# Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(df.drop('sales', axis=1), df['sales'], test_size=0.2, random_state=42)
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. Our goal is to build a model that can accurately predict sales and help the company make informed decisions. 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() # Split data into training and testing sets
As a data scientist, you're constantly looking for ways to efficiently and effectively build and deploy data science solutions. With the rise of big data and artificial intelligence, the demand for data scientists has increased exponentially. In this story, we'll explore how to build data science solutions using Anaconda, a popular Python distribution for data science. # Create histogram plt
from sklearn.metrics import mean_squared_error, r2_score