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Hands-on Machine Learning with Scikit-Learn

Challenge Solution Review

Explore the solution process for a linear regression challenge using Scikit-Learn. Learn to load and prepare data, split it into training and test sets, build and train a linear regression model, and evaluate performance with mean squared error.

We'll cover the following...
Python 3.5
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import sklearn.metrics as metrcis
df = pd.read_csv("./auto_insurance_sweden.csv", sep=",", header=0)
df.columns = ["x", "y"]
y = df.pop("y").values
X = df
train_x, test_x, train_y, test_y = train_test_split(X,
                                                    y,
                                                    test_size=0.2,
                                                    random_state=42)
lr = LinearRegression()
lr.fit(train_x, train_y)
pred_y = lr.predict(test_x)
mse = metrcis.mean_squared_error(test_y, pred_y)
print("The MSE is {}.".format(mse))

First, you need to load the dataset from auto_insurance_sweden.csv by using read_csv at line 6. Here we use the pandas library, which is a widely used library for data processing. If you are not familiar with this library, we highly ...