machine_learning_error_metrics.py

# -*- coding: utf-8 -*-
"""Machine Learning Error Metrics.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1T1i11V5YlyZE2RsEj1V3zzDqWwlJFxXk
"""

# MSE RMSE MAE MAPE

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error,mean_absolute_percentage_error

df=pd.read_csv('insurance.csv')

df.head(3)

df = pd.get_dummies(df, columns=["sex", "smoker", "region"])

y=df[["charges"]]
x=df.drop("charges",axis=1)

lm=LinearRegression()
model=lm.fit(x,y)

model.score(x,y)

df.head(3)

model.predict([[22,27,0,0,1,0,1,0,0,0,1]])

df_hata=pd.DataFrame()

df_hata["y"]=y

y_tahmin=model.predict(x)

df_hata["tahmin"]=y_tahmin

df_hata.head(3)

df_hata["error"]=y-y_tahmin

df_hata.head(7)

"""Long Way"""

df_hata["squared_error"]=np.square(df_hata["error"]) #df_hata["squared_error"]=df_hata["error"]**2

df_hata["abs_error"]=np.abs(df_hata["error"])

df_hata["percent_error"]=np.abs((y-y_tahmin)/y)

df_hata.mean()

"""Short Way with Methods"""

mean_squared_error(y,y_tahmin)

mean_absolute_error(y,y_tahmin)

mean_absolute_percentage_error(y,y_tahmin)