端到端的机器学习项目-加州住房.zip

# California Housing ## Source This dataset is a modified version of the California Housing dataset available from [Luís Torgo's page](http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html) (University of Porto). Luís Torgo obtained it from the StatLib repository (which is closed now). The dataset may also be downloaded from StatLib mirrors. This dataset appeared in a 1997 paper titled *Sparse Spatial Autoregressions* by Pace, R. Kelley and Ronald Barry, published in the *Statistics and Probability Letters* journal. They built it using the 1990 California census data. It contains one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people). ## Tweaks The dataset in this directory is almost identical to the original, with two differences: * 207 values were randomly removed from the `total_bedrooms` column, so we can discuss what to do with missing data. * An additional categorical attribute called `ocean_proximity` was added, indicating (very roughly) whether each block group is near the ocean, near the Bay area, inland or on an island. This allows discussing what to do with categorical data. Note that the block groups are called "districts" in the Jupyter notebooks, simply because in some contexts the name "block group" was confusing. ## Data description >>> housing.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 20640 entries, 0 to 20639 Data columns (total 10 columns): longitude 20640 non-null float64 latitude 20640 non-null float64 housing_median_age 20640 non-null float64 total_rooms 20640 non-null float64 total_bedrooms 20433 non-null float64 population 20640 non-null float64 households 20640 non-null float64 median_income 20640 non-null float64 median_house_value 20640 non-null float64 ocean_proximity 20640 non-null object dtypes: float64(9), object(1) memory usage: 1.6+ MB >>> housing["ocean_proximity"].value_counts() <1H OCEAN 9136 INLAND 6551 NEAR OCEAN 2658 NEAR BAY 2290 ISLAND 5 Name: ocean_proximity, dtype: int64 >>> housing.describe() longitude latitude housing_median_age total_rooms \ count 16513.000000 16513.000000 16513.000000 16513.000000 mean -119.575972 35.639693 28.652335 2622.347605 std 2.002048 2.138279 12.576306 2138.559393 min -124.350000 32.540000 1.000000 6.000000 25% -121.800000 33.940000 18.000000 1442.000000 50% -118.510000 34.260000 29.000000 2119.000000 75% -118.010000 37.720000 37.000000 3141.000000 max -114.310000 41.950000 52.000000 39320.000000 total_bedrooms population households median_income count 16355.000000 16513.000000 16513.000000 16513.000000 mean 534.885112 1419.525465 496.975050 3.875651 std 412.716467 1115.715084 375.737945 1.905088 min 2.000000 3.000000 2.000000 0.499900 25% 295.000000 784.000000 278.000000 2.566800 50% 433.000000 1164.000000 408.000000 3.541400 75% 644.000000 1718.000000 602.000000 4.745000 max 6210.000000 35682.000000 5358.000000 15.000100