Data Science Project on Area and Population
In this project we’ll use the size of points to indicate the area and populations of California cities. We would like a legend that specifies the scale of the sizes of the points, and we’ll accomplish this by plotting some labeled data with no entries.
You can download the dataset required for this project from here.
cities = pd.read_csv("california_cities.csv")
latitude, longitude = cities["latd"], cities["longd"]
population, area = cities["population_total"], cities["area_total_km2"]
import numpy as np
import matplotlib.pyplot as plt
plt.scatter(longitude, latitude, label=None, c=np.log10(population),
cmap='viridis', s=area, linewidth=0, alpha=0.5)
# now we will craete a legend, we will plot empty lists with the desired size and label
for area in [100, 300, 500]:
plt.scatter(, , c='k', alpha=0.3, s=area, label=str(area) + 'km$^2$')
plt.legend(scatterpoints=1, frameon=False, labelspacing=1, title='City Areas')
plt.title("Area and Population of California Cities")
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
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