- This is a link to the regression model data csv file!
https://drive.google.com/file/d/1P1IAq7q-3u8C6CyQoFCiuCch9eh3bn60/view?usp=sharing
I'm making a regression model with this data, but I'm asking because I'm not sure because it's my first time with Python.
According to this data, is it multiple regression because there are three x_0x_1x_2x? Or is it just a regression model?
And in the case of multiple regression analysis, do we model the data right away without making it into visual data? I'm going to make a regression model with that data, so please give me some tips or teach me.
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
Study linear regression and look at the document above. For general machine learning, scikit-learn is simple and good. The document is well-written.
The simple linear regression example code is
import pandas as pd
from sklearn.linear_model import LinearRegression
# # read data and set X and y
df = pd.read_csv('test.csv')
print(df.info())
print(df.head())
X = df[['x_0', 'x_1', 'x_2']]
y = df['y']
# # regression using sklearn lin-reg model
reg = LinearRegression()
reg.fit(X, y)
print(reg.coef_)
print(reg.intercept_)
Execution result
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 5 columns):
# # Column Non-Null Count Dtype
--- ------ -------------- -----
0 Unnamed: 0 200 non-null int64
1 x_0 200 non-null float64
2 x_1 200 non-null float64
3 x_2 200 non-null float64
4 y 200 non-null float64
dtypes: float64(4), int64(1)
memory usage: 7.9 KB
None
Unnamed: 0 x_0 x_1 x_2 y
0 0 -1.774915 0.627609 0.320547 44.371864
1 1 -0.433889 -0.271014 -0.726314 -52.123380
2 2 -2.132202 -0.329896 2.034855 5.649362
3 3 -2.368874 0.282574 -1.004728 -41.428106
4 4 1.089158 0.624181 -0.427276 61.875930
[16.38626013 92.78638947 33.74001825]
4.139570957198153
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