Diabetes mellitus

"""

Tahmin programları v1


"""

from sklearn.cluster import KMeans
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning) 
warnings.filterwarnings("ignore", category=FutureWarning)
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score

from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn import model_selection
import matplotlib.pyplot as plt
from sklearn.linear_model import RidgeCV,Lasso,Ridge,LassoCV,ElasticNet,ElasticNetCV
from sklearn.linear_model import LassoCV
import numpy as np
import pandas as pd 
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import mean_squared_error, r2_score, accuracy_score
import matplotlib.pyplot as plt
from sklearn.preprocessing import scale 
from sklearn.preprocessing import StandardScaler
from sklearn import model_selection
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn import neighbors
from sklearn.svm import SVR,SVC
from lightgbm import LGBMRegressor
from xgboost import XGBRegressor
from warnings import filterwarnings
filterwarnings('ignore')

from sklearn.svm import SVR
df = pd.read_csv("diabetes.csv",sep=",")
df = df.dropna()

y = df["Outcome"]
X = df.drop(["Outcome"], axis = 1)
X_train, X_test, y_train, y_test = train_test_split(X, 
                                                    y, 
                                                    test_size=0.30, 
                                                    random_state=42)

svm_model = SVC(kernel = "linear").fit(X_train, y_train)
y_pred = svm_model.predict(X_test)
accuracy_score(y_test, y_pred)

svm = SVC()
svm_params = {"C": np.arange(1,10), "kernel": ["linear","rbf"]}
svm_cv_model = GridSearchCV(svm, svm_params, cv = 5, n_jobs = -1, verbose = 2).fit(X_train, y_train)
svm_cv_model.best_score_
svm_cv_model.best_params_
svm_tuned = SVC(C = 2, kernel = "linear").fit(X_train, y_train)
y_pred = svm_tuned.predict(X_test)
accuracy_score(y_test, y_pred)

# çooook önemli burası bize karşılaştırma sonucunu veriyor
Pregnancies=input("Pregnancies:")
Glucose=input("Glucose:")
BloodPressure=input("BloodPressure:")
SkinThickness=input("SkinThickness:")
Insulin=input("Insulin:")
BMI=input("BMI:")
DiabetesPedigreeFunction=input("DiabetesPedigreeFunction:")
Age=input("Age:")

x_degerler = np.array([[Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age]])
print("x_yeni: {}".format(x_degerler.shape))

prediction = svm_tuned.predict(x_degerler)
print("tahmin: {}".format(prediction))
print("tahmin sonucu: {}".format(prediction))

İndir: