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Questions tagged [missing-data]

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1answer
15 views

Time Series - Values recorded every 10 minutes - Fill missing values

I have a time series data from a sensor that records value periodically - sometimes - every 10 minute period, other times every 5 minute period etc. I have to find out anomalies in real time (as and ...
2
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2answers
20 views

Dealing with diverse groups in regression

What happens if a certain dataset contains different "groups" that follow different linear models? For example, let's imagine that examining the scatterplot of a certain feature $x_i$ against $y$ we ...
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1answer
79 views

Does encoding missing data with fixed values help in classification?

I have a lot of missing values for some variables in my data (70-80%). I have seen some people deal with missing values this way: encode the variable with missing values as 0 or 1. Where 0 is the ...
3
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1answer
136 views

Handling missing values to optimize polynomial features

I was playing around with some data to practice my Python and machine learning skills and wanted to create polynomial features from two features that I think are related and have a strong influence on ...
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1answer
185 views
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3answers
40 views

What best/correct algorithm/procedure to cluster a dataset with a lot 0's?

I'm new to statistics so sorry any major lack of knowledge in the topic, just doing a project for graduation. I'm trying to cluster a Health dataset containing Diseases(3456) and Symptoms(25) ...
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2answers
43 views

what to do if the missing data in one column is based on some value/condition in another column in r?

I have a dataset with 20,000 observations and 19 variables. To start off with I have a gender column which has three levels namely 'M', 'F' and 'U' where U can be taken as not disclosed. Whenever ...
2
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1answer
29 views

Cluster Analysis - Comparing Same Individuals Clustered Across Different Datasets with different features

I have an interesting problem, and I think my Google is failing me since I can't find the same problem anywhere. I have a set of individuals. I have 4 different datasets, with (some) to (all) of ...
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0answers
19 views

How to incorporate an attribute that only exists in some observations?

In a binary classification problem, some of my observations have an event that occurs. I can, obviously, add a 1/0 flag if the event occurs ("event_occurred" in the data below). However, my intuition ...
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2answers
49 views

Column With Many Missing Values (36%)

Hello this is my first machine learning project, I got a dataset with 18.000 rows and I have a column with 4244 values missing. I don't know why the values are missing since when it's appropriate ...
2
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1answer
163 views

Data prepration for logistic regression : Value either “not available” or a “year”

I have some data of houses that have been renovated. In my data there is one column (among others) that captures this information. It is either "-1" if there has not been yet any renovation, or the ...
0
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0answers
11 views

Learning with missing features (MNAR)

I want to learn from features that may have some missing informations. The value of the variable that's missing is related to the reason it's missing (MNAR) To better understand my case, here is an ...
3
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2answers
136 views

How does the naive Bayes classifier handle missing data in testing?

Assume that a classier has been trained already (no missing training data), but a prediction has been requested based on an observation that does not include every feature. How can we handle this ...
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0answers
12 views

Finding features for missing classes

I have some data with labels from n-1 classes. There is no datapoint with the n-th class in the data but we know that data from the n-th exist. How can we generate data from the missing n+1-the class? ...
2
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1answer
21 views

Training on data with inherently non-applicable data cells

I am training a model on a chemical sample dataset to find outliers and perform imputation where it makes sense. Chemical Dataset Contains thousands of rows of chemical mixtures with many columns of ...
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0answers
5 views

Calculating target mean to validate if I should drop column with missing values is correct?

I am working on the KDD 2009 Cup Data Set (The Small one) and I have a question about preprocessing data. It has a lot of columns with null values, some of them have more than 90% of missing. Reading ...
2
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1answer
109 views

How to fill in missing value of the mean of the other columns?

I had a movie dataset including 'budget' and 'genres' attributes. I'd like to fill in the missing value of budget with the mean budget of each genre. I first create two dataframes with or without ...
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0answers
56 views

Finding unaffected data in a dataset

The data set has missing values. Further examination tells that they are spread along 1.5 standard deviation from the median with distribution mean = 0 & variance = 5. How much data (in percentage)...
1
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1answer
115 views

Missing population values in census data

I have population data from Census.gov: Total US population by age by year from 1940 through 2010 Depending on the range of decades, the data is missing discrete population values for ages greater ...
0
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1answer
24 views

Handling NA Values in the Chicago Crime Rate data set

I am doing a little project on the Chicago Crime Rate data set and I noticed that there are over 600,000 NA values, primarily in the location fields. I feel that ...
0
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1answer
1k views

To calculate unaffected part of the data set with missing values and positive skewness

A dataset has some missing values with positive skewness = 1. It is known that it is spread over 1.5 standard deviation from the median. How much % of data will remain unaffected?
2
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2answers
26 views

How to think about - and sometimes impute - geographic distances

I have a dataset with one of the (important) features being the geographic distances from NYC. Of course, some of the values are missing.... The goal is predicting whether people with certain ...
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2answers
1k views
1
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1answer
50 views

R mice doesn't give a 'valid' sollution

EDITED: See below for additional information.. TL;DR: How can I add missing data in a dataset like the sample in a way that it doesn't deviate much from the original dataset. ORIGINAL: I have a ...
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0answers
26 views

Detect Missing Records in Dataset

I have a dataset that contains several measures from various widgets on a daily basis. While the widgets remain relatively stable over time, sometimes there are legitimate reasons for one to disappear ...
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5answers
2k views

filling missing data with other than mean values

What all options are available for filling missing data ? One obvious choice is the mean, but if the percentage of missing data is large, it will decrease the accuracy. So how do we deal with ...
2
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2answers
84 views

What is the difference between Missing at Random and Missing not at Random data?

