The example we will look at below seeks to predict life span based on weight, height, physical activity, BMI, gender, and whether the person has a history of smoking.

With linear regression we assume that the output variable (lifespan in this example) is linearly related to the features we have (we will look at non-linear models in the next module).

This example uses a synthetic data set.

## Load data

We’ll load the data from the web…

```
import pandas as pd
filename = 'https://gitlab.com/michaelallen1966/1804_python_healthcare_wordpress/raw/master/jupyter_notebooks/life_expectancy.csv'
df = pd.read_csv(filename)
df.head()
```

## Exploratory data analysis

```
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set(style = 'whitegrid', context = 'notebook')
sns.pairplot(df, size = 2.5)
plt.show()
```

We can show the correlation matrix with np.corrcoef (np.cov would show the non-standardised covariance matrix; a correlation matrix has the same values as a covariance matrix on standardised data). Note that we need to transpose our data so that each feature is in a row rather than a column.

When building a linear regression model we are most interested in those features which have the strongest correlation with our outcome. If there are high degrees of covariance between features we may wish to consider using principal component analysis to reduce the data set.

```
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
%matplotlib inline
np.set_printoptions(precision=3)
corr_mat = np.corrcoef(df.values.T)
print ('Correlation matrix:\n')
print (corr_mat)
print ()
# Plot correlation matrix
plt.imshow(corr_mat, interpolation='nearest')
plt.colorbar()
plt.xlabel('Feature')
plt.ylabel('Feature')
plt.title('Correlation between life expectancy features')
plt.show()
```

## Fitting a linear regression model using a single feature

To illustrate linear regression, we’ll start with a single feature. We’ll pick BMI.

```
X = df['BMI'].values.reshape(-1, 1)
X = X.astype('float')
y = df['life_expectancy'].values.reshape(-1, 1)
y = y.astype('float')
# Standardise X and y
# Though this may often not be necessary it may help when features are on
# very different scales. We won't use the standardised data here,
# but here is how it would be done
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X_std = sc_X.fit_transform(X)
Y_std = sc_y.fit_transform(X)
# Create linear regression model
from sklearn.linear_model import LinearRegression
slr = LinearRegression()
slr.fit(X, y)
# Print model coefficients
print ('Slope =', slr.coef_[0])
print ('Intercept =', slr.intercept_)
```

## Predicting values

We can simply use the predict method of the linear regression model to predict values for any given X. (We use ‘flatten’ below to change from a column array toa row array).

```
y_pred = slr.predict(X)
print ('Actual = ', y[0:5].flatten())
print ('Predicted = ', y_pred[0:5].flatten())
```

## Obtaining metrics of observed vs predicted

The metrics module from sklearn contains simple methods of reporting metrics given observed and predicted values.

```
from sklearn import metrics
print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_pred))
print('Mean Squared Error:', metrics.mean_squared_error(y, y_pred))
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y, y_pred)))
print('R-square:',metrics.r2_score(y, y_pred))
```

## Plotting observed values and fitted line

```
plt.scatter (X, y, c = 'blue')
plt.plot (X, slr.predict(X), color = 'red')
plt.xlabel('X')
plt.ylabel('y')
plt.show()
```

## Plotting observed vs. predicted values

Plotting observed vs. predicted can give a good sense of the accuracy of the model, and is also suitable when there are multiple X features.

```
plt.scatter (y, slr.predict(X), c = 'blue')
plt.xlabel('Observed')
plt.ylabel('Predicted')
plt.show()
```

## Fitting a model to multiple X features¶

The method described above works with any number of X features. Generally we may wish to pick those features with the highest correlation to the outcome value, but here we will use them all.

```
X = df.values[:, :-1]
y = df.values[:, -1]
```

```
# Create linear regression model
from sklearn.linear_model import LinearRegression
slr = LinearRegression()
slr.fit(X, y)
# Print model coefficients
print ('Slope =', slr.coef_)
print ('Intercept =', slr.intercept_)
```

Show metrics (notice the improvement)

```
y_pred = slr.predict(X)
from sklearn import metrics
print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_pred))
print('Mean Squared Error:', metrics.mean_squared_error(y, y_pred))
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y, y_pred)))
print('R-square:',metrics.r2_score(y, y_pred))
```

Plot observed vs. predicted:

```
plt.scatter (y, slr.predict(X), c = 'blue')
plt.xlabel('Observed')
plt.ylabel('Predicted')
plt.show()
```

## Plotting residuals

Residuals are simply the difference between an observed value and its predicted value. We can plot the relationship between observed values and residuals. Ideally we like to see that there is no clear relationship between predicted value and residual – residuals should be randomly distributed. Residual plotting may also be used to look to see if there are any outliers which might be having an effect on our model (in which case we may decide that it better to remove the outliers and re-fit).

```
residuals = slr.predict(X) - y # predicted - observed
plt.scatter (y, residuals, c = 'blue')
plt.xlabel('Observed')
plt.ylabel('Residual')
plt.show()
```

## 2 thoughts on “86. Linear regression and multiple linear regression”