covid-19.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Introductıon\n","* In this notebook i will create a ML model to predict death situation of covid 19."]},{"cell_type":"markdown","metadata":{},"source":["### The notebook includes:\n","* Data Importing\n","* Data Preprocessing\n","* Data Preparing & Cleaning\n","* Missing Value Analysis\n","* Data Visualization\n","* Future Selection\n","* Creating Model\n","* Model Analysis\n","* Improving Model\n","* Solving Imbalance Dataset Problem With Undersampling"]},{"cell_type":"code","execution_count":48,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:40.023923Z","iopub.status.busy":"2022-12-08T18:11:40.022687Z","iopub.status.idle":"2022-12-08T18:11:40.839605Z","shell.execute_reply":"2022-12-08T18:11:40.837409Z","shell.execute_reply.started":"2022-12-08T18:11:40.023713Z"},"trusted":true},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"markdown","metadata":{"execution":{"iopub.execute_input":"2022-12-08T15:22:38.109187Z","iopub.status.busy":"2022-12-08T15:22:38.108434Z","iopub.status.idle":"2022-12-08T15:22:38.121459Z","shell.execute_reply":"2022-12-08T15:22:38.119817Z","shell.execute_reply.started":"2022-12-08T15:22:38.109135Z"}},"source":["# Abot The Dataset\n","#### This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients. In the Boolean features, 1 means \"yes\" and 2 means \"no\". values as 97 and 99 are missing data.\n","\n","* sex: female or male\n","* age: of the patient.\n","* classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different\n","* degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.\n","* patient type: hospitalized or not hospitalized.\n","* pneumonia: whether the patient already have air sacs inflammation or not.\n","* pregnancy: whether the patient is pregnant or not.\n","* diabetes: whether the patient has diabetes or not.\n","* copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.\n","* asthma: whether the patient has asthma or not.\n","* inmsupr: whether the patient is immunosuppressed or not.\n","* hypertension: whether the patient has hypertension or not.\n","* cardiovascular: whether the patient has heart or blood vessels related disease.\n","* renal chronic: whether the patient has chronic renal disease or not.\n","* other disease: whether the patient has other disease or not.\n","* obesity: whether the patient is obese or not.\n","* tobacco: whether the patient is a tobacco user.\n","* usmr: Indicates whether the patient treated medical units of the first, second or third level.\n","* medical unit: type of institution of the National Health System that provided the care.\n","* intubed: whether the patient was connected to the ventilator.\n","* icu: Indicates whether the patient had been admitted to an Intensive Care Unit.\n","* death: indicates whether the patient died or recovered."]},{"cell_type":"markdown","metadata":{},"source":["# Overview Dataset"]},{"cell_type":"code","execution_count":49,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:40.84405Z","iopub.status.busy":"2022-12-08T18:11:40.843501Z","iopub.status.idle":"2022-12-08T18:11:43.659892Z","shell.execute_reply":"2022-12-08T18:11:43.65876Z","shell.execute_reply.started":"2022-12-08T18:11:40.844Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Shape of df : (1048575, 21)\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>USMER</th>\n","      <th>MEDICAL_UNIT</th>\n","      <th>SEX</th>\n","      <th>PATIENT_TYPE</th>\n","      <th>DATE_DIED</th>\n","      <th>INTUBED</th>\n","      <th>PNEUMONIA</th>\n","      <th>AGE</th>\n","      <th>PREGNANT</th>\n","      <th>DIABETES</th>\n","      <th>...</th>\n","      <th>ASTHMA</th>\n","      <th>INMSUPR</th>\n","      <th>HIPERTENSION</th>\n","      <th>OTHER_DISEASE</th>\n","      <th>CARDIOVASCULAR</th>\n","      <th>OBESITY</th>\n","      <th>RENAL_CHRONIC</th>\n","      <th>TOBACCO</th>\n","      <th>CLASIFFICATION_FINAL</th>\n","      <th>ICU</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>03/05/2020</td>\n","      <td>97</td>\n","      <td>1</td>\n","      <td>65</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>97</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>03/06/2020</td>\n","      <td>97</td>\n","      <td>1</td>\n","      <td>72</td>\n","      <td>97</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>97</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>09/06/2020</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>55</td>\n","      <td>97</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>12/06/2020</td>\n","      <td>97</td>\n","      <td>2</td>\n","      <td>53</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>7</td>\n","      <td>97</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>21/06/2020</td>\n","      <td>97</td>\n","      <td>2</td>\n","      <td>68</td>\n","      <td>97</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>97</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 21 columns</p>\n","</div>"],"text/plain":["   USMER  MEDICAL_UNIT  SEX  PATIENT_TYPE   DATE_DIED  INTUBED  PNEUMONIA  \\\n","0      2             1    1             1  03/05/2020       97          1   \n","1      2             1    2             1  03/06/2020       97          1   \n","2      2             1    2             2  09/06/2020        1          2   \n","3      2             1    1             1  12/06/2020       97          2   \n","4      2             1    2             1  21/06/2020       97          2   \n","\n","   AGE  PREGNANT  DIABETES  ...  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n","0   65         2         2  ...       2        2             1              2   \n","1   72        97         2  ...       2        2             1              2   \n","2   55        97         1  ...       2        2             2              2   \n","3   53         2         2  ...       2        2             2              2   \n","4   68        97         1  ...       2        2             1              2   \n","\n","   CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  CLASIFFICATION_FINAL  ICU  \n","0               2        2              2        2                     3   97  \n","1               2        1              1        2                     5   97  \n","2               2        2              2        2                     3    2  \n","3               2        2              2        2                     7   97  \n","4               2        2              2        2                     3   97  \n","\n","[5 rows x 21 columns]"]},"execution_count":49,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv(\"./Covid Data.csv\")\n","print(\"Shape of df :\",df.shape)\n","df.head()"]},{"cell_type":"code","execution_count":64,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:43.662164Z","iopub.status.busy":"2022-12-08T18:11:43.661432Z","iopub.status.idle":"2022-12-08T18:11:43.830207Z","shell.execute_reply":"2022-12-08T18:11:43.828978Z","shell.execute_reply.started":"2022-12-08T18:11:43.662119Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1048575 entries, 0 to 1048574\n","Data columns (total 21 columns):\n"," #   Column                Non-Null Count    Dtype \n","---  ------                --------------    ----- \n"," 0   USMER                 1048575 non-null  int64 \n"," 1   MEDICAL_UNIT          1048575 non-null  int64 \n"," 2   SEX                   1048575 non-null  int64 \n"," 3   PATIENT_TYPE          1048575 non-null  int64 \n"," 4   DATE_DIED             1048575 non-null  object\n"," 5   INTUBED               1048575 non-null  int64 \n"," 6   PNEUMONIA             1048575 non-null  int64 \n"," 7   AGE                   1048575 non-null  int64 \n"," 8   PREGNANT              1048575 non-null  int64 \n"," 9   DIABETES              1048575 non-null  int64 \n"," 10  COPD                  1048575 non-null  int64 \n"," 11  ASTHMA                1048575 non-null  int64 \n"," 12  INMSUPR               1048575 non-null  int64 \n"," 13  HIPERTENSION          1048575 non-null  int64 \n"," 14  OTHER_DISEASE         1048575 non-null  int64 \n"," 15  CARDIOVASCULAR        1048575 non-null  int64 \n"," 16  OBESITY               1048575 non-null  int64 \n"," 17  RENAL_CHRONIC         1048575 non-null  int64 \n"," 18  TOBACCO               1048575 non-null  int64 \n"," 19  CLASIFFICATION_FINAL  1048575 non-null  int64 \n"," 20  ICU                   1048575 non-null  int64 \n","dtypes: int64(20), object(1)\n","memory usage: 168.0+ MB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":65,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:43.83408Z","iopub.status.busy":"2022-12-08T18:11:43.833237Z","iopub.status.idle":"2022-12-08T18:11:43.926292Z","shell.execute_reply":"2022-12-08T18:11:43.925351Z","shell.execute_reply.started":"2022-12-08T18:11:43.834016Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0"]},"execution_count":65,"metadata":{},"output_type":"execute_result"}],"source":["df.isna().sum().sum()"]},{"cell_type":"markdown","metadata":{},"source":["* We have no NaN values but we will have missing values."]},{"cell_type":"markdown","metadata":{},"source":["### Number of unique values by columns"]},{"cell_type":"code","execution_count":66,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:43.928616Z","iopub.status.busy":"2022-12-08T18:11:43.927849Z","iopub.status.idle":"2022-12-08T18:11:44.172061Z","shell.execute_reply":"2022-12-08T18:11:44.170794Z","shell.execute_reply.started":"2022-12-08T18:11:43.928559Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["USMER =>\t 2\n","MEDICAL_UNIT =>\t 13\n","SEX =>\t 2\n","PATIENT_TYPE =>\t 2\n","DATE_DIED =>\t 401\n","INTUBED =>\t 4\n","PNEUMONIA =>\t 3\n","AGE =>\t 121\n","PREGNANT =>\t 4\n","DIABETES =>\t 3\n","COPD =>\t 3\n","ASTHMA =>\t 3\n","INMSUPR =>\t 3\n","HIPERTENSION =>\t 3\n","OTHER_DISEASE =>\t 3\n","CARDIOVASCULAR =>\t 3\n","OBESITY =>\t 3\n","RENAL_CHRONIC =>\t 3\n","TOBACCO =>\t 3\n","CLASIFFICATION_FINAL =>\t 7\n","ICU =>\t 4\n"]}],"source":["for i in df.columns:\n","    print(i,\"=>\\t\",len(df[i].unique()))"]},{"cell_type":"code","execution_count":67,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:44.173889Z","iopub.status.busy":"2022-12-08T18:11:44.173519Z","iopub.status.idle":"2022-12-08T18:11:44.260637Z","shell.execute_reply":"2022-12-08T18:11:44.258844Z","shell.execute_reply.started":"2022-12-08T18:11:44.173854Z"},"trusted":true},"outputs":[{"data":{"text/plain":["9999-99-99    971633\n","06/07/2020      1000\n","07/07/2020       996\n","13/07/2020       990\n","16/06/2020       979\n","               ...  \n","24/11/2020         1\n","17/12/2020         1\n","08/12/2020         1\n","16/03/2021         1\n","22/04/2021         1\n","Name: DATE_DIED, Length: 401, dtype: int64"]},"execution_count":67,"metadata":{},"output_type":"execute_result"}],"source":["df.DATE_DIED.value_counts()"]},{"cell_type":"code","execution_count":68,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:44.263615Z","iopub.status.busy":"2022-12-08T18:11:44.26281Z","iopub.status.idle":"2022-12-08T18:11:44.282429Z","shell.execute_reply":"2022-12-08T18:11:44.280991Z","shell.execute_reply.started":"2022-12-08T18:11:44.263574Z"},"trusted":true},"outputs":[{"data":{"text/plain":["2     892534\n","1     140038\n","99     16003\n","Name: PNEUMONIA, dtype: int64"]},"execution_count":68,"metadata":{},"output_type":"execute_result"}],"source":["df.PNEUMONIA.value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["### INFERENCE:\n","* 1) We have some features that we expect them to have just 2 unique values but we see that these features have 3 or 4 unique values. For example the feature \"PNEUMONIA\" has 3 unique values (1,2,99) 99 represents NaN values. Hence we will just take the rows that includes 1 and 2 values.\n","#### \n","* 2) In \"DATE_DIED\" column, we have 971633 \"9999-99-99\" values which represent alive patients so i will take this feature as a \"DEATH\" that includes wether the patient died or not."]},{"cell_type":"markdown","metadata":{},"source":["# Data Preprocessing"]},{"cell_type":"markdown","metadata":{},"source":["### Getting rid of the missing values of features except \"INTUBED\", \"PREGNANT\", \"ICU\""]},{"cell_type":"code","execution_count":50,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:44.285531Z","iopub.status.busy":"2022-12-08T18:11:44.28418Z","iopub.status.idle":"2022-12-08T18:11:45.851343Z","shell.execute_reply":"2022-12-08T18:11:45.849917Z","shell.execute_reply.started":"2022-12-08T18:11:44.285462Z"},"trusted":true},"outputs":[],"source":["df = df[(df.PNEUMONIA == 1) | (df.PNEUMONIA == 2)]\n","df = df[(df.DIABETES == 1) | (df.DIABETES == 2)]\n","df = df[(df.COPD == 1) | (df.COPD == 2)]\n","df = df[(df.ASTHMA == 1) | (df.ASTHMA == 2)]\n","df = df[(df.INMSUPR == 1) | (df.INMSUPR == 2)]\n","df = df[(df.HIPERTENSION == 1) | (df.HIPERTENSION == 2)]\n","df = df[(df.OTHER_DISEASE == 1) | (df.OTHER_DISEASE == 2)]\n","df = df[(df.CARDIOVASCULAR == 1) | (df.CARDIOVASCULAR == 2)]\n","df = df[(df.OBESITY == 1) | (df.OBESITY == 2)]\n","df = df[(df.RENAL_CHRONIC == 1) | (df.RENAL_CHRONIC == 2)]\n","df = df[(df.TOBACCO == 1) | (df.TOBACCO == 2)]"]},{"cell_type":"markdown","metadata":{},"source":["### Preaparing \"DATE_DIED\" column"]},{"cell_type":"code","execution_count":51,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:45.85381Z","iopub.status.busy":"2022-12-08T18:11:45.853369Z","iopub.status.idle":"2022-12-08T18:11:46.305633Z","shell.execute_reply":"2022-12-08T18:11:46.304005Z","shell.execute_reply.started":"2022-12-08T18:11:45.853763Z"},"trusted":true},"outputs":[],"source":["# If we have \"9999-99-99\" values that means this patient is alive.\n","\n","df[\"DEATH\"] = [0 if each==\"9999-99-99\" else 1 for each in df.DATE_DIED]"]},{"cell_type":"markdown","metadata":{},"source":["### Pregnant-Sex Bar Plot"]},{"cell_type":"code","execution_count":71,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:46.311699Z","iopub.status.busy":"2022-12-08T18:11:46.310109Z","iopub.status.idle":"2022-12-08T18:11:46.854872Z","shell.execute_reply":"2022-12-08T18:11:46.853238Z","shell.execute_reply.started":"2022-12-08T18:11:46.311652Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.figure()\n","ax = sns.countplot(df.SEX, hue = df.PREGNANT)\n","for bars in ax.containers:\n","    ax.bar_label(bars)\n","plt.title(\"PREGNANCIES - SEX\",color=\"red\");"]},{"cell_type":"markdown","metadata":{},"source":["* We see that all \"97\" values are for males and males can not be pregnant so we will convert 97 to 2."]},{"cell_type":"code","execution_count":52,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:46.857228Z","iopub.status.busy":"2022-12-08T18:11:46.85674Z","iopub.status.idle":"2022-12-08T18:11:47.145883Z","shell.execute_reply":"2022-12-08T18:11:47.144822Z","shell.execute_reply.started":"2022-12-08T18:11:46.857179Z"},"trusted":true},"outputs":[],"source":["# Converting process according to inference above\n","df.PREGNANT = df.PREGNANT.replace(97,2)\n","\n","# Getting rid of the missing values\n","df = df[(df.PREGNANT == 1) | (df.PREGNANT == 2)]"]},{"cell_type":"markdown","metadata":{},"source":["### Missing value analysis of \"INTUBED\" feature"]},{"cell_type":"code","execution_count":73,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:47.148212Z","iopub.status.busy":"2022-12-08T18:11:47.147456Z","iopub.status.idle":"2022-12-08T18:11:47.501144Z","shell.execute_reply":"2022-12-08T18:11:47.499453Z","shell.execute_reply.started":"2022-12-08T18:11:47.148165Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["ax = sns.countplot(df.INTUBED)\n","plt.bar_label(ax.containers[0])\n","plt.title(\"Count of Intubed\",color=\"red\");"]},{"cell_type":"markdown","metadata":{},"source":["### Missing value analysis of \"ICU\" feature"]},{"cell_type":"code","execution_count":74,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:47.503657Z","iopub.status.busy":"2022-12-08T18:11:47.503136Z","iopub.status.idle":"2022-12-08T18:11:47.86024Z","shell.execute_reply":"2022-12-08T18:11:47.858766Z","shell.execute_reply.started":"2022-12-08T18:11:47.5036Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["ax = sns.countplot(df.ICU)\n","plt.bar_label(ax.containers[0])\n","plt.title(\"Count of ICU\",color=\"red\");"]},{"cell_type":"markdown","metadata":{},"source":["### Droping the columns\n","* In \"INTUBED\" and \"ICU\" features there are too many missing values so i will drop them. Also we don't need \"DATE_DIED\" column anymore because we used this feature as a \"DEATH\" feature."]},{"cell_type":"code","execution_count":53,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:47.862992Z","iopub.status.busy":"2022-12-08T18:11:47.861823Z","iopub.status.idle":"2022-12-08T18:11:47.934817Z","shell.execute_reply":"2022-12-08T18:11:47.933599Z","shell.execute_reply.started":"2022-12-08T18:11:47.862952Z"},"trusted":true},"outputs":[],"source":["#df = df[(df.INTUBED == 1) | (df.INTUBED == 2)]\n","#df = df[(df.ICU == 1) | (df.ICU == 2)]\n","#df.drop(columns=\"DATE_DIED\", inplace=True)\n","df.drop(columns=[\"INTUBED\",\"ICU\",\"DATE_DIED\"], inplace=True)"]},{"cell_type":"markdown","metadata":{},"source":["### Number of unique values by columns "]},{"cell_type":"code","execution_count":76,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:47.93833Z","iopub.status.busy":"2022-12-08T18:11:47.937451Z","iopub.status.idle":"2022-12-08T18:11:48.07275Z","shell.execute_reply":"2022-12-08T18:11:48.071376Z","shell.execute_reply.started":"2022-12-08T18:11:47.938275Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["USMER =>\t 2\n","MEDICAL_UNIT =>\t 13\n","SEX =>\t 2\n","PATIENT_TYPE =>\t 2\n","PNEUMONIA =>\t 2\n","AGE =>\t 121\n","PREGNANT =>\t 2\n","DIABETES =>\t 2\n","COPD =>\t 2\n","ASTHMA =>\t 2\n","INMSUPR =>\t 2\n","HIPERTENSION =>\t 2\n","OTHER_DISEASE =>\t 2\n","CARDIOVASCULAR =>\t 2\n","OBESITY =>\t 2\n","RENAL_CHRONIC =>\t 2\n","TOBACCO =>\t 2\n","CLASIFFICATION_FINAL =>\t 7\n","DEATH =>\t 2\n"]}],"source":["for i in df.columns:\n","    print(i,\"=>\\t\",len(df[i].unique()))"]},{"cell_type":"markdown","metadata":{},"source":["* As we see, we have just one numeric feature which is called \"AGE\" the rest of them are categorical."]},{"cell_type":"code","execution_count":54,"metadata":{},"outputs":[],"source":["df.mask(df==2, df-2,inplace=True)"]},{"cell_type":"code","execution_count":78,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>USMER</th>\n","      <th>MEDICAL_UNIT</th>\n","      <th>SEX</th>\n","      <th>PATIENT_TYPE</th>\n","      <th>PNEUMONIA</th>\n","      <th>AGE</th>\n","      <th>PREGNANT</th>\n","      <th>DIABETES</th>\n","      <th>COPD</th>\n","      <th>ASTHMA</th>\n","      <th>INMSUPR</th>\n","      <th>HIPERTENSION</th>\n","      <th>OTHER_DISEASE</th>\n","      <th>CARDIOVASCULAR</th>\n","      <th>OBESITY</th>\n","      <th>RENAL_CHRONIC</th>\n","      <th>TOBACCO</th>\n","      <th>CLASIFFICATION_FINAL</th>\n","      <th>DEATH</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>65</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>72</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>55</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>68</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1048570</th>\n","      <td>0</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>40</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1048571</th>\n","      <td>1</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>51</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1048572</th>\n","      <td>0</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>55</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1048573</th>\n","      <td>0</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>28</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1048574</th>\n","      <td>0</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>52</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1021977 rows × 19 columns</p>\n","</div>"],"text/plain":["         USMER  MEDICAL_UNIT  SEX  PATIENT_TYPE  PNEUMONIA  AGE  PREGNANT  \\\n","0            0             1    1             1          1   65         0   \n","1            0             1    0             1          1   72         0   \n","2            0             1    0             0          0   55         0   \n","3            0             1    1             1          0   53         0   \n","4            0             1    0             1          0   68         0   \n","...        ...           ...  ...           ...        ...  ...       ...   \n","1048570      0            13    0             1          0   40         0   \n","1048571      1            13    0             0          0   51         0   \n","1048572      0            13    0             1          0   55         0   \n","1048573      0            13    0             1          0   28         0   \n","1048574      0            13    0             1          0   52         0   \n","\n","         DIABETES  COPD  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n","0               0     0       0        0             1              0   \n","1               0     0       0        0             1              0   \n","2               1     0       0        0             0              0   \n","3               0     0       0        0             0              0   \n","4               1     0       0        0             1              0   \n","...           ...   ...     ...      ...           ...            ...   \n","1048570         0     0       0        0             0              0   \n","1048571         0     0       0        0             1              0   \n","1048572         0     0       0        0             0              0   \n","1048573         0     0       0        0             0              0   \n","1048574         0     0       0        0             0              0   \n","\n","         CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  \\\n","0                     0        0              0        0   \n","1                     0        1              1        0   \n","2                     0        0              0        0   \n","3                     0        0              0        0   \n","4                     0        0              0        0   \n","...                 ...      ...            ...      ...   \n","1048570               0        0              0        0   \n","1048571               0        0              0        0   \n","1048572               0        0              0        0   \n","1048573               0        0              0        0   \n","1048574               0        0              0        0   \n","\n","         CLASIFFICATION_FINAL  DEATH  \n","0                           3      1  \n","1                           5      1  \n","2                           3      1  \n","3                           7      1  \n","4                           3      1  \n","...                       ...    ...  \n","1048570                     7      0  \n","1048571                     7      0  \n","1048572                     7      0  \n","1048573                     7      0  \n","1048574                     7      0  \n","\n","[1021977 rows x 19 columns]"]},"execution_count":78,"metadata":{},"output_type":"execute_result"}],"source":["df"]},{"cell_type":"markdown","metadata":{},"source":["# Data Visualization"]},{"cell_type":"code","execution_count":79,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:48.076155Z","iopub.status.busy":"2022-12-08T18:11:48.07521Z","iopub.status.idle":"2022-12-08T18:11:48.401735Z","shell.execute_reply":"2022-12-08T18:11:48.400141Z","shell.execute_reply.started":"2022-12-08T18:11:48.076107Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["ax = sns.countplot(df.DEATH,palette=sns.cubehelix_palette(2))\n","plt.bar_label(ax.containers[0])\n","plt.title(\"Death Distribution\", fontsize=18,color=\"red\");"]},{"cell_type":"markdown","metadata":{},"source":["* The values are distributed unbalanced in target column. This will probably cause an imbalance problem. Nonetheless i want you to see the problem. Hence i will go ahead for now and if we meet this problem i will try to figure it out."]},{"cell_type":"code","execution_count":80,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:48.407284Z","iopub.status.busy":"2022-12-08T18:11:48.406367Z","iopub.status.idle":"2022-12-08T18:11:49.445782Z","shell.execute_reply":"2022-12-08T18:11:49.444399Z","shell.execute_reply.started":"2022-12-08T18:11:48.407211Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.histplot(x=df.AGE)\n","plt.title(\"Age Distribution\", color=\"red\", fontsize=18);"]},{"cell_type":"markdown","metadata":{},"source":["* Patients are roughly between 20-60 years old."]},{"cell_type":"code","execution_count":81,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:49.448564Z","iopub.status.busy":"2022-12-08T18:11:49.447994Z","iopub.status.idle":"2022-12-08T18:11:49.832609Z","shell.execute_reply":"2022-12-08T18:11:49.831204Z","shell.execute_reply.started":"2022-12-08T18:11:49.448504Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.boxplot(x=\"DEATH\", y=\"AGE\",data=df,palette=sns.color_palette([\"#2f4f4f\",\"#eedd82\"]))\n","plt.title(\"Age-DEATH\",fontsize=18, color=\"red\");"]},{"cell_type":"markdown","metadata":{},"source":["* The older patients are more likely to die compare to younger ones."]},{"cell_type":"code","execution_count":82,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:49.836138Z","iopub.status.busy":"2022-12-08T18:11:49.835136Z","iopub.status.idle":"2022-12-08T18:11:50.422731Z","shell.execute_reply":"2022-12-08T18:11:50.421715Z","shell.execute_reply.started":"2022-12-08T18:11:49.836074Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.boxplot(x=\"DEATH\", y=\"AGE\",hue=\"SEX\",data=df,palette=sns.color_palette([\"#2f4f4f\",\"#eedd82\"]))\n","plt.title(\"Age-Death-Sex\",fontsize=18, color=\"red\")\n","plt.legend(loc=\"best\");"]},{"cell_type":"markdown","metadata":{},"source":["* The older patients are more likely to die compare to younger ones.\n","* There is no serious difference between males and females in terms of average rate of patients."]},{"cell_type":"code","execution_count":83,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:50.425074Z","iopub.status.busy":"2022-12-08T18:11:50.42401Z","iopub.status.idle":"2022-12-08T18:11:50.840902Z","shell.execute_reply":"2022-12-08T18:11:50.838608Z","shell.execute_reply.started":"2022-12-08T18:11:50.425031Z"},"trusted":true},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZcAAAEaCAYAAADdSBoLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZeUlEQVR4nO3de7xdZX3n8c8PQgzKHQKlOdgEybQGaxXCraijoBBSh1CLDqgl3JoZhQ6dTqvxMlJvM7bTjgVB+2IkkmhLoFgkIoLxgk473IJyp8oRdDgZkOSEqxqQ8Js/1nNkc9jnZEeevffJOZ/367VfZ6/fetZ6ng3J+Wat9ay1IzORJKmmbfo9AEnS5GO4SJKqM1wkSdUZLpKk6gwXSVJ1hoskqTrDRdKWiUgiLur3MDSxGS6aWiL2JeICIv6ViJ8R8TARdxOxnIg39HFc2fJ6hojHibiXiMuJOIWI7Xs8nr8g4rie9qlJZVq/ByD1TMR84NvAL4AVwJ3A9sBc4CjgceBbfRsf3AL8TXn/YuClNONaBnyAiD8g89YejeVsYDnwpR71p0nGcNFUcjbNL+1Xtf0lHfFrPR/Rc60l8wujah8k4q3A3wNfJWJ/Mh/uw9ikLeJpMU0lc4HhMf/1n/ngc5Yj3kjE14h4hIiNRNxGxH8c1eYSIjYR8fpR9aPL6a0VL3jUmf8I/BWwN3DGqH6CiHcRcXM5zfcEEd9qe4ov4t3l86wl4ikiHiDiC0TMbmkzm4iRZ0Itfs7puufv7zAivk3ET4kYJuKzROzwgj+vJoXw2WKaMiK+AiwE/oDMf9pM2yXA3wHXA5cDPwXeBBwH/DWZf17a7Qx8D5hOc0S0vhwB3Qo8ChxA5hMdjC2Br5D55jHWzwbuA64n87CW+heAE4HLgP8NvAh4B/BK4C1krmppe2/5PLcCG4BXAKeXcf42mcNEvAT4feDzZX8X/HL7kaOqZqy3AvsAnwPuAV4PnAD8LzKXbPbzavLLTF++psYLDkt4KiETfpCwLOFdCS8f1W7vhI0J/9BmH+ckbErYt6V2SNnvlxO2SVid8GTCAVswtky4cjNtHksYbln+/bLdklHtpiWsSbgvyz8gS/0lbfZ5ZNnHe9qM56JxxvpMwiGj6l9J+EXCDn3/f+2r7y9Pi2nqyLwOOJDmQvXOwCnAp4G7iPgOEfuWlsfTHAFcSMQez3nBl2lOJ7+xZb83AB8E3gx8p6xbSuZ3K3+Cx4CdWpbfSTMJ4UujxrhLGedsmlOBI+P8KQAR2xCxc2k7coR1yBaO5bryuVt9k+Y67uwt3JcmIS/oa2rJvB04GYCI3wD+Lc2podcCVxBxIPDy0vrr4+xpr1HL/4MmXF4LfA342+esbaYS7zxqm0fJ/PkWjH4nmoAZ8XJgR+AnmxnnD8oYjgA+RBMkM0a123ULxgFwb5vacPm5+xbuS5OQ4aKpK/PHwAoiRq4vHA4cDERpcRLwwBhbj/7lOpvmOgfAfsAONEcVI/49zfWJVqcAF3U01uaay47Ada1VYB3w9nG2vKNsfxBN6A0CS2mu3/wcSGAlWz65Z9N4o93CfWkSMlykzCTiBppwmUVzgRpgPZnjHb00IqYBF9P8ffpPwDnAZ2hOW424hmZCQKs7t2CUp5efX2mp3QP8G5qL/JubNPB2YFvgGDLvaxn7S9jyoxZps7zmoqkj4k0lCEbXt6e5WRHgLuBS4Engw23vjG+uV7yopfIxmlNNZ5L5KZobId9BxOJftsh8gMyvj3qNdVQ0ur+3Au8B/h9wfsuaFTR/h//7GNu1nrobOdIYfVTxftr/HngC2K2j8UlteOSiqeSTwO5ErAJuB35GM5327TRHACvKNRmIeBfwWeDuctrsx8BM4LdppiPPA35ExJtofvH/A5kXlX7eT3Mt5zwi/g+ZI0dCmzOLiJGjne159g79g2lOZ72FzEd+2TrzMiI+B5xJxAHAlcB6YAA4jOb03MgkhcuB/wxcRcQFwFM0R1KvLNuMdj3wRiLeC/xfIMlc2eHnkLzPRVNIxFHAIuA1NKe/dqGZKXUbzX0dF5H5TEv7w4E/ozldtgvNL+Hv0/wSP5/mAvutNPfAvJrMx1u2fRnN/S8/AH6XzKc2M7bRfxF/SnM95VZgFXDxmBf/I/4QWAL8Ds39Ng8C3wUuIfOSlnbHAf8V+C2a6y1fpwnG7wA/IvP1LW3nls94KM21HsiMlrEuJ/PkUeM4mea60hvIvHbcz6tJz3CRJFXnNRdJUnWGiySpOsNFklSd4SJJqq6rU5Ej4kc0dylvAp7OzPkRsRtwCc0dzT8C3paZD0dE0Nx8tpBmiujJWZ7NFM39Ah8su/1YZi4v9QNp7nDeHrgKOCszc6w+xhvrHnvskbNnz67xsSVpyrj55pvXZ+bM0fWuzhYr4TI/M9e31P4K2JCZn4iIpcCumfneiFgI/DFNuBwCnJOZh5SgWAPMp3lUxc3AgSWQbqS5I/oGmnA5NzO/OlYf4411/vz5uWbNmsr/BSRpcouImzNz/uh6P06LLaJ5Ki3l53Et9RXZuB7YJSL2Bo4GVmfmhnL0sRpYUNbtlJnXZ5OQK0btq10fkqQe6Ha4JPC1iLg5mi9fAtgrn33sxYM8+3TZWcD9LdsOldp49aE29fH6kCT1QLcf//KazFwbEXsCqyPiX1tXlusjXb2Lc7w+SuAtAXjpS1/azWFI0pTS1XDJzLXl50MRcTnNM5J+EhF7Z+YD5dTWQ6X5WprnPI0YKLW1NF+h2lq/ttQH2rRnnD5Gj+8Cyte4zp8/30cVSOq5X/ziFwwNDbFx48Z+D2VcM2bMYGBggO22266j9l0Ll2ge5b1NZj5e3h8FfITmOUmLgU+Un1eUTVYBZ0bESpoL+o+WcLgG+G8RMfJY8KOA92Xmhoh4LCIOpbmgfxLwqZZ9tetDkiaUoaEhdtxxR2bPnk0zaXbiyUyGh4cZGhpizpw5HW3TzSOXvYDLy3+saTTfR351RNwEXBoRp9E8afZtpf1VNDPFBmmmIp8CUELko8BNpd1HMnNDef9unp2K/NXygiZU2vUhSRPKxo0bJ3SwAEQEu+++O+vWret4m66FS2beS/OU1tH1YeDINvUEzhhjX8uAZW3qa4BXdNqHJE1EEzlYRmzpGL1DX5JUnV8WJkkTyEN33lp1f3vu/7wTSG1dffXVnHXWWWzatInTTz+dpUuXvqB+DRdpCqj9C2tr1ukv26lk06ZNnHHGGaxevZqBgQEOOuggjj32WObNm/cr79PTYpI0xd14443st99+7LvvvkyfPp0TTjiBK654YZNsDRdJmuLWrl3LPvs8e5vhwMAAa9euHWeLzTNcJEnVGS6SNMXNmjWL++9/9hGOQ0NDzJo1a5wtNs9wkaQp7qCDDuKee+7hvvvu46mnnmLlypUce+yxL2ifzhaTpAmkH7PZpk2bxnnnncfRRx/Npk2bOPXUU9l///1f2D4rjU2StBVbuHAhCxcurLY/w6Ui7yV4lvcSSFOb11wkSdUZLpKk6gwXSVJ1hoskqTrDRZJUnbPFJGkCeeXLDq+6v9t++C+bbXPqqady5ZVXsueee3LHHXdU6dcjF0ma4k4++WSuvvrqqvs0XCRpinvd617HbrvtVnWfhoskqTrDRZJUneEiSarOcJEkVedUZEmaQDqZOlzbiSeeyLXXXsv69esZGBjgwx/+MKeddtoL2qfhIklT3MUXX1x9n54WkyRVZ7hIkqozXCSpzzKz30PYrC0do+EiSX00Y8YMhoeHJ3TAZCbDw8PMmDGj4228oC9JfTQwMMDQ0BDr1q3r91DGNWPGDAYGBjpub7hIUh9tt912zJkzp9/DqM7TYpKk6gwXSVJ1hoskqbquh0tEbBsR34uIK8vynIi4ISIGI+KSiJhe6i8qy4Nl/eyWfbyv1L8fEUe31BeU2mBELG2pt+1DktQbvThyOQu4u2X5L4FPZuZ+wMPAyANsTgMeLvVPlnZExDzgBGB/YAHw6RJY2wLnA8cA84ATS9vx+pAk9UBXwyUiBoDfAz5blgM4ArisNFkOHFfeLyrLlPVHlvaLgJWZ+WRm3gcMAgeX12Bm3puZTwErgUWb6UOS1APdPnL5W+A9wDNleXfgkcx8uiwPAbPK+1nA/QBl/aOl/S/ro7YZqz5eH88REUsiYk1ErJnoc8wlaWvStXCJiDcDD2Xmzd3q44XKzAsyc35mzp85c2a/hyNJk0Y3b6I8HDg2IhYCM4CdgHOAXSJiWjmyGADWlvZrgX2AoYiYBuwMDLfUR7Ru064+PE4fkqQe6NqRS2a+LzMHMnM2zQX5b2bmO4BvAceXZouBK8r7VWWZsv6b2TxsZxVwQplNNgeYC9wI3ATMLTPDppc+VpVtxupDktQD/bjP5b3An0bEIM31kQtL/UJg91L/U2ApQGbeCVwK3AVcDZyRmZvKUcmZwDU0s9EuLW3H60OS1AM9ebZYZl4LXFve30sz02t0m43AW8fY/uPAx9vUrwKualNv24ckqTe8Q1+SVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqrmvhEhEzIuLGiLg1Iu6MiA+X+pyIuCEiBiPikoiYXuovKsuDZf3sln29r9S/HxFHt9QXlNpgRCxtqbftQ5LUG908cnkSOCIzfwd4FbAgIg4F/hL4ZGbuBzwMnFbanwY8XOqfLO2IiHnACcD+wALg0xGxbURsC5wPHAPMA04sbRmnD0lSD3QtXLLxRFncrrwSOAK4rNSXA8eV94vKMmX9kRERpb4yM5/MzPuAQeDg8hrMzHsz8ylgJbCobDNWH5KkHujqNZdyhHEL8BCwGvgh8EhmPl2aDAGzyvtZwP0AZf2jwO6t9VHbjFXffZw+Ro9vSUSsiYg169atewGfVJLUqqvhkpmbMvNVwADNkcZvdbO/LZWZF2Tm/MycP3PmzH4PR5ImjZ7MFsvMR4BvAYcBu0TEtLJqAFhb3q8F9gEo63cGhlvro7YZqz48Th+SpB7o5myxmRGxS3m/PfAm4G6akDm+NFsMXFHeryrLlPXfzMws9RPKbLI5wFzgRuAmYG6ZGTad5qL/qrLNWH1Iknpg2uab/Mr2BpaXWV3bAJdm5pURcRewMiI+BnwPuLC0vxD4fEQMAhtowoLMvDMiLgXuAp4GzsjMTQARcSZwDbAtsCwz7yz7eu8YfUiSeqBr4ZKZtwGvblO/l+b6y+j6RuCtY+zr48DH29SvAq7qtA9JUm94h74kqTrDRZJUneEiSaquo3CJiG90UpMkCTZzQT8iZgAvBvaIiF2BKKt2Yoy73iVJ2txssf8A/Anw68DNPBsujwHndW9YkqSt2bjhkpnnAOdExB9n5qd6NCZJ0lauo/tcMvNTEfG7wOzWbTJzRZfGJUnainUULhHxeeBlwC3AplJOwHCRJD1Pp3fozwfmled2SZI0rk7vc7kD+LVuDkSSNHl0euSyB3BXRNxI8/XFAGTmsV0ZlSRpq9ZpuPxFNwchSZpcOp0t9u1uD0SSNHl0OlvscZrZYQDTge2An2bmTt0amCRp69XpkcuOI+8jIoBFwKHdGpQkaeu2xU9FzsaXgKPrD0eSNBl0elrsLS2L29Dc97KxKyOSJG31Op0t9u9a3j8N/Ijm1JgkSc/T6TWXU7o9EEnS5NHpl4UNRMTlEfFQeX0xIga6PThJ0tap0wv6nwNW0Xyvy68DXy41SZKep9NwmZmZn8vMp8vrImBmF8clSdqKdRouwxHxzojYtrzeCQx3c2CSpK1Xp+FyKvA24EHgAeB44OQujUmStJXrdCryR4DFmfkwQETsBvw1TehIkvQcnR65vHIkWAAycwPw6u4MSZK0tes0XLaJiF1HFsqRS6dHPZKkKabTgPgb4LqI+Mey/Fbg490ZkiRpa9fpHforImINcEQpvSUz7+resCRJW7OOT22VMDFQJEmbtcWP3JckaXMMF0lSdYaLJKm6roVLROwTEd+KiLsi4s6IOKvUd4uI1RFxT/m5a6lHRJwbEYMRcVtEHNCyr8Wl/T0RsbilfmBE3F62Obd8BfOYfUiSeqObRy5PA/8lM+cBhwJnRMQ8YCnwjcycC3yjLAMcA8wtryXAZ+CX99ScDRwCHAyc3RIWnwH+qGW7BaU+Vh+SpB7oWrhk5gOZ+d3y/nHgbmAWzTdYLi/NlgPHlfeLgBXZuB7YJSL2Bo4GVmfmhvKUgNXAgrJup8y8PjMTWDFqX+36kCT1QE+uuUTEbJrHxdwA7JWZD5RVDwJ7lfezgPtbNhsqtfHqQ23qjNPH6HEtiYg1EbFm3bp1v8InkyS10/VwiYgdgC8Cf5KZj7WuK0cc2c3+x+sjMy/IzPmZOX/mTL+eRpJq6Wq4RMR2NMHy95n5T6X8k3JKi/LzoVJfC+zTsvlAqY1XH2hTH68PSVIPdHO2WAAXAndn5v9sWbUKGJnxtRi4oqV+Upk1dijwaDm1dQ1wVETsWi7kHwVcU9Y9FhGHlr5OGrWvdn1Iknqgm082Phz4Q+D2iLil1N4PfAK4NCJOA35M8yVkAFcBC4FB4GfAKdA83j8iPgrcVNp9pDzyH+DdwEXA9sBXy4tx+pAk9UDXwiUz/xmIMVYf2aZ9AmeMsa9lwLI29TXAK9rUh9v1IUnqDe/QlyRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqrrWrhExLKIeCgi7mip7RYRqyPinvJz11KPiDg3IgYj4raIOKBlm8Wl/T0RsbilfmBE3F62OTciYrw+JEm9080jl4uABaNqS4FvZOZc4BtlGeAYYG55LQE+A01QAGcDhwAHA2e3hMVngD9q2W7BZvqQJPVI18IlM78DbBhVXgQsL++XA8e11Fdk43pgl4jYGzgaWJ2ZGzLzYWA1sKCs2ykzr8/MBFaM2le7PiRJPdLray57ZeYD5f2DwF7l/Szg/pZ2Q6U2Xn2oTX28Pp4nIpZExJqIWLNu3bpf4eNIktrp2wX9csSR/ewjMy/IzPmZOX/mzJndHIokTSm9DpeflFNalJ8PlfpaYJ+WdgOlNl59oE19vD4kST3S63BZBYzM+FoMXNFSP6nMGjsUeLSc2roGOCoidi0X8o8CrinrHouIQ8sssZNG7atdH5KkHpnWrR1HxMXA64E9ImKIZtbXJ4BLI+I04MfA20rzq4CFwCDwM+AUgMzcEBEfBW4q7T6SmSOTBN5NMyNte+Cr5cU4fUiSeqRr4ZKZJ46x6sg2bRM4Y4z9LAOWtamvAV7Rpj7crg9JUu94h74kqTrDRZJUneEiSarOcJEkVWe4SJKqM1wkSdUZLpKk6gwXSVJ1hoskqTrDRZJUneEiSarOcJEkVWe4SJKqM1wkSdUZLpKk6gwXSVJ1XfuyME1tr3zZ4f0ewoRw2w//pd9DkPrCIxdJUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqozXCRJ1RkukqTqvIlS0pTiDb7P6uZNvh65SJKqM1wkSdUZLpKk6gwXSVJ1hoskqTrDRZJUneEiSarOcJEkVTdpwyUiFkTE9yNiMCKW9ns8kjSVTMpwiYhtgfOBY4B5wIkRMa+/o5KkqWNShgtwMDCYmfdm5lPASmBRn8ckSVPGZH222Czg/pblIeCQ0Y0iYgmwpCw+ERHf78HYpoo9gPX9HkS/RUS/h6Dn889mUenP52+0K07WcOlIZl4AXNDvcUxGEbEmM+f3exzSaP7Z7I3JelpsLbBPy/JAqUmSemCyhstNwNyImBMR04ETgFV9HpMkTRmT8rRYZj4dEWcC1wDbAssy884+D2uq8XSjJir/bPZAZGa/xyBJmmQm62kxSVIfGS6SpOoMF1XlY3c0UUXEsoh4KCLu6PdYpgLDRdX42B1NcBcBC/o9iKnCcFFNPnZHE1ZmfgfY0O9xTBWGi2pq99idWX0ai6Q+MlwkSdUZLqrJx+5IAgwX1eVjdyQBhosqysyngZHH7twNXOpjdzRRRMTFwHXAb0bEUESc1u8xTWY+/kWSVJ1HLpKk6gwXSVJ1hoskqTrDRZJUneEiSarOcJH6LCI+EBF3RsRtEXFLRBwSEdeWp0vfUl6XlbbnRsSHRm17fv9GL7U3Kb/mWNpaRMRhwJuBAzLzyYjYA5heVr8jM9eM2uSDwC0R8YWyfDrw6t6MVuqc4SL1197A+sx8EiAz1wNERNvGmflYRHwAOK+UPpSZj/RgnNIW8SZKqY8iYgfgn4EXA18HLsnMb0fEtTTB8/PSdHVm/nnLdtcBmzLzNT0estQRj1ykPsrMJyLiQOC1wBuAS1q+wbPdaTEiYoAmeJ6JiB0y84nejVjqjEcu0gQSEccDi4EdgT8bI1y+SPNA0JcD27Ye0UgThUcuUh9FxG8Cz2TmPaX0KuDHwCvGaH8MsCewguZU2m0R8bnMvKsHw5U65pGL1EfllNingF2Ap4FBYAlwGc+95rKeZlbZrcDxmXl72f4twJmZeURvRy6Nz3CRJFXnTZSSpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqvv/pd8c5nhyfewAAAAASUVORK5CYII=","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.countplot(df.SEX,hue=df.DEATH, palette=sns.cubehelix_palette(2))\n","plt.title(\"Sex-Death\",fontsize=18, color=\"red\")\n","plt.legend(loc=\"best\");"]},{"cell_type":"markdown","metadata":{},"source":["* Males are more likely to die of covid compare to females."]},{"cell_type":"code","execution_count":84,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:50.842977Z","iopub.status.busy":"2022-12-08T18:11:50.842621Z","iopub.status.idle":"2022-12-08T18:11:51.310942Z","shell.execute_reply":"2022-12-08T18:11:51.309638Z","shell.execute_reply.started":"2022-12-08T18:11:50.842944Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["ax=sns.countplot(df.OBESITY,hue=df.DEATH, palette=sns.color_palette([\"#7fffd4\",\"#a52a2a\"]))\n","plt.title(\"Obesity-Death\",fontsize=18, color=\"red\")\n","plt.bar_label(ax.containers[0])\n","plt.bar_label(ax.containers[1])\n","plt.legend(loc=\"best\");"]},{"cell_type":"markdown","metadata":{},"source":["* Obese patients are more likely to die from covid compare to non-obese patients."]},{"cell_type":"code","execution_count":57,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:51.313242Z","iopub.status.busy":"2022-12-08T18:11:51.312639Z","iopub.status.idle":"2022-12-08T18:11:55.022323Z","shell.execute_reply":"2022-12-08T18:11:55.021012Z","shell.execute_reply.started":"2022-12-08T18:11:51.313206Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1296x1080 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.figure(figsize=(18,15))\n","sns.heatmap(df.corr(),annot=True, fmt=\".2f\",cmap='Blues')\n","plt.title(\"Correlation Between Features\",fontsize=18,color=\"blue\");"]},{"cell_type":"markdown","metadata":{},"source":["# Future Selection\n","* I will drop the features that have low correlation with \"DEATH\" feature."]},{"cell_type":"code","execution_count":58,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:55.024751Z","iopub.status.busy":"2022-12-08T18:11:55.024364Z","iopub.status.idle":"2022-12-08T18:11:55.062136Z","shell.execute_reply":"2022-12-08T18:11:55.060527Z","shell.execute_reply.started":"2022-12-08T18:11:55.024718Z"},"trusted":true},"outputs":[],"source":["unrelevant_columns = [\"SEX\",\"PREGNANT\",\"COPD\",\"ASTHMA\",\"INMSUPR\",\"OTHER_DISEASE\",\"CARDIOVASCULAR\",\n","                      \"OBESITY\",\"TOBACCO\"]\n","\n","df.drop(columns=unrelevant_columns,inplace=True)"]},{"cell_type":"code","execution_count":59,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:55.065318Z","iopub.status.busy":"2022-12-08T18:11:55.064759Z","iopub.status.idle":"2022-12-08T18:11:55.083087Z","shell.execute_reply":"2022-12-08T18:11:55.08195Z","shell.execute_reply.started":"2022-12-08T18:11:55.065226Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>USMER</th>\n","      <th>MEDICAL_UNIT</th>\n","      <th>PATIENT_TYPE</th>\n","      <th>PNEUMONIA</th>\n","      <th>AGE</th>\n","      <th>DIABETES</th>\n","      <th>HIPERTENSION</th>\n","      <th>RENAL_CHRONIC</th>\n","      <th>CLASIFFICATION_FINAL</th>\n","      <th>DEATH</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>65</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>72</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>55</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>68</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   USMER  MEDICAL_UNIT  PATIENT_TYPE  PNEUMONIA  AGE  DIABETES  HIPERTENSION  \\\n","0      0             1             1          1   65         0             1   \n","1      0             1             1          1   72         0             1   \n","2      0             1             0          0   55         1             0   \n","3      0             1             1          0   53         0             0   \n","4      0             1             1          0   68         1             1   \n","\n","   RENAL_CHRONIC  CLASIFFICATION_FINAL  DEATH  \n","0              0                     3      1  \n","1              1                     5      1  \n","2              0                     3      1  \n","3              0                     7      1  \n","4              0                     3      1  "]},"execution_count":59,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","metadata":{},"source":["### Preparing the categorical features which are not binary"]},{"cell_type":"code","execution_count":60,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:55.086045Z","iopub.status.busy":"2022-12-08T18:11:55.085625Z","iopub.status.idle":"2022-12-08T18:11:55.301567Z","shell.execute_reply":"2022-12-08T18:11:55.299789Z","shell.execute_reply.started":"2022-12-08T18:11:55.08601Z"},"trusted":true},"outputs":[],"source":["df = pd.get_dummies(df,columns=[\"MEDICAL_UNIT\",\"CLASIFFICATION_FINAL\"],drop_first=True)"]},{"cell_type":"markdown","metadata":{},"source":["### Scaling the numeric feature"]},{"cell_type":"code","execution_count":61,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:55.30419Z","iopub.status.busy":"2022-12-08T18:11:55.303631Z","iopub.status.idle":"2022-12-08T18:11:55.469787Z","shell.execute_reply":"2022-12-08T18:11:55.468416Z","shell.execute_reply.started":"2022-12-08T18:11:55.304136Z"},"trusted":true},"outputs":[],"source":["from sklearn.preprocessing import RobustScaler\n","scaler = RobustScaler()\n","df.AGE = scaler.fit_transform(df.AGE.values.reshape(-1,1))"]},{"cell_type":"markdown","metadata":{},"source":["### Determining the \"x\" and \"y\""]},{"cell_type":"code","execution_count":62,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:55.472456Z","iopub.status.busy":"2022-12-08T18:11:55.471546Z","iopub.status.idle":"2022-12-08T18:11:55.530288Z","shell.execute_reply":"2022-12-08T18:11:55.528369Z","shell.execute_reply.started":"2022-12-08T18:11:55.472401Z"},"trusted":true},"outputs":[],"source":["x = df.drop(columns=\"DEATH\")\n","y = df[\"DEATH\"]"]},{"cell_type":"markdown","metadata":{},"source":["# Train Test Split"]},{"cell_type":"code","execution_count":63,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:55.538915Z","iopub.status.busy":"2022-12-08T18:11:55.538486Z","iopub.status.idle":"2022-12-08T18:11:56.085766Z","shell.execute_reply":"2022-12-08T18:11:56.084364Z","shell.execute_reply.started":"2022-12-08T18:11:55.538882Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Train_x : (817581, 25)\n","Test_x : (204396, 