How do you handle missing data?

Handling missing data involves identifying the reason behind the loss. If it's random, incomplete data points can be removed, but this may reduce the sample size and introduce bias. Imputation methods can also be used to estimate missing values, such as mean imputation or multiple imputation which accounts for variability. Predictive modeling can be employed to predict missing values based on observed data. In some cases, treating missing data as a separate category can be useful. The choice of approach depends on the dataset, missing data mechanism, and analysis goals, aiming to minimize bias and maintain integrity.

There are many ways to handle missing values in your dataset. Deletion, replacing etc. You can convert your missing/ null values into 0 ( zero) or some other values from your dataset. Another one is you can delete missing values columns but in this Ase you might losss your data. This is a best practice to convert missing values with zero for modeling statistics and anywhere.

Primarily when I got into data analytics, concentrating dealing with large financial data I started with familiarizing myself with Barndorff-Nielsen and Bandi and Russell methodologies on cleaning up the noisy data to avoid skewing the results. However, specific approaches always depend on characteristics of the data and reasons of missing values — whether it's random or systematic. Filling in blanks with average values or using other data-driven methods can help (KNN – filling values based on the nearest neighbors or more advance approaches).

Missing data matters depending on the business question. If you are building a model and th feature with the mission values is not apart of the model then missing data may not matter. If it does matter than running a random sample of the data that can be a statistical representation of the set is what I do and compare that run to a normalized run of that data. If the result is similar this can help fine tune the model building and feature engineering.

Depending on the nature of the data and the outputs requested, I would either use a median/mean calculation of the existing data to forecast missing data (moving averages can also help), or else I would probably use a linear regression model with the existing data to try and pinpoint where the probable missing data in the model lies.

Missing data can have serious impact on quality improvement and also mitigation of similar events . As once said beat the data it tells the truth, missing data can affect the changes if any changes or improvements made based on the data at hand

Even seemingly insignificant missing data can have significant consequences, sometimes leading to unnoticed errors or even complete failures in the data processing pipeline. Managing the number of valid entries, as you process data is crucial to ensuring accurate and reliable results. Regularly monitor and control the presence of missing data and be aware that a wide variety of issues can arise from it.

Dealing with missing data will certainly depend on the nature of the data. Sometimes deletion is the first choice but not accurate. Imputation may be easy but will also need a deep understanding of the data and a need of testing multiple scenarios (i.e use mean, median or other approaches). Then statistical methods for sure are more accurate but will need expertise and evaluation to arrive at a final selection or an ensemble model for a high accuracy in the modelling process.

Selecting the method will often depend on the business rules surrounding the data. For example, if you had a financial services app that tracked a users contributions to a pot and there were empty records for dates where the user was unable to make contributions, to prevent the service from crashing or retrieving null values, you could impute a zero as the value of contributions. This would impact the calculations in the same way as a null but allow the calculation logic to continue. Ultimately, context is key to informing the method of handling missing data.

Handling missing data involves identifying the reason behind the loss. If it's random, incomplete data points can be removed, but this may reduce the sample size and introduce bias. Imputation methods can also be used to estimate missing values, such as mean imputation or multiple imputation which accounts for variability. Predictive modeling can be employed to predict missing values based on observed data. In some cases, treating missing data as a separate category can be useful. The choice of approach depends on the dataset, missing data mechanism, and analysis goals, aiming to minimize bias and maintain integrity.

Communication with stakeholders is often overlooked when it comes to missing data. For example, if stakeholders are more business- oriented, they may prefer to leave the values null so that the data is in no way fabricated. Others may have no issue with imputing, understanding it may improve the output of the model or analysis You can also use context clues to deduce if imputed data is appropriate (i.e. is this reporting to a client, being fed into a predictive model, etc) There are many useful ways to impute missing data, like K- nearest neighbors, which fills in values based on similarity scores from the NN algorithm. Another is multi chained imputation, which assumes the missing values are randomly distributed across the observations

I have never imputed data. It is a slippery slope to go down once you start making edits to data that you plan on modelling on to make business decisions down the line. In my six years in the analytics field working with Fortune 100 and Fortune 500 clients never have I had a request to impute the value and add it into the model. In the first place, if you are building a model you should have more than the minimum amount to model on and you can disregard a certain percent of your data knowing there will be missing values or incorrect data. Identifying missing values and trying to impute them is also a time suck. Each feature will have a different calculation and you need to do additional QA to make sure you didn't skew the data.

There are a number of tools to address missing data, but as Eralda said above, they should be used judiciously and with an understanding of the missing data. For example, the pandas package has a .dropna() and a .fillna() method but knowing when it's appropriate to use them comes first.

pandas.DataFrame.info This is the quickest method I have found so far. There are additional methods beyond this but this is almost always my first step. It answers a lot of questions around datatypes and population of the features/columns across your dataset/dataframe. For example, if I have a list of contacts I can quickly tell how good it is by seeing that the 'email' column is 80% non-null value. Then I could identify duplicates in that column. In two easy steps I've just identified not only missing values but distinct contacts with unique email addresses and the overall total population for the list.

Consider incorporating missing data into your charts. For example, if you are visualizing a categorical column with a frequency chart (showing counts of categories), you can add an extra bar for the missing values. If categorical bars use solid colors, consider using the chart's background color as fill color for the bar visualizing the missing data, and a neutral color (e.g. grey) for its outline.

The evaluation of missing data is imperative in assessing potential compliance risks within regulatory frameworks. It is essential to thoroughly analyze the absence of data and ascertain the most likely cause behind its loss. Implementing strategic measures to mitigate the recurrence of data loss is crucial in ensuring sustained data integrity.

To avoid bias of data in a limited number of observations try lasso regression to regularise the coefficients. Improving regression means the model will be more flexible.

I agree missing data is one of the biggest issues that anyone will face while doing any kind of data analyses or modelling tasks. The method can vary from easiest to more advance that is from removing specific rule to finding a correlation between different variables and then tried to fill in the missing part. A model performance can significantly be boosted by using correct method of handling the missing value. The steps I will suggest is to 1st analyse the variable that what it contains and how important it is for our purpose. Assuming it's a name column which has a missing value those columns doesn't hold enough use.