How can you ensure that your machine learning models are not impacted by data cleaning?

??? Prioritize robust validation mechanisms! I'd emphasize the need for rigorous validation processes to ensure that data cleaning doesn't inadvertently introduce biases or anomalies in the dataset, which can skew the model's performance. Always validate model performance on untouched test data to provide an unbiased evaluation of the model's real-world applicability. Why leave anything to chance when precision and reliability are paramount in machine learning applications? ????

Data cleaning is like sifting through a treasure trove. But before you dive in, become a data detective. Understand your data's origins, formats, and flavors. Is it structured or unstructured? Diverse sources, each with its quirks, await. Keep your eye on the prize—business goals. What does your machine learning project seek to achieve? Clarity here is your compass. Now, the real detective work begins. Unveil the secrets of your data with EDA. Descriptive stats give you the lay of the land, while visualizations paint vivid portraits. Spot anomalies, uncover patterns, and befriend summary tables. So, before you cleanse, become a data connoisseur. In this journey, knowledge is power, and the outcome, a masterpiece.

Data cleaning is the meticulous art of refining the raw ore of information. Once you've unraveled your data's mysteries, it's time to craft your cleaning strategy. Inconsistencies and errors, the gremlins of data, must be exorcised. Data validation rules, your incantations, ensure purity. Data types, ranges, formats—all are sacred runes. Your data should be a harmonious symphony, not a cacophony. Consistency is your mantra. Data must sing the same tune across sources and systems. In this quest, you are the data whisperer, coaxing order from chaos. Your choices shape not just the data but the insights it yields. Master this alchemy, and your data will reveal its secrets willingly.

There is not necessarily 1 true dataset for your machine learning models. Understanding that complex systems e.g., refinery units or assembly lines may have different modes of operation mean that data cleaning should ultimately produce multiple datasets, each representing a different but equal reality with its own modeling efforts.

Data transformation is like crafting a symphony from raw notes. Once your data's pristine, it's time to refine its tune for machine learning. Transformation and normalization are your conductor's baton. Feature engineering, the virtuoso's touch, brings forth new melodies. Crafted from domain wisdom, statistics, or machine learning, it elevates relevance, quality, and usability. Feature extraction, selection, encoding—each note has its purpose. With these tools, your data orchestra harmonizes, ensuring no outlier's discordant note disrupts the melody. As the conductor, you ensure your data sings in unison, primed for the grand performance of machine learning.

Think of data cleaning as the backstage crew of a theater production. Once their work is done, it's showtime for your machine learning models. But how do you ensure the spotlight isn't stolen by errors? Enter data quality indicators and performance metrics, the critics and reviewers of your data cleaning performance. They scrutinize the before-and-after, revealing any changes. Are the models still hitting the high notes? These insights uncover potential pitfalls and trade-offs. Adjust your strategy and parameters until the symphony is perfect. Your goal? The best possible results, a standing ovation for your data cleaning prowess, and a memorable performance from your machine learning models.

Imagine data as a vast universe, each point a star in its constellation. Here, we navigate the cosmos of information, drawing insights from the void. It's a journey where curiosity knows no bounds, where anomalies spark curiosity, and outliers unveil hidden truths. In this uncharted space, we're the explorers, charting the unknown, one data point at a time.

A probable way could be to apply data cleansing in landing area before passing it to machine learning model. Anyways the model prediction would not give correct output with raw unrefined data input.