25)\n","Train_y : (817581,)\n","Test_y : (204396,)\n"]}],"source":["from sklearn.model_selection import train_test_split\n","train_x, test_x, train_y, test_y = train_test_split(x,y, test_size=0.2, random_state=42)\n","print(\"Train_x :\",train_x.shape)\n","print(\"Test_x :\",test_x.shape)\n","print(\"Train_y :\",train_y.shape)\n","print(\"Test_y :\",test_y.shape)"]},{"cell_type":"markdown","metadata":{},"source":["# Logistic Regression"]},{"cell_type":"code","execution_count":64,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:11:56.088618Z","iopub.status.busy":"2022-12-08T18:11:56.088086Z","iopub.status.idle":"2022-12-08T18:12:13.593755Z","shell.execute_reply":"2022-12-08T18:12:13.592403Z","shell.execute_reply.started":"2022-12-08T18:11:56.088569Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Logistic Regression Accuracy : 0.9394459774163878\n"]}],"source":["from sklearn.linear_model import LogisticRegression\n","\n","logreg = LogisticRegression()\n","logreg.fit(train_x,train_y)\n","print(\"Logistic Regression Accuracy :\",logreg.score(test_x, test_y))"]},{"cell_type":"code","execution_count":65,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:12:13.596597Z","iopub.status.busy":"2022-12-08T18:12:13.595734Z","iopub.status.idle":"2022-12-08T18:12:13.785Z","shell.execute_reply":"2022-12-08T18:12:13.783537Z","shell.execute_reply.started":"2022-12-08T18:12:13.596546Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Logistic Regression F1 Score : [0.96768821 0.51920911]\n"]}],"source":["from sklearn.metrics import f1_score\n","\n","print(\"Logistic Regression F1 Score :\",f1_score(test_y,logreg.predict(test_x),average=None))"]},{"cell_type":"code","execution_count":69,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:12:13.786903Z","iopub.status.busy":"2022-12-08T18:12:13.786448Z","iopub.status.idle":"2022-12-08T18:12:14.363508Z","shell.execute_reply":"2022-12-08T18:12:14.362441Z","shell.execute_reply.started":"2022-12-08T18:12:13.786865Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["from sklearn.metrics import confusion_matrix\n","\n","sns.heatmap(confusion_matrix(test_y, logreg.predict(test_x)), annot=True, fmt=\".0f\",cmap='Blues_r')\n","plt.title(\"Logistic Regression Confusion Matrix\",fontsize=18, color=\"black\");"]},{"cell_type":"markdown","metadata":{},"source":["## Inference:\n","* We got well accuracy with Logistic Regression.\n","* But it can mislead us so we have to check the other metrics.\n","* When we look at the F1 Score it says that we predicted the patients who survived well but we can't say the same thing for dead patients.\n","* Also we see the same thing when we check the confusion matrix. This problem is based on imbalance dataset as i mentioned about it."]},{"cell_type":"code","execution_count":89,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:12:14.366084Z","iopub.status.busy":"2022-12-08T18:12:14.365328Z","iopub.status.idle":"2022-12-08T18:12:14.701083Z","shell.execute_reply":"2022-12-08T18:12:14.699606Z","shell.execute_reply.started":"2022-12-08T18:12:14.366035Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["ax = sns.countplot(df.DEATH, palette=sns.cubehelix_palette(2, start=1.5, rot=-0.75))\n","plt.bar_label(ax.containers[0])\n","plt.title(\"Death Distribution\\n 0:dead 1:alive\", fontsize=18,color=\"black\");"]},{"cell_type":"markdown","metadata":{},"source":["# How To Solve Imbalance Dataset Problem\n","* Loading More Datas\n","* Changing The Perfomance Metrics\n","* Resampling (Undersampling or Oversampling)\n","* Changing The Algorithm\n","* Penalized Models etc."]},{"cell_type":"markdown","metadata":{},"source":["##### I am going to use Undersampling for this case because we already have too many patients.\n","* Undersampling : Undersampling is a technique to balance uneven datasets by keeping all of the data in the minority class and decreasing the size of the majority class. \n","* If we use Oversampling our row number will increase so this is too many rows for computer.\n","* If i can't solve the problem with Undersampling i will use the others."]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:12:14.702973Z","iopub.status.busy":"2022-12-08T18:12:14.702588Z","iopub.status.idle":"2022-12-08T18:12:15.470928Z","shell.execute_reply":"2022-12-08T18:12:15.469535Z","shell.execute_reply.started":"2022-12-08T18:12:14.702934Z"},"trusted":true},"outputs":[],"source":["from imblearn.under_sampling import RandomUnderSampler\n","\n","rus = RandomUnderSampler(random_state=0)\n","x_resampled,y_resampled = rus.fit_resample(x,y)"]},{"cell_type":"code","execution_count":97,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:12:15.473546Z","iopub.status.busy":"2022-12-08T18:12:15.473032Z","iopub.status.idle":"2022-12-08T18:12:15.71471Z","shell.execute_reply":"2022-12-08T18:12:15.713476Z","shell.execute_reply.started":"2022-12-08T18:12:15.473504Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["ax = sns.countplot(y_resampled, palette=sns.cubehelix_palette(2))\n","plt.bar_label(ax.containers[0])\n","plt.title(\"Death Distribution\", fontsize=18,color=\"red\");"]},{"cell_type":"markdown","metadata":{},"source":["# Train Test Split After Undersampling"]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:12:15.71704Z","iopub.status.busy":"2022-12-08T18:12:15.716667Z","iopub.status.idle":"2022-12-08T18:12:15.777031Z","shell.execute_reply":"2022-12-08T18:12:15.775542Z","shell.execute_reply.started":"2022-12-08T18:12:15.717008Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Train_x : (119451, 25)\n","Test_x : (29863, 25)\n","Train_y : (119451,)\n","Test_y : (29863,)\n"]}],"source":["train_x, test_x, train_y, test_y = train_test_split(x_resampled,y_resampled, test_size=0.2, random_state=42)\n","print(\"Train_x :\",train_x.shape)\n","print(\"Test_x :\",test_x.shape)\n","print(\"Train_y :\",train_y.shape)\n","print(\"Test_y :\",test_y.shape)"]},{"cell_type":"markdown","metadata":{},"source":["# Logistic Regression After Undersampling"]},{"cell_type":"code","execution_count":99,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:12:15.779116Z","iopub.status.busy":"2022-12-08T18:12:15.778707Z","iopub.status.idle":"2022-12-08T18:12:18.363312Z","shell.execute_reply":"2022-12-08T18:12:18.360174Z","shell.execute_reply.started":"2022-12-08T18:12:15.77908Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Logistic Regression Accuracy : 0.9052673877373338\n"]}],"source":["logreg = LogisticRegression()\n","logreg.fit(train_x,train_y)\n","print(\"Logistic Regression Accuracy :\",logreg.score(test_x, test_y))"]},{"cell_type":"code","execution_count":100,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:12:18.368871Z","iopub.status.busy":"2022-12-08T18:12:18.365762Z","iopub.status.idle":"2022-12-08T18:12:18.451796Z","shell.execute_reply":"2022-12-08T18:12:18.449775Z","shell.execute_reply.started":"2022-12-08T18:12:18.368796Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Logistic Regression F1 Score : [0.90360173 0.90687646]\n"]}],"source":["print(\"Logistic Regression F1 Score :\",f1_score(test_y,logreg.predict(test_x),average=None))"]},{"cell_type":"code","execution_count":101,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:12:18.45498Z","iopub.status.busy":"2022-12-08T18:12:18.454157Z","iopub.status.idle":"2022-12-08T18:12:18.901629Z","shell.execute_reply":"2022-12-08T18:12:18.900146Z","shell.execute_reply.started":"2022-12-08T18:12:18.454911Z"},"trusted":true},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXsAAAEMCAYAAAAlGRZyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAApJUlEQVR4nO3deXxU1fnH8c9DQMWNXZSlgoJVrGiFui8oirgirVZri6go+lPrvlbrrtVaa90tCgIuUJcqKLggouKCEjdcUEEEWSs7gqzJ8/vjnCSTZJJMQpJJZr7vvO4rM+eee++5M3eeOXPuM3fM3RERkczWIN0NEBGRmqdgLyKSBRTsRUSygIK9iEgWULAXEckCCvYiIlmg7gd7sx6YOWanVfN638RsZrWuU8qmx7s0sxMw+wyz1fEY71FD2xmKmXKsK1JTsaaOqDjYFz0Al9V8c6qZ2UW1+sSFxylxWovZdMz+hVmLWmuHlM+sLWZ/x2wKZj/F52kmZk9g1rOW2rATMAJYDpwP9AOm1sq208HstITXRfJYYvbrhDpDN2Jbtfu6rycaprsBKXgbaAysr8KyFwEzgaFJ5vUCrKqNKsenwF3xdrO4nQuBwzDbE/d1NbDN+qCmHu/KMTuaEGQ3BZ4BBgGrgQ7A8cDrmB2N+9gabkkPwuvvItw/ruFtnQWcU8PbSNUa4HTgH0nmnRHnb7aR27iIsl/35dmYWFPn1f1g755POACqe701FXTn4v5Ewv37MHueEEiOBZ6roe2Wz2wr3H9Ky7ahJh/v1JntSgjwS4DDcZ9aYv51wB+pieOttG3j/yU1viX39dSdAPY88AfM9sL9w8JSs02BU4D/xv+1p+C1UVOxpo6o3jF7s4MwG4fZ8jgO+TFmA8qo+7s4XrkGsx8wux6zw0qNmSUbRzNrED+qFXwMX4HZN5gNxqxRrOPA9sDBJYZWOsT5yceQzTph9hhmczBbh9k8zEZh1m0jHpnX4//OJba1KWZ/wezL+Dgsw+xFzH6dpF0tMBuC2WLMVmL2RvzYW3o/wpDEm3H+q5gtB6YkzO+M2eOYzY/7OBOzOzHbosR62sdtzopDHT9i9h5m/RPqVPxchHplPd6pHTMFy5u1wWwEZksx+znu305lPO4l3UTouZ1ZKtADuDvuT+D+RsJ2G2J2JWZfxedoMWbPY7ZbifZ1iMfXDZgdg9nkWH9+fGwbJtR14MZ47/u43Mw4r+zx9WTDG2anYvZhPHZWYTYDsycxa5VQJ/k6zbrGfVkc2/oVZldgllOi3tC47SaYPRSPgzWYvYvZ3knbWrYXgUWE3n2iPkBz4LEy9v0kzEYTYsVazBZh9gJmXUvUq+h1X/ZrI3ms+Q9meZQ8n2J2BGb5mA2v5P6nTfX17M2OJbxrLyAMY/wEnAw8itkOuF+TUPckwkfp7wgH/QagP6Hnm4prCC/cF4GHgTygI3Ac4eP5esIY6N2EA+vWhGUXlrMP3YHxQCNgMPAF4QA8GNgP+CjF9pW0Y/xf1IsLgfCVuN7HgfuBJoSP3O9idhDuubHupoQ3jD0IH00/BLrGsrJ6hr8A3iD0ZJ8Dtozr6hbLlwH/BuYCuwMXAPtjdjDu62NwGge0BR4Evo3t6wocCAyL20nluUiuMsdMsAXho/Yk4C9xOxcCozD7Fe555WxrM+BoYDbur5RZr7Qngd8THouHCD3y84D3MTsQ909K1D8KOJfwWAwhBLHLgKXAbbFOP+C3QF/gYsIxurISbSrYp36E52EicB1hOKp9bMM2VHysv0V4fh4gPAfHAncQjoc/Jlnq1bjOm4AWwCXAGMw6VuJT43rgCeA0zC7GvaAnfQbwCWEYNJnzgcWEYbcFhNfUQMJrZU/cp8V6qbzuk782khsI/AZ4ArM9cF+E2bbAcGA64bmuH0JnppwJeji4w2Xl1MlxmOWwzKFNQvkmDu865Dl0jmUNHeY6/M+hWULdLR1mxG2dlmT7iWUfO3yVQttnOrxZxrw3HWYm3DeHLxzWOHRNUr9BCttzh1cdWsapk8N5DmsdfnLYJqHuxbH+ESXWsbXDD8XaDefGuteUqFtQPjPJfrvDmUna+JnD1w5blSjvW+xxhq7x/hUV7HOqz0XJxzv1Y6Zo+dLtgcuTPo6lt79brDe6wrYWLXN4XOY/DpZQvrvDBoeJCWUdYt1VDh2SHFfzS6z7hli/Q4nyoQ5ezvE1NOH+fx1WODSsYD9KrzM8xhuKHeuhrU/H7fQstTw8WGIdJ8bys1N4LE+LdU9IeC5OifPaxef7/Pi6Kb6foc4WSda5S3xtlWxXea/78l4bpWNNKN/bYZ3Diw4NHMbF7e6Z8rFUB6bqGsbpRni3HIL7vIR3knXA3wnDRX0S6rYBhuK+NKHuSkJvKBXLgbaYHbDRLS+yB7Ar8BjuU0rNDeN5qehF6EUsBKYReuxfAIfh/mNCvT8BXwMfYdaycIJNCL3IAzBrHOseS+gx31NiW48SHotkllDyI3EYeugKPAVsWmK77wCrYvtJWO8hmG1Tzv5W9bmozDFTIB+4t0RZwZBLZ8q3dfy/ohJt7Bv/34q7J7TxM8InmQOKDZcEL+A+M6GuAxOAbTErrwdZFcuBzYGjMUv95Hd4PvcDRhc71kNbC3rDfZMseXeJ+6k+9sW5fw7kUjSU05/Q43+ynGVWxbYbZlvHY3Yh8A1Q2aGk0q+N8tv7AXAtcAzhk+VhwFXU/In1alVdwb5j/P9lknkFZTuUqPtNkrrJypL5C+FEykTM5sYxylMw2yTF5ZMpOGA/2Yh1AHwAHE4ImmcCXwHtgJInKHcBdqbojSFxOgPIAVrGuh2BefENsUgIjN+X0Y7vKD2ssUv8f2OSbf5IGCZpHdc9i/DC7wXMx+wjQrrib0qss6rPRWWOmQLzKPrYX2Bx/F9RamtBkN+qgnqJOhLeYJKlRH6ZUCfRjCR1U21jZd0GzAJeABZi9hxmZ2JW0T6W99hPJexzycceSu6b+8bs12PAoZhtD5wGjCrW+SspjLG/RBjqW07RcbsbIeutMpK9NipyJ2G4bH/gNeBflVw+7ep+Nk4y7u9jtiNwBHBInE4BrsXsANxrPsOhbItwf73wXsjE+Rx4DrNdcV9dMCeWX1LOusoec63Yz0nKCnp/dxHOFyST+GnrWsyGEMa6DyS8eV2O2d9xvzLWqc3norwXaEU922nAWsInuJq0MW0E8KSliSd4C2v6NMy6AD3jdDDwCHBjPOfzXQrbS13ZAbIqKbVPEY7DR4BOhDH5MtZuvyD0qFcANxM6hasIj9W/KH/MPZlkr42KdCB8KobQ3i0Jbzz1RnUF+4J3/F2TzOtSos7M+P+XSeomK0su9HKfoyCV0excwommAYR3YSjrhZPct/H/HpVYpmLuSzC7lnCy7mKKTtJNA1oBb6QwRDSTkKe/ZbHefTjJ25FwsjUVBSex8oq9IZXf/hnAfYQU0s0IJ+muwOyuwmGp1J6LkipzzGw89zWYjQX6YtYL99dSWGoG4dPvLiRmMxVvY1mfrKoqvDmaNS/xRpmspw3ua4GxcQKzo4AxhE7EeWVso6DNyR77nQn7XH2PfTLuy2JH6A/AbMLQZVn6EoLrcbhPKDYnfFlxbcm1V2NLC95oRxDi5QWE4dSHCEOx9UZ1DeN8DPwAnB7PVAchGF1OePBHxdJcYD7hbHyzhLpbkuoXP8J4XbI2QMieKbCyxP3yfEb4WHsGIR+75DY35gtBjxNePJdhVjB2PJyQ2ZG8Z2/WOuHei4RhnQtL1DqLkCGTqk8I5w/Owax08Ahphs3j7SYkpk4CcQilYEijWayX6nORrE6qx0x1KchYeRSz5B2LMAR1aLz3Qvx/dbHn3+xXhGyjd3DfmE9fyRR0Og4rUX5pqZpVfezDm/R7wLFxXwrWZ8DV8d7zKbV249xOGFI8v4IOT8EniuKvQbOzKPq+QqLKvO5TcQvhvMD5uN9H+ETyRxJTkOuByvTse8aeXUmLcH8Ys/MJB8hkzAYRPuKcBOwD3EZBapT7BsLXpZ8EPsRsMCH18jTC2GZHKn5nnorZJML4+DxgO0KK1DpgZEK9ScAAzG6maCzyxcKTPYncHbPTCamXBe36AmhK+Hj8CqGHW3lhn/9G+Mh6IeGj6D2Esf07Y3B5g/Ax9ReEj+RrCEMiEE7Eng3cglknilIvf09I/0rteQz72C9ua0ocovmScJKvEyEd8GpCeuchwCDMniN8bF5JOKl6JvAB7gXnV1J9Lkq2JS/lY6a6uH+B2YmEXtpnmD0d272akJvdh5B2eGSsPy7WORloFseMC1Iv1xB6edVtBOHT3yDMdib09HtTdP4m0WuYLSOMJc8mHKunEV4/j1ewnQsJqZcTMStIvTyGMBz3FO7jN3ZHKhRODpdOhijtZcLQy+OY3U8YatyfkGL6HaWP/9Rf9xUxOxy4gvCYDI2lfyHEhPsxe6/aj9OakkLKVEE6UlnT1wl1D45pSSs8pDB+4jCgjPWe6DAlpjD94HC9F6X//b7cdCi4yuFthx/j8rMdnimVCgXbODznsMQh3xNT3UqmAhYt80uHJxwWxHSreQ4vpJRmFdb/UhnzGnlINVzq0CSWNXS4wGGyh5S9VQ7THJ506FVi+VYeUuCWxHpvOOzhkOslUx/LSz0L87d3eDjWW+ew2OEjh785tI91OsY6U+PzuSrevqmw/ZV7Lsp6vFM7ZspeviDl8YaU09CgrcOdDp87rIzt/t7hcYceJeo2dLgy7vva+Pi/4LBbyu1IlmZZVuplmLe3h9TINQ6LHAY5NPXSqZdnxceu4Fid7zDW4ZAS60uezhlSSF+I+7Q27uMVDjkpLV90zA9NOq94vaLUy/LrlZV6eZDDOx5SmJc5jHH4VdLjovzXfXlpmcVjTVjPfIfpXjpVecd4zOY6bJLysZfGydw93e83RcwuJVwzY1/cJ6W7OXVe+KbjIkJPu3e6myMidVd6LnFstgmlv5K9JeHj8WKKxh2lQFHOfaJzCB/dyzu5JSKSttTLHYCXMRtJyAzYjvDFio7A/1EXLppV9zwSz5m8R8g+2JeQ4jid8BVyEZEypWcYJ6RL3U84ybIN4QTt58DduD9d+w2qB8xOJXzy2YmQhvY/QrrdX3H/XzqbJiJ1X90asxcRkRpRp79Bu/a7SXonklJada3dy51L3bdi1YyN/mGc9YtmpBxvGrXcIf0/xFNJdTrYi4jUmvzKXi6nflGwFxEBSPnCtvWTgr2ICEC+gr2ISMZL/Scr6icFexERgLwN6W5BjVKwFxEBnaAVEckKGsYREckCOkErIpL5dIJWRCQbqGcvIpIF8tanuwU1SsFeRAR0glZEJCtoGEdEJAuoZy8ikgXUsxcRyXyerxO0IiKZTz17EZEsoDF7EZEsoAuhiYhkAfXsRUSygMbsRUSygH68REQkC6hnLyKS+dx1glZEJPOpZy8ikgWUjSMikgUyvGffIN0NEBGpE/I2pD5VwMyGmNmPZvZFQtmdZva1mU0xs+fNrGnCvKvNbLqZfWNmRySU945l083sqoTyjmb2QSz/j5ltUlGbFOxFRCAM46Q6VWwo0LtE2TjgV+7eFfgWuBrAzLoAJwO7xmUeNLMcM8sBHgCOBLoAf4h1Ae4A7nb3TsBSYEBFDVKwFxGBMIyT6lQBd38bWFKi7DV3L/hYMAloF2/3AUa6+1p3/x6YDuwVp+nuPsPd1wEjgT5mZsChwLNx+WHA8RW1ScFeRAQqFezNbKCZ5SZMAyu5tTOAl+PttsDshHlzYllZ5S2AZQlvHAXl5dIJWhERqFQ2jrsPAgZVZTNmdg2wAXiyKstXlYK9iAjUyuUSzOw04Bigp7t7LJ4LtE+o1i6WUUb5YqCpmTWMvfvE+mXSMI6ICFTrmH0yZtYbuAI4zt1/Tpg1GjjZzDY1s45AZ+BDYDLQOWbebEI4iTs6vklMAE6Iy/cHRlW0ffXsRUSgWr9UZWYjgB5ASzObA1xPyL7ZFBgXzrEyyd3Pcfcvzexp4CvC8M55Hq/dYGbnA68COcAQd/8ybuJKYKSZ3QJ8AgyuqE0K9iIiUK1fqnL3PyQpLjMgu/utwK1JyscCY5OUzyBk66RMwV5EBDL+G7QK9iIiAIXnSzOTgr2ICMAG/XiJiEjm01UvRUSygMbsRUSygMbsRUSygHr2IiJZQMFeRCTzeZ5+cFxEJPOpZy8ikgWUeikikgXylY0jIpL5NIwjIpIFdIJWqst1dz/KWx9+SvOmW/P8Q7cBcP/w55gw6WMaNGhA8yZbcfMlZ7FNi2aMmfAeQ54ZgztssflmXHtef365wy8A6H3apWzeeDNychqQ06ABI++9EYBvZvzAzfcP5efVa2nTuiW3X3EOW27eOG37K5X3wEN30PvIQ1i4cDH7/ObIwvKzzzmVswb2Iy8vj1dfncB1195Bt25duef+cByZGX+79R5eevE1AA47/CDu+Pt15OQ0YNiwp7n7rofTsj/1Sob37M3r8LfG1n43qe42rgpyP/+azRtvxjV3DSoM9it/Xl0YkJ8c9RozfpjHX/98Gp9+NY0d2rdh6622YOLkz3joyRd46l/XAyHYj7jnBpo12arY+v9w4Q1ceubJdN9tZ55/7W3mLljI+af+rnZ3sha06npKuptQY/bb/zesWvUz/37kH4XB/sCD9uGyK87jxN8OYN26dbRs1YJFCxfTuPFmrFu3nry8PFpv24r3Jo1hpx33xd355LPx9Dn2VObOXcCbE1/gjNMu5Juvp6d572rOilUzbGPX8fM/zkw53mx+2aMbvb3app8lrEXdd9uZJlttUawssee9es1aiIfQHl06s3Wsu/vOnfhx8ZIK1z9r7gK6/eqXAOz76115/d3camq51Jb33p3M0iXLipUNOPOP3H3Xw6xbtw6ARQsXA7B69Rry4tDDZptuWvht/+7dd2fGjFnMnDmb9evX89yzL3H0MYfX2j7UW56f+lQP1ViwN7OdzexKM7s3Tlea2S41tb367N5hz3L4qRcz5s33Oa/fb0vN/+9rb7F/t65FBQZnX3snJ11wHc++PKGweMft2zLh/Y8BeG3iZBYsqvgNQuq+Tp07st9+v+GNN//L2FdGsOeeRcdC9+6788HkV3j/w5e56IJrycvLY7s22zJnzvzCOvPmzqfNdq3T0fT6Jd9Tn+qhGgn2ZnYlMJLQT/0wTgaMMLOrKlh2oJnlmlnuoyNfqInm1TkX9D+BccPv5uge+zLixdeLzfvws6k8/9rbXHzGSYVlw+68hqfvu4kHb7qMkS+NJ/fzrwG46aIB/GfMeE664DpWrV5No4Y5tbofUjMaNsyhWbMmHNrjt/z1mr8x9PH7Cufl5n7G3r/pTY+DjufSy/6PTTfdJI0trd88Pz/lqT6qqRO0A4Bd3X19YqGZ/RP4Eri9rAXdfRAwCDJvzL4iRx+yH+defxfn/Sn07r/9/gduuGcwD950GU233rKwXuuWzQFo0XRrDt23G198O4Puu+1Mx/Zt+PetVwAwc84CJk7+rPZ3QqrdvLkLGD36VQA++mgKnp9Pi5bNWZzwye3bb75j5apVdOnyS+bPW0C7dtsVzmvTdjvmzf9frbe73snwbJyaGsbJB9okKd8uzpNo1twFhbcnTPqYjvFFOv/HxVx8y33cdtnZdGi3bWGdn9esZdXPqwtvv//JF3Tavh0Ai5etACA/P59BI0dx4lGH1tZuSA166cVxHHTQPgB06tSRRps0YvGiJWy/fTtycsKnt/bt27DTTjsy64c5fPTRFHbYsQPbb9+ORo0a8bsTjmHsmNfL24RAxg/j1FTP/iJgvJlNA2bHsl8AnYDza2ibdd4VdzxI7pSvWbZiJYf1u4hz/9SXiZOnMHPufBqYsd02Lfnr+f0BePipF1j200pufXA4QGGK5ZKly7nolnsByMvL48ge+3JA9zCG+/Kbk/jPS+FF3XP/7hx/+IFp2EvZGEOG3sMBB+5NixbNmPrtu9x2yz08PvwZHnz4DiZNfpl169ZzzsDLAdh3v+5cfMk5rN+wgfz8fC656DqWLF4KwOWX3sDzo4aRk9OAx4c/w9dTp6Vzt+qHejo8k6oaS700swbAXkDbWDQXmOzuKX9WyrZhHElNJqdeStVUR+rlqutOTjnebHHTyHqXelljX6py93xgUk2tX0SkWtXTlMpU6Ru0IiJQb8fiU6UvVYmIAL4hL+WpImY2xMx+NLMvEsqam9k4M5sW/zeL5Ra/izTdzKaY2Z4Jy/SP9aeZWf+E8m5m9nlc5l4zq3BYScFeRASqOxtnKNC7RNlVwHh37wyMj/cBjgQ6x2kg8BCENwfgemBvwvnP6wveIGKdsxKWK7mtUhTsRUSgWi+X4O5vAyW/wt4HGBZvDwOOTygf7sEkoKmZbQccAYxz9yXuvhQYB/SO87Z290keMmyGJ6yrTBqzFxGB2hizb+3uBdexWAAUXMOiLUUp6gBzYll55XOSlJdLPXsREcDzPeUp8bIucRpYqW2FHnmtnhFWz15EBCCFE68FEi/rUgn/M7Pt3H1+HIr5MZbPBdon1GsXy+YCPUqUvxnL2yWpXy717EVEoDYulzAaKMio6Q+MSig/NWbl7AMsj8M9rwK9zKxZPDHbC3g1zlthZvvELJxTE9ZVJvXsRUSgWsfszWwEoVfe0szmELJqbgeeNrMBwCzg97H6WOAoYDrwM3A6gLsvMbObgcmx3k3uXnDS91xCxk9j4OU4lUvBXkQEqM5Lx7j7H8qY1TNJXQfOK2M9Q4AhScpzgV9Vpk0K9iIikPHfoFWwFxEBBXsRkWzgG3QhNBGRzJfZsV7BXkQEwpeqMpmCvYgIaMxeRCQraBhHRCTzaRhHRCQL+AYFexGRzKdhHBGRzJfhvzeuYC8iAqhnLyKSDdSzFxHJAr4h3S2oWQr2IiKoZy8ikhUU7EVEsoFbultQoxTsRURQz15EJCt4vnr2IiIZLz9PwV5EJONpGEdEJAtoGEdEJAt4Zl/0UsFeRATUsxcRyQo6QSsikgUyvWffIN0NEBGpC9wt5SkVZnaxmX1pZl+Y2Qgz28zMOprZB2Y23cz+Y2abxLqbxvvT4/wOCeu5OpZ/Y2ZHVHX/FOxFRAipl6lOFTGztsAFQHd3/xWQA5wM3AHc7e6dgKXAgLjIAGBpLL871sPMusTldgV6Aw+aWU5V9k/BXkQEyHdLeUpRQ6CxmTUENgfmA4cCz8b5w4Dj4+0+8T5xfk8zs1g+0t3Xuvv3wHRgr6rsn4K9iAiVG8Yxs4FmlpswDSy+Lp8L/AP4gRDklwMfAcvcC6+cPwdoG2+3BWbHZTfE+i0Sy5MsUyk6QSsiQuWycdx9EDCorPlm1ozQK+8ILAOeIQzDpI2CvYgI1Z6NcxjwvbsvBDCz/wL7A03NrGHsvbcD5sb6c4H2wJw47NMEWJxQXiBxmUrRMI6ICNU+Zv8DsI+ZbR7H3nsCXwETgBNinf7AqHh7dLxPnP+Gu3ssPzlm63QEOgMfVmX/1LMXEYGUUypTW5d/YGbPAh8DG4BPCMM+Y4CRZnZLLBscFxkMPG5m04ElhAwc3P1LM3ua8EaxATjP3fOq0ibzOnxBiLXfTaq7jZO0adX1lHQ3QeqYFatmbHSkntLh2JTjTdeZL9a7b2CpZy8iApVJqayXFOxFRID8DL9cgoK9iAjq2afVFrv8Lt1NkDpo9byJ6W6CZKDqPEFbF9XpYC8iUlvUsxcRyQKZnvqnYC8iAuTlZ/Z3TBXsRUSAFK5cXK8p2IuIAI7G7EVEMl5+hg/aK9iLiAD56tmLiGQ+DeOIiGSBPAV7EZHMp2wcEZEsoGAvIpIFNGYvIpIFMvwKxwr2IiKg1EsRkaxQpR92rUcU7EVEgHxTz15EJONl+NUSFOxFRECplyIiWUHZOCIiWUCXSxARyQLq2YuIZIFMH7PP7B9dFBFJkVdiSoWZNTWzZ83sazObamb7mllzMxtnZtPi/2axrpnZvWY23cymmNmeCevpH+tPM7P+Vd0/BXsREcIwTqpTiu4BXnH3nYHdganAVcB4d+8MjI/3AY4EOsdpIPAQgJk1B64H9gb2Aq4veIOoLAV7ERHCME6qU0XMrAlwEDAYwN3XufsyoA8wLFYbBhwfb/cBhnswCWhqZtsBRwDj3H2Juy8FxgG9q7J/CvYiIkCepT6Z2UAzy02YBpZYXUdgIfCYmX1iZo+a2RZAa3efH+ssAFrH222B2QnLz4llZZVXmk7QiohQuRO07j4IGFROlYbAnsCf3f0DM7uHoiGbgnW4mdXaF3fVsxcRoXqHcQg98Dnu/kG8/ywh+P8vDs8Q//8Y588F2ics3y6WlVVeaQr2IiJUbzaOuy8AZpvZL2NRT+ArYDRQkFHTHxgVb48GTo1ZOfsAy+Nwz6tALzNrFk/M9opllaZhHBERauRLVX8GnjSzTYAZwOmEDvbTZjYAmAX8PtYdCxwFTAd+jnVx9yVmdjMwOda7yd2XVKUxCvYiIlT/l6rc/VOge5JZPZPUdeC8MtYzBBiyse1RsBcRQT9eIiKSFXRtHBGRLJDp18ZRsBcRQb9UJSKSFfIzPNwr2IuIoBO0IiJZQWP2IiJZQNk4IiJZQGP2IiJZILNDvYK9iAigMXsRkayQl+F9ewV7ERHUsxcRyQo6QSsikgUyO9Qr2IuIABrGERHJCjpBKyKSBTRmLzXikUF3cfRRh/HjwkXs8evwK2W/+90xXPfXS9hl587su9/RfPTxFAAaNWrEQw/eQbduXcnPdy655Dreevt9AE488TiuvurP5OTkMHbs61z9l9vStk9SNdfe9k/efvdDmjdrygtPPAzAfYOG88Y779PAGtC8WRNuveZStmnVgiFPPsuY1yYAkJeXx4xZs5k4ZiRLli7nsuv+VrjOOfPmc/6Z/eh3Ul8eGPwEz41+hWZNmwBw4dn9OWi/vWp/R+u4zA71YOGnD+umhpu0rbuN20gHHrA3K1eu4rHH7ikM9jvv3In8fOehB27niitvLgz2/3dOf7p1250zz7qEVq1a8NKLT7DPvkfRrFlTcj98lb326c2iRUsYMvhfPPHEs7wx4Z107lqNWz1vYrqbUK1yP/2czRs35i83/6Mw2K9ctYott9gCgCeeGcV33//A9Vf8udhyb74zieH/eYEh991erDwvL49Dj+/HiEfups22rXlg8BNs3ngzTj/lhNrZoTRo1HKHjb6yzdkdTkw53vx75jP17ko6DdLdgGw18Z0PWLJ0WbGyr7+ezrfffleq7i677MSEN98FYOHCxSxftoLu3XZnh46/YPr071m0KPzY/Pg3JtK371E13napXt332I0mW29VrKwg0AOsXr0GSxJaxr7+FkcdfnCp8km5n9K+7Xa02bZ1tbc1k+VXYqqPFOzrgSlTvuLYY3qRk5NDhw7t2XPP3WjXvg3Tv5vJTjvtyPbbtyMnJ4c+xx1B+/Zt0t1cqSb3/HsoPfv2Y8xrEzj/zH7F5q1es4Z3JuVyeI8DSi338vi3OOqw4m8CI557kb6n/h/X3vZPlq/4qUbbXV95Jf7qo7QEezM7vZx5A80s18xy8/NX1Waz6qzHho5k7pz5fDDpZf551428/34ueXl5LFu2nPP/fDUjnnyItyY8z6yZc8jLy/SfYMgeF559GuOff5yjex3CU8+9WGzem+98wK+7din1iWD9+vW8+c4H9Dr0wMKyk/oezctPD+G5oQ/QqkVz7rz/kVppf32Th6c81Ufp6tnfWNYMdx/k7t3dvXuDBluUVS2r5OXlcenlN9D9N7347e/OoGnTJkybNgOAl8aMY78DjuWAg47jm2+/KyyXzHFMr0N4PQ7jFQi99x6l6k6clMsuO+1Iy+bNCstaNm9GTk4ODRo04ITjjuSLr76t6SbXSxrGqSIzm1LG9DmgwcRKaNx4MzbfvDEAh/U8kA0bNjB16jQAWrVqAUDTpk0455z+DB4yIm3tlOoza/bcwttvTHyfjtu3K7z/08pV5H7yOYccuG+p5caOe5OjDu9RrGxhPKcDMP6t9+i0w/bV3+AMkO+e8lQf1WTqZWvgCGBpiXID3qvB7dYLTzz+AAcftC8tWzZn5oxcbrzpHyxZuox77r6FVq2aM3rUcD777EuOOuaPbLNNS8aOeYr8/HzmzV1A/9MvKFzP3f+8ia5duwBwy613q2dfD11+/e1M/mQKy5atoOfxf+LcAf2Y+P5kZv4wB2tgtNl2G667vCgTZ/xb77HfXnuyeePNiq3n59VreH/yJ1x/xQXFyu96cDDfTJsBBm23bV1qvgQ1EcLNLAfIBea6+zFm1hEYCbQAPgL6ufs6M9sUGA50AxYDJ7n7zLiOq4EBhJ/JvcDdX61SW2oq9dLMBgOPuXupPEAze8rdT6loHZmceilVl2mpl7LxqiP18pTt+6Ycb56a9XxK2zOzS4DuwNYx2D8N/NfdR5rZw8Bn7v6QmZ0LdHX3c8zsZKCvu59kZl2AEcBeQBvgdWAnd6/0ybkaG8Zx9wHJAn2cV2GgFxGpTdWdjWNm7YCjgUfjfQMOBZ6NVYYBx8fbfeJ94vyesX4fYKS7r3X374HphMBfaUq9FBEBNuApT4lZg3EamGSV/wKuoOicbgtgmbtviPfnAG3j7bbAbIA4f3msX1ieZJlK0eUSRESgUvnz7j4IGFTWfDM7BvjR3T8ysx4b3bhqoGAvIkK1p1TuDxxnZkcBmwFbA/cATc2sYey9twMK0q7mAu2BOWbWEGhCOFFbUF4gcZlK0TCOiAjg7ilPKazrandv5+4dgJOBN9z9j8AEoOAiRf2BUfH26HifOP8NDxsaDZxsZpvGTJ7OwIdV2T/17EVEqLVLHF8JjDSzW4BPgMGxfDDwuJlNB5YQ3iBw9y9jBs9XwAbgvKpk4oCuein1kFIvpaTqSL085hdHpxxvXvphTL276qV69iIi6MdLRESyQl0e5agOCvYiItTfC5ylSsFeRITK5dnXRwr2IiJozF5EJCvkeWYP5CjYi4igYRwRkaxQX3+UJFUK9iIi1MyPl9QlCvYiIugErYhIVlCwFxHJAsrGERHJAsrGERHJAro2johIFtCYvYhIFlDPXkQkC+Rl+HUvFexFRNA3aEVEsoKycUREsoB69iIiWUA9exGRLKCevYhIFtDlEkREsoCGcUREsoCrZy8ikvky/XIJDdLdABGRusDdU54qYmbtzWyCmX1lZl+a2YWxvLmZjTOzafF/s1huZnavmU03sylmtmfCuvrH+tPMrH9V90/BXkSE0LNPdUrBBuBSd+8C7AOcZ2ZdgKuA8e7eGRgf7wMcCXSO00DgIQhvDsD1wN7AXsD1BW8QlaVgLyIC5OXnpzxVxN3nu/vH8fZPwFSgLdAHGBarDQOOj7f7AMM9mAQ0NbPtgCOAce6+xN2XAuOA3lXZPwV7ERFCNk6qf2Y20MxyE6aBZa3XzDoAvwY+AFq7+/w4awHQOt5uC8xOWGxOLCurvNJ0glZEhMpd4tjdBwGDKqpnZlsCzwEXufsKM0tch5tZrZ0VVs9eRIRqH7PHzBoRAv2T7v7fWPy/ODxD/P9jLJ8LtE9YvF0sK6u80hTsRUSo9mwcAwYDU939nwmzRgMFGTX9gVEJ5afGrJx9gOVxuOdVoJeZNYsnZnvFskrTMI6ICKR04rUS9gf6AZ+b2aex7C/A7cDTZjYAmAX8Ps4bCxwFTAd+Bk4HcPclZnYzMDnWu8ndl1SlQVaXf4qr4SZt627jJG1Wz5uY7iZIHdOo5Q5Wca3yNdlyx5TjzfKV32309mqbevYiIug3aEVEsoIucSwikgV01UsRkSygnr2ISBbI1yWORUQyn07QiohkAQV7EZEskNmhvo5/qUqKmNnAePElkUI6LiRVujZO/VHmJVQlq+m4kJQo2IuIZAEFexGRLKBgX39oXFaS0XEhKdEJWhGRLKCevYhIFlCwFxHJAgr2dZyZ9Tazb8xsupldle72SPqZ2RAz+9HMvkh3W6T+ULCvw8wsB3gAOBLoAvzBzLqkt1VSBwwFeqe7EVK/KNjXbXsB0919hruvA0YCfdLcJkkzd38bqNLvkEr2UrCv29oCsxPuz4llIiKVomAvIpIFFOzrtrlA+4T77WKZiEilKNjXbZOBzmbW0cw2AU4GRqe5TSJSDynY12HuvgE4H3gVmAo87e5fprdVkm5mNgJ4H/ilmc0xswHpbpPUfbpcgohIFlDPXkQkCyjYi4hkAQV7EZEsoGAvIpIFFOxFRLKAgr2ISBZQsBcRyQL/D2eDsNoBhNFgAAAAAElFTkSuQmCC","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(test_y, logreg.predict(test_x)), annot=True, fmt=\".0f\")\n","plt.title(\"Logistic Regression Confusion Matrix\",fontsize=18, color=\"red\");"]},{"cell_type":"markdown","metadata":{},"source":["* We solved the problem with Undersampling. We also could've used Oversampling and probably we would get better accuracy but i think it would be so tiring for computer."]