Building Data Science Solutions With Anaconda Pdf __link__ -

# Build linear regression model model = LinearRegression() model.fit(X_train, y_train)

# 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

We evaluate our model's performance using metrics such as mean squared error and R-squared. # Build linear regression model model = LinearRegression()

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. y_pred) r2 = r2_score(y_test

We split our data into training and testing sets and build a linear regression model using scikit-learn.

We start by importing the necessary libraries and loading our dataset into a Pandas dataframe.