T_8af1f_row4_col6, #T_8af1f_row4_col7, #T_8af1f_row5_col0, #T_8af1f_row5_col1, #T_8af1f_row5_col2, #T_8af1f_row5_col3, #T_8af1f_row5_col4, #T_8af1f_row5_col5, #T_8af1f_row5_col6, #T_8af1f_row5_col7, #T_8af1f_row6_col0, #T_8af1f_row6_col1, #T_8af1f_row6_col2, #T_8af1f_row6_col3, #T_8af1f_row6_col4, #T_8af1f_row6_col5, #T_8af1f_row6_col6, #T_8af1f_row6_col7, #T_8af1f_row7_col0, #T_8af1f_row7_col1, #T_8af1f_row7_col2, #T_8af1f_row7_col3, #T_8af1f_row7_col4, #T_8af1f_row7_col5, #T_8af1f_row7_col6, #T_8af1f_row7_col7, #T_8af1f_row8_col0, #T_8af1f_row8_col1, #T_8af1f_row8_col2, #T_8af1f_row8_col3, #T_8af1f_row8_col4, #T_8af1f_row8_col5, #T_8af1f_row8_col6, #T_8af1f_row8_col7, #T_8af1f_row9_col0, #T_8af1f_row9_col1, #T_8af1f_row9_col2, #T_8af1f_row9_col3, #T_8af1f_row9_col4, #T_8af1f_row9_col5, #T_8af1f_row9_col6, #T_8af1f_row9_col7, #T_8af1f_row10_col0, #T_8af1f_row10_col1, #T_8af1f_row10_col2, #T_8af1f_row10_col3, #T_8af1f_row10_col4, #T_8af1f_row10_col5, #T_8af1f_row10_col6, #T_8af1f_row10_col7, #T_8af1f_row11_col0, #T_8af1f_row11_col1, #T_8af1f_row11_col2, #T_8af1f_row11_col3, #T_8af1f_row11_col4, #T_8af1f_row11_col5, #T_8af1f_row11_col6, #T_8af1f_row11_col7, #T_8af1f_row12_col0, #T_8af1f_row12_col1, #T_8af1f_row12_col2, #T_8af1f_row12_col3, #T_8af1f_row12_col4, #T_8af1f_row12_col5, #T_8af1f_row12_col6, #T_8af1f_row12_col7, #T_8af1f_row13_col0, #T_8af1f_row13_col1, #T_8af1f_row13_col2, #T_8af1f_row13_col3, #T_8af1f_row13_col4, #T_8af1f_row13_col5, #T_8af1f_row13_col6, #T_8af1f_row13_col7 {\n  background-color: #2a9d8f;\n  color: white;\n  border: 1.5px  solid black;\n}\n</style>\n<table id=\"T_8af1f_\">\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_8af1f_level0_row0\" class=\"row_heading level0 row0\" >AGE</th>\n      <td id=\"T_8af1f_row0_col0\" class=\"data row0 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row0_col1\" class=\"data row0 col1\" >62.673139</td>\n      <td id=\"T_8af1f_row0_col2\" class=\"data row0 col2\" >8.210301</td>\n      <td id=\"T_8af1f_row0_col3\" class=\"data row0 col3\" >21.000000</td>\n      <td id=\"T_8af1f_row0_col4\" class=\"data row0 col4\" >57.000000</td>\n      <td id=\"T_8af1f_row0_col5\" class=\"data row0 col5\" >62.000000</td>\n      <td id=\"T_8af1f_row0_col6\" class=\"data row0 col6\" >69.000000</td>\n      <td id=\"T_8af1f_row0_col7\" class=\"data row0 col7\" >87.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row1\" class=\"row_heading level0 row1\" >SMOKING</th>\n      <td id=\"T_8af1f_row1_col0\" class=\"data row1 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row1_col1\" class=\"data row1 col1\" >1.563107</td>\n      <td id=\"T_8af1f_row1_col2\" class=\"data row1 col2\" >0.496806</td>\n      <td id=\"T_8af1f_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row1_col5\" class=\"data row1 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row1_col6\" class=\"data row1 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row1_col7\" class=\"data row1 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row2\" class=\"row_heading level0 row2\" >YELLOW_FINGERS</th>\n      <td id=\"T_8af1f_row2_col0\" class=\"data row2 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row2_col1\" class=\"data row2 col1\" >1.569579</td>\n      <td id=\"T_8af1f_row2_col2\" class=\"data row2 col2\" >0.495938</td>\n      <td id=\"T_8af1f_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row2_col5\" class=\"data row2 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row2_col6\" class=\"data row2 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row2_col7\" class=\"data row2 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row3\" class=\"row_heading level0 row3\" >ANXIETY</th>\n      <td id=\"T_8af1f_row3_col0\" class=\"data row3 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row3_col1\" class=\"data row3 col1\" >1.498382</td>\n      <td id=\"T_8af1f_row3_col2\" class=\"data row3 col2\" >0.500808</td>\n      <td id=\"T_8af1f_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row3_col5\" class=\"data row3 col5\" >1.000000</td>\n      <td id=\"T_8af1f_row3_col6\" class=\"data row3 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row3_col7\" class=\"data row3 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row4\" class=\"row_heading level0 row4\" >PEER_PRESSURE</th>\n      <td id=\"T_8af1f_row4_col0\" class=\"data row4 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row4_col1\" class=\"data row4 col1\" >1.501618</td>\n      <td id=\"T_8af1f_row4_col2\" class=\"data row4 col2\" >0.500808</td>\n      <td id=\"T_8af1f_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row4_col5\" class=\"data row4 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row4_col6\" class=\"data row4 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row4_col7\" class=\"data row4 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row5\" class=\"row_heading level0 row5\" >CHRONIC DISEASE</th>\n      <td id=\"T_8af1f_row5_col0\" class=\"data row5 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row5_col1\" class=\"data row5 col1\" >1.504854</td>\n      <td id=\"T_8af1f_row5_col2\" class=\"data row5 col2\" >0.500787</td>\n      <td id=\"T_8af1f_row5_col3\" class=\"data row5 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row5_col4\" class=\"data row5 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row5_col5\" class=\"data row5 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row5_col6\" class=\"data row5 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row5_col7\" class=\"data row5 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row6\" class=\"row_heading level0 row6\" >FATIGUE </th>\n      <td id=\"T_8af1f_row6_col0\" class=\"data row6 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row6_col1\" class=\"data row6 col1\" >1.673139</td>\n      <td id=\"T_8af1f_row6_col2\" class=\"data row6 col2\" >0.469827</td>\n      <td id=\"T_8af1f_row6_col3\" class=\"data row6 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row6_col4\" class=\"data row6 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row6_col5\" class=\"data row6 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row6_col6\" class=\"data row6 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row6_col7\" class=\"data row6 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row7\" class=\"row_heading level0 row7\" >ALLERGY </th>\n      <td id=\"T_8af1f_row7_col0\" class=\"data row7 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row7_col1\" class=\"data row7 col1\" >1.556634</td>\n      <td id=\"T_8af1f_row7_col2\" class=\"data row7 col2\" >0.497588</td>\n      <td id=\"T_8af1f_row7_col3\" class=\"data row7 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row7_col4\" class=\"data row7 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row7_col5\" class=\"data row7 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row7_col6\" class=\"data row7 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row7_col7\" class=\"data row7 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row8\" class=\"row_heading level0 row8\" >WHEEZING</th>\n      <td id=\"T_8af1f_row8_col0\" class=\"data row8 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row8_col1\" class=\"data row8 col1\" >1.556634</td>\n      <td id=\"T_8af1f_row8_col2\" class=\"data row8 col2\" >0.497588</td>\n      <td id=\"T_8af1f_row8_col3\" class=\"data row8 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row8_col4\" class=\"data row8 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row8_col5\" class=\"data row8 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row8_col6\" class=\"data row8 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row8_col7\" class=\"data row8 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row9\" class=\"row_heading level0 row9\" >ALCOHOL CONSUMING</th>\n      <td id=\"T_8af1f_row9_col0\" class=\"data row9 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row9_col1\" class=\"data row9 col1\" >1.556634</td>\n      <td id=\"T_8af1f_row9_col2\" class=\"data row9 col2\" >0.497588</td>\n      <td id=\"T_8af1f_row9_col3\" class=\"data row9 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row9_col4\" class=\"data row9 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row9_col5\" class=\"data row9 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row9_col6\" class=\"data row9 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row9_col7\" class=\"data row9 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row10\" class=\"row_heading level0 row10\" >COUGHING</th>\n      <td id=\"T_8af1f_row10_col0\" class=\"data row10 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row10_col1\" class=\"data row10 col1\" >1.579288</td>\n      <td id=\"T_8af1f_row10_col2\" class=\"data row10 col2\" >0.494474</td>\n      <td id=\"T_8af1f_row10_col3\" class=\"data row10 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row10_col4\" class=\"data row10 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row10_col5\" class=\"data row10 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row10_col6\" class=\"data row10 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row10_col7\" class=\"data row10 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row11\" class=\"row_heading level0 row11\" >SHORTNESS OF BREATH</th>\n      <td id=\"T_8af1f_row11_col0\" class=\"data row11 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row11_col1\" class=\"data row11 col1\" >1.640777</td>\n      <td id=\"T_8af1f_row11_col2\" class=\"data row11 col2\" >0.480551</td>\n      <td id=\"T_8af1f_row11_col3\" class=\"data row11 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row11_col4\" class=\"data row11 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row11_col5\" class=\"data row11 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row11_col6\" class=\"data row11 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row11_col7\" class=\"data row11 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row12\" class=\"row_heading level0 row12\" >SWALLOWING DIFFICULTY</th>\n      <td id=\"T_8af1f_row12_col0\" class=\"data row12 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row12_col1\" class=\"data row12 col1\" >1.469256</td>\n      <td id=\"T_8af1f_row12_col2\" class=\"data row12 col2\" >0.499863</td>\n      <td id=\"T_8af1f_row12_col3\" class=\"data row12 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row12_col4\" class=\"data row12 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row12_col5\" class=\"data row12 col5\" >1.000000</td>\n      <td id=\"T_8af1f_row12_col6\" class=\"data row12 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row12_col7\" class=\"data row12 col7\" >2.000000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8af1f_level0_row13\" class=\"row_heading level0 row13\" >CHEST PAIN</th>\n      <td id=\"T_8af1f_row13_col0\" class=\"data row13 col0\" >309.000000</td>\n      <td id=\"T_8af1f_row13_col1\" class=\"data row13 col1\" >1.556634</td>\n      <td id=\"T_8af1f_row13_col2\" class=\"data row13 col2\" >0.497588</td>\n      <td id=\"T_8af1f_row13_col3\" class=\"data row13 col3\" >1.000000</td>\n      <td id=\"T_8af1f_row13_col4\" class=\"data row13 col4\" >1.000000</td>\n      <td id=\"T_8af1f_row13_col5\" class=\"data row13 col5\" >2.000000</td>\n      <td id=\"T_8af1f_row13_col6\" class=\"data row13 col6\" >2.000000</td>\n      <td id=\"T_8af1f_row13_col7\" class=\"data row13 col7\" >2.000000</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"code","source":"data.describe(include=object).T.style.set_properties(**{\"background-color\": \"#2a9d8f\",\"color\":\"white\",\"border\": \"1.5px  solid black\"})","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.646860Z","iopub.execute_input":"2022-11-30T14:44:34.647367Z","iopub.status.idle":"2022-11-30T14:44:34.671747Z","shell.execute_reply.started":"2022-11-30T14:44:34.647317Z","shell.execute_reply":"2022-11-30T14:44:34.670289Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f4e94c09290>","text/html":"<style type=\"text/css\">\n#T_eb602_row0_col0, #T_eb602_row0_col1, #T_eb602_row0_col2, #T_eb602_row0_col3, #T_eb602_row1_col0, #T_eb602_row1_col1, #T_eb602_row1_col2, #T_eb602_row1_col3 {\n  background-color: #2a9d8f;\n  color: white;\n  border: 1.5px  solid black;\n}\n</style>\n<table id=\"T_eb602_\">\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\" >unique</th>\n      <th class=\"col_heading level0 col2\" >top</th>\n      <th class=\"col_heading level0 col3\" >freq</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_eb602_level0_row0\" class=\"row_heading level0 row0\" >GENDER</th>\n      <td id=\"T_eb602_row0_col0\" class=\"data row0 