diabetes model prediction

"""
# @Time    :  2020/9/6
# @Author  :  Jimou Chen
"""
from sklearn.linear_model import LogisticRegression
import pandas as pd
import matplotlib.pyplot as plt
import seaborn
import numpy as np
import missingno as msn
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split


def label_distribution(data):
    p = data.Outcome.value_counts().plot(kind='bar')  # 使用柱狀圖畫出
    plt.show()
    # 視覺化資料釋出, 有些資料本不該為0的卻為0，其實是空的
    p = seaborn.pairplot(data, hue='Outcome')
    plt.show()
    # 把空值的用柱狀圖畫出來
    p = msn.bar(data)
    plt.show()


def handle_data():
    data = pd.read_csv('data/diabetes.csv')
    # 檢視標籤分佈
    print(data.Outcome.value_counts())
    # 把葡萄糖，血壓，面板厚度，胰島素，身體質量指數中的0替換為nan
    handle_col = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']
    data[handle_col] = data[handle_col].replace(0, np.nan)

    # 設定閥值
    thresh_count = data.shape[0] * 0.8
    # 若某一列資料缺失的數量超過20%就會被刪除
    data = data.dropna(thresh=thresh_count, axis=1)

    # 填充資料，得到新的資料集data
    data['Glucose'] = data['Glucose'].fillna(data['Glucose'].mean())
    data['BloodPressure'] = data['BloodPressure'].fillna(data['BloodPressure'].mean())
    data['BMI'] = data['BMI'].fillna(data['BMI'].mean())

    return data


if __name__ == '__main__':
    new_data = handle_data()
    label_distribution(new_data)

    # 切分資料集
    x_data = new_data.drop('Outcome', axis=1)
    y_data = new_data.Outcome
    x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.3, stratify=y_data)

    # 建模
    model = LogisticRegression()
    model.fit(x_train, y_train)

    # 預測
    pred = model.predict(x_test)
    # 評估
    print(classification_report(pred, y_test))
 

D:\Anaconda\Anaconda3\python.exe D:/Appication/PyCharm/Git/kaggle-project/DiabetesPrediction/diabetes_predict.py
0    500
1    268
Name: Outcome, dtype: int64
              precision    recall  f1-score   support

           0       0.90      0.80      0.85       169
           1       0.58      0.76      0.66        62

    accuracy                           0.79       231
   macro avg       0.74      0.78      0.75       231
weighted avg       0.81      0.79      0.80       231


Process finished with exit code 0