Building Data Science Solutions With Anaconda Pdf Work (5000+ DIRECT)

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

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

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. building data science solutions with anaconda pdf

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