Data Science and Machine Learning (Theory and Projects) A to Z - Matplotlib for Data Visualization: Matplotlib Histogram

The video tutorial covers the use of histograms in data analysis, focusing on their ability to display data distribution, peaks, and tails. It demonstrates how to generate data with normal distribution using numpy and plot histograms using Matplotlib in a Jupyter Notebook. The tutorial also explores advanced customization options for histograms, such as setting alpha values, increasing the number of bins, and using step filled types. Additionally, it includes a practical example using the Iris dataset to visualize attribute distributions. The video concludes with a preview of upcoming topics, including subplots and 3D plots.

What is the primary purpose of using histograms in statistical analysis?

• To determine the median of the data

• To visualize the distribution of data

• To calculate the mean of the data

• To find the mode of the data

Which library in Python is used for creating histograms as mentioned in the video?

• Seaborn

• Pandas

• Scikit-learn

• Matplotlib

What function is used to generate random data following a normal distribution in NumPy?

• np.random.normal

• np.random.uniform

• np.random.choice

• np.random.randint

What does setting an alpha value in a histogram plot do?

• Changes the color of the plot

• Adds a legend to the plot

• Adjusts the transparency of the plot

• Increases the size of the plot

How does increasing the number of bins affect a histogram?

• It makes the histogram more continuous-looking

• It decreases the plot size

• It changes the color of the histogram

• It reduces the transparency

What parameter is used to create a filled histogram in Matplotlib?

• histtype='line'

• histtype='stepfilled'

• histtype='step'

• histtype='bar'

What dataset is used to demonstrate histogram plotting in the final section?

• CIFAR-10 dataset

• MNIST dataset

• Iris dataset

• Boston Housing dataset