hellozeyu

import numpy as np
import pygame

pygame.init()

WIDTH, HEIGHT = 400, 600
SCREEN = pygame.display.set_mode((WIDTH,HEIGHT))
GameOn = True
CLOCK = pygame.time.Clock()
FRAMERATE = 60

hellozeyu

hellozeyu

import numpy as np
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
from sklearn.base import clone

def transformer(y, func=None):
    """Transforms target variable and prediction"""
    if func is None:
        return y
    else:

hellozeyu

# Idea borrowed from this script: https://www.kaggle.com/serigne/stacked-regressions-top-4-on-leaderboard


def impute(all_data):

	# PoolQC : data description says NA means "No Pool". That make sense, given the huge ratio of missing value (+99%) and majority of houses have no Pool at all in general.
	all_data["PoolQC"] = all_data["PoolQC"].fillna("None")

	# MiscFeature : data description says NA means "no misc feature"
	all_data["MiscFeature"] = all_data["MiscFeature"].fillna("None")

hellozeyu

# restore_packages.R
#
# installs each package from the stored list of packages
# source: http://hlplab.wordpress.com/2012/06/01/transferring-installed-packages-between-different-installations-of-r/

load("~/installed_packages.rda")

for (count in 1:length(installedpackages)) {
    install.packages(installedpackages[count])
}