Building Data Science Solutions With Anaconda Pdf ((install)) -

# Evaluate model performance mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) print(f'MSE: {mse:.2f}, R2: {r2:.2f}')

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

In this story, we demonstrated how to build a data science solution using Anaconda. We covered data preparation, exploration, feature engineering, model building, evaluation, and deployment. Anaconda provides a comprehensive platform for data science, making it easy to build and deploy data science solutions. In this story, we demonstrated how to build

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

Finally, we deploy our model using Anaconda's built-in deployment tools, such as Anaconda Enterprise or Docker. This allows us to integrate our model with other applications and services. # Explore the data print(df

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

from sklearn.metrics import mean_squared_error, r2_score