col0\" >309</td>\n      <td id=\"T_eb602_row0_col1\" class=\"data row0 col1\" >2</td>\n      <td id=\"T_eb602_row0_col2\" class=\"data row0 col2\" >M</td>\n      <td id=\"T_eb602_row0_col3\" class=\"data row0 col3\" >162</td>\n    </tr>\n    <tr>\n      <th id=\"T_eb602_level0_row1\" class=\"row_heading level0 row1\" >LUNG_CANCER</th>\n      <td id=\"T_eb602_row1_col0\" class=\"data row1 col0\" >309</td>\n      <td id=\"T_eb602_row1_col1\" class=\"data row1 col1\" >2</td>\n      <td id=\"T_eb602_row1_col2\" class=\"data row1 col2\" >YES</td>\n      <td id=\"T_eb602_row1_col3\" class=\"data row1 col3\" >270</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"code","source":"data.isna().sum().to_frame().T.style.set_properties(**{\"background-color\": \"#2a9d8f\",\"color\":\"white\",\"border\": \"1.5px  solid black\"})","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.673013Z","iopub.execute_input":"2022-11-30T14:44:34.673585Z","iopub.status.idle":"2022-11-30T14:44:34.705576Z","shell.execute_reply.started":"2022-11-30T14:44:34.673531Z","shell.execute_reply":"2022-11-30T14:44:34.704122Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f4e838e24d0>","text/html":"<style type=\"text/css\">\n#T_a0a92_row0_col0, #T_a0a92_row0_col1, #T_a0a92_row0_col2, #T_a0a92_row0_col3, #T_a0a92_row0_col4, #T_a0a92_row0_col5, #T_a0a92_row0_col6, #T_a0a92_row0_col7, #T_a0a92_row0_col8, #T_a0a92_row0_col9, #T_a0a92_row0_col10, #T_a0a92_row0_col11, #T_a0a92_row0_col12, #T_a0a92_row0_col13, #T_a0a92_row0_col14, #T_a0a92_row0_col15 {\n  background-color: #2a9d8f;\n  color: white;\n  border: 1.5px  solid black;\n}\n</style>\n<table id=\"T_a0a92_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >GENDER</th>\n      <th class=\"col_heading level0 col1\" >AGE</th>\n      <th class=\"col_heading level0 col2\" >SMOKING</th>\n      <th class=\"col_heading level0 col3\" >YELLOW_FINGERS</th>\n      <th class=\"col_heading level0 col4\" >ANXIETY</th>\n      <th class=\"col_heading level0 col5\" >PEER_PRESSURE</th>\n      <th class=\"col_heading level0 col6\" >CHRONIC DISEASE</th>\n      <th class=\"col_heading level0 col7\" >FATIGUE </th>\n      <th class=\"col_heading level0 col8\" >ALLERGY </th>\n      <th class=\"col_heading level0 col9\" >WHEEZING</th>\n      <th class=\"col_heading level0 col10\" >ALCOHOL CONSUMING</th>\n      <th class=\"col_heading level0 col11\" >COUGHING</th>\n      <th class=\"col_heading level0 col12\" >SHORTNESS OF BREATH</th>\n      <th class=\"col_heading level0 col13\" >SWALLOWING DIFFICULTY</th>\n      <th class=\"col_heading level0 col14\" >CHEST PAIN</th>\n      <th class=\"col_heading level0 col15\" >LUNG_CANCER</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a0a92_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n      <td id=\"T_a0a92_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_a0a92_row0_col1\" class=\"data row0 col1\" >0</td>\n      <td id=\"T_a0a92_row0_col2\" class=\"data row0 col2\" >0</td>\n      <td id=\"T_a0a92_row0_col3\" class=\"data row0 col3\" >0</td>\n      <td id=\"T_a0a92_row0_col4\" class=\"data row0 col4\" >0</td>\n      <td id=\"T_a0a92_row0_col5\" class=\"data row0 col5\" >0</td>\n      <td id=\"T_a0a92_row0_col6\" class=\"data row0 col6\" >0</td>\n      <td id=\"T_a0a92_row0_col7\" class=\"data row0 col7\" >0</td>\n      <td id=\"T_a0a92_row0_col8\" class=\"data row0 col8\" >0</td>\n      <td id=\"T_a0a92_row0_col9\" class=\"data row0 col9\" >0</td>\n      <td id=\"T_a0a92_row0_col10\" class=\"data row0 col10\" >0</td>\n      <td id=\"T_a0a92_row0_col11\" class=\"data row0 col11\" >0</td>\n      <td id=\"T_a0a92_row0_col12\" class=\"data row0 col12\" >0</td>\n      <td id=\"T_a0a92_row0_col13\" class=\"data row0 col13\" >0</td>\n      <td id=\"T_a0a92_row0_col14\" class=\"data row0 col14\" >0</td>\n      <td id=\"T_a0a92_row0_col15\" class=\"data row0 col15\" >0</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"Here, we can see there is **no null value** exists in this dataset. Let's check if there exists any duplicate entry in this dataset. If exists then we will remove them from the dataset. After that we will initialize the visualization style and custom pallete for visualization.","metadata":{}},{"cell_type":"code","source":"dup = data[data.duplicated()].shape[0]\nprint(f\"There are {dup} duplicate entries among {data.shape[0]} entries in this dataset.\")\n\ndata.drop_duplicates(keep='first',inplace=True)\nprint(f\"\\nAfter removing duplicate entries there are {data.shape[0]} entries in this dataset.\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.707529Z","iopub.execute_input":"2022-11-30T14:44:34.707930Z","iopub.status.idle":"2022-11-30T14:44:34.725174Z","shell.execute_reply.started":"2022-11-30T14:44:34.707897Z","shell.execute_reply":"2022-11-30T14:44:34.723489Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"There are 33 duplicate entries among 309 entries in this dataset.\n\nAfter removing duplicate entries there are 276 entries in this dataset.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"5\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Digging Deeper</p>\n\n","metadata":{}},{"cell_type":"markdown","source":"Let's replace all the **numeric values** into **categorical values** for better and efficient visualization. All the **2**'s and **1**'s will be replaced by **\"YES\"** and **\"NO\"** accordingly. And replace **\"M\"** and **\"F\"** by **\"Male\"** and **\"Female\"** accordingly in **\"GENDER\"** column.","metadata":{}},{"cell_type":"code","source":"data_temp = data.copy()\ndata_temp[\"GENDER\"] = data_temp[\"GENDER\"].replace({\"M\" : \"Male\" , \"F\" : \"Female\"})\n\nfor column in data_temp.columns:\n    data_temp[column] = data_temp[column].replace({2: \"Yes\" , 1 : \"No\"})\n\ndata_temp.head().style.set_properties(**{\"background-color\": \"#2a9d8f\",\"color\":\"white\",\"border\": \"1.5px  solid black\"})    ","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.727419Z","iopub.execute_input":"2022-11-30T14:44:34.727790Z","iopub.status.idle":"2022-11-30T14:44:34.773721Z","shell.execute_reply.started":"2022-11-30T14:44:34.727757Z","shell.execute_reply":"2022-11-30T14:44:34.772472Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f4e882d6510>","text/html":"<style type=\"text/css\">\n#T_daa5d_row0_col0, #T_daa5d_row0_col1, #T_daa5d_row0_col2, #T_daa5d_row0_col3, #T_daa5d_row0_col4, #T_daa5d_row0_col5, #T_daa5d_row0_col6, #T_daa5d_row0_col7, #T_daa5d_row0_col8, #T_daa5d_row0_col9, #T_daa5d_row0_col10, #T_daa5d_row0_col11, #T_daa5d_row0_col12, #T_daa5d_row0_col13, #T_daa5d_row0_col14, #T_daa5d_row0_col15, #T_daa5d_row1_col0, #T_daa5d_row1_col1, #T_daa5d_row1_col2, #T_daa5d_row1_col3, #T_daa5d_row1_col4, #T_daa5d_row1_col5, #T_daa5d_row1_col6, #T_daa5d_row1_col7, #T_daa5d_row1_col8, #T_daa5d_row1_col9, #T_daa5d_row1_col10, #T_daa5d_row1_col11, #T_daa5d_row1_col12, #T_daa5d_row1_col13, #T_daa5d_row1_col14, #T_daa5d_row1_col15, #T_daa5d_row2_col0, #T_daa5d_row2_col1, #T_daa5d_row2_col2, #T_daa5d_row2_col3, #T_daa5d_row2_col4, #T_daa5d_row2_col5, #T_daa5d_row2_col6, #T_daa5d_row2_col7, #T_daa5d_row2_col8, #T_daa5d_row2_col9, #T_daa5d_row2_col10, #T_daa5d_row2_col11, #T_daa5d_row2_col12, #T_daa5d_row2_col13, #T_daa5d_row2_col14, #T_daa5d_row2_col15, #T_daa5d_row3_col0, #T_daa5d_row3_col1, #T_daa5d_row3_col2, #T_daa5d_row3_col3, #T_daa5d_row3_col4, #T_daa5d_row3_col5, #T_daa5d_row3_col6, #T_daa5d_row3_col7, #T_daa5d_row3_col8, #T_daa5d_row3_col9, #T_daa5d_row3_col10, #T_daa5d_row3_col11, #T_daa5d_row3_col12, #T_daa5d_row3_col13, #T_daa5d_row3_col14, #T_daa5d_row3_col15, #T_daa5d_row4_col0, #T_daa5d_row4_col1, #T_daa5d_row4_col2, #T_daa5d_row4_col3, #T_daa5d_row4_col4, #T_daa5d_row4_col5, #T_daa5d_row4_col6, #T_daa5d_row4_col7, #T_daa5d_row4_col8, #T_daa5d_row4_col9, #T_daa5d_row4_col10, #T_daa5d_row4_col11, #T_daa5d_row4_col12, #T_daa5d_row4_col13, #T_daa5d_row4_col14, #T_daa5d_row4_col15 {\n  background-color: #2a9d8f;\n  color: white;\n  border: 1.5px  solid black;\n}\n</style>\n<table id=\"T_daa5d_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >GENDER</th>\n      <th class=\"col_heading level0 col1\" >AGE</th>\n      <th class=\"col_heading level0 col2\" >SMOKING</th>\n      <th class=\"col_heading level0 col3\" >YELLOW_FINGERS</th>\n      <th class=\"col_heading level0 col4\" >ANXIETY</th>\n      <th class=\"col_heading level0 col5\" >PEER_PRESSURE</th>\n      <th class=\"col_heading level0 col6\" >CHRONIC DISEASE</th>\n      <th class=\"col_heading level0 col7\" >FATIGUE </th>\n      <th class=\"col_heading level0 col8\" >ALLERGY </th>\n      <th class=\"col_heading level0 col9\" >WHEEZING</th>\n      <th class=\"col_heading level0 col10\" >ALCOHOL CONSUMING</th>\n      <th class=\"col_heading level0 col11\" >COUGHING</th>\n      <th class=\"col_heading level0 col12\" >SHORTNESS OF BREATH</th>\n      <th class=\"col_heading level0 col13\" >SWALLOWING DIFFICULTY</th>\n      <th class=\"col_heading level0 col14\" >CHEST PAIN</th>\n      <th class=\"col_heading level0 col15\" >LUNG_CANCER</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_daa5d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n      <td id=\"T_daa5d_row0_col0\" class=\"data row0 col0\" >Male</td>\n      <td id=\"T_daa5d_row0_col1\" class=\"data row0 col1\" >69</td>\n      <td id=\"T_daa5d_row0_col2\" class=\"data row0 col2\" >No</td>\n      <td id=\"T_daa5d_row0_col3\" class=\"data row0 col3\" >Yes</td>\n      <td id=\"T_daa5d_row0_col4\" class=\"data row0 col4\" >Yes</td>\n      <td id=\"T_daa5d_row0_col5\" class=\"data row0 col5\" >No</td>\n      <td id=\"T_daa5d_row0_col6\" class=\"data row0 col6\" >No</td>\n      <td id=\"T_daa5d_row0_col7\" class=\"data row0 col7\" >Yes</td>\n      <td id=\"T_daa5d_row0_col8\" class=\"data row0 col8\" >No</td>\n      <td id=\"T_daa5d_row0_col9\" class=\"data row0 col9\" >Yes</td>\n      <td id=\"T_daa5d_row0_col10\" class=\"data row0 col10\" >Yes</td>\n      <td id=\"T_daa5d_row0_col11\" class=\"data row0 col11\" >Yes</td>\n      <td id=\"T_daa5d_row0_col12\" class=\"data row0 col12\" >Yes</td>\n      <td id=\"T_daa5d_row0_col13\" class=\"data row0 col13\" >Yes</td>\n      <td id=\"T_daa5d_row0_col14\" class=\"data row0 col14\" >Yes</td>\n      <td id=\"T_daa5d_row0_col15\" class=\"data row0 col15\" >YES</td>\n    </tr>\n    <tr>\n      <th id=\"T_daa5d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n      <td id=\"T_daa5d_row1_col0\" class=\"data row1 col0\" >Male</td>\n      <td id=\"T_daa5d_row1_col1\" class=\"data row1 col1\" >74</td>\n      <td id=\"T_daa5d_row1_col2\" class=\"data row1 col2\" >Yes</td>\n      <td id=\"T_daa5d_row1_col3\" class=\"data row1 col3\" >No</td>\n      <td id=\"T_daa5d_row1_col4\" class=\"data row1 col4\" >No</td>\n      <td id=\"T_daa5d_row1_col5\" class=\"data row1 col5\" >No</td>\n      <td id=\"T_daa5d_row1_col6\" class=\"data row1 col6\" >Yes</td>\n      <td id=\"T_daa5d_row1_col7\" class=\"data row1 col7\" >Yes</td>\n      <td id=\"T_daa5d_row1_col8\" class=\"data row1 col8\" >Yes</td>\n      <td id=\"T_daa5d_row1_col9\" class=\"data row1 col9\" >No</td>\n      <td id=\"T_daa5d_row1_col10\" class=\"data row1 col10\" >No</td>\n      <td id=\"T_daa5d_row1_col11\" class=\"data row1 col11\" >No</td>\n      <td id=\"T_daa5d_row1_col12\" class=\"data row1 col12\" >Yes</td>\n      <td id=\"T_daa5d_row1_col13\" class=\"data row1 col13\" >Yes</td>\n      <td id=\"T_daa5d_row1_col14\" class=\"data row1 col14\" >Yes</td>\n      <td id=\"T_daa5d_row1_col15\" class=\"data row1 col15\" >YES</td>\n    </tr>\n    <tr>\n      <th id=\"T_daa5d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n      <td id=\"T_daa5d_row2_col0\" class=\"data row2 col0\" >Female</td>\n      <td id=\"T_daa5d_row2_col1\" class=\"data row2 col1\" >59</td>\n      <td id=\"T_daa5d_row2_col2\" class=\"data row2 col2\" >No</td>\n      <td id=\"T_daa5d_row2_col3\" class=\"data row2 col3\" >No</td>\n      <td id=\"T_daa5d_row2_col4\" class=\"data row2 col4\" >No</td>\n      <td id=\"T_daa5d_row2_col5\" class=\"data row2 col5\" >Yes</td>\n      <td id=\"T_daa5d_row2_col6\" class=\"data row2 col6\" >No</td>\n      <td id=\"T_daa5d_row2_col7\" class=\"data row2 col7\" >Yes</td>\n      <td id=\"T_daa5d_row2_col8\" class=\"data row2 col8\" >No</td>\n      <td id=\"T_daa5d_row2_col9\" class=\"data row2 col9\" >Yes</td>\n      <td id=\"T_daa5d_row2_col10\" class=\"data row2 col10\" >No</td>\n      <td id=\"T_daa5d_row2_col11\" class=\"data row2 col11\" >Yes</td>\n      <td id=\"T_daa5d_row2_col12\" class=\"data row2 col12\" >Yes</td>\n      <td id=\"T_daa5d_row2_col13\" class=\"data row2 col13\" >No</td>\n      <td id=\"T_daa5d_row2_col14\" class=\"data row2 col14\" >Yes</td>\n      <td