32 lines
920 B
Python
32 lines
920 B
Python
# Regression Linéaire Simple
|
|
|
|
# Importer les librairies
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import pandas as pd
|
|
|
|
# Importer le dataset
|
|
dataset = pd.read_csv('Salary_Data.csv')
|
|
X = dataset.iloc[:, :-1].values
|
|
y = dataset.iloc[:, -1].values
|
|
|
|
# Diviser le dataset entre le Training set et le Test set
|
|
from sklearn.model_selection import train_test_split
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1.0/3, random_state = 0)
|
|
|
|
# Construction du modèle
|
|
from sklearn.linear_model import LinearRegression
|
|
regressor = LinearRegression()
|
|
regressor.fit(X_train, y_train)
|
|
|
|
# Faire de nouvelles prédictions
|
|
y_pred = regressor.predict(X_test)
|
|
regressor.predict(15)
|
|
|
|
# Visualiser les résultats
|
|
plt.scatter(X_test, y_test, color = 'red')
|
|
plt.plot(X_train, regressor.predict(X_train), color = 'blue')
|
|
plt.title('Salaire vs Experience')
|
|
plt.xlabel('Experience')
|
|
plt.ylabel('Salaire')
|
|
plt.show() |