import pandas as pd from sklearn.model_selection import train_test_split
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We start by importing the necessary libraries and loading our dataset into a Pandas dataframe. building data science solutions with anaconda pdf
We identify relevant features that can help improve our model's performance. We create new features, such as the average sales per customer and the sales growth rate. import pandas as pd from sklearn
# Build linear regression model model = LinearRegression() model.fit(X_train, y_train) building data science solutions with anaconda pdf