id=\"T_daa5d_row2_col15\" class=\"data row2 col15\" >NO</td>\n    </tr>\n    <tr>\n      <th id=\"T_daa5d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n      <td id=\"T_daa5d_row3_col0\" class=\"data row3 col0\" >Male</td>\n      <td id=\"T_daa5d_row3_col1\" class=\"data row3 col1\" >63</td>\n      <td id=\"T_daa5d_row3_col2\" class=\"data row3 col2\" >Yes</td>\n      <td id=\"T_daa5d_row3_col3\" class=\"data row3 col3\" >Yes</td>\n      <td id=\"T_daa5d_row3_col4\" class=\"data row3 col4\" >Yes</td>\n      <td id=\"T_daa5d_row3_col5\" class=\"data row3 col5\" >No</td>\n      <td id=\"T_daa5d_row3_col6\" class=\"data row3 col6\" >No</td>\n      <td id=\"T_daa5d_row3_col7\" class=\"data row3 col7\" >No</td>\n      <td id=\"T_daa5d_row3_col8\" class=\"data row3 col8\" >No</td>\n      <td id=\"T_daa5d_row3_col9\" class=\"data row3 col9\" >No</td>\n      <td id=\"T_daa5d_row3_col10\" class=\"data row3 col10\" >Yes</td>\n      <td id=\"T_daa5d_row3_col11\" class=\"data row3 col11\" >No</td>\n      <td id=\"T_daa5d_row3_col12\" class=\"data row3 col12\" >No</td>\n      <td id=\"T_daa5d_row3_col13\" class=\"data row3 col13\" >Yes</td>\n      <td id=\"T_daa5d_row3_col14\" class=\"data row3 col14\" >Yes</td>\n      <td id=\"T_daa5d_row3_col15\" class=\"data row3 col15\" >NO</td>\n    </tr>\n    <tr>\n      <th id=\"T_daa5d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n      <td id=\"T_daa5d_row4_col0\" class=\"data row4 col0\" >Female</td>\n      <td id=\"T_daa5d_row4_col1\" class=\"data row4 col1\" >63</td>\n      <td id=\"T_daa5d_row4_col2\" class=\"data row4 col2\" >No</td>\n      <td id=\"T_daa5d_row4_col3\" class=\"data row4 col3\" >Yes</td>\n      <td id=\"T_daa5d_row4_col4\" class=\"data row4 col4\" >No</td>\n      <td id=\"T_daa5d_row4_col5\" class=\"data row4 col5\" >No</td>\n      <td id=\"T_daa5d_row4_col6\" class=\"data row4 col6\" >No</td>\n      <td id=\"T_daa5d_row4_col7\" class=\"data row4 col7\" >No</td>\n      <td id=\"T_daa5d_row4_col8\" class=\"data row4 col8\" >No</td>\n      <td id=\"T_daa5d_row4_col9\" class=\"data row4 col9\" >Yes</td>\n      <td id=\"T_daa5d_row4_col10\" class=\"data row4 col10\" >No</td>\n      <td id=\"T_daa5d_row4_col11\" class=\"data row4 col11\" >Yes</td>\n      <td id=\"T_daa5d_row4_col12\" class=\"data row4 col12\" >Yes</td>\n      <td id=\"T_daa5d_row4_col13\" class=\"data row4 col13\" >No</td>\n      <td id=\"T_daa5d_row4_col14\" class=\"data row4 col14\" >No</td>\n      <td id=\"T_daa5d_row4_col15\" class=\"data row4 col15\" >NO</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"6\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Custom Palette For Visualization</p>","metadata":{}},{"cell_type":"code","source":"sns.set_style(\"whitegrid\")\nsns.set_context(\"poster\",font_scale = .7)\n\npalette = [\"#1d7874\",\"#679289\",\"#f4c095\",\"#ee2e31\",\"#ffb563\",\"#918450\",\"#f85e00\",\"#a41623\",\"#9a031e\",\"#d6d6d6\",\"#ffee32\",\"#ffd100\",\"#333533\",\"#202020\"]\n\n# sns.palplot(sns.color_palette(palette))\n# plt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.775470Z","iopub.execute_input":"2022-11-30T14:44:34.776060Z","iopub.status.idle":"2022-11-30T14:44:34.783171Z","shell.execute_reply.started":"2022-11-30T14:44:34.776023Z","shell.execute_reply":"2022-11-30T14:44:34.781348Z"},"trusted":true},"execution_count":70,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"7\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Positive Lung Cancer Cases</p>","metadata":{}},{"cell_type":"markdown","source":"Let's create a new dataframe containing only positive cases data.","metadata":{}},{"cell_type":"code","source":"data_temp_pos = data_temp[data_temp[\"LUNG_CANCER\"] == \"YES\"]\ndata_temp_pos.head().style.set_properties(**{\"background-color\": \"#2a9d8f\",\"color\":\"white\",\"border\": \"1.5px  solid black\"})","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.789464Z","iopub.execute_input":"2022-11-30T14:44:34.789894Z","iopub.status.idle":"2022-11-30T14:44:34.826533Z","shell.execute_reply.started":"2022-11-30T14:44:34.789856Z","shell.execute_reply":"2022-11-30T14:44:34.825279Z"},"trusted":true},"execution_count":71,"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f4e2b76ba10>","text/html":"<style type=\"text/css\">\n#T_3bcc5_row0_col0, #T_3bcc5_row0_col1, #T_3bcc5_row0_col2, #T_3bcc5_row0_col3, #T_3bcc5_row0_col4, #T_3bcc5_row0_col5, #T_3bcc5_row0_col6, #T_3bcc5_row0_col7, #T_3bcc5_row0_col8, #T_3bcc5_row0_col9, #T_3bcc5_row0_col10, #T_3bcc5_row0_col11, #T_3bcc5_row0_col12, #T_3bcc5_row0_col13, #T_3bcc5_row0_col14, #T_3bcc5_row0_col15, #T_3bcc5_row1_col0, #T_3bcc5_row1_col1, #T_3bcc5_row1_col2, #T_3bcc5_row1_col3, #T_3bcc5_row1_col4, #T_3bcc5_row1_col5, #T_3bcc5_row1_col6, #T_3bcc5_row1_col7, #T_3bcc5_row1_col8, #T_3bcc5_row1_col9, #T_3bcc5_row1_col10, #T_3bcc5_row1_col11, #T_3bcc5_row1_col12, #T_3bcc5_row1_col13, #T_3bcc5_row1_col14, #T_3bcc5_row1_col15, #T_3bcc5_row2_col0, #T_3bcc5_row2_col1, #T_3bcc5_row2_col2, #T_3bcc5_row2_col3, #T_3bcc5_row2_col4, #T_3bcc5_row2_col5, #T_3bcc5_row2_col6, #T_3bcc5_row2_col7, #T_3bcc5_row2_col8, #T_3bcc5_row2_col9, #T_3bcc5_row2_col10, #T_3bcc5_row2_col11, #T_3bcc5_row2_col12, #T_3bcc5_row2_col13, #T_3bcc5_row2_col14, #T_3bcc5_row2_col15, #T_3bcc5_row3_col0, #T_3bcc5_row3_col1, #T_3bcc5_row3_col2, #T_3bcc5_row3_col3, #T_3bcc5_row3_col4, #T_3bcc5_row3_col5, #T_3bcc5_row3_col6, #T_3bcc5_row3_col7, #T_3bcc5_row3_col8, #T_3bcc5_row3_col9, #T_3bcc5_row3_col10, #T_3bcc5_row3_col11, #T_3bcc5_row3_col12, #T_3bcc5_row3_col13, #T_3bcc5_row3_col14, #T_3bcc5_row3_col15, #T_3bcc5_row4_col0, #T_3bcc5_row4_col1, #T_3bcc5_row4_col2, #T_3bcc5_row4_col3, #T_3bcc5_row4_col4, #T_3bcc5_row4_col5, #T_3bcc5_row4_col6, #T_3bcc5_row4_col7, #T_3bcc5_row4_col8, #T_3bcc5_row4_col9, #T_3bcc5_row4_col10, #T_3bcc5_row4_col11, #T_3bcc5_row4_col12, #T_3bcc5_row4_col13, #T_3bcc5_row4_col14, #T_3bcc5_row4_col15 {\n  background-color: #2a9d8f;\n  color: white;\n  border: 1.5px  solid black;\n}\n</style>\n<table id=\"T_3bcc5_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >GENDER</th>\n      <th class=\"col_heading level0 col1\" >AGE</th>\n      <th class=\"col_heading level0 col2\" >SMOKING</th>\n      <th class=\"col_heading level0 col3\" >YELLOW_FINGERS</th>\n      <th class=\"col_heading level0 col4\" >ANXIETY</th>\n      <th class=\"col_heading level0 col5\" >PEER_PRESSURE</th>\n      <th class=\"col_heading level0 col6\" >CHRONIC DISEASE</th>\n      <th class=\"col_heading level0 col7\" >FATIGUE </th>\n      <th class=\"col_heading level0 col8\" >ALLERGY </th>\n      <th class=\"col_heading level0 col9\" >WHEEZING</th>\n      <th class=\"col_heading level0 col10\" >ALCOHOL CONSUMING</th>\n      <th class=\"col_heading level0 col11\" >COUGHING</th>\n      <th class=\"col_heading level0 col12\" >SHORTNESS OF BREATH</th>\n      <th class=\"col_heading level0 col13\" >SWALLOWING DIFFICULTY</th>\n      <th class=\"col_heading level0 col14\" >CHEST PAIN</th>\n      <th class=\"col_heading level0 col15\" >LUNG_CANCER</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_3bcc5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n      <td id=\"T_3bcc5_row0_col0\" class=\"data row0 col0\" >Male</td>\n      <td id=\"T_3bcc5_row0_col1\" class=\"data row0 col1\" >69</td>\n      <td id=\"T_3bcc5_row0_col2\" class=\"data row0 col2\" >No</td>\n      <td id=\"T_3bcc5_row0_col3\" class=\"data row0 col3\" >Yes</td>\n      <td id=\"T_3bcc5_row0_col4\" class=\"data row0 col4\" >Yes</td>\n      <td id=\"T_3bcc5_row0_col5\" class=\"data row0 col5\" >No</td>\n      <td id=\"T_3bcc5_row0_col6\" class=\"data row0 col6\" >No</td>\n      <td id=\"T_3bcc5_row0_col7\" class=\"data row0 col7\" >Yes</td>\n      <td id=\"T_3bcc5_row0_col8\" class=\"data row0 col8\" >No</td>\n      <td id=\"T_3bcc5_row0_col9\" class=\"data row0 col9\" >Yes</td>\n      <td id=\"T_3bcc5_row0_col10\" class=\"data row0 col10\" >Yes</td>\n      <td id=\"T_3bcc5_row0_col11\" class=\"data row0 col11\" >Yes</td>\n      <td id=\"T_3bcc5_row0_col12\" class=\"data row0 col12\" >Yes</td>\n      <td id=\"T_3bcc5_row0_col13\" class=\"data row0 col13\" >Yes</td>\n      <td id=\"T_3bcc5_row0_col14\" class=\"data row0 col14\" >Yes</td>\n      <td id=\"T_3bcc5_row0_col15\" class=\"data row0 col15\" >YES</td>\n    </tr>\n    <tr>\n      <th id=\"T_3bcc5_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n      <td id=\"T_3bcc5_row1_col0\" class=\"data row1 col0\" >Male</td>\n      <td id=\"T_3bcc5_row1_col1\" class=\"data row1 col1\" >74</td>\n      <td id=\"T_3bcc5_row1_col2\" class=\"data row1 col2\" >Yes</td>\n      <td id=\"T_3bcc5_row1_col3\" class=\"data row1 col3\" >No</td>\n      <td id=\"T_3bcc5_row1_col4\" class=\"data row1 col4\" >No</td>\n      <td id=\"T_3bcc5_row1_col5\" class=\"data row1 col5\" >No</td>\n      <td id=\"T_3bcc5_row1_col6\" class=\"data row1 col6\" >Yes</td>\n      <td id=\"T_3bcc5_row1_col7\" class=\"data row1 col7\" >Yes</td>\n      <td id=\"T_3bcc5_row1_col8\" class=\"data row1 col8\" >Yes</td>\n      <td id=\"T_3bcc5_row1_col9\" class=\"data row1 col9\" >No</td>\n      <td id=\"T_3bcc5_row1_col10\" class=\"data row1 col10\" >No</td>\n      <td id=\"T_3bcc5_row1_col11\" class=\"data row1 col11\" >No</td>\n      <td id=\"T_3bcc5_row1_col12\" class=\"data row1 col12\" >Yes</td>\n      <td id=\"T_3bcc5_row1_col13\" class=\"data row1 col13\" >Yes</td>\n      <td id=\"T_3bcc5_row1_col14\" class=\"data row1 col14\" >Yes</td>\n      <td id=\"T_3bcc5_row1_col15\" class=\"data row1 col15\" >YES</td>\n    </tr>\n    <tr>\n      <th id=\"T_3bcc5_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n      <td id=\"T_3bcc5_row2_col0\" class=\"data row2 col0\" >Female</td>\n      <td id=\"T_3bcc5_row2_col1\" class=\"data row2 col1\" >75</td>\n      <td id=\"T_3bcc5_row2_col2\" class=\"data row2 col2\" >No</td>\n      <td id=\"T_3bcc5_row2_col3\" class=\"data row2 col3\" >Yes</td>\n      <td id=\"T_3bcc5_row2_col4\" class=\"data row2 col4\" >No</td>\n      <td id=\"T_3bcc5_row2_col5\" class=\"data row2 col5\" >No</td>\n      <td id=\"T_3bcc5_row2_col6\" class=\"data row2 col6\" >Yes</td>\n      <td id=\"T_3bcc5_row2_col7\" class=\"data row2 col7\" >Yes</td>\n      <td id=\"T_3bcc5_row2_col8\" class=\"data row2 col8\" >Yes</td>\n      <td id=\"T_3bcc5_row2_col9\" class=\"data row2 col9\" >Yes</td>\n      <td id=\"T_3bcc5_row2_col10\" class=\"data row2 col10\" >No</td>\n      <td id=\"T_3bcc5_row2_col11\" class=\"data row2 col11\" >Yes</td>\n      <td id=\"T_3bcc5_row2_col12\" class=\"data row2 col12\" >Yes</td>\n      <td id=\"T_3bcc5_row2_col13\" class=\"data row2 col13\" >No</td>\n      <td id=\"T_3bcc5_row2_col14\" class=\"data row2 col14\" >No</td>\n      <td id=\"T_3bcc5_row2_col15\" class=\"data row2 col15\" >YES</td>\n    </tr>\n    <tr>\n      <th id=\"T_3bcc5_level0_row3\" class=\"row_heading level0 row3\" >6</th>\n      <td id=\"T_3bcc5_row3_col0\" class=\"data row3 col0\" >Male</td>\n      <td id=\"T_3bcc5_row3_col1\" class=\"data row3 col1\" >52</td>\n      <td id=\"T_3bcc5_row3_col2\" class=\"data row3 col2\" >Yes</td>\n      <td id=\"T_3bcc5_row3_col3\" class=\"data row3 col3\" >No</td>\n      <td id=\"T_3bcc5_row3_col4\" class=\"data row3 col4\" >No</td>\n      <td id=\"T_3bcc5_row3_col5\" class=\"data row3 col5\" >No</td>\n      <td id=\"T_3bcc5_row3_col6\" class=\"data row3 col6\" >No</td>\n      <td id=\"T_3bcc5_row3_col7\" class=\"data row3 col7\" >Yes</td>\n      <td id=\"T_3bcc5_row3_col8\" class=\"data row3 col8\" >No</td>\n      <td id=\"T_3bcc5_row3_col9\" class=\"data row3 col9\" >Yes</td>\n      <td id=\"T_3bcc5_row3_col10\" class=\"data row3 col10\" >Yes</td>\n      <td id=\"T_3bcc5_row3_col11\" class=\"data row3 col11\" >Yes</td>\n      <td id=\"T_3bcc5_row3_col12\" class=\"data row3 col12\" >Yes</td>\n      <td id=\"T_3bcc5_row3_col13\" class=\"data row3 col13\" >No</td>\n      <td id=\"T_3bcc5_row3_col14\" class=\"data row3 col14\" >Yes</td>\n      <td id=\"T_3bcc5_row3_col15\" class=\"data row3 col15\" >YES</td>\n    </tr>\n    <tr>\n      <th id=\"T_3bcc5_level0_row4\" class=\"row_heading level0 row4\" >7</th>\n      <td id=\"T_3bcc5_row4_col0\" class=\"data row4 col0\" >Female</td>\n      <td id=\"T_3bcc5_row4_col1\" class=\"data row4 col1\" >51</td>\n      <td id=\"T_3bcc5_row4_col2\" class=\"data row4 col2\" >Yes</td>\n      <td id=\"T_3bcc5_row4_col3\" class=\"data row4 col3\" >Yes</td>\n      <td id=\"T_3bcc5_row4_col4\" class=\"data row4 col4\" >Yes</td>\n      <td id=\"T_3bcc5_row4_col5\" class=\"data row4 col5\" >Yes</td>\n      <td id=\"T_3bcc5_row4_col6\" class=\"data row4 col6\" >No</td>\n      <td id=\"T_3bcc5_row4_col7\" class=\"data row4 col7\" >Yes</td>\n      <td id=\"T_3bcc5_row4_col8\" class=\"data row4 col8\" >Yes</td>\n      <td id=\"T_3bcc5_row4_col9\" class=\"data row4 col9\" >No</td>\n      <td id=\"T_3bcc5_row4_col10\" class=\"data row4 col10\" >No</td>\n      <td id=\"T_3bcc5_row4_col11\" class=\"data row4 col11\" >No</td>\n      <td id=\"T_3bcc5_row4_col12\" class=\"data row4 col12\" >Yes</td>\n      <td id=\"T_3bcc5_row4_col13\" class=\"data row4 col13\" >Yes</td>\n      <td id=\"T_3bcc5_row4_col14\" class=\"data row4 col14\" >No</td>\n      <td id=\"T_3bcc5_row4_col15\" class=\"data row4 col15\" >YES</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"8\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Positive Cases' Age Distribution</p>","metadata":{}},{"cell_type":"code","source":"_, axs = plt.subplots(2,1,figsize=(20,10),sharex=True,sharey=True)\nplt.tight_layout(pad=4.0)\n\nsns.histplot(data_temp_pos[data_temp_pos[\"GENDER\"]==\"Male\"][\"AGE\"],color=palette[11],kde=True,ax=axs[0],bins=20,alpha=1,fill=True)\naxs[0].lines[0].set_color(palette[12])\naxs[0].set_title(\"\\nPositive Male Cases Age Distribution\\n\",fontsize=20)\naxs[0].set_xlabel(\"Age\")\naxs[0].set_ylabel(\"Quantity\")\n\nsns.histplot(data_temp_pos[data_temp_pos[\"GENDER\"]==\"Female\"][\"AGE\"],color=palette[12],kde=True,ax=axs[1],bins=20,alpha=1,fill=True)\naxs[1].lines[0].set_color(palette[11])\naxs[1].set_title(\"\\nPositive Female Cases Age Distribution\\n\",fontsize=20)\naxs[1].set_xlabel(\"Age\")\naxs[1].set_ylabel(\"Quantity\")\n\nsns.despine(left=True, bottom=True)\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:34.827867Z","iopub.execute_input":"2022-11-30T14:44:34.828216Z","iopub.status.idle":"2022-11-30T14:44:35.452671Z","shell.execute_reply.started":"2022-11-30T14:44:34.828170Z","shell.execute_reply":"2022-11-30T14:44:35.451318Z"},"trusted":true},"execution_count":72,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x720 with 2 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stack them together in a same figure.**","metadata":{}},{"cell_type":"code","source":"plt.subplots(figsize=(20, 8))\np = sns.histplot(data=data_temp_pos,x=\"AGE\",hue=\"GENDER\",multiple=\"stack\",palette=palette[11:13],kde=True,shrink=.99,bins=20,alpha=1,fill=True)\np.axes.lines[0].set_color(palette[11])\np.axes.lines[1].set_color(palette[12])\np.axes.set_title(\"\\nPositive Cases Age Distribution\\n\",fontsize=20)\nplt.ylabel(\"Count\")\nplt.xlabel(\"Age\")\n\nsns.despine(left=True, bottom=True)\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:35.454839Z","iopub.execute_input":"2022-11-30T14:44:35.455435Z","iopub.status.idle":"2022-11-30T14:44:35.944747Z","shell.execute_reply.started":"2022-11-30T14:44:35.455383Z","shell.execute_reply":"2022-11-30T14:44:35.941952Z"},"trusted":true},"execution_count":73,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"9\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Positive Cases' Gender Distribution</p>","metadata":{}},{"cell_type":"code","source":"plt.subplots(figsize=(12, 12))\n\nlabels = \"Male\",\"Female\"\nsize = 0.5\n\nwedges, texts, autotexts = plt.pie([len(data_temp_pos[data_temp_pos[\"GENDER\"]==\"Male\"][\"GENDER\"]),\n                                    len(data_temp_pos[data_temp_pos[\"GENDER\"]==\"Female\"][\"GENDER\"])],\n                                    explode = (0,0),\n                                    textprops=dict(size= 25, color= \"white\"),\n                                    autopct=\"%.2f%%\", \n                                    pctdistance = 0.7,\n                                    radius=.9, \n                                    colors = [\"#0f4c5c\",\"#FFC300\"], \n                                    shadow = True,\n                                    wedgeprops=dict(width = size, edgecolor = \"white\", \n                                    linewidth = 5),\n                                    startangle = 0)\n\nplt.legend(wedges, labels, title=\"Category\",loc=\"center left\",bbox_to_anchor=(1, 0, 0.5, 1))\nplt.title(\"\\nPositive Cases' Gender Distribution\",fontsize=20)\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:35.946787Z","iopub.execute_input":"2022-11-30T14:44:35.947577Z","iopub.status.idle":"2022-11-30T14:44:36.234502Z","shell.execute_reply.started":"2022-11-30T14:44:35.947527Z","shell.execute_reply":"2022-11-30T14:44:36.233131Z"},"trusted":true},"execution_count":74,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x864 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"10\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Gender-wise Positive Cases' Reasons</p>","metadata":{}},{"cell_type":"code","source":"_, axs = plt.subplots(1,2,figsize=(20,8),sharex=True,sharey=True)\nplt.tight_layout(pad=4.0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"SMOKING\",hue_order=[\"Yes\",\"No\"],ax=axs[0],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[0].set_title(\"\\nEffect of Smoking\\n\",fontsize=20)\naxs[0].set_xlabel(\"Gender\")\naxs[0].set_ylabel(\"Quantity\")\nfor container in axs[0].containers:\n    axs[0].bar_label(container,label_type=\"center\",padding=2,size=25,color=\"white\",rotation=0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"ALCOHOL CONSUMING\",hue_order=[\"Yes\",\"No\"],ax=axs[1],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[1].set_title(\"\\nEffect of Alcohol Consuming\\n\",fontsize=20)\naxs[1].set_xlabel(\"Gender\")\naxs[1].set_ylabel(\"Quantity\")\nfor container in axs[1].containers:\n    axs[1].bar_label(container,label_type=\"center\",padding=2,size=25,color=\"white\",rotation=0)\n\nsns.despine(left=True, bottom=True)\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:36.237458Z","iopub.execute_input":"2022-11-30T14:44:36.238146Z","iopub.status.idle":"2022-11-30T14:44:36.808635Z","shell.execute_reply.started":"2022-11-30T14:44:36.238081Z","shell.execute_reply":"2022-11-30T14:44:36.807359Z"},"trusted":true},"execution_count":75,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x576 with 2 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SxdVrFhRZcuWVevWrTVt2jSFhITE2/fIkSPl7++f4LHLly+vkSNHxlsfEhKiadOmqXXr1ipbtqwqVqyoLl26aNWqVTIYDCk6X3OePHmimTNnql27dqpQoYL8/f3VtGlTjR07VpcvX47X//z58xo6dKhq1aqlMmXKqHbt2ho6dKguXLgQr29s7ubr66vjx4/Ha//ss8/k6+trss5gMGjNmjXq2LGjqlatqnLlyqlRo0Z67733tHv37nj7jpsXxjVlyhT5+vrq7t27xnUNGjSQr6+vunfvbnabVatWGeM9ePBgovsaOXKkfH191bJlS7NzXib0/oaHh2vx4sXq3LmzqlSpojJlyqhWrVrq27evVq1apcjISLOxpQZHi+0ZAAAgDRw7dkzdunWTl5eX2rVrp9y5c+uff/7R6dOnNXv2bA0aNMjY18XFRdeuXdOJEydUtmxZk/2sXr1aLi4uZu/IeOTIEfXp00eS9Oabb6po0aK6e/eufv31V3Xu3Fkffvihunbt+txYx48frx9//FHdu3fXBx988Nz+ERERmjlzpj777LPn9jUYDProo4/0888/y8fHR506dVKePHkUEhKiEydOaOrUqdq8ebNWrFjx3H0BAIC0s3r1anl7e+vKlSs6efKkXnnllRfe18qVKzV69Gh5eHjo1VdfVdGiRRUaGqojR47oyy+/1IULFzR27FhJUlRUlIYOHaoNGzaoUqVKGjx4sFxdXXXo0CFNnTpVGzdu1IIFC5QzZ84Und+9e/fUo0cPBQQEqGXLlnrrrbcUGhqqLVu2aMSIEdq1a5e++uqrVLvR0o0bN9SrVy9du3ZNTZo0Udu2beXq6qpLly5p/fr1WrZsmf78809j/82bN+v999+Xh4eHXn/9deXPn183btzQL7/8ok2bNmny5MkJfjE+ZcoUzZs377kxffHFF1q0aJHq1aunfv36GXPSvXv3av369apTp06KztnFxUV//PGH7ty5Iy8vL5O2NWvWJJjjJuTixYvasGGDWrZs+dy+Dx8+VJ8+fXTy5EnVqlVL7777rrJly6b79+/rwIEDGjlypC5cuKBhw4Yl+7ySwqqFzatXr2revHk6ceKELly4oKJFi2rt2rXx+u3atUvffvutLl68qNy5c6t79+5m//Mwb948LV68WPfu3VPx4sU1bNgwVa9ePS1OBQAAWMmMGTPk5uamn3/+WdmzZzdpu3fvnsnzfPnyycHBQWvWrDEpbEZERGjTpk2qV6+eNm3aZLJNUFCQ/vOf/8jV1VX//e9/VbBgQWNbz5491atXL40bN06lS5dWhQoVEoxz4sSJ+uGHH5Jc1JQkPz8/rVy5Uu+++67y5cuXaN958+bp559/Vo8ePTRy5Mh4E7E/ePBAy5YtS9JxAQBA2rh9+7YOHTqkr7/+WhMnTtTq1atfuLB58uRJjR49Wv7+/pozZ46yZMlibOvatauuXLminTt3GtfNnTtXGzZsUM+ePTVixAjj+g4dOqh58+YaOHCgRo4cqblz577w+UnSiBEjFBAQoKlTp6phw4bG9d27d9fEiRM1f/58+fn5Gb9ETomoqCgNGjRIt2/f1o8//qgqVaqYtL/33nuaPHmy8fm1a9c0fPhwFShQQIsXL5anp6dJfF26dNHw4cO1evVqFShQwGRffn5+2rNnj44fP65y5colGNO9e/e0ePFitW3bVhMmTDDbnlLlypXTuXPntH79evXo0cO4Pra42LBhw3g5bkKcnJxUqFAhTZ8+Xc2bN5e9feIXe48YMUJ//vmnpkyZoiZNmpi09enTR+fOndPp06eTfU5JZdVL0S9cuKBdu3apUKFCKlasmNk+x44dU//+/eXn56c5c+aoXbt2GjdunP773/+a9Js3b54mT56sLl26aNasWSpcuLDxBQQAABnXtWvXVKxYsXhFTUlmRxi0atVKGzZsUFRUlHHd77//rqCgILVq1Spe/2XLlunu3bsaNmyYSVFTklxdXTVhwgTZ2dlp2rRpCcYYm7Qnp6gpyZjgz5w5M9F+oaGhmj17tkqUKKHhw4ebvbukp6en+vXrl+RjAwAAy1uzZo1cXV3VoEEDtWjRIl6OkhxTpkyRwWDQV199ZVLUjFW4cGFj0Ss0NFTz5s1T4cKFNXTo0Hh9GzZsqDZt2uj33383e7l1Uh0/flx79uxR27ZtTYqasYYOHarChQtr7ty5Cg0NfeHjxNq0aZPOnDmjvn37xitqSjEjG+NeSj137lw9ffpUY8eONSlqSjG502effaaQkBDNmTMn3r66dOmi7Nmzm0x9ZM6NGzcUHR2tihUrmm1P6YhYKaYY2aRJE61evdpk/fr16+Xs7JzgFEbm2NnZqX///rp48aLWr1+faN/jx49r165devPNN+MVNWOVLFlSr7/+epKPn1xWLWw2aNBAu3bt0vfff6/SpUub7TNt2jSVKlVK48aNU7Vq1dS/f3+98cYbmjZtmvF6//DwcM2YMUPdunVTr169VL16dU2aNEkFChTQjBkz0vKUAABAGvP29tbZs2eT/GVmq1atdO/ePe3fv9+4bu3atapQoYLy5s0br//27dvl4uKiFi1amN1fgQIFVLFiRR08eNBsQj5p0qQXKmpKMSNMX3/9da1cudLsXJuxjhw5oqCgILVs2TLVLuMCAACWt3r1ajVs2FCurq5q2bKl7t27p7179yZ7P6Ghodq/f78qVaqk/PnzP7d/bO7QunVrOTqav5i3TZs2kqQdO3bEa3vw4IHZx7/Fbhu7r39zdHRUq1atFBQUZHbe8uTavn17osczF5+3t7cqVapktr1y5cry9vbWrl274rW5ubmpZ8+e2rNnj44dO5bgMWKvutm0aZPZeUtTS6tWrXT69GmTOUTXrl2rRo0aydXVNVn7at68uYoXL67p06ebnWszVuz7++qrr75Y0KnAqpeiP284a3h4uA4cOBDv24NWrVpp+fLlOn36tPz9/XX06FE9evTI5Np/BwcHNW/eXPPnz5fBYDA7cgEAANi+3r17q2fPnmrbtq3KlCmjSpUqqWrVqqpevbpcXFzi9S9QoIDKlSunNWvWqFatWnry5Im2b99ucglWXAEBASpSpIicnZ0TjMHX11d//PGHrl69ajJZ/NKlS3Xz5s0XKmrGevfdd/XLL78kOtdmQECAJKlEiRIm66OiouLdMMjDw+O5ORjg0fr5c8Yi/Qtcs8jaIQBIxLlz53T+/Hm9//77kqQyZcqocOHCWr16dbLnXLxy5YoiIiLi3bQmIRcvXpQUM5ouIbFtly5dMlkfHh6e5Gn/knOcgIAA1ahRI0n7TUhAQICyZMny3Cl8JOnRo0e6c+eO2ZGkcfn6+mr79u16/PixMmfObNL21ltvaf78+Zo6dWqCc23GzgO/cuVK1alTR5UrV1aFChVUu3btRF+X5KpSpYpy586tNWvWaPDgwbp+/bqOHz+uAQMGJLugam9vrwEDBmjIkCFav3692auapGc5qI+Pj8n6sLAwPXnyxPjcwcFB2bJlS+YZJTFWi+w1lVy7dk0RERHxLlOPTdpj/3HFvpD/7le8eHGFhITon3/+SYNoAQCANVSvXl2LFy9WvXr1dOHCBc2fP199+/ZVjRo19Msvv5jdplWrVtqyZYtCQ0O1detWRUZGqlmzZmb7PnnyRO7u7onGENv++PFjk/WxcyYVLlw4mWf1TN68eY2jNm/evGm2T+xx/x3nlStXVL16dZNHYiM/AQBA2lm9erU8PDxUq1Yt47qWLVtq27ZtyS5EJZQLJCS26JRY/4TyGycnJy1YsMDs499fKqfkOC/i8ePHqfoaxG2PW6iLFXfUZmIjTseOHasPP/xQ3t7e2rFjh7766iu99tpreuONN+IVjl+Uvb29WrRoYbx3zZo1a5QjR44XLhY3a9ZMJUqUMLli+t9i3zM3NzeT9StXrjTJP9u2bftCMSRFur4reuwIg6xZs5qsj30e2x4cHCxnZ+d4Q2tjq8GBgYHKkydPimI5cuRIirYHAAAvxtXV1WwiGZevr6+++uorRURE6PLly9q9e7cWLlyoDz74QJ6enqpSpYqioqJkMBj05MkT1atXT+PHj9eGDRu0evVqVatWTc7OzsZLyeN+y+zm5qZHjx4lGkNsTmJvb68nT57o6dOnkmIm6v/jjz80duxYubi4mC2emjtm7F0rnz59qidPnqhbt2765ZdfNGXKFI0ePVqRkZGKjo429ndycpIUM0F83DizZctmnJZny5YtWrlypXGf5kRHRxvvnJqQhOaHgmWkdQ7K+5sx8X8ZwDoSy2Gio6O1du1alS9f3jiqUYoZ+RYSEqJ169aZTIMTHR2tqKioBPcXOxVNYGDgc/MmKeHcIa7g4GBJMfNSxvaJjIyUnZ2dyU0Y47K3t1dkZKSxf2yd5u7du2bn/YyNITam2O2ed74JcXNz04MHD5K1XXBwcKL9Y18HKaa4+e/crV27dpo/f76+++47TZ8+XREREca+cb3++ut6/fXX9ejRI508eVJr1qzR5s2b1bdvXy1fvjzBXDSu8PBwSTLJ5+K+Vg0bNtSCBQt08OBBrV69Wo0aNVJYWJgxtwwNDTVuF7uvkJAQk/c3buy9evXSyJEjtXLlSjVv3lwGgyFJ72+1atWMOei0adMS/T2LPYfEctDE8pN0PWITAAAgOZycnOTj46PevXvrq6++kiSzk557enqqcuXKWrp0qf74448E58+UpCJFiujq1avG5M+cCxcuyNHRMd7NhTJlyqQpU6aoWLFi+vjjj7V79+4XOq88efKoTZs2WrNmjdkRl0WKFJH07CqWuMevWrWqqlatGi82AABgPYcPH9Y///yjHTt2qE2bNsbHf/7zH0nm85fEFChQQI6OjiZF0sTE5g4XLlxIsE9sW9GiRZMVS1yxV62cP3/eoseJVaRIET1+/Fi3b99+bt8sWbIoZ86cib4GsfF5eXnFuww9VqZMmdS1a1cdOHBAJ06cSNJxa9asqQkTJqhly5a6du2a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id=\"11\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Gender-wise Positive Cases' Symptoms</p>","metadata":{}},{"cell_type":"code","source":"_, axs = plt.subplots(2,5,figsize=(20,14),sharex=False,sharey=True)\nplt.tight_layout(pad=4.0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"YELLOW_FINGERS\",hue_order=[\"Yes\",\"No\"],ax=axs[0,0],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[0,0].set_ylabel(\"Total\")\naxs[0,0].legend(title=\"Yellow Fingers\",loc=\"upper right\")\nfor container in axs[0,0].containers:\n    axs[0,0].bar_label(container,label_type=\"center\",padding=2,size=17,color=\"white\",rotation=0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"ANXIETY\",hue_order=[\"Yes\",\"No\"],ax=axs[0,1],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[0,1].set_ylabel(\"Total\")\naxs[0,1].legend(title=\"Anxiety\",loc=\"upper right\")\nfor container in axs[0,1].containers:\n    axs[0,1].bar_label(container,label_type=\"center\",padding=2,size=17,color=\"white\",rotation=0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"CHRONIC DISEASE\",hue_order=[\"Yes\",\"No\"],ax=axs[0,2],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[0,2].set_ylabel(\"Total\")\naxs[0,2].legend(title=\"Chronic Disease\",loc=\"upper right\")\nfor container in axs[0,2].containers:\n    axs[0,2].bar_label(container,label_type=\"center\",padding=2,size=17,color=\"white\",rotation=0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"CHEST PAIN\",hue_order=[\"Yes\",\"No\"],ax=axs[0,3],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[0,3].set_ylabel(\"Total\")\naxs[0,3].legend(title=\"Chest Pain\",loc=\"upper right\")\nfor container in axs[0,3].containers:\n    