Anaconda Pdf - Building Data Science Solutions With

from sklearn.linear_model import LinearRegression

# Load dataset df = pd.read_csv('sales_data.csv')

# 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}') building data science solutions with anaconda pdf

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.

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. from sklearn

Next, we use Jupyter Notebook to explore and visualize our data. We create a histogram to understand the distribution of sales values.

We split our data into training and testing sets and build a linear regression model using scikit-learn. This allows us to integrate our model with

We evaluate our model's performance using metrics such as mean squared error and R-squared.