ML-Ch2-P2-End to End Machine Learning Project

Introduction

Welcome to this exciting chapter Again!

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Step 3: Explore and Visualize the Data to Gain Insights

• make a copy of the original dataset so we can revert to it afterwards

Visualizing geographical data

Since the dataset includes geographical information, it is a good idea to create a scatterplot of all the districts to visualize the data.

housing.plot(kind='scatter', x='longitude', y='latitude', grid=True)
plt.show()

But this is hard to see any particular pattern. Setting the alpha option to 0.2 makes it easier to visualize the places where there is a high density of data points.

housing.plot(kind='scatter', x='longitude', y='latitude', grid=True, alpha=0.2)
plt.show()

Next, let’s look at the housing prices. The radius of each circle represents the district’s population (option s), and the color represents the price (option c). We can use a predefined color map (option cmap) called jet, which ranges from blue (low values) to red (high prices).

housing.plot(kind='scatter', x='longitude', y='latitude', grid=True,
             s=housing["population"] / 100, label="population",
             c="median_house_value", cmap="jet", colorbar=True,
             legend=True, sharex=False, figsize=(10,7), alpha=0.2)
plt.show()

Look for corrections

Experiment with attribute combinations

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Step 4: Prepare the Data for Machine Learning Algorithms

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Step 5: Select a Model

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Step 6: Fine-Tune the Model

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Step 7: Present the Solution

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Step 8: Launch, Monitor, and Maintain the System