axs[0,3].bar_label(container,label_type=\"center\",padding=2,size=17,color=\"white\",rotation=0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"FATIGUE \",hue_order=[\"Yes\",\"No\"],ax=axs[0,4],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[0,4].set_ylabel(\"Total\")\naxs[0,4].legend(title=\"Fatigue\",loc=\"upper right\")\nfor container in axs[0,4].containers:\n    axs[0,4].bar_label(container,label_type=\"center\",padding=2,size=17,color=\"white\",rotation=0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"WHEEZING\",hue_order=[\"Yes\",\"No\"],ax=axs[1,0],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[1,0].set_ylabel(\"Total\")\naxs[1,0].legend(title=\"Wheezing\",loc=\"upper right\")\nfor container in axs[1,0].containers:\n    axs[1,0].bar_label(container,label_type=\"center\",padding=2,size=17,color=\"white\",rotation=0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"COUGHING\",hue_order=[\"Yes\",\"No\"],ax=axs[1,1],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[1,1].set_ylabel(\"Total\")\naxs[1,1].legend(title=\"Coughing\",loc=\"upper right\")\nfor container in axs[1,1].containers:\n    axs[1,1].bar_label(container,label_type=\"center\",padding=2,size=17,color=\"white\",rotation=0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"SHORTNESS OF BREATH\",hue_order=[\"Yes\",\"No\"],ax=axs[1,2],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[1,2].set_ylabel(\"Total\")\naxs[1,2].legend(title=\"Short Breath\",loc=\"upper right\")\nfor container in axs[1,2].containers:\n    axs[1,2].bar_label(container,label_type=\"center\",padding=2,size=17,color=\"white\",rotation=0)\n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"SWALLOWING DIFFICULTY\",hue_order=[\"Yes\",\"No\"],ax=axs[1,3],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[1,3].set_ylabel(\"Total\")\naxs[1,3].legend(title=\"Swallowing Difficulty\",loc=\"upper right\")\nfor container in axs[1,3].containers:\n    axs[1,3].bar_label(container,label_type=\"center\",padding=2,size=17,color=\"white\",rotation=0)    \n\nsns.countplot(data=data_temp_pos,x=\"GENDER\",hue=\"ALLERGY \",hue_order=[\"Yes\",\"No\"],ax=axs[1,4],palette=[\"#0f4c5c\",\"#FFC300\"],saturation=1)\naxs[1,4].set_ylabel(\"Total\")\naxs[1,4].legend(title=\"Allergy\",loc=\"upper right\")\nfor container in axs[1,4].containers:\n    axs[1,4].bar_label(container,label_type=\"center\",padding=2,size=17,color=\"white\",rotation=0)    \n\nsns.despine(left=True, bottom=True)\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:36.810718Z","iopub.execute_input":"2022-11-30T14:44:36.811575Z","iopub.status.idle":"2022-11-30T14:44:38.659740Z","shell.execute_reply.started":"2022-11-30T14:44:36.811521Z","shell.execute_reply":"2022-11-30T14:44:38.658748Z"},"trusted":true},"execution_count":76,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x1008 with 10 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KfP333/rxIkT5l4RFSpUUNGiRbVy5UqrAuWZM2e0Zs0aFSlSRJJUq1YtdejQQWvWrNFzzz0nSapYsaL69eunmTNnqnnz5oqOjtbixYv1+uuvWxQN/ysiIkIffPCBOnXqpE8++cTc3qVLF7Vs2VIzZszQpEmTVKVKFRUtWlS7du2yurjRqlUrffTRR1q5cqXFvn766Se5ubnZ9KULOH36tIoVK2bTLcrXr1/XhAkT1LFjR0mm92+TJk20dOlSqwLl3bt3tXbtWothTCZMmCAfHx8tXLhQAQEBkkz/h7dt21aff/65pkyZYvEcZcuWtThfQkJCtGTJkmQLlCtWrNDOnTs1fPhw9e3b19z+8ssvKyEhIcV/a1JKlSol6f6X/6Rs27ZN3t7eFp+H/uvbb7/VmTNntGzZMvOH9q5du+qJJ57Q5MmT9dJLL6l48eKSpA0bNihbtmzmbXv16qU+ffqYe1ZK0h9//KGQkBB9/fXXCgwMNK87ZMgQ87//+OMP/d///Z/F6ydJ9evXV8+ePbVixQp17drVxiMCZBzplfNWr16tX375RfPmzVPNmjXN21aoUEHDhw/Xrl27VK9ePe3atUvR0dH66quvLCYwffPNNyVJuXLlUoMGDTRlyhRVrlzZps4i9+7d061bt8xjUCbmzcRZyD/77DOLPNKtWzcVKVJEkydP1ltvvaX8+fMn+/znzp3T999/b/5u1qpVKzVs2FDLli1L9R1vAGTxeSXR/v37zedwog4dOqh169ZasmSJBg4caHPeuHHjhr799ls1btxYM2fONNcopk2b9li9taWUf8exB27xziDeeOMNBQQEKCEhQaNHj1ZISIh+/fVXtWrVShEREbp165b5p379+rp69arF7UuP8uuvvyomJka9e/e2+DDeoUMH5cqVS9u2bZNk6tlYpUoVcwEysafkiy++KE9PT3Mvyv379yt37twqWrRoivY/efJkzZ071/zzsJm9H/Tss89aPK5WrZpFN+VffvlFLi4uFh/WnZ2d1bNnT4vtHudYPvvss1ZjQWTPnl0nT5606bgDeLSVK1fK399f9erVM7e1adNGmzdvtrhNWzIVJBOLk5JUpkwZ+fj46J9//rFYb+DAgSpXrpxGjhypd955R5UqVXrkANS//vqr7ty5o3bt2lnkibi4OFWvXj3ZiyqJfH191aRJE61evVrx8fGSTD03Vq9erfr161t84QEeJTw8XN7e3jZt4+npqfbt25sfu7u7KzAw0OockUyfAR4sTl67dk1Hjx5Vp06dzMVJyTRmdpMmTfTLL79Y3d783/+rq1evrpCQEIWHhz80xg0bNsjPz0+9e/e2WpbayXQS/567d+8+dJ3s2bPr3r175l5RSVm3bp2qVaumgIAAi3xQp04dSabb4BMlFhViYmIUEhKiW7duqVatWrpw4YL5Lozs2bNLMhVHY2JiHrpPLy8v1a9f32KfxYsXV65cuVKUg4CMLL1y3rp161S0aFGVLFnS4tyqWbOmnJyczOdW4nm6efNm8//haSWxo0jdunXVq1cvXbp0SaNHjzbfhZWYR+Lj4809tapVq6aEhAQdOXLkkc9ftGhRi44jOXLkULFixZLM/QBsN3r0aIt6xty5cy0uKty7d0+3b9+Wt7e3ihUrlqLzNim7d+8234Hx4GeixI4YjyMtvuOkFXpQZhA+Pj4qVqyYrl+/rjx58ujgwYNKSEjQ1KlTH1opT/zQmhKXLl2SZD3TlIuLi4oUKaLg4GBzW/Xq1fXFF18oPDxc+/btk4+PjwIDA1WpUiXt27dPrVq10r59+8zdk1OievXqD50kJylubm7KkyePRZufn5/u3Llj8TflzJnT6oNM4cKFLR5fuHDB5mP53xm6JFMvh0GDBqlly5YqWbKk6tWrZx7PE8DjiY+P15o1a1SjRg1znpJkHkZh06ZNFl88kupB4Ofnp9DQUIs2Nzc3ffLJJ2rfvr3c3Nw0b968h/aUSpR48SGpXtiS6QJISnTq1Enr1q3T3r17Vbt2be3bt0/BwcF66623UrQ9kMjHxyfZQltS8ubNa/Ve9fPzsxgPKdF//7982GcFSSpevLg2bNig27dvK1euXOb2AgUKWKzn6+sryTS0y4MzaT/owoULKlq0aLpMXpN4USO5IkePHj20fv169evXT3ny5FGdOnXUvHlzNWnSxPxl4Ny5c/r7779Vu3btJJ/j5s2b5n9v2rRJM2bM0N9//21VwA0LC1P27NlVs2ZNtWjRQtOmTdPcuXNVs2ZNNWnSRG3btjUXVc+dO6eIiAhzETS5fQKZUXrlvHPnzuns2bOPPJ8Tez2NHj1an332mYKCgtSsWTO1atUq1ZNYdOnSRW3atJGLi4vy5MmjggULWuTAEydOaOLEifrtt98sxqWVlOwFn0T/zcWS9XcnAI8vMDDQapKcqKgoTZo0SStXrtT169ctliU3nF1yEj+LPdghI/H5/Pz8Hus50+o7TlqgQJlBJV61e+GFF6xuyUqUXuOJVK9eXXFxcdq/f7/27dunqlWrytnZWdWrV9emTZt069YtnTlzxnyrdXpIbQ+KBz3OsUy8DfxBNWrU0M8//6ytW7dq586dWrFihb777ju98cYbevnll9MsXiAr2bt3r65cuaIrV67o559/tlq+cuVKiwLlw/4DTeq20MTeUTExMTp79uwjL+gkPsf48eOVN2/eFP8N/1WvXj3lypVLK1euVO3atbVy5Upzz0rAFsWLF9fRo0cVHR2d4mKeLR8yk/q/zla2nJP2kDiZz3+Lrw/KmTOnli9frt27d2vHjh365ZdftGLFCovbqeLj4xUUFKT+/fsn+RyJFzL37dunwYMHq1q1aho7dqzy5MkjNzc3bd++Xd9++635M4iTk5OmTJmigwcPauvWrfr111/17rvvavbs2Vq0aJFy5syp+Ph4+fv7P3S8ucTiL5BZpVfOi4+PV8mSJfXOO+8kuTyxU4Snp6e+//57/f7779q+fbt27typN998U3PnztWCBQtSlTOLFCny0IsPYWFh6t27t7y8vDR06FAVLlxYnp6eunr1qt5+++0U9ea0Z4EBgMkHH3ygpUuXmiee8fX1lbOzsz7++GO7fA56WM3kvxdL0+o7TlqgQJlBJX7wdXFxeeh/ZrZIvKp29uxZi54R8fHxOn/+vMqWLWtuCwwMlIeHh/bt26d9+/bp6aeflmQq0M2cOVNbtmwxPzZSgQIFtHv3bt29e9eip8R/x51Ky2Pp5+enjh07qmPHjoqMjNTLL7+sqVOnqk+fPo/snQXA2sqVKxUQEKD333/fatnOnTu1fPly3bx5Uzlz5rTpeU+dOqXJkyerXbt2+ueffzRmzBhVrVrV4rbV/0rMFTly5HhkrkjuIoqLi4vatWunRYsWaeTIkdqwYYNatmyZLr3FkLk1adJEBw4c0Pr16y0K9enlwc8K/3XmzBl5eXklew6lVOHChXXgwAGbihAptXLlSjk5Oalu3brJrufu7q6GDRuqYcOGSkhI0KRJk/TVV1/pjz/+ULVq1VS4cGHdvXv3kblgw4YN8vDwMI8jnuhht0tVrFhRFStW1Guvvabt27erX79+WrRokQYMGKDChQvr119/VaVKlWy+zRXIDNIr5xUuXFhHjhxR7dq1H9kJwtnZWbVq1VKtWrX01ltvacGCBRo7dqw2btxo0/j/tti7d69u376tKVOmWIyRuWvXrjTfF4C0s379enXs2NHq4sedO3csPi/ZkjcSP4udP3/eYji927dvW/WITrxwGRoaanER88G70iTbvuOkNy6lZFA5c+ZUrVq1tHjxYl29etVq+X9nkHqUOnXqyM3NTfPnz7e4Crdy5UrduHFDjRs3Nre5u7urYsWK2rhxo86ePWueqbtSpUpycXHR119/LV9fX/NA9EapV6+e4uLi9H//93/mtvj4eP3www8W66XVsbx9+7bFY09PTxUvXlxRUVFWt2IAeLSoqCht3LhRDRs2VMuWLa1+XnzxRcXGxmrNmjU2PW9sbKzeeustBQQEaMyYMRo/frzCw8M1duzYZLerX7++fH19NWvWrCRnyXswVySOOfOwW6c6duyou3fvasyYMbpz545NA+kDibp166a8efNqwoQJOn36tNXy8PDwFM3umlJ58uRR+fLltWLFCoWEhJjbL1y4oC1btqh+/fppcjGuRYsWunPnjubNm2e1LDU9Dr788kvt3LlTrVu3TnaM7P/+f+7k5KRy5cpJknnMyFatWunQoUPavn271fbh4eHmHOHi4mLucZnozp07Wrp0qcU2d+7csfrbypcvL0nmISpat26t+Ph4TZ8+3WqfcXFx3KqJTC+9cl7r1q1148YNLViwwGpZdHS0+Rbq/+YG6f55mpgbEv///+/QMqmR2PvxwRwRHx+vuXPnptk+AKQ9FxcXq//bV69erWvXrlm02ZI3ateuLVdXV/34448W7f+tcUj37xZJnCdEMg11s2LFCov1bPmOk97oQZmBvf/+++revbvat2+vZ555RoULF9bNmzf1119/6fTp00neDvkwOXLk0MCBA/XFF1+oT58+atasmf755x/98MMPKlOmjJ555hmL9atVq6ZZs2bJ09PTPMail5eXypUrp7/++kuNGjUy/FaCZs2aqWLFipo4caIuXryoEiVKaMuWLeYP8A9eqUiLY9m6dWvVqFFDFSpUUEBAgI4fP64lS5aocePG9HQAHsPmzZsVHh7+0FufS5QoYZ7NO6nJNB5m1qxZOnLkiL766iv5+vrK19dXb7zxhj766CM1b95crVu3TnI7Hx8fjR07Vm+++aY6deqkNm3aKFeuXLp06ZJ++eUXPfnkkxo/frwk08yfkjRu3Dg1aNBArq6uaty4sXksuTJlyqhMmTJat26dnnjiCZvG7AUS+fr6avr06erXr586depkHvfY2dlZJ0+e1OrVq+Xn56ehQ4em2T7feustvfTSS+rataueffZZRUVFacGCBfLw8Eiz/XTo0EEr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id=\"12\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Correlation Heatmap</p>","metadata":{}},{"cell_type":"markdown","source":"Let's convert the target feature **\"LUNG_CANCER\"** from **Categorical** to **Numerical** data type by using **Label Encoder**. And converting the **\"GENDER\"** column from **Categorical** to **Numerical** data type by using **\"One Hot Encoder\"** for avoiding unexpected gender bias.","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nLabelEncoder = LabelEncoder()\n\ndata[\"GENDER\"] = data[\"GENDER\"].replace({\"M\" : \"Male\" , \"F\" : \"Female\"})\ndata[\"LUNG_CANCER\"] = LabelEncoder.fit_transform(data[\"LUNG_CANCER\"])\n\ndata = pd.get_dummies(data, columns= [\"GENDER\"])\ndata.rename(columns={\"GENDER_Male\" : \"MALE\", \"GENDER_Female\" : \"FEMALE\", \"YELLOW_FINGERS\" : \"YELLOW FINGERS\", \"PEER_PRESSURE\" : \"PEER PRESSURE\", \"LUNG_CANCER\" : \"LUNG CANCER\", \"FATIGUE \" : \"FATIGUE\", \"ALLERGY \" : \"ALLERGY\"}, inplace=True)\ndata = data[[\"AGE\",\"MALE\",\"FEMALE\",\"ALCOHOL CONSUMING\",\"CHEST PAIN\",\"SHORTNESS OF BREATH\",\"COUGHING\",\"PEER PRESSURE\",\"CHRONIC DISEASE\",\"SWALLOWING DIFFICULTY\",\"YELLOW FINGERS\",\"ANXIETY\",\"FATIGUE\",\"ALLERGY\",\"WHEEZING\",\"LUNG CANCER\"]]\ndata.head().style.set_properties(**{\"background-color\": \"#2a9d8f\",\"color\":\"white\",\"border\": \"1.5px  solid black\"})","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:38.661054Z","iopub.execute_input":"2022-11-30T14:44:38.661419Z","iopub.status.idle":"2022-11-30T14:44:38.711033Z","shell.execute_reply.started":"2022-11-30T14:44:38.661387Z","shell.execute_reply":"2022-11-30T14:44:38.709967Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f4e89e4a110>","text/html":"<style type=\"text/css\">\n#T_b85e0_row0_col0, #T_b85e0_row0_col1, #T_b85e0_row0_col2, #T_b85e0_row0_col3, #T_b85e0_row0_col4, #T_b85e0_row0_col5, #T_b85e0_row0_col6, #T_b85e0_row0_col7, #T_b85e0_row0_col8, #T_b85e0_row0_col9, #T_b85e0_row0_col10, #T_b85e0_row0_col11, #T_b85e0_row0_col12, #T_b85e0_row0_col13, #T_b85e0_row0_col14, #T_b85e0_row0_col15, #T_b85e0_row1_col0, #T_b85e0_row1_col1, #T_b85e0_row1_col2, #T_b85e0_row1_col3, #T_b85e0_row1_col4, #T_b85e0_row1_col5, #T_b85e0_row1_col6, #T_b85e0_row1_col7, #T_b85e0_row1_col8, #T_b85e0_row1_col9, #T_b85e0_row1_col10, #T_b85e0_row1_col11, #T_b85e0_row1_col12, #T_b85e0_row1_col13, #T_b85e0_row1_col14, #T_b85e0_row1_col15, #T_b85e0_row2_col0, #T_b85e0_row2_col1, #T_b85e0_row2_col2, #T_b85e0_row2_col3, #T_b85e0_row2_col4, #T_b85e0_row2_col5, #T_b85e0_row2_col6, #T_b85e0_row2_col7, #T_b85e0_row2_col8, #T_b85e0_row2_col9, #T_b85e0_row2_col10, #T_b85e0_row2_col11, #T_b85e0_row2_col12, #T_b85e0_row2_col13, #T_b85e0_row2_col14, #T_b85e0_row2_col15, #T_b85e0_row3_col0, #T_b85e0_row3_col1, #T_b85e0_row3_col2, #T_b85e0_row3_col3, #T_b85e0_row3_col4, #T_b85e0_row3_col5, #T_b85e0_row3_col6, #T_b85e0_row3_col7, #T_b85e0_row3_col8, #T_b85e0_row3_col9, #T_b85e0_row3_col10, #T_b85e0_row3_col11, #T_b85e0_row3_col12, #T_b85e0_row3_col13, #T_b85e0_row3_col14, #T_b85e0_row3_col15, #T_b85e0_row4_col0, #T_b85e0_row4_col1, #T_b85e0_row4_col2, #T_b85e0_row4_col3, #T_b85e0_row4_col4, #T_b85e0_row4_col5, #T_b85e0_row4_col6, #T_b85e0_row4_col7, #T_b85e0_row4_col8, #T_b85e0_row4_col9, #T_b85e0_row4_col10, #T_b85e0_row4_col11, #T_b85e0_row4_col12, #T_b85e0_row4_col13, #T_b85e0_row4_col14, #T_b85e0_row4_col15 {\n  background-color: #2a9d8f;\n  color: white;\n  border: 1.5px  solid black;\n}\n</style>\n<table id=\"T_b85e0_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >AGE</th>\n      <th class=\"col_heading level0 col1\" >MALE</th>\n      <th class=\"col_heading level0 col2\" >FEMALE</th>\n      <th class=\"col_heading level0 col3\" >ALCOHOL CONSUMING</th>\n      <th class=\"col_heading level0 col4\" >CHEST PAIN</th>\n      <th class=\"col_heading level0 col5\" >SHORTNESS OF BREATH</th>\n      <th class=\"col_heading level0 col6\" >COUGHING</th>\n      <th class=\"col_heading level0 col7\" >PEER PRESSURE</th>\n      <th class=\"col_heading level0 col8\" >CHRONIC DISEASE</th>\n      <th class=\"col_heading level0 col9\" >SWALLOWING DIFFICULTY</th>\n      <th class=\"col_heading level0 col10\" >YELLOW FINGERS</th>\n      <th class=\"col_heading level0 col11\" >ANXIETY</th>\n      <th class=\"col_heading level0 col12\" >FATIGUE</th>\n      <th class=\"col_heading level0 col13\" >ALLERGY</th>\n      <th class=\"col_heading level0 col14\" >WHEEZING</th>\n      <th class=\"col_heading level0 col15\" >LUNG CANCER</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b85e0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n      <td id=\"T_b85e0_row0_col0\" class=\"data row0 col0\" >69</td>\n      <td id=\"T_b85e0_row0_col1\" class=\"data row0 col1\" >1</td>\n      <td