},{"cell_type":"markdown","metadata":{},"source":["## Logistic Regression Curve"]},{"cell_type":"code","execution_count":102,"metadata":{"execution":{"iopub.execute_input":"2022-12-08T18:12:18.904141Z","iopub.status.busy":"2022-12-08T18:12:18.903556Z","iopub.status.idle":"2022-12-08T18:12:19.176008Z","shell.execute_reply":"2022-12-08T18:12:19.174653Z","shell.execute_reply.started":"2022-12-08T18:12:18.904087Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["from sklearn.metrics import roc_curve\n","#test_y = test_y.replace({2:1,1:0})\n","# Probabilities\n","logreg_pred_proba = logreg.predict_proba(test_x)\n","\n","fpr, tpr, thresholds = roc_curve(test_y, logreg_pred_proba[:,1])\n","plt.plot([0,1],[0,1],\"k--\")\n","plt.plot(fpr, tpr, label = \"Logistic Regression\")\n","plt.xlabel(\"False Positive Rate\")\n","plt.ylabel(\"True Positive Rate\")\n","plt.title(\"Logistic Regression ROC Curve\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["# Conclusion\n","* In this project i predicted \"Death\" situation with %90 accuracy.\n","* I just used one algorithm Logistic Regression.\n","* The important point in this project was solving the imbalance dataset problem. I figured it out with Undersampling method.\n","* If you found this notebook beneficial please upvote it and follow me. Thanks..."]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# MLP"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[],"source":["import torch.nn as nn\n","import torch\n"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[],"source":["x_train, x_test, y_train, y_test = train_test_split(x_resampled,y_resampled, test_size=0.2, random_state=42)\n","x_train =np.array(x_train)\n","y_train = np.array(y_train)\n","x_test = np.array(x_test)\n","y_test = np.array(y_test)\n","x_train = torch.FloatTensor((x_train))\n","y_train = torch.LongTensor((y_train))\n","x_test = torch.FloatTensor((x_test))\n","y_test = torch.LongTensor((y_test))\n","train_size=len(y_train)"]},{"cell_type":"code","execution_count":105,"metadata":{},"outputs":[],"source":["class NET(nn.Module):\n","    def __init__(self, n_feature, n_hidden1, n_hidden2, n_hidden3, n_output):\n","        super(NET, self).__init__()\n","        self.hidden1 = nn.Linear(n_feature, n_hidden1)\n","        self.relu1 = nn.ReLU()\n","\n","        self.hidden2 = nn.Linear(n_hidden1, n_hidden2)\n","        self.relu2 = nn.ReLU()\n","\n","        self.hidden3 = nn.Linear(n_hidden2, n_hidden3)\n","        self.relu3 = nn.ReLU()\n","\n","        self.out = nn.Linear(n_hidden3, n_output)\n","        self.softmax = nn.Softmax(dim=1)\n","\n","    def forward(self, x):\n","        hidden1 = self.hidden1(x)\n","        relu1 = self.relu1(hidden1)\n","\n","        hidden2 = self.hidden2(relu1)\n","        relu2 = self.relu2(hidden2)\n","\n","        hidden3 = self.hidden3(relu2)\n","        relu3 = self.relu3(hidden3)\n","        out = self.out(relu3)\n","\n","        return out\n","\n","    def test(self, x):\n","        y_pred = self.forward(x)\n","        y_predict = self.softmax(y_pred)\n","\n","        return y_predict\n"]},{"cell_type":"code","execution_count":106,"metadata":{},"outputs":[{"data":{"text/plain":["25"]},"execution_count":106,"metadata":{},"output_type":"execute_result"}],"source":["x_train.shape[1]"]},{"cell_type":"code","execution_count":107,"metadata":{},"outputs":[{"data":{"text/plain":["<torch.optim.lr_scheduler.ReduceLROnPlateau at 0x19fbf9001c0>"]},"execution_count":107,"metadata":{},"output_type":"execute_result"}],"source":["net = NET(n_feature=x_train.shape[1], n_hidden1=30,\n","          n_hidden2=60, n_hidden3=30, n_output=2)\n","optimizer=torch.optim.Adagrad(net.parameters(), lr=0.001, lr_decay=0, weight_decay=0)\n","torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=2,\n","                                           threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=1e-5, eps=1e-08, verbose=True)\n"]},{"cell_type":"code","execution_count":108,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["training set accuracy 0.9130187273442667\n","test set accuracy 0.9101228945517865\n"]}],"source":["loss_func = torch.nn.CrossEntropyLoss()\n","costs = []\n","num_epoch = 2000\n","\n","#训练网络\n","for  epoch in range(num_epoch):\n","    cost = 0\n","    out = net.forward(x_train)\n","    loss = loss_func(out,y_train)\n","    optimizer.zero_grad()\n","    loss.backward()\n","    optimizer.step()\n","    cost += loss.cpu().detach().numpy()\n","    costs.append(cost/train_size)\n"," \n","plt.plot(costs)\n","plt.title('graph of cost')\n","plt.xlabel('epoch')\n","plt.ylabel('cost')\n","plt.show()\n","#print(costs)\n","#training set accuracy\n","out = net.test(x_train)\n","#prediction = torch.argmax(out,dim = 1)\n","prediction = torch.max(out,1)[1]\n","pred_y = prediction.numpy()\n","target_y = y_train.numpy()\n","accuracy = float((pred_y == target_y).astype(int).sum())/float(target_y.size)\n","print(\"training set accuracy\",accuracy)\n","#test set accuracy\n","out1 = net.test(x_test)\n","prediction1 = torch.argmax(out1,dim = 1)\n","#prediction1 = torch.max(out1,1)[1]\n","pred_y1 = prediction1.numpy()\n","target_y1 = y_test.numpy()\n","accuracy1 = float((pred_y1 == target_y1).astype(int).sum())/float(target_y1.size)\n","print(\"test set accuracy\",accuracy1)"]},{"cell_type":"code","execution_count":109,"metadata":{},"outputs":[{"data":{"text/plain":["array([0.90725639, 0.91281751])"]},"execution_count":109,"metadata":{},"output_type":"execute_result"}],"source":["f1_score(target_y1,pred_y1,average=None)"]},{"cell_type":"code","execution_count":110,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'MLP Confusion Matrix')"]},"execution_count":110,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(target_y1, pred_y1), annot=True, fmt=\".0f\")\n","plt.title(\"MLP Confusion Matrix\",fontsize=18, color=\"red\")"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# RANDOM FOREST"]},{"cell_type":"code","execution_count":111,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.9049325251984061\n"]}],"source":["from sklearn.ensemble import RandomForestClassifier\n","#from sklearn.metrics import plot_confusion_matrix\n","model1 = RandomForestClassifier(\n","    ccp_alpha=0.001, criterion='entropy', max_depth=8, max_leaf_nodes=3)\n","model1.fit(x_train, y_train)\n","pred2=model1.predict(x_test)\n","print(model1.score(x_test, y_test))"]},{"cell_type":"code","execution_count":112,"metadata":{},"outputs":[{"data":{"text/plain":["array([0.90199192, 0.90770181])"]},"execution_count":112,"metadata":{},"output_type":"execute_result"}],"source":["f1_score(y_test.numpy(),pred2,average=None)"]},{"cell_type":"code","execution_count":113,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Random Forest Confusion Matrix')"]},"execution_count":113,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(target_y1, pred2), annot=True, fmt=\".0f\")\n","plt.title(\"Random Forest Confusion Matrix\",fontsize=18, color=\"red\")"]},{"cell_type":"code","execution_count":114,"metadata":{},"outputs":[],"source":["from sklearn.model_selection import GridSearchCV\n"]},{"cell_type":"code","execution_count":115,"metadata":{},"outputs":[],"source":["param_grid=[{'ccp_alpha':[0.0, 0.005, 0.007, 0.01, 0.015, 0.02, 0.03, 0.05, 0.1],\n","'criterion':['gini','entropy'],'max_depth':[2,3,4,5,6,7,8,9],'max_leaf_nodes':[2,3,4,5,6,7,8,9]}]"]},{"cell_type":"code","execution_count":116,"metadata":{},"outputs":[],"source":["search=GridSearchCV(estimator=model1,param_grid=param_grid)\n","best_model=search.fit(x_train,y_train)"]},{"cell_type":"code","execution_count":117,"metadata":{},"outputs":[{"data":{"text/plain":["array([0.90199192, 0.90770181])"]},"execution_count":117,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.metrics import f1_score\n","f1_score(y_test.numpy(),pred2,average=None)"]},{"cell_type":"code","execution_count":118,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Random Forest Confusion Matrix')"]},"execution_count":118,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(y_test, pred2), annot=True, fmt=\".0f\")\n","plt.title(\"Random Forest Confusion Matrix\",fontsize=18, color=\"red\")"]},{"cell_type":"code","execution_count":126,"metadata":{},"outputs":[{"data":{"text/plain":["array([0.90360771, 0.91172697])"]},"execution_count":126,"metadata":{},"output_type":"execute_result"}],"source":["pred_best=best_model.predict(x_test)\n","f1_score(y_test.numpy(),pred_best,average=None)"]},{"cell_type":"code","execution_count":127,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Best Random Forest Confusion Matrix')"]},"execution_count":127,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(y_test, pred_best), annot=True, fmt=\".0f\")\n","plt.title(\"Best Random Forest Confusion Matrix\",fontsize=18, color=\"red\")"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# SVM"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[],"source":["from sklearn import