Paris Saclay Center for Data Science¶
Boston RAMP challenge: prediction of Boston housing prices
Balázs Kégl (LAL/CNRS)
Introduction¶
Boston housing is a small standard regression data set from the UCI Machine Learning Repository.
In [1]:
from __future__ import print_function
%matplotlib inline
import numpy as np
import pandas as pd
import pylab as plt
import seaborn as sns; sns.set()
Fetch the data and load it in pandas¶
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local_filename = 'data/train.csv'
# Open file and print the first 3 lines
with open(local_filename) as fid:
for line in fid.readlines()[:3]:
print(line)
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data = pd.read_csv(local_filename)
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data.head()
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data.shape
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data.describe()
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data.hist(figsize=(10, 20), bins=50, layout=(7, 3));
Let's look as scatter plots between pairs of variables¶
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sns.pairplot(data.iloc[:, :5]); # take only 5 to make it fast enough
Building predictive models¶
For submitting to the RAMP site, you will need to create a estimator.py file that defines a get_estimator function which returns a scikit-learn estimator. You can find an example estimator.py file in submissions/starting_kit.
The initial example classifier in your sandbox is:
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from sklearn.ensemble import RandomForestRegressor
def get_estimator():
reg = RandomForestRegressor(
n_estimators=2, max_leaf_nodes=2, random_state=61)
return reg
Submission¶
Before you make your submission it is important that you test your code locally first. To submit your code, you can refer to the online documentation.