id=\"T_b85e0_row0_col2\" class=\"data row0 col2\" >0</td>\n      <td id=\"T_b85e0_row0_col3\" class=\"data row0 col3\" >2</td>\n      <td id=\"T_b85e0_row0_col4\" class=\"data row0 col4\" >2</td>\n      <td id=\"T_b85e0_row0_col5\" class=\"data row0 col5\" >2</td>\n      <td id=\"T_b85e0_row0_col6\" class=\"data row0 col6\" >2</td>\n      <td id=\"T_b85e0_row0_col7\" class=\"data row0 col7\" >1</td>\n      <td id=\"T_b85e0_row0_col8\" class=\"data row0 col8\" >1</td>\n      <td id=\"T_b85e0_row0_col9\" class=\"data row0 col9\" >2</td>\n      <td id=\"T_b85e0_row0_col10\" class=\"data row0 col10\" >2</td>\n      <td id=\"T_b85e0_row0_col11\" class=\"data row0 col11\" >2</td>\n      <td id=\"T_b85e0_row0_col12\" class=\"data row0 col12\" >2</td>\n      <td id=\"T_b85e0_row0_col13\" class=\"data row0 col13\" >1</td>\n      <td id=\"T_b85e0_row0_col14\" class=\"data row0 col14\" >2</td>\n      <td id=\"T_b85e0_row0_col15\" class=\"data row0 col15\" >1</td>\n    </tr>\n    <tr>\n      <th id=\"T_b85e0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n      <td id=\"T_b85e0_row1_col0\" class=\"data row1 col0\" >74</td>\n      <td id=\"T_b85e0_row1_col1\" class=\"data row1 col1\" >1</td>\n      <td id=\"T_b85e0_row1_col2\" class=\"data row1 col2\" >0</td>\n      <td id=\"T_b85e0_row1_col3\" class=\"data row1 col3\" >1</td>\n      <td id=\"T_b85e0_row1_col4\" class=\"data row1 col4\" >2</td>\n      <td id=\"T_b85e0_row1_col5\" class=\"data row1 col5\" >2</td>\n      <td id=\"T_b85e0_row1_col6\" class=\"data row1 col6\" >1</td>\n      <td id=\"T_b85e0_row1_col7\" class=\"data row1 col7\" >1</td>\n      <td id=\"T_b85e0_row1_col8\" class=\"data row1 col8\" >2</td>\n      <td id=\"T_b85e0_row1_col9\" class=\"data row1 col9\" >2</td>\n      <td id=\"T_b85e0_row1_col10\" class=\"data row1 col10\" >1</td>\n      <td id=\"T_b85e0_row1_col11\" class=\"data row1 col11\" >1</td>\n      <td id=\"T_b85e0_row1_col12\" class=\"data row1 col12\" >2</td>\n      <td id=\"T_b85e0_row1_col13\" class=\"data row1 col13\" >2</td>\n      <td id=\"T_b85e0_row1_col14\" class=\"data row1 col14\" >1</td>\n      <td id=\"T_b85e0_row1_col15\" class=\"data row1 col15\" >1</td>\n    </tr>\n    <tr>\n      <th id=\"T_b85e0_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n      <td id=\"T_b85e0_row2_col0\" class=\"data row2 col0\" >59</td>\n      <td id=\"T_b85e0_row2_col1\" class=\"data row2 col1\" >0</td>\n      <td id=\"T_b85e0_row2_col2\" class=\"data row2 col2\" >1</td>\n      <td id=\"T_b85e0_row2_col3\" class=\"data row2 col3\" >1</td>\n      <td id=\"T_b85e0_row2_col4\" class=\"data row2 col4\" >2</td>\n      <td id=\"T_b85e0_row2_col5\" class=\"data row2 col5\" >2</td>\n      <td id=\"T_b85e0_row2_col6\" class=\"data row2 col6\" >2</td>\n      <td id=\"T_b85e0_row2_col7\" class=\"data row2 col7\" >2</td>\n      <td id=\"T_b85e0_row2_col8\" class=\"data row2 col8\" >1</td>\n      <td id=\"T_b85e0_row2_col9\" class=\"data row2 col9\" >1</td>\n      <td id=\"T_b85e0_row2_col10\" class=\"data row2 col10\" >1</td>\n      <td id=\"T_b85e0_row2_col11\" class=\"data row2 col11\" >1</td>\n      <td id=\"T_b85e0_row2_col12\" class=\"data row2 col12\" >2</td>\n      <td id=\"T_b85e0_row2_col13\" class=\"data row2 col13\" >1</td>\n      <td id=\"T_b85e0_row2_col14\" class=\"data row2 col14\" >2</td>\n      <td id=\"T_b85e0_row2_col15\" class=\"data row2 col15\" >0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b85e0_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n      <td id=\"T_b85e0_row3_col0\" class=\"data row3 col0\" >63</td>\n      <td id=\"T_b85e0_row3_col1\" class=\"data row3 col1\" >1</td>\n      <td id=\"T_b85e0_row3_col2\" class=\"data row3 col2\" >0</td>\n      <td id=\"T_b85e0_row3_col3\" class=\"data row3 col3\" >2</td>\n      <td id=\"T_b85e0_row3_col4\" class=\"data row3 col4\" >2</td>\n      <td id=\"T_b85e0_row3_col5\" class=\"data row3 col5\" >1</td>\n      <td id=\"T_b85e0_row3_col6\" class=\"data row3 col6\" >1</td>\n      <td id=\"T_b85e0_row3_col7\" class=\"data row3 col7\" >1</td>\n      <td id=\"T_b85e0_row3_col8\" class=\"data row3 col8\" >1</td>\n      <td id=\"T_b85e0_row3_col9\" class=\"data row3 col9\" >2</td>\n      <td id=\"T_b85e0_row3_col10\" class=\"data row3 col10\" >2</td>\n      <td id=\"T_b85e0_row3_col11\" class=\"data row3 col11\" >2</td>\n      <td id=\"T_b85e0_row3_col12\" class=\"data row3 col12\" >1</td>\n      <td id=\"T_b85e0_row3_col13\" class=\"data row3 col13\" >1</td>\n      <td id=\"T_b85e0_row3_col14\" class=\"data row3 col14\" >1</td>\n      <td id=\"T_b85e0_row3_col15\" class=\"data row3 col15\" >0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b85e0_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n      <td id=\"T_b85e0_row4_col0\" class=\"data row4 col0\" >63</td>\n      <td id=\"T_b85e0_row4_col1\" class=\"data row4 col1\" >0</td>\n      <td id=\"T_b85e0_row4_col2\" class=\"data row4 col2\" >1</td>\n      <td id=\"T_b85e0_row4_col3\" class=\"data row4 col3\" >1</td>\n      <td id=\"T_b85e0_row4_col4\" class=\"data row4 col4\" >1</td>\n      <td id=\"T_b85e0_row4_col5\" class=\"data row4 col5\" >2</td>\n      <td id=\"T_b85e0_row4_col6\" class=\"data row4 col6\" >2</td>\n      <td id=\"T_b85e0_row4_col7\" class=\"data row4 col7\" >1</td>\n      <td id=\"T_b85e0_row4_col8\" class=\"data row4 col8\" >1</td>\n      <td id=\"T_b85e0_row4_col9\" class=\"data row4 col9\" >1</td>\n      <td id=\"T_b85e0_row4_col10\" class=\"data row4 col10\" >2</td>\n      <td id=\"T_b85e0_row4_col11\" class=\"data row4 col11\" >1</td>\n      <td id=\"T_b85e0_row4_col12\" class=\"data row4 col12\" >1</td>\n      <td id=\"T_b85e0_row4_col13\" class=\"data row4 col13\" >1</td>\n      <td id=\"T_b85e0_row4_col14\" class=\"data row4 col14\" >2</td>\n      <td id=\"T_b85e0_row4_col15\" class=\"data row4 col15\" >0</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"code","source":"plt.subplots(figsize =(16, 12))\n\np=sns.heatmap(data.corr(), cmap = palette, square=True, cbar_kws=dict(shrink =.99), \n            annot=True, vmin=-1, vmax=1, linewidths=0.1,linecolor='white',annot_kws=dict(fontsize =12))\np.axes.set_title(\"Pearson Correlation Of Features\\n\", fontsize=25)\nplt.xticks(rotation=90)\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:38.712719Z","iopub.execute_input":"2022-11-30T14:44:38.713102Z","iopub.status.idle":"2022-11-30T14:44:40.252433Z","shell.execute_reply.started":"2022-11-30T14:44:38.713067Z","shell.execute_reply":"2022-11-30T14:44:40.251333Z"},"trusted":true},"execution_count":78,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x864 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"13\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Preprocessing For Classification</p>","metadata":{}},{"cell_type":"code","source":"x = data.drop(\"LUNG CANCER\", axis = 1)\ny = data[\"LUNG CANCER\"]\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\n\nscaler = StandardScaler()\nx = scaler.fit_transform(x)\nx_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=42)\n\nprint(f\"Shape of training data : {x_train.shape}, {y_train.shape}\")\nprint(f\"Shape of testing data : {x_test.shape}, {y_test.shape}\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:40.253828Z","iopub.execute_input":"2022-11-30T14:44:40.254447Z","iopub.status.idle":"2022-11-30T14:44:40.271755Z","shell.execute_reply.started":"2022-11-30T14:44:40.254408Z","shell.execute_reply":"2022-11-30T14:44:40.270148Z"},"trusted":true},"execution_count":79,"outputs":[{"name":"stdout","text":"Shape of training data : (220, 15), (220,)\nShape of testing data : (56, 15), (56,)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"14\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Logistic Regression Model</p>","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n\n\nlr = LogisticRegression()\nlr.fit(x_train, y_train)\nlr_pred = lr.predict(x_test)\nlr_conf = confusion_matrix(y_test, lr_pred)\nlr_report = classification_report(y_test, lr_pred)\nlr_acc = round(accuracy_score(y_test, lr_pred)*100, ndigits = 2)\nprint(f\"Confusion Matrix : \\n\\n{lr_conf}\")\nprint(f\"\\nClassification Report : \\n\\n{lr_report}\")\nprint(f\"\\nThe Accuracy of Logistic Regression is {lr_acc} %\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:40.273343Z","iopub.execute_input":"2022-11-30T14:44:40.273707Z","iopub.status.idle":"2022-11-30T14:44:40.299251Z","shell.execute_reply.started":"2022-11-30T14:44:40.273674Z","shell.execute_reply":"2022-11-30T14:44:40.297559Z"},"trusted":true},"execution_count":80,"outputs":[{"name":"stdout","text":"Confusion Matrix : \n\n[[ 5  7]\n [ 0 44]]\n\nClassification Report : \n\n              precision    recall  f1-score   support\n\n           0       1.00      0.42      0.59        12\n           1       0.86      1.00      0.93        44\n\n    accuracy                           0.88        56\n   macro avg       0.93      0.71      0.76        56\nweighted avg       0.89      0.88      0.85        56\n\n\nThe Accuracy of Logistic Regression is 87.5 %\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"15\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Gaussian Naive Bayes Model</p>","metadata":{}},{"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\n\ngnb = GaussianNB()\ngnb.fit(x_train, y_train)\ngnb_pred = gnb.predict(x_test)\ngnb_conf = confusion_matrix(y_test, gnb_pred)\ngnb_report = classification_report(y_test, gnb_pred)\ngnb_acc = round(accuracy_score(y_test, gnb_pred)*100, ndigits = 2)\nprint(f\"Confusion Matrix : \\n\\n{gnb_conf}\")\nprint(f\"\\nClassification Report : \\n\\n{gnb_report}\")\nprint(f\"\\nThe Accuracy of Gaussian Naive Bayes is {gnb_acc} %\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:40.300991Z","iopub.execute_input":"2022-11-30T14:44:40.301422Z","iopub.status.idle":"2022-11-30T14:44:40.319179Z","shell.execute_reply.started":"2022-11-30T14:44:40.301385Z","shell.execute_reply":"2022-11-30T14:44:40.317700Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"Confusion Matrix : \n\n[[ 8  4]\n [ 1 43]]\n\nClassification Report : \n\n              precision    recall  f1-score   support\n\n           0       0.89      0.67      0.76        12\n           1       0.91      0.98      0.95        44\n\n    accuracy                           0.91        56\n   macro avg       0.90      0.82      0.85        56\nweighted avg       0.91      0.91      0.91        56\n\n\nThe Accuracy of Gaussian Naive Bayes is 91.07 %\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"16\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Bernoulli Naive Bayes Model</p>","metadata":{}},{"cell_type":"code","source":"from sklearn.naive_bayes import BernoulliNB\n\nbnb = BernoulliNB()\nbnb.fit(x_train, y_train)\nbnb_pred = bnb.predict(x_test)\nbnb_conf = confusion_matrix(y_test, bnb_pred)\nbnb_report = classification_report(y_test, bnb_pred)\nbnb_acc = round(accuracy_score(y_test, bnb_pred)*100, ndigits = 2)\nprint(f\"Confusion Matrix : \\n\\n{bnb_conf}\")\nprint(f\"\\nClassification Report : \\n\\n{bnb_report}\")\nprint(f\"\\nThe Accuracy of Bernoulli Naive Bayes is {bnb_acc} %\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:40.321110Z","iopub.execute_input":"2022-11-30T14:44:40.322583Z","iopub.status.idle":"2022-11-30T14:44:40.339111Z","shell.execute_reply.started":"2022-11-30T14:44:40.322525Z","shell.execute_reply":"2022-11-30T14:44:40.337491Z"},"trusted":true},"execution_count":82,"outputs":[{"name":"stdout","text":"Confusion Matrix : \n\n[[ 8  4]\n [ 1 43]]\n\nClassification Report : \n\n              precision    recall  f1-score   support\n\n           0       0.89      0.67      0.76        12\n           1       0.91      0.98      0.95        44\n\n    accuracy                           0.91        56\n   macro avg       0.90      0.82      0.85        56\nweighted avg       0.91      0.91      0.91        56\n\n\nThe Accuracy of Bernoulli Naive Bayes is 91.07 %\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"17\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Support Vector Machine Model</p>","metadata":{}},{"cell_type":"code","source":"from sklearn.svm import SVC\n\nsvm = SVC(C = 100, gamma = 0.002)\nsvm.fit(x_train, y_train)\nsvm_pred = svm.predict(x_test)\nsvm_conf = confusion_matrix(y_test, svm_pred)\nsvm_report = classification_report(y_test, svm_pred)\nsvm_acc = round(accuracy_score(y_test, svm_pred)*100, ndigits = 2)\nprint(f\"Confusion Matrix : \\n\\n{svm_conf}\")\nprint(f\"\\nClassification Report : \\n\\n{svm_report}\")\nprint(f\"\\nThe Accuracy of Support Vector Machine is {svm_acc} %\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:40.340735Z","iopub.execute_input":"2022-11-30T14:44:40.342006Z","iopub.status.idle":"2022-11-30T14:44:40.360659Z","shell.execute_reply.started":"2022-11-30T14:44:40.341951Z","shell.execute_reply":"2022-11-30T14:44:40.358880Z"},"trusted":true},"execution_count":83,"outputs":[{"name":"stdout","text":"Confusion Matrix : \n\n[[ 4  8]\n [ 0 44]]\n\nClassification Report : \n\n              precision    recall  f1-score   support\n\n           0       1.00      0.33      0.50        12\n           1       0.85      1.00      0.92        44\n\n    accuracy                           0.86        56\n   macro avg       0.92      0.67      0.71        56\nweighted avg       0.88      0.86      0.83        56\n\n\nThe Accuracy of Support Vector Machine is 85.71 %\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"18\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Random Forest Model</p>","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\n\nrfg = RandomForestClassifier(n_estimators = 100, random_state = 42) \nrfg.fit(x_train, y_train)\nrfg_pred = rfg.predict(x_test)\nrfg_conf = confusion_matrix(y_test, rfg_pred)\nrfg_report = classification_report(y_test, rfg_pred)\nrfg_acc = round(accuracy_score(y_test, rfg_pred)*100, ndigits = 2)\nprint(f\"Confusion Matrix : \\n\\n{rfg_conf}\")\nprint(f\"\\nClassification Report : \\n\\n{rfg_report}\")\nprint(f\"\\nThe Accuracy of Random Forest Classifier is {rfg_acc} %\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:40.365597Z","iopub.execute_input":"2022-11-30T14:44:40.366050Z","iopub.status.idle":"2022-11-30T14:44:40.549562Z","shell.execute_reply.started":"2022-11-30T14:44:40.365997Z","shell.execute_reply":"2022-11-30T14:44:40.547895Z"},"trusted":true},"execution_count":84,"outputs":[{"name":"stdout","text":"Confusion Matrix : \n\n[[ 4  8]\n [ 0 44]]\n\nClassification Report : \n\n              precision    recall  f1-score   support\n\n           0       1.00      0.33      0.50        12\n           1       0.85      1.00      0.92        44\n\n    accuracy                           0.86        56\n   macro avg       0.92      0.67      0.71        56\nweighted avg       0.88      0.86      0.83        56\n\n\nThe Accuracy of Random Forest Classifier is 85.71 %\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"19\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">K Nearest Neighbors Model</p>","metadata":{}},{"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier\n\nknn = KNeighborsClassifier(n_neighbors=2)\nknn.fit(x_train, y_train)\nknn_pred = knn.predict(x_test)\nknn_conf = confusion_matrix(y_test, knn_pred)\nknn_report = classification_report(y_test, knn_pred)\nknn_acc = round(accuracy_score(y_test, knn_pred)*100, ndigits = 2)\nprint(f\"Confusion Matrix : \\n\\n{knn_conf}\")\nprint(f\"\\nClassification Report : \\n\\n{knn_report}\")\nprint(f\"\\nThe Accuracy of K Nearest Neighbors Classifier is {knn_acc} %\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:40.551621Z","iopub.execute_input":"2022-11-30T14:44:40.552023Z","iopub.status.idle":"2022-11-30T14:44:40.570671Z","shell.execute_reply.started":"2022-11-30T14:44:40.551986Z","shell.execute_reply":"2022-11-30T14:44:40.568842Z"},"trusted":true},"execution_count":85,"outputs":[{"name":"stdout","text":"Confusion Matrix : \n\n[[ 9  3]\n [ 1 43]]\n\nClassification Report : \n\n              precision    recall  f1-score   support\n\n           0       0.90      0.75      0.82        12\n           1       0.93      0.98      0.96        44\n\n    accuracy                           0.93        56\n   macro avg       0.92      0.86      0.89        56\nweighted avg       0.93      0.93      0.93        56\n\n\nThe Accuracy of K Nearest Neighbors Classifier is 92.86 %\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"20\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Extreme Gradient Boosting Model</p>","metadata":{}},{"cell_type":"code","source":"from xgboost import XGBClassifier\n\nxgb = XGBClassifier(use_label_encoder = False)\nxgb.fit(x_train, y_train)\nxgb_pred = xgb.predict(x_test)\nxgb_conf = confusion_matrix(y_test, xgb_pred)\nxgb_report = classification_report(y_test, xgb_pred)\nxgb_acc = round(accuracy_score(y_test, xgb_pred)*100, ndigits = 2)\nprint(f\"Confusion Matrix : \\n\\n{xgb_conf}\")\nprint(f\"\\nClassification Report : \\n\\n{xgb_report}\")\nprint(f\"\\nThe Accuracy of Extreme Gradient Boosting Classifier is {xgb_acc} %\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:40.572819Z","iopub.execute_input":"2022-11-30T14:44:40.573369Z","iopub.status.idle":"2022-11-30T14:44:40.994124Z","shell.execute_reply.started":"2022-11-30T14:44:40.573306Z","shell.execute_reply":"2022-11-30T14:44:40.992919Z"},"trusted":true},"execution_count":86,"outputs":[{"name":"stdout","text":"Confusion Matrix : \n\n[[ 7  5]\n [ 0 44]]\n\nClassification Report : \n\n              precision    recall  f1-score   support\n\n           0       1.00      0.58      0.74        12\n           1       0.90      1.00      0.95        44\n\n    accuracy                           0.91        56\n   macro avg       0.95      0.79      0.84        56\nweighted avg       0.92      0.91      0.90        56\n\n\nThe Accuracy of Extreme Gradient Boosting Classifier is 91.07 %\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"21\"></a>\n# <p style=\"padding:10px;background-color:orange;margin:0;color:black;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Neural Network Architecture</p>","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras import regularizers\nfrom tensorflow.keras.optimizers import Adam\n\nregularization_parameter = 0.003\n\nneural_model = Sequential([tf.keras.layers.Dense(units=32, input_dim=(x_train.shape[-1]), activation=\"relu\", kernel_regularizer = regularizers.l1(regularization_parameter)),\n                    tf.keras.layers.Dense(units=64, activation=\"relu\", kernel_regularizer = regularizers.l1(regularization_parameter)),\n                    tf.keras.layers.Dense(units=128, activation=\"relu\", kernel_regularizer = regularizers.l1(regularization_parameter)),\n                    tf.keras.layers.Dropout(0.3),\n                    tf.keras.layers.Dense(units=16,activation=\"relu\", kernel_regularizer = regularizers.l1(regularization_parameter)),\n                    tf.keras.layers.Dense(units=1, activation=\"sigmoid\")\n                    ])\n\nprint(neural_model.summary())","metadata":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:40.995940Z","iopub.execute_input":"2022-11-30T14:44:40.996996Z","iopub.status.idle":"2022-11-30T14:44:41.065711Z","shell.execute_reply.started":"2022-11-30T14:44:40.996952Z","shell.execute_reply":"2022-11-30T14:44:41.063729Z"},"trusted":true},"execution_count":87,"outputs":[{"name":"stdout","text":"Model: \"sequential_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_10 (Dense)             (None, 32)                512       \n_________________________________________________________________\ndense_11 (Dense)             (None, 64)                2112      \n_________________________________________________________________\ndense_12 (Dense)             (None, 128)               8320      \n_________________________________________________________________\ndropout_2 (Dropout)          (None, 128)               0         \n_________________________________________________________________\ndense_13 (Dense)             (None, 16)                2064      \n_________________________________________________________________\ndense_14 (Dense)             (None, 1)                 17        \n=================================================================\nTotal params: 13,025\nTrainable params: 13,025\nNon-trainable params: 0\n_________________________________________________________________\nNone\n","output_type":"stream"}]},{"cell_type":"code","source":"class myCallback(tf.keras.callbacks.Callback):\n    def on_epoch_end(self, epoch, logs={}):\n        if(logs.get(\"accuracy\") == 1.0):\n            print(\"\\nAccuracy is 100% so canceling training!\")\n            self.model.stop_training = True\n\ncallbacks = myCallback()\n\n\nneural_model.compile(optimizer = Adam(learning_rate = 0.001), \n                     loss = \"binary_crossentropy\", \n                     metrics = [\"accuracy\"])\n\nhistory = neural_model.fit(x_train, y_train,\n                           epochs = 150,\n                           verbose = 1,\n                           batch_size = 64,\n                           validation_data = (x_test, y_test),\n                           callbacks = [callbacks])","metadata":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:41.068886Z","iopub.execute_input":"2022-11-30T14:44:41.069492Z","iopub.status.idle":"2022-11-30T14:44:52.530917Z","shell.execute_reply.started":"2022-11-30T14:44:41.069418Z","shell.execute_reply":"2022-11-30T14:44:52.529372Z"},"trusted":true},"execution_count":88,"outputs":[{"name":"stdout","text":"Epoch 1/150\n4/4 [==============================] - 1s 92ms/step - loss: 4.4587 - accuracy: 0.5727 - val_loss: 4.3342 - val_accuracy: 0.7857\nEpoch 2/150\n4/4 [==============================] - 0s 18ms/step - loss: 4.2574 - accuracy: 0.8636 - val_loss: 4.1814 - val_accuracy: 0.7857\nEpoch 3/150\n4/4 [==============================] - 0s 18ms/step - loss: 4.0778 - accuracy: 0.8818 - val_loss: 4.0458 - val_accuracy: 0.7857\nEpoch 4/150\n4/4 [==============================] - 0s 16ms/step - loss: 3.9063 - accuracy: 0.8818 - val_loss: 3.9211 - val_accuracy: 0.7857\nEpoch 5/150\n4/4 [==============================] - 0s 16ms/step - loss: 3.7662 - accuracy: 0.8818 - val_loss: 3.8074 - val_accuracy: 0.7857\nEpoch 6/150\n4/4 [==============================] - 0s 16ms/step - loss: 3.6402 - accuracy: 0.8818 - val_loss: 3.7000 - val_accuracy: 0.7857\nEpoch 7/150\n4/4 [==============================] - 0s 17ms/step - loss: 3.5040 - accuracy: 0.8818 - val_loss: 3.5934 - val_accuracy: 0.7857\nEpoch 8/150\n4/4 [==============================] - 0s 17ms/step - loss: 3.3851 - accuracy: 0.8818 - val_loss: 3.4847 - val_accuracy: 0.7857\nEpoch 9/150\n4/4 [==============================] - 0s 17ms/step - loss: 3.2713 - accuracy: 0.8818 - val_loss: 3.3720 - val_accuracy: 0.7857\nEpoch 10/150\n4/4 [==============================] - 0s 17ms/step - loss: 3.1570 - accuracy: 0.8818 - val_loss: 3.2551 - val_accuracy: 0.7857\nEpoch 11/150\n4/4 [==============================] - 0s 20ms/step - loss: 3.0366 - accuracy: 0.8818 - val_loss: 3.1397 - val_accuracy: 0.7857\nEpoch 12/150\n4/4 [==============================] - 0s 17ms/step - loss: 2.9307 - accuracy: 0.8818 - val_loss: 3.0260 - val_accuracy: 0.7857\nEpoch 13/150\n4/4 [==============================] - 0s 17ms/step - loss: 2.8269 - accuracy: 0.8818 - val_loss: 2.9115 - val_accuracy: 0.7857\nEpoch 14/150\n4/4 [==============================] - 0s 16ms/step - loss: 2.7253 - accuracy: 0.8818 - val_loss: 2.8009 - val_accuracy: 0.7857\nEpoch 15/150\n4/4 [==============================] - 0s 16ms/step - loss: 2.6196 - accuracy: 0.8818 - val_loss: 2.6957 - val_accuracy: 0.7857\nEpoch 16/150\n4/4 [==============================] - 0s 18ms/step - loss: 2.5219 - accuracy: 0.8818 - val_loss: 2.5931 - val_accuracy: 0.7857\nEpoch 17/150\n4/4 [==============================] - 0s 16ms/step - loss: 2.4207 - accuracy: 0.8818 - val_loss: 2.4942 - val_accuracy: 0.7857\nEpoch 18/150\n4/4 [==============================] - 0s 17ms/step - loss: 2.3264 - accuracy: 0.8818 - val_loss: 2.3985 - val_accuracy: 0.7857\nEpoch 19/150\n4/4 [==============================] - 0s 18ms/step - loss: 2.2350 - accuracy: 0.8818 - val_loss: 2.3037 - val_accuracy: 0.7857\nEpoch 20/150\n4/4 [==============================] - 0s 18ms/step - loss: 2.1427 - accuracy: 0.8818 - val_loss: 2.2149 - val_accuracy: 0.7857\nEpoch 21/150\n4/4 [==============================] - 0s 18ms/step - loss: 2.0577 - accuracy: 0.8818 - val_loss: 2.1300 - val_accuracy: 0.7857\nEpoch 22/150\n4/4 [==============================] - 0s 19ms/step - loss: 1.9678 - accuracy: 0.8818 - val_loss: 2.0460 - val_accuracy: 0.7857\nEpoch 23/150\n4/4 [==============================] - 0s 17ms/step - loss: 1.8890 - accuracy: 0.8818 - val_loss: 1.9599 - val_accuracy: 0.7857\nEpoch 24/150\n4/4 [==============================] - 0s 17ms/step - loss: 1.8127 - accuracy: 0.8818 - val_loss: 1.8786 - val_accuracy: 0.7857\nEpoch 25/150\n4/4 [==============================] - 0s 16ms/step - loss: 1.7291 - accuracy: 0.8818 - val_loss: 1.7998 - val_accuracy: 0.7857\nEpoch 26/150\n4/4 [==============================] - 0s 17ms/step - loss: 1.6532 - accuracy: 0.8818 - val_loss: 1.7263 - val_accuracy: 0.7857\nEpoch 27/150\n4/4 [==============================] - 0s 16ms/step - loss: 1.5853 - accuracy: 0.8818 - val_loss: 1.6543 - val_accuracy: 0.7857\nEpoch 28/150\n4/4 [==============================] - 0s 16ms/step - loss: 1.5145 - accuracy: 0.8818 - val_loss: 1.5878 - val_accuracy: 0.7857\nEpoch 29/150\n4/4 [==============================] - 0s 17ms/step - loss: 1.4489 - accuracy: 0.8818 - val_loss: 1.5212 - val_accuracy: 0.7857\nEpoch 30/150\n4/4 [==============================] - 0s 17ms/step - loss: 1.3827 - accuracy: 0.8818 - val_loss: 1.4572 - val_accuracy: 0.7857\nEpoch 31/150\n4/4 [==============================] - 0s 19ms/step - loss: 1.3256 - accuracy: 0.8818 - val_loss: 1.3953 - val_accuracy: 0.7857\nEpoch 32/150\n4/4 [==============================] - 0s 17ms/step - loss: 1.2693 - accuracy: 0.8818 - val_loss: 1.3345 - val_accuracy: 0.7857\nEpoch 33/150\n4/4 [==============================] - 0s 17ms/step - loss: 1.2069 - accuracy: 0.8818 - val_loss: 1.2781 - val_accuracy: 0.7857\nEpoch 34/150\n4/4 [==============================] - 0s 16ms/step - loss: 1.1544 - accuracy: 0.8818 - val_loss: 1.2256 - val_accuracy: 0.7857\nEpoch 35/150\n4/4 [==============================] - 0s 15ms/step - loss: 1.1063 - accuracy: 0.8818 - val_loss: 1.1739 - val_accuracy: 0.7857\nEpoch 36/150\n4/4 [==============================] - 0s 17ms/step - loss: 1.0582 - accuracy: 0.8818 - val_loss: 1.1266 - val_accuracy: 0.7857\nEpoch 37/150\n4/4 [==============================] - 0s 16ms/step - loss: 1.0102 - accuracy: 0.8818 - val_loss: 1.0818 - val_accuracy: 0.7857\nEpoch 38/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.9691 - accuracy: 0.8818 - val_loss: 1.0378 - val_accuracy: 0.7857\nEpoch 39/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.9249 - accuracy: 0.8818 - val_loss: 0.9972 - val_accuracy: 0.7857\nEpoch 40/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.8894 - accuracy: 0.8818 - val_loss: 0.9591 - val_accuracy: 0.7857\nEpoch 41/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.8546 - accuracy: 0.8818 - val_loss: 0.9250 - val_accuracy: 0.7857\nEpoch 42/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.8192 - accuracy: 0.8818 - val_loss: 0.8942 - val_accuracy: 0.7857\nEpoch 43/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.7896 - accuracy: 0.8818 - val_loss: 0.8637 - val_accuracy: 0.7857\nEpoch 44/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.7643 - accuracy: 0.8909 - val_loss: 0.8349 - val_accuracy: 0.7857\nEpoch 45/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.7367 - accuracy: 0.8909 - val_loss: 0.8085 - val_accuracy: 0.7857\nEpoch 46/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.7103 - accuracy: 0.9091 - val_loss: 0.7844 - val_accuracy: 0.7857\nEpoch 47/150\n4/4 [==============================] - 0s 19ms/step - loss: 0.6922 - accuracy: 0.9091 - val_loss: 0.7628 - val_accuracy: 0.8036\nEpoch 48/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.6667 - accuracy: 0.9182 - val_loss: 0.7433 - val_accuracy: 0.8036\nEpoch 49/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.6432 - accuracy: 0.9136 - val_loss: 0.7242 - val_accuracy: 0.8036\nEpoch 50/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.6299 - accuracy: 0.9136 - val_loss: 0.7061 - val_accuracy: 0.8036\nEpoch 51/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.6150 - accuracy: 0.9227 - val_loss: 0.6898 - val_accuracy: 0.8036\nEpoch 52/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.5993 - accuracy: 0.9318 - val_loss: 0.6747 - val_accuracy: 0.8036\nEpoch 53/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.5887 - accuracy: 0.9273 - val_loss: 0.6599 - val_accuracy: 0.8036\nEpoch 54/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.5690 - accuracy: 0.9318 - val_loss: 0.6468 - val_accuracy: 0.8036\nEpoch 55/150\n4/4 [==============================] - 0s 19ms/step - loss: 0.5568 - accuracy: 0.9273 - val_loss: 0.6334 - val_accuracy: 0.8036\nEpoch 56/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.5435 - accuracy: 0.9273 - val_loss: 0.6205 - val_accuracy: 0.8214\nEpoch 57/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.5330 - accuracy: 0.9318 - val_loss: 0.6074 - val_accuracy: 0.8214\nEpoch 58/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.5207 - accuracy: 0.9182 - val_loss: 0.5958 - val_accuracy: 0.8929\nEpoch 59/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.5086 - accuracy: 0.9227 - val_loss: 0.5834 - val_accuracy: 0.8929\nEpoch 60/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.5042 - accuracy: 0.9273 - val_loss: 0.5742 - val_accuracy: 0.8929\nEpoch 61/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.4903 - accuracy: 0.9273 - val_loss: 0.5657 - val_accuracy: 0.8929\nEpoch 62/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.4830 - accuracy: 0.9318 - val_loss: 0.5587 - val_accuracy: 0.8929\nEpoch 63/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.4750 - accuracy: 0.9227 - val_loss: 0.5524 - val_accuracy: 0.8929\nEpoch 64/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.4674 - accuracy: 0.9227 - val_loss: 0.5445 - val_accuracy: 0.9107\nEpoch 65/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.4622 - accuracy: 0.9318 - val_loss: 0.5370 - val_accuracy: 0.9107\nEpoch 66/150\n4/4 [==============================] - 0s 19ms/step - loss: 0.4557 - accuracy: 0.9273 - val_loss: 0.5305 - val_accuracy: 0.9107\nEpoch 67/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.4493 - accuracy: 0.9318 - val_loss: 0.5240 - val_accuracy: 0.9107\nEpoch 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96/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.3385 - accuracy: 0.9409 - val_loss: 0.4096 - val_accuracy: 0.9107\nEpoch 97/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.3391 - accuracy: 0.9500 - val_loss: 0.4075 - val_accuracy: 0.9107\nEpoch 98/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.3372 - accuracy: 0.9500 - val_loss: 0.4052 - val_accuracy: 0.9107\nEpoch 99/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.3293 - accuracy: 0.9500 - val_loss: 0.4031 - val_accuracy: 0.9107\nEpoch 100/150\n4/4 [==============================] - 0s 19ms/step - loss: 0.3306 - accuracy: 0.9500 - val_loss: 0.4011 - val_accuracy: 0.9107\nEpoch 101/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.3296 - accuracy: 0.9591 - val_loss: 0.3981 - val_accuracy: 0.9107\nEpoch 102/150\n4/4 [==============================] - 0s 19ms/step - loss: 0.3252 - accuracy: 0.9545 - val_loss: 0.3957 - val_accuracy: 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0.3647 - val_accuracy: 0.9464\nEpoch 117/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.3000 - accuracy: 0.9409 - val_loss: 0.3621 - val_accuracy: 0.9464\nEpoch 118/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.2954 - accuracy: 0.9455 - val_loss: 0.3596 - val_accuracy: 0.9464\nEpoch 119/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2969 - accuracy: 0.9591 - val_loss: 0.3578 - val_accuracy: 0.9464\nEpoch 120/150\n4/4 [==============================] - 0s 20ms/step - loss: 0.2916 - accuracy: 0.9455 - val_loss: 0.3568 - val_accuracy: 0.9464\nEpoch 121/150\n4/4 [==============================] - 0s 19ms/step - loss: 0.2908 - accuracy: 0.9500 - val_loss: 0.3559 - val_accuracy: 0.9464\nEpoch 122/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2880 - accuracy: 0.9500 - val_loss: 0.3538 - val_accuracy: 0.9464\nEpoch 123/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2944 - accuracy: 0.9500 - val_loss: 0.3523 - val_accuracy: 0.9464\nEpoch 124/150\n4/4 [==============================] - 0s 20ms/step - loss: 0.2875 - accuracy: 0.9500 - val_loss: 0.3507 - val_accuracy: 0.9464\nEpoch 125/150\n4/4 [==============================] - 0s 19ms/step - loss: 0.2869 - accuracy: 0.9500 - val_loss: 0.3501 - val_accuracy: 0.9464\nEpoch 126/150\n4/4 [==============================] - 0s 20ms/step - loss: 0.2872 - accuracy: 0.9545 - val_loss: 0.3500 - val_accuracy: 0.9464\nEpoch 127/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2827 - accuracy: 0.9500 - val_loss: 0.3497 - val_accuracy: 0.9464\nEpoch 128/150\n4/4 [==============================] - 0s 19ms/step - loss: 0.2844 - accuracy: 0.9545 - val_loss: 0.3481 - val_accuracy: 0.9464\nEpoch 129/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2788 - accuracy: 0.9500 - val_loss: 0.3455 - val_accuracy: 0.9464\nEpoch 130/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.2787 - accuracy: 0.9455 - val_loss: 0.3440 - val_accuracy: 0.9464\nEpoch 131/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2811 - accuracy: 0.9500 - val_loss: 0.3433 - val_accuracy: 0.9464\nEpoch 132/150\n4/4 [==============================] - 0s 19ms/step - loss: 0.2783 - accuracy: 0.9500 - val_loss: 0.3422 - val_accuracy: 0.9464\nEpoch 133/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2752 - accuracy: 0.9545 - val_loss: 0.3403 - val_accuracy: 0.9464\nEpoch 134/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2751 - accuracy: 0.9500 - val_loss: 0.3383 - val_accuracy: 0.9464\nEpoch 135/150\n4/4 [==============================] - 0s 19ms/step - loss: 0.2735 - accuracy: 0.9545 - val_loss: 0.3365 - val_accuracy: 0.9464\nEpoch 136/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.2737 - accuracy: 0.9545 - val_loss: 0.3354 - val_accuracy: 0.9464\nEpoch 137/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.2699 - accuracy: 0.9591 - val_loss: 0.3349 - val_accuracy: 0.9464\nEpoch 138/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.2724 - accuracy: 0.9500 - val_loss: 0.3345 - val_accuracy: 0.9464\nEpoch 139/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.2692 - accuracy: 0.9500 - val_loss: 0.3324 - val_accuracy: 0.9464\nEpoch 140/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.2718 - accuracy: 0.9500 - val_loss: 0.3312 - val_accuracy: 0.9464\nEpoch 141/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2700 - accuracy: 0.9500 - val_loss: 0.3308 - val_accuracy: 0.9464\nEpoch 142/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.2653 - accuracy: 0.9455 - val_loss: 0.3295 - val_accuracy: 0.9464\nEpoch 143/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.2662 - accuracy: 0.9545 - val_loss: 0.3280 - val_accuracy: 0.9464\nEpoch 144/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.2710 - accuracy: 0.9500 - val_loss: 0.3263 - val_accuracy: 0.9464\nEpoch 145/150\n4/4 [==============================] - 0s 16ms/step - loss: 0.2661 - accuracy: 0.9455 - val_loss: 0.3235 - val_accuracy: 0.9464\nEpoch 146/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.2683 - accuracy: 0.9455 - val_loss: 0.3228 - val_accuracy: 0.9464\nEpoch 147/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.2655 - accuracy: 0.9591 - val_loss: 0.3213 - val_accuracy: 0.9464\nEpoch 148/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2606 - accuracy: 0.9455 - val_loss: 0.3207 - val_accuracy: 0.9464\nEpoch 149/150\n4/4 [==============================] - 0s 18ms/step - loss: 0.2611 - accuracy: 0.9545 - val_loss: 0.3195 - val_accuracy: 0.9464\nEpoch 150/150\n4/4 [==============================] - 0s 17ms/step - loss: 0.2587 - accuracy: 0.9500 - val_loss: 0.3184 - val_accuracy: 0.9464\n","output_type":"stream"}]},{"cell_type":"code","source":"acc = history.history[\"accuracy\"]\nval_acc = history.history[\"val_accuracy\"]\nloss = history.history[\"loss\"]\nval_loss = history.history[\"val_loss\"]\n\nepochs = range(len(acc)) # number of epochs\n\nplt.figure(figsize=(20, 12))\nplt.subplot(2,1,1)\nplt.plot(epochs, acc, \"yellow\", label= \"Training Accuracy\")\nplt.plot(epochs, val_acc, \"black\", label= \"Validation Accuracy\")\nplt.title(\"Training and validation accuracy\")\nplt.legend()\n\nplt.subplot(2,1,2)\nplt.plot(epochs, loss, \"yellow\", label= \"Training Loss\")\nplt.plot(epochs, val_loss, \"black\", label= \"Validation Loss\")\nplt.title(\"Training and validation loss\")\nplt.legend()\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-11-30T14:44:52.533011Z","iopub.execute_input":"2022-11-30T14:44:52.533540Z","iopub.status.idle":"2022-11-30T14:44:53.115632Z","shell.execute_reply.started":"2022-11-30T14:44:52.533489Z","shell.execute_reply":"2022-11-30T14:44:53.114025Z"},"trusted":true},"execution_count":89,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x864 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"22\"></a>\n# <p style=\"padding:10px;background-color:#0f4c5c;margin:0;color:white;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Thank You</p>\n\n\n<p>\n<h3><font color=\"#0f4c5c\">If you liked this notebook please upvote. Your feedback will be highly appreciated.</font></h3>\n\n<br>\n\n<h4><b>Author :</b></h4>\n\n<h3>Hasib Al Muzdadid</h3>\n\n<b>👉Shoot me mails :</b> muzdadid@gmail.com<br>\n<b>👉Connect on LinkedIn :</b> https://www.linkedin.com/in/hasibalmuzdadid <br>\n<b>👉Explore Github :</b> https://github.com/HasibAlMuzdadid    \n    \n</p> \n","metadata":{}}]}