資料來源：GrandmaCan -我阿嬤都會教學影片

邏輯回歸 Logistic Regression可以用來解決分類問題，像是糖尿病的原因，如果有年齡、血糖、體重等資料，可以用來做回歸預測。

可以透過Sigmoid Function(S型函數)來讓回歸的直線更加貼合數據。

1. 輸入檔案

import pandas as pd

url = "https://raw.githubusercontent.com/GrandmaCan/ML/main/Classification/Diabetes_Data.csv"
data = pd.read_csv(url)
data

2. 資料預處理

• label encoding:

data["Gender"] = data["Gender"].map({"男生": 1, "女生": 0})
data

3. 訓練資料：將資料拆分成訓練集與測試集，通常訓練及佔比7-8成。

from sklearn.model_selection import train_test_split
x = data[["Age", "Weight", "BloodSugar", "Gender"]]
y = data["Diabetes"]

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=87)
x_train = x_train.to_numpy()
x_test = x_test.to_numpy()

資料標準化

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)

4. 尋找cost function 以及設定optimizer

import numpy as np
def sigmoid(z):
  return 1/(1+np.exp(-z))

w = np.array([1, 2, 3, 4])
b = 1
z = (w*x_train).sum(axis=1) + b 
sigmoid(z)