deck.md

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California housing prediction

Goal of Analysis: Use ML algorithms to get best accuracy of predictions for California housing prices given the attributes in the dataset.

(Link to the dataset for California housing pricesc in 1990)

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Data

• Histogram of the raw data
• Quick view of the data before doing further analysis
• Generated automatically from a data frame using matplotlib

---

    # visualize data based on geographical info
    import matplotlib.image as mpimg
    import matplotlib.pyplot as mppyplot

    california_img=mpimg.imread('./images/california.png')
    ax = housing.plot(kind="scatter", x="longitude", y="latitude", figsize=(10,7),
                    s=housing['population']/100, label="Population",
                    c="median_house_value", cmap=plt.get_cmap("jet"),
                    colorbar=False, alpha=0.4)
    plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,
            cmap=plt.get_cmap("jet"))
    plt.ylabel("Latitude", fontsize=14)
    plt.xlabel("Longitude", fontsize=14)

    prices = housing["median_house_value"]
    tick_values = np.linspace(prices.min(), prices.max(), 11)
    cbar = plt.colorbar(ticks=tick_values/prices.max())
    cbar.ax.set_yticklabels(["$%dk"%(round(v/1000)) for v in tick_values], fontsize=14)
    cbar.set_label('Median House Value', fontsize=16)

    plt.legend(fontsize=16)
    plt.show()

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