Questions tagged [missing-data]

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20 views

when to drop na values? [on hold]

It's very basic question, but I wanted some clarity on this topic. Normally, I drop na values from columns if na values are less than 5% of total values. I wanted to ask, does this approach is ...
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16 views

Filling 2 different values for missing/NaN columns

I am doing a binary classification problem (TARGET = 0 or 1). My dataset contains some NaN ...
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14 views

How to use KNN for NaN imputation? [closed]

I am trying to use KNN for null value imputation using the following: ...
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16 views

Encode missing data and unseen data

Let's assume that I have a classification problem and all my features are categorical data. I have missing data (and I do not want to do any imputation). Also, I know that I will have some unseen ...
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24 views

Linear/Logistic Regression for unknown values or how to get a good prior for new coefficients

Suppose, we model the probability of making holidays by country and town. The input data are people and how many people actually made holiday in that particular town: ...
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2answers
35 views

Dealing with informative missingness

How can I deal with a time series that contains missing data which means something? So the value that is missing is not wrong. It's missing on purpose and imputing those missing values would mean a ...
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3answers
56 views

Dealing with no data

I am working on predictive maintenance and get temperature data from assets. In few months or few days asset remains down and we do not get temperature value. In this scenario i cannot fill data with ...
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20 views

Fill na for categorical variables [duplicate]

I am working on a dataset with around 250 rows. There are 2 columns in the dataset gender and education which are generally ...
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11 views

What is the default method for Orange to deal with missing values in random forest classification?

Good day, I have built a random forest classification model in Orange, but some of my input data (all continuous) are missing. Up to 30% of the data for some variables are missing. I understand the ...
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2answers
32 views

When to use missing data imputation in the data analysis problem?

I want to run statistical analysis of a dataset and build a logistic regression model and multinominal linear model by R according to the research question. But I was wondering which step should I use ...
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1answer
25 views

Handling unwanted negative numbers

With me is a dataset collected from IoT sensors with one column labeled “Soil Humidity” measured in percentage. It stands to reason then that all the values be positive percentages, however there’s a ...
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1answer
25 views

What is the difference between the value -99 and NaN in a data column?

I am new to data science. I was looking into some datasets and I saw some values like -99, which I discovered later that it means that there is a missing value. Does this mean the same thing as NaN? ...
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9 views

What's the meaning of Err in Online Time Series Predicion with Missing Values

I'm reading THIS paper about online predictions on time series with missing values. And trying to code the third algorithm in C++. The thing is that I don’t understand what they mean by $Err_{\tau}...
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8 views

How to combine multiple data sources where subjects are unknown and there is no ID attribute to match subjects?

I have two data sets. Data set A: data about lifestyles, health conditions and SOCIOECONOMIC and Data set B: data about travel behavior and also SOCIOECONOMIC. We don't know whether they are the same ...
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13 views

How to handle variables with text and number?

This is a post with two related questions in one. The first question is: What is the correct procedure when I have variables with different kind of information? Imagine you have a column which has ...
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1answer
23 views

Do word embeddings help with out of vocab tokens?

I am performing sentiment analysis on a custom dataset of text with Keras but am a little confused about word embeddings. I have been able to train an "Embedding" layer and have also learned to load ...
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27 views

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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2answers
2k views
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56 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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13 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
251 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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13 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
49 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
72 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
166 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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1answer
33 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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13 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
28 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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10 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 ...
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1answer
1k 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 ...
3
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2answers
283 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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64 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
28 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
39 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
56 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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31 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
58 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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1answer
291 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
370 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
36 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
46 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
2k 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
22 views

Investigate why data is missing? After finding out reasons, what should I do next?

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1answer
328 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
23 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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44 views
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2answers
190 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
85 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 ...