Machine learning initiatives can be very difficult. The quality and quantity of data that needs to be processed, the complexity of the underlying algorithms, and the requirement for accuracy and dependability can all contribute to their difficulty. Additionally, the development and implementation of machine learning applications can be costly and time-consuming. 

Despite the fact that data preparation is frequently a relatively routine operation, it is undoubtedly the step that determines whether the entire model performs well or not. The majority of data science difficulties arise during the data preparation phase. Although the creation of a complete ML project is thought to be a sequential process, in practise it is frequently cyclical. To attain the desired performance, you switch between data preparation, model modification, and model refinement. Due to the volume of data involved, one of the biggest hurdles in big data analytics is the data preparation stage.

Here, we will examine the main obstacles that nearly every machine learning project faces on the route to success. Despite their wide variety, these can be categorised into four main groups:

1. Data Applicability
2. Erroneous Data
3. Scale of Data
4. Data Integration

Data Applicability

Simply indicates data relevance. Choosing the appropriate subset that best captures the problem at hand is one of the major difficulties in big data analytics. Issues with data relevance can frequently necessitate replacing the dataset.

Biased data can often result in problems that cannot be solved. Most ML models will be biased if there are different numbers of entries. Models, for instance, can favour one group over another as a result of this. When a certain subset’s quality deteriorates, the problem gets worse. When we have separate preferences for different labels, having corrupt records for either category (missing data, errors, etc.) might seriously jeopardise the project as a whole. 

Seasonal effects in dataset is also to be considered before training the dataset. Trends are typically present in financial data. This problem necessitates careful selection of subset or the implementation of the detrending technique, which removes values responsible for seasonal effects from the dataset. 

Observations typically evolve over time. Data collected in last decade is no longer relevant. However, because historical data depicts procedures that are not comparable to those used today, it frequently has more positive effects than negative ones. This characteristic of the data can call for dataset replacement.Proper train-test split plays a vital role in model building. Using test data that only takes into account a portion of all possible events or features might produce subpar results, just like using biased training data. ML models that were statistically incorrectly trained using biased datasets will almost certainly introduce an error into the model. As a result, having accurate and comprehensive test/training data is essential. Implementing cross validation can remove this issue.

Erroneous Data

There is a broad range of problems appear due to error in a dataset. Removing all erroneous and null entries leads to substantial data loss. To avoid that, imputing those values with utmost care is required. There are numerous ways to approach such issues. 

An outlier is a data point, that is extreme and wide apart or very different from the rest of the datapoints. Basically, outliers appear due to erroneous entry due to human or system error, erratic production process, malfunctioned machineries etc. Presence of outliers is the indicator of possible irregularities in the system or process. In huge datasets, outliers are predicted and are highly common. For building regression models and forecast, outlier treatment is a must. For classification purpose, outliers do not have much implication as those models are based on the principle of similarity or distance.

While eliminating duplicate records is not always difficult, failing to do so can be a serious error. Duplicate values might emphasise certain observations, which improves the performance of ML systems for repeated cases in the training dataset. For those that validate ML models, having duplicate train data records in the test subset can be perplexing. Duplicate values must therefore be removed from the data.

If missing values are not fixed, the performance of the subsequent models may be drastically reduced. Depending on the algorithm, missing values may be replaced, missing records may be deleted, or missing values may be fixed using a particular algorithm. I t’s critical to understand how many values are missing or whether missing features are related to other features. 

Scale of Data

More benefits come from having a lot of data than not enough. It can be simple to eliminate outliers and missing variables when more data is required, or you can just utilise a sample for training. However, even after preprocessing, a modest amount of data can frequently cause problems. As a result, one of the key difficulties in data analytics is choosing the appropriate amount of data without overfitting the model.

Massive datasets typically need to be processed via cloud computing or specialised data storage methods. Large subsets, however, might lengthen training times and complicate development infrastructure. It can also take a long time to process enormous amounts of data or records. Careful sample selection or even resampling can considerably enhance the model’s output when there is significantly more data than can be processed.

When it comes to training and testing models, using the right data split methodologies is crucial. Engineers frequently disregard sequential data and use it instead. Such writings are limited to describing a certain time or group. Any machine learning model will perform worse as a result of this data utilisation. This problem can be overcome by randomising test/train subsets. 

Data can be represented or stored in a variety of ways. Making the right choice can increase performance during the training process greatly. The clean data principle is a fantastic way to avoid any potential ambiguity.  Data matching frequently refers to the straightforward elimination of duplicates. However, comparing several datasets from various sources/in various formats presents a significant challenge. The process of choosing the comparative metric, appropriate data standards, etc., is difficult and time-consuming. One of the key difficulties in big data analysis is finding matches across numerous enormous datasets.

Data Integration

The data itself is the main source of problems throughout the data preparation step. It’s not simple to put a preparation technique into production. Setting up a functional pipeline might be a substantial difficulty for large data analysis on its own.

Numerous initiatives like inserting new set of data are implemented in machine learning process.  Models are frequently continually trained or tested. Additionally, since data evolves over time, new preparation methods must be devised. It’s important to real-time verification of your model’s performance (and how performance changes over time) and construct some sort of continuous training pipeline.

In certain cases, gathering and standardising the data necessary for the model to function is the foremost job. Take a look at some web crawling scenarios where the dataset is being continuously updated with new records. The extraction procedure first gets pretty challenging. Second, transforming continuous data might be quite difficult. Third, it may be impossible to accurately determine how a particular value is distributed. Furthermore, supplying such input to the model can be challenging. The data preparation technique becomes an essential component of the entire project. To overcome this automated ETL is being used in most of the cases.

Numerous machine learning issues are well-known, common, and even basic. However, a lot of the processing-stage difficulties call for a customised strategy. While many problems can be resolved instantly, others may call for strenuous manual labour. Many difficulties in data analysis can be attributed to the data preparation stage.  The majority of problems in the data preparation step have several remedies. To differentiate between the best possible and just good approaches, some theoretical understanding is needed. There is a strong belief that data processing might consume up to 80% of the time spent on an ML project.