3.6.10.11. A simple regression analysis on the Boston housing dataΒΆ

Here we perform a simple regression analysis on the Boston housing data, exploring two types of regressors.

```from sklearn.datasets import load_boston
```

Print a histogram of the quantity to predict: price

```import matplotlib.pyplot as plt
plt.figure(figsize=(4, 3))
plt.hist(data.target)
plt.xlabel('price (\$1000s)')
plt.ylabel('count')
plt.tight_layout()
```

Print the join histogram for each feature

```for index, feature_name in enumerate(data.feature_names):
plt.figure(figsize=(4, 3))
plt.scatter(data.data[:, index], data.target)
plt.ylabel('Price', size=15)
plt.xlabel(feature_name, size=15)
plt.tight_layout()
```

Simple prediction

```from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target)

from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train)
predicted = clf.predict(X_test)
expected = y_test

plt.figure(figsize=(4, 3))
plt.scatter(expected, predicted)
plt.plot([0, 50], [0, 50], '--k')
plt.axis('tight')
plt.xlabel('True price (\$1000s)')
plt.ylabel('Predicted price (\$1000s)')
plt.tight_layout()
```

```from sklearn.ensemble import GradientBoostingRegressor

clf.fit(X_train, y_train)

predicted = clf.predict(X_test)
expected = y_test

plt.figure(figsize=(4, 3))
plt.scatter(expected, predicted)
plt.plot([0, 50], [0, 50], '--k')
plt.axis('tight')
plt.xlabel('True price (\$1000s)')
plt.ylabel('Predicted price (\$1000s)')
plt.tight_layout()
```

Print the error rate

```import numpy as np
print("RMS: %r " % np.sqrt(np.mean((predicted - expected) ** 2)))

plt.show()
```

Out:

```RMS: 3.288354213919203
```

Total running time of the script: ( 0 minutes 0.725 seconds)

Gallery generated by Sphinx-Gallery