I have been working with a dataset where the missing data seem to following a few particular patterns. I have gone through a lot websites and articles related to missing data but I haven't been able ...
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1answer
958 views

Dealing with NaN (missing) values for Logistic Regression- Best practices?

I am working with a data-set of patient information and trying to calculate the Propensity Score from the data using MATLAB. After removing features with many missing values, I am still left with ...
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5answers
72k views

How to replace NA values with another value in factors in R? [closed]

I have a factor variable in my data frame with values where in the original CSV "NA" was intended to mean simply "None", not missing data. Hence I want replace every value in the given column with "...
0
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1answer
32 views

Missing Values In New Data

(Before someone marks this as duplicate - I'm not asking about training data, I'm asking about new data which has come in and needs to be classified) Suppose I've got a dataset which has 5 predictors ...
0
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1answer
41 views

Missing Values in Classification

I'm working on a classification problem. I'm trying to build a model which can predict if a bank client will get a loan or not. Some of clients have co-borrower and the majority don't. I also have ...
2
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1answer
193 views

How to treat missing data for survival analysis

I have a dataset consisting of questionnaires from patient survey data. There are around 10 questions which are asked during several stages of treatment like during first day of visit, after a week, ...
1
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1answer
795 views

Fill missing values AND normalise

I have two columns of training data for a neural net which are missing values. (There are many other columns which aren't missing values.) For example ...
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2answers
2k views

What to do when testing data has less features than training data?

Let's say we are predicting the sales of a shop and my training data has two sets of features: One about the store sales with the dates (the field "Store" is not unique) One about the store types (...
1
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1answer
223 views

Imputation missing values other than using Mean, Median in python

I heard that Mean, Median isn't the best way to impute the missing values, why would that be? In my scenario, I have data like this ...
0
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1answer
98 views

Orange imports numeric features as categorical (“file” widget)

Why are some of my numeric features not being recognized as 'numeric' types AND why can't I reclassify them? I can't share my CSV here but I can assure you those features are indeed numeric (I use ...
3
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1answer
24 views

How to deal with Missing Not at Random Data for k-means clustering?

I am running k-means clustering on a customer dataset. One of the available demographic fields is inferred homevalue, represented as an integer. This field has value 0 when it's inferred that the ...
1
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1answer
47 views

Missing value in continuous variable: Indicator variable vs. Indicator value

Most data has missing values, and as far as I'm aware, these are the options: Imputation (mean, hot-deck, etc.) Indicator variable. A categorical variable that tells what type the primary variable is....
4
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2answers
649 views

How to handle missing data for machine learning

I'm trying to come up with a data structure to predict water visibility in a lake. I have some measured samples but would like to take other features into the equation. As an example, I would like to ...
2
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1answer
126 views

Toolbox for handling NaNs in Python 2.7

Is there a good toolbox for handling and analyzing missing values in Python 2.7? There is a good toolbox for doing this in Python 3.6 here (missingno): https://github.com/ResidentMario/missingno I ...
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0answers
18 views

Linear Model: How to deal with predictors with a lot of missing/small values?

I have a linear model used for prediction, with around 30 predictors, which are car usage rate as in percentage, across different zip codes. All these predictors have the same unit, as they are all ...
2
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1answer
432 views

Missing outputs in multiple-output neural net

I am looking at a task, where I want to predict multiple things from an image (an animal's breed [categorical], age [continuous number] and gender [categorical]). Unsurprisingly, my first thought was ...
3
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1answer
79 views

Correlation with missing values. Is least squares an acceptable option?

I have been tasked with finding a correlation matrix for a lot of variables. Many of them have missing values. I read here that pairwise deletion may not be the best way of dealing with this situation,...
3
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1answer
59 views

Setting “missing” distance values to zero when training a neural network

Not sure if missing values is the right name to use here. I want to train a DNN on data given by a sensor. The sensor gives the (x,y) coordinates of the founded objects. The sensor can keep track of ...
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2answers
224 views

How would one impute missing values for a Discrete variable?

How would one imputing missing values (without using the mode) for a discrete variable, e.g. a variable corresponding to a count.
2
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1answer
2k views

Missing data imputation with KNN

-1 down vote favorite I have a dataset including missing data for most of the variables. Assume the dataset is as follows: ...
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0answers
39 views

Fix missing data by adding another feature instead of using the mean?

I am trying to build a model which predicts whether a user will unsubscribe from a service. There is a particular column which tells the number of hours until a report was written for the user. These ...
3
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1answer
249 views

Predicting Missing Features

I have "millions" of items each having N binary features. When a feature is "0" it could be that the information is simply missing. So, given the data with the currently observed 1's, I would like to ...