How can you engineer features when data is missing?

To handle missing data in feature engineering: Impute: Fill missing values with mean, median, mode, or model predictions. Indicator Variables: Create flags for missing data. Delete: Remove rows or columns with missing values. Use Algorithms that Handle Missing Data: Such as certain tree-based models. Dimensionality Reduction: Apply techniques like PCA to lessen the impact of missing data. Choose a method based on data type, missing data volume, and analysis needs.

At some point or another, we all deal with missing data. When I was analyzing the Fatality Analysis Reporting System (FARS) for automotive crashes, I had to deal with certain information being omitted for personal privacy. One important feature was the VIN of the vehicles involved in the crashes. If I had those then I would be able to identify exactly what was in the vehicle for safety features. Instead, I had to first build a translator that took the information FARS gives along with the partial VIN to determine what driver safety features each vehicle possessed. Once I did, I had all the data I needed to start analyzing the crashes. Often times, you will need to interpolate the existing information and draw from external sources.

Several ways can be adopted when dealing with missing values However, some factors must be put into consideration before choosing the right method such as: 1, The Goal and Objectives 2, The number of missing values 3, the impact of the missing values 4, the data collection source Having this above idea will give you an edge in deciding if inputting or filling the missing value or deletion is the best method to adopt. Assuming you decide to fill in the data, - Aggregated using mean median mode. -Filled backwards or forward - Filled by predicting the best value using other variable features by machine learning algorithm - and so on. The most important factor is to maintain the data quality while dealing with handling missing values.

Identifying the type of missingness will help in understanding how to treat them. So let us suppose if data is missing due to complete randomness we can fill this based on rest data.

To handle missing data in feature engineering: Impute: Fill missing values with mean, median, mode, or model predictions. Indicator Variables: Create flags for missing data. Delete: Remove rows or columns with missing values. Use Algorithms that Handle Missing Data: Such as certain tree-based models. Dimensionality Reduction: Apply techniques like PCA to lessen the impact of missing data. Choose a method based on data type, missing data volume, and analysis needs.

Unusually we take mean and median to fill these missing values but other values can also be used but first we need to determine the type and cause of missingness. Let's suppose it not random then we need to fill some constant value.

After identifying the type of missingness, the next crucial step is to impute the missing values, which essentially means filling in the gaps with estimated values. There are various methods to carry out imputation, and the choice depends on factors like the type and pattern of missingness, the variable's distribution and scale, and the specific feature engineering goal. Common imputation methods include using the mean, median, mode, constant value, regression-based imputation, interpolation, k-nearest neighbors, or multiple imputation. For instance, if the aim is to engineer a feature that captures the variability of a variable, a suitable approach might involve imputing missing values with random values drawn from the same distribution.

Using indicator variables in feature engineering is a popular method for handling missing data. Make binary indicators to fill in the gaps in pertinent feature values. Introduce a new binary feature called "X_missing" where 1 denotes missing values and 0 denotes present values, for instance, if a column named "X" has missing values. Information concerning missingness is preserved in this way. Additionally, take into account methods such as mode imputation for categorical values and mean imputation for numerical features. By using indicator variables, you can make sure the model can discriminate between seen and missing data, which helps you avoid losing important information when you're doing your analysis.

It is really very helpful method/step in feature engineering. The performance of algorithm totally depends on number of feature. If we use correlated feature then definitely it will impact the performance of the model. So it is better to check impact of the feature on the model and correlation among features,

It should be noted that we should consider the computational cost which choosing any method. It would be time consuming we use a large range of parameters. But overall experimenting and evaluating the model with upgraded features will help in finding the best results.

Experimenting and evaluating theresults involves trying out different methods and techniques for feature engineering and observing how they impact your predictive model. To measure how well your model is doing, you can use metrics like accuracy, precision, recall, F1-score, AUC, or RMSE. Additionally, methods like cross-validation, grid search, or random search can be applied to fine-tune your model's parameters and features. The ultimate goal is to discover the best combination of features and parameters that leads to the highest model performance. It's like testing different ingredients and recipes to find the perfect mix for baking the most successful model.

Feature engineering for missing data entails trying out several approaches to handle or impute missing values and then doing a thorough assessment. Among the methods are binary indicators for missingness, predictive modeling for imputation, and mean or median imputation. Evaluating the effects of experiments on model performance, such as alterations in robustness or accuracy, is part of the process. Decisions for feature engineering are guided by evaluation metrics such as domain-specific measurements or cross-validation. By optimizing feature handling in the presence of missing data, this iterative method guarantees the accuracy and robustness of the models.

Dive into blogs, articles, books, or papers where experienced individuals share their insights on overcoming issues related to missing data and feature engineering. Joining online communities, forums, or groups focused on data analytics allows you to engage in discussions, seek advice, and learn from the collective wisdom of the community. Additionally, participating in courses, workshops, or webinars dedicated to best practices and techniques for handling missing data and feature engineering enhances your skills and knowledge. The more you absorb from the experiences and expertise of others, the better equipped you become to navigate these challenges effectively.

A wise man once said and I quote "the day you stop learning, you start dying". There is no problem in the data field that has not been encountered by certain persons. As a Data Analyst, wisdom demands learning from others when faced with the problem of engineering features when data is missing. Several avenues to learn from others are via YouTube video, blogs, articles on Medium and the likes. You could as well reach out to your mentor.

Handling missing data in feature engineering is complex, and there's no one-size-fits-all solution. The key? Document every step (think data version control) and be mindful of the assumptions you make. ???? Whether you're imputing, omitting, or using algorithms that handle missing values, your decisions will shape your results. Regularly evaluate your approach, considering how these choices affect your outcomes. It's a blend of art and science, requiring careful thought and continuous assessment. Stay vigilant and reflective; your data's integrity depends on it!

I would add to the above, a more complex technique for which there a packages in python. This is for features which have data in them enough to represent the statistical space of the feature. If the feature has a correct statistical representation (check them with someone that understands that feature well), then the user can try create synthetic data based on the statistical representation clearly correlation from other features around them is vital. This way you can populate more data in a feature that has enough statistical support and more complete features around it. Yes you can also try to predic them, but using packages to create synthetic data will require less work and less features.

What I do when I see a lot of zeros and null values on my data set, during my prelim or BAU analysis is a mix and match of the following, let ke know which one resonated the best with you!!! -Imputation Techniques: I try to use statistical methods to fill missing values, such as mean, median, or mode imputation, or more advanced methods like regression imputation. - Creation of Indicator Variables: Introduce binary variables indicating whether data is missing or not, providing the model information about missingness patterns. - Time-Based Interpolation: - Predictive Modeling: - Domain-Specific Methods: - Multiple Imputation: - Use of External Data: