Building Data Science Solutions With Anaconda Pdf -

# Explore the data print(df.head())

We start by importing the necessary libraries and loading our dataset into a Pandas dataframe. building data science solutions with anaconda pdf

# Create histogram plt.hist(df['sales'], bins=50) plt.title('Distribution of Sales') plt.xlabel('Sales') plt.ylabel('Frequency') plt.show() # Explore the data print(df

from sklearn.linear_model import LinearRegression 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.