# Comment implémenter une régression linéaire simple avec scikit-learn et python 3

Pour faire une régression linéaire simple avec python 3 on peut utiliser le module scikit-learn, exemple de code:

````from sklearn import linear_model`

`import matplotlib.pyplot as plt`
`import numpy as np`
`import random`

`#----------------------------------------------------------------------------------------#`
`# Step 1: training data`

`X = [i for i in range(10)]`
`Y = [random.gauss(x,0.75) for x in X]`

`X = np.asarray(X)`
`Y = np.asarray(Y)`

`X = X[:,np.newaxis]`
`Y = Y[:,np.newaxis]`

`plt.scatter(X,Y)`

`#----------------------------------------------------------------------------------------#`
`# Step 2: define and train a model`

`model = linear_model.LinearRegression()`
`model.fit(X, Y)`

`print(model.coef_, model.intercept_)`

`#----------------------------------------------------------------------------------------#`
`# Step 3: prediction`

`x_new_min = 0.0`
`x_new_max = 10.0`

`X_NEW = np.linspace(x_new_min, x_new_max, 100)`
`X_NEW = X_NEW[:,np.newaxis]`

`Y_NEW = model.predict(X_NEW)`

`plt.plot(X_NEW, Y_NEW, color='coral', linewidth=3)`

`plt.grid()`
`plt.xlim(x_new_min,x_new_max)`
`plt.ylim(0,10)`

`plt.title("Simple Linear Regression using scikit-learn and python 3",fontsize=10)`
`plt.xlabel('x')`
`plt.ylabel('y')`

`plt.savefig("simple_linear_regression.png", bbox_inches='tight')`
`plt.show()`
```

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