svm\n"]},{"cell_type":"code","execution_count":120,"metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.8, gamma=20, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.8, gamma=20, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"],"text/plain":["SVC(C=0.8, gamma=20, kernel='linear')"]},"execution_count":120,"metadata":{},"output_type":"execute_result"}],"source":["clf = svm.SVC(C=0.8, kernel='linear', gamma=20, decision_function_shape='ovr')\n","clf.fit(x_train, y_train.ravel())\n"]},{"cell_type":"code","execution_count":121,"metadata":{},"outputs":[{"data":{"text/plain":["array([0.88573486, 0.89092516])"]},"execution_count":121,"metadata":{},"output_type":"execute_result"}],"source":["pred3 = clf.predict(x_test)\n","f1_score(y_test.numpy(),pred3,average=None)"]},{"cell_type":"code","execution_count":122,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'SVM Confusion Matrix')"]},"execution_count":122,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(target_y1, pred3), annot=True, fmt=\".0f\")\n","plt.title(\"SVM Confusion Matrix\",fontsize=18, color=\"blue\")"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[{"data":{"text/plain":["array([0.89920949, 0.90901561])"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["clf1 = svm.SVC(C=1, kernel='rbf', gamma='auto',\n","              decision_function_shape='ovr')\n","clf1.fit(x_train, y_train.ravel())\n","pred_ = clf1.predict(x_test)\n","f1_score(y_test.numpy(), pred_, average=None)\n"]},{"cell_type":"code","execution_count":37,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'SVM Confusion Matrix')"]},"execution_count":37,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(y_test, pred_), annot=True, fmt=\".0f\")\n","plt.title(\"SVM Confusion Matrix\",fontsize=18, color=\"blue\")"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# CNN"]},{"cell_type":"code","execution_count":123,"metadata":{},"outputs":[],"source":["from torchvision import models\n"]},{"cell_type":"code","execution_count":124,"metadata":{},"outputs":[{"data":{"text/plain":["<torch.optim.lr_scheduler.ReduceLROnPlateau at 0x19fbeb1e820>"]},"execution_count":124,"metadata":{},"output_type":"execute_result"}],"source":["res=models.resnet50(pretrained=\"True\")\n","optimizer = torch.optim.Adagrad(\n","    res.parameters(), lr=0.001, lr_decay=0, weight_decay=0)\n","torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=2,\n","                                           threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=1e-5, eps=1e-08, verbose=True)\n"]},{"cell_type":"code","execution_count":125,"metadata":{},"outputs":[{"ename":"RuntimeError","evalue":"Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [119451, 25]","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)","\u001b[1;32md:\\codefield\\Oxford\\11 - Assignment\\covid-19.ipynb Cell 108\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/codefield/Oxford/11%20-%20Assignment/covid-19.ipynb#Y354sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mfor\u001b[39;00m  epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_epoch):\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/codefield/Oxford/11%20-%20Assignment/covid-19.ipynb#Y354sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m     cost \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/codefield/Oxford/11%20-%20Assignment/covid-19.ipynb#Y354sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m     out \u001b[39m=\u001b[39m res\u001b[39m.\u001b[39;49mforward(x_train)\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/codefield/Oxford/11%20-%20Assignment/covid-19.ipynb#Y354sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m     loss \u001b[39m=\u001b[39m loss_func(out,y_train)\n\u001b[0;32m     <a href='vscode-notebook-cell:/d%3A/codefield/Oxford/11%20-%20Assignment/covid-19.ipynb#Y354sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m     optimizer\u001b[39m.\u001b[39mzero_grad()\n","File \u001b[1;32mc:\\Users\\liu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\resnet.py:283\u001b[0m, in \u001b[0;36mResNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    282\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 283\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_forward_impl(x)\n","File \u001b[1;32mc:\\Users\\liu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\resnet.py:266\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    264\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_forward_impl\u001b[39m(\u001b[39mself\u001b[39m, x: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[0;32m    265\u001b[0m     \u001b[39m# See note [TorchScript super()]\u001b[39;00m\n\u001b[1;32m--> 266\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconv1(x)\n\u001b[0;32m    267\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbn1(x)\n\u001b[0;32m    268\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrelu(x)\n","File \u001b[1;32mc:\\Users\\liu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1186\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1187\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1188\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1189\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1190\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1191\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n","File \u001b[1;32mc:\\Users\\liu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\conv.py:463\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    462\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 463\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_conv_forward(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n","File \u001b[1;32mc:\\Users\\liu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\conv.py:459\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode \u001b[39m!=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mzeros\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m    456\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39mconv2d(F\u001b[39m.\u001b[39mpad(\u001b[39minput\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode),\n\u001b[0;32m    457\u001b[0m                     weight, bias, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstride,\n\u001b[0;32m    458\u001b[0m                     _pair(\u001b[39m0\u001b[39m), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdilation, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgroups)\n\u001b[1;32m--> 459\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mconv2d(\u001b[39minput\u001b[39;49m, weight, bias, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstride,\n\u001b[0;32m    460\u001b[0m                 \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpadding, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdilation, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgroups)\n","\u001b[1;31mRuntimeError\u001b[0m: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [119451, 25]"]}],"source":["loss_func = torch.nn.CrossEntropyLoss()\n","costs = []\n","num_epoch = 2000\n","\n","#训练网络\n","for  epoch in range(num_epoch):\n","    cost = 0\n","    out = res.forward(x_train)\n","    loss = loss_func(out,y_train)\n","    optimizer.zero_grad()\n","    loss.backward()\n","    optimizer.step()\n","    cost += loss.cpu().detach().numpy()\n","    costs.append(cost/train_size)\n"," \n","plt.plot(costs)\n","plt.title('graph of cost')\n","plt.xlabel('epoch')\n","plt.ylabel('cost')\n","plt.show()\n","#print(costs)\n","#training set accuracy\n","out = net.test(x_train)\n","#prediction = torch.argmax(out,dim = 1)\n","prediction = torch.max(out,1)[1]\n","pred_y = prediction.numpy()\n","target_y = y_train.numpy()\n","accuracy = float((pred_y == target_y).astype(int).sum())/float(target_y.size)\n","print(\"training set accuracy\",accuracy)\n","#test set accuracy\n","out1 = net.test(x_test)\n","prediction1 = torch.argmax(out1,dim = 1)\n","#prediction1 = torch.max(out1,1)[1]\n","pred_y1 = prediction1.numpy()\n","target_y1 = y_test.numpy()\n","accuracy1 = float((pred_y1 == target_y1).astype(int).sum())/float(target_y1.size)\n","print(\"test set accuracy\",accuracy1)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# LDA"]},{"cell_type":"code","execution_count":128,"metadata":{},"outputs":[],"source":["from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n"]},{"cell_type":"code","execution_count":129,"metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearDiscriminantAnalysis()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearDiscriminantAnalysis</label><div class=\"sk-toggleable__content\"><pre>LinearDiscriminantAnalysis()</pre></div></div></div></div></div>"],"text/plain":["LinearDiscriminantAnalysis()"]},"execution_count":129,"metadata":{},"output_type":"execute_result"}],"source":["lda = LinearDiscriminantAnalysis()\n","lda.fit(x_train, y_train)\n"]},{"cell_type":"code","execution_count":130,"metadata":{},"outputs":[{"data":{"text/plain":["{'covariance_estimator': None,\n"," 'n_components': None,\n"," 'priors': None,\n"," 'shrinkage': None,\n"," 'solver': 'svd',\n"," 'store_covariance': False,\n"," 'tol': 0.0001}"]},"execution_count":130,"metadata":{},"output_type":"execute_result"}],"source":["lda.get_params()\n"]},{"cell_type":"code","execution_count":131,"metadata":{},"outputs":[{"data":{"text/plain":["array([0.89421431, 0.89874164])"]},"execution_count":131,"metadata":{},"output_type":"execute_result"}],"source":["pred4=lda.predict(x_test)\n","f1_score(y_test.numpy(),pred4,average=None)"]},{"cell_type":"code","execution_count":132,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'LDA Confusion Matrix')"]},"execution_count":132,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(target_y1, pred4), annot=True, fmt=\".0f\")\n","plt.title(\"LDA Confusion Matrix\",fontsize=18, color=\"red\")"]},{"cell_type":"code","execution_count":46,"metadata":{},"outputs":[{"data":{"text/plain":["array([0.90782074, 0.91513651])"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.tree import DecisionTreeClassifier\n","model = DecisionTreeClassifier(random_state=0, max_depth=10, ccp_alpha=0.00001)\n","model.fit(x_train,y_train)\n","pred_tree=model.predict(x_test)\n","f1_score(y_test.numpy(),pred_tree,average=None)"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.6"},"vscode":{"interpreter":{"hash":"b90105dbfe2381468827b5b56105670c6ca908540c811c939494055059dce230"}}},"nbformat":4,"nbformat_minor":4}