Missing Data:

A Machine Learning

Approach

DAMIAN MINGLE

CHIEF DATA SCIENTIST, WPC Healthcare

@DamianMingle

GET THE FULL STORY

bit.ly/UseSciKitNow

What’s Imputation Anyway?

Some models don’t do well with missing values, so filling with values could

prove useful.

Missing values can be replaced by the mean, median, or frequent value.

Why Imputation Matters

Imputing the missing values can give better results than discarding the

samples containing any missing value.

Imputing does not always improve the predictions – cross-validation is good to

review.

In some cases, dropping rows or using marker values is more effective.

Preprocessing

Clustering

Regression

Classification

Dimensionality Reduction

Model Selection

Let’s Look

at an

ML Recipe

Imputation

The Imports

import numpy as np

import urllib

from sklearn.preprocessing import Imputer

Load Dataset with Missing Values

url = “https://goo.gl/3jvZXE”

raw_data = urllib.urlopen(url)

dataset = np.loadtxt(raw_data, delimiter=",") print(dataset.shape)

Separate Features from Target

X = dataset[:,0:7]

y = dataset[:,8

Mark Values with 0

X[X==0]=np.nan

Impute Missing Values with Mean

imp = Imputer(missing_values =‘NaN’, strategy=‘mean’)

imputed_X = imp.fit_transform(X)

Imputation Recipe

# Impute missing values with the mean

import numpy as np

import urllib

from sklearn.preprocessing import Imputer

# Load dataset from UCI Machine Learning Repo

url = “https://goo.gl/3jvZXE”

raw_data = urllib.urlopen(url)

dataset = np.loadtxt(raw_data, delimiter=",") print(dataset.shape)

# Segregate the data by features and target

X = dataset[:,0:7] y = dataset[:,8]

# All values with 0 become “not actual number” (NaN)

X[X==0]=np.nan

# Make use of the mean value for attribute

imp = Imputer(missing_values='NaN', strategy='mean')

imputed_X = imp.fit_transform(X)

Missing Data:

A Machine Learning

Approach

DAMIAN MINGLE

CHIEF DATA SCIENTIST, WPC Healthcare

@DamianMingle

GET THE FULL STORY

bit.ly/UseSciKitNow

Resources

Society of Data Scientists

SciKit Learn

Also:

Fit the imputer on X, fit(X[,y])

Fit to data, then transform it, fit_transform (X[,y])

Impute all missing values in X, transform(X)