Let's say we're a data scientist at a retail company, and we're tasked with building a predictive model to forecast sales for the next quarter. We have a large dataset containing historical sales data, customer demographics, and market trends.
We evaluate our model's performance using metrics such as mean squared error and R-squared. 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() Let's say we're a data scientist at a