Building Data Science Solutions With Anaconda Pdf < PROVEN ✯ >

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

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import pandas as pd from sklearn.model_selection import train_test_split 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()

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. We evaluate our model's performance using metrics such

# 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}')

# Create new features df['avg_sales_per_customer'] = df['sales'] / df['customers'] df['sales_growth_rate'] = df['sales'].pct_change() such as Microsoft Word

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