Fitting a line

Setup

import pandas as pd

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

Data

Import data

df = pd.read_csv('https://raw.githubusercontent.com/kirenz/datasets/master/possum.csv')

Data structure

df

	site	pop	sex	age	head_l	skull_w	total_l	tail_l
0	1	Vic	m	8.0	94.1	60.4	89.0	36.0
1	1	Vic	f	6.0	92.5	57.6	91.5	36.5
2	1	Vic	f	6.0	94.0	60.0	95.5	39.0
3	1	Vic	f	6.0	93.2	57.1	92.0	38.0
4	1	Vic	f	2.0	91.5	56.3	85.5	36.0
...	...	...	...	...	...	...	...	...
99	7	other	m	1.0	89.5	56.0	81.5	36.5
100	7	other	m	1.0	88.6	54.7	82.5	39.0
101	7	other	f	6.0	92.4	55.0	89.0	38.0
102	7	other	m	4.0	91.5	55.2	82.5	36.5
103	7	other	f	3.0	93.6	59.9	89.0	40.0

104 rows × 8 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 104 entries, 0 to 103
Data columns (total 8 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   site     104 non-null    int64  
 1   pop      104 non-null    object 
 2   sex      104 non-null    object 
 3   age      102 non-null    float64
 4   head_l   104 non-null    float64
 5   skull_w  104 non-null    float64
 6   total_l  104 non-null    float64
 7   tail_l   104 non-null    float64
dtypes: float64(5), int64(1), object(2)
memory usage: 6.6+ KB

Variable lists

Prepara data for scikit-learn model:

y_label = "head_l"

X = df[["total_l"]]
y = df[y_label]

Model

Select model

# Choose the linear regression model
reg = LinearRegression()

Fit model

# Fit the model to the data
reg.fit(X, y)

LinearRegression()

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Coefficients

# Intercept
reg.intercept_

42.70979314896378

# Slope
reg.coef_

array([0.57290128])

Make predictions

# Make predictions on the data
y_pred = reg.predict(X)

Evaluation

Mean squared error

mean_squared_error(y, y_pred)

6.6061634260446445

Root mean squared error

mean_squared_error(y, y_pred, squared=False)

2.570245790978879