Machine learning model with a linear regression algorithm in Python

There must be data and an algorithm that operates on that data

In CSV the data is separated by commas, the first line is the title.

takes 1 or more minutes:

you must use the pip or pip3 dependency manager

pip install pandas

or

pip3 install pandas

If you understand Python, you know what I mean.

If you don’t understand, you must learn Python first.

the code:

import pandas as pd
import seaborn as sb
from sklearn.linear_model import LinearRegression

data = pd.read_csv('data.csv')

sb.scatterplot(x='x', y='y', data=data, hue='y', palette='coolwarm')

x_processed = data['x'].values.reshape(-1,1)
y_processed = data['y'].values.reshape(-1,1)

model = LinearRegression()

# training
model.fit(x_processed, y_processed)

worth = 1
prediction = model.predict([[worth]])

print(f"The prediction for {worth} is {prediction}")

# know if the model is well trained, 1.0 indicates that it is perfect
print(model.score(x_processed, y_processed))