Questions tagged [missing-data]

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How to choose feature which is used to fill another feature's missng values?

I am dealing with a house prediction problem. However, it has about 10% missing values in buildYear which is one of the most important features. I tried filling ...
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1answer
102 views
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20 views

Anomaly detection in time series data from multiple sensors

I've build a classification model based on 15 features coming in real time from 15 different sensors. The window time is 60 seconds (which means that the classification model needs 60 records from ...
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12 views

“Best guess” identification of previously seen entities with imperfect data

First of all, I hope that this question does not fall too far out of scope. Choice of approach is certainly discussion-heavy, but I don't consider it unanswerable. I am importing data (person records)...
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3answers
140 views

What predictive model to use to impute Gender?

My data looks like this: birth_date has 634,990 missing values gender has 328,849 missing values Both of these are a ...
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0answers
12 views

How to use marker values to deal with missing values?

could someone tell me how to use marker values to deal efficiently with missing values for numerical and categorical features ? I am mainly working with pandas and sklearn libraries.
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1answer
35 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 ...
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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
57 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 ...
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1answer
164 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 ...
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0answers
14 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 ...
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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
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? ...
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1answer
24 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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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 ...
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1answer
289 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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2answers
216 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
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)...
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2answers
27 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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1answer
33 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 ...
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1answer
53 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
29 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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3answers
45 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) ...
2
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1answer
227 views

How to deal with missing data for Bernoulli Naive Bayes?

I am dealing with a dataset of categorical data that looks like this: ...
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1answer
215 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
35 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 ...
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1answer
43 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 ...
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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?
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1answer
1k 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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1answer
21 views
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1answer
245 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, ...
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2answers
21 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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0answers
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2answers
106 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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3answers
55 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 ...
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1answer
358 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 ...
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 ...
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1answer
136 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 ...
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1answer
57 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....
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1answer
1k 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 ...
2
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1answer
177 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
20 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
505 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
60 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
252 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.
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1answer
164 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 ...
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0answers
40 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
280 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 ...
3
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1answer
88 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,...
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0answers
619 views

predict() returns NA values

I have the problem. predict() method returns NA. My plan is: Read data from file and separate data to 2 sets: test and train Remove column with NA fraction over 95% Replace NA values with mean value ...