기본사용방법 결과
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from test_module import check
from sklearn.linear_model import LinearRegression
print(check(1,100))
x = np.random.randn(1000)
y = np.random.randn(1000)
fig, ax = plt.subplots()
ax.scatter(x, y)
fig
pyscript&ML 비만도 알아보기
이곳에 키와 몸무게를 입력해주세요(저장되지 않습니다)
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split, cross_val_score
import pandas as pd
from js import console
def info_to_df(height, weight, gender):
height, weight = height / 100, weight / 100
check = pd.DataFrame(columns=["Height", "Weight", "gender"])
data_to_insert = {"Height": height, "Weight": weight, "gender": gender}
infodf = check.append(data_to_insert, ignore_index=True)
return infodf
def make_ml_answer(*args,**kwargs):
data = pd.DataFrame([['Male',174,96,4],
['Male',189,87,2],
['Female',185,110,4],
['Male',149,61,3],
['Male',189,104,3],
['Male',147,92,5],
['Male',154,111,5],
['Male',174,90,3],
['Female',169,103,4],
['Male',195,81,2],
['Female',159,80,4],
['Female',192,101,3],
['Male',155,51,2],
['Male',191,79,2],
['Female',153,107,5],
['Female',157,110,5],
['Male',140,129,5],
['Male',144,145,5],
['Male',172,139,5],
['Male',157,110,5],
['Female',153,149,5],
['Female',169,97,4],
['Female',195,104,3],
['Male',149,61,3]], columns = ['Gender','Height','Weight','Index'])
df = data
le = LabelEncoder()
df["gender"] = le.fit_transform(df["Gender"])
df["Height"] = df["Height"] / 100
df["Weight"] = df["Weight"] / 100
X = df.drop(["Index", "Gender"], axis="columns")
y = df["Index"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15)
model = KNeighborsClassifier(n_neighbors=5)
test = model.fit(X_train, y_train)
string_height = document.getElementById('height').value;
string_weight = document.getElementById('weight').value;
string_gender = document.getElementById('gender').value;
check_height = int(round(float(string_height)))
check_weight = int(round(float(string_weight)))
check_gender = int(round(float(string_gender)))
console.log(check_height);
console.log(check_weight);
console.log(check_gender);
console.log("test",int(check_height));
height,weight,gender = int(check_height),int(check_weight), int(check_gender)
print("키몸무게성별 잘나오나??",height,weight,gender)
print("키몸무게성별 잘나오나??",type(height),type(weight),gender)
if height:
print("키 나옴??")
prediction = test.predict(info_to_df(height, weight, gender))
print("prediction",prediction)
prediction_dict = {1: "깡마름", 2: "마름", 3: "보통", 4: "통통", 5: "뚱뚱"}
prediction_name = prediction_dict[prediction[0]]
print("결과값:",prediction_name)
pyscript.write("result",prediction_name)
키
cm
몸무게
kg
성별
남자:1 / 여자:2
누르기
이곳에서 결과가 나옵니다(깡마름/마름/보통/통통/뚱뚱)