from sklearn.metrics import mean_squared_error, r2_score
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. from sklearn
# Create new features df['avg_sales_per_customer'] = df['sales'] / df['customers'] df['sales_growth_rate'] = df['sales'].pct_change() from sklearn.metrics import mean_squared_error
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.
# 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}')
To solve this problem, we'll use Anaconda, which provides a comprehensive platform for data science. Anaconda includes Python, Jupyter Notebook, Conda, scikit-learn, and Pandas.
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.linear_model import LinearRegression
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.
# Create new features df['avg_sales_per_customer'] = df['sales'] / df['customers'] df['sales_growth_rate'] = df['sales'].pct_change()
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.
# 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}')
To solve this problem, we'll use Anaconda, which provides a comprehensive platform for data science. Anaconda includes Python, Jupyter Notebook, Conda, scikit-learn, and Pandas.