Here's how you can effectively navigate complex data mining algorithms with confidence.

Cleaning up your data before running any model is really important. If you understand the business logic, the relevant business KPIs affecting it and then try to implement the model, it will go a long way. Please make sure the data is clean and valid as well!

One effective strategy I found helpful is to first grasp a broad understanding of the concepts rather than diving deeply into each one initially. By covering a wide range of foundational concepts, you can identify which specific algorithms are most relevant and worth exploring in greater detail. This approach allows you to focus your efforts on the areas that are most applicable to your needs, making the learning process more efficient and targeted.

Certain job requirements not only technical skills but also problem solving. Especially in the field of data science, data-mining, AI. There is a lot of complexs that need to solve. Based on my experiences, to overcome that problem, it easy to starting with some of benchmarking from another company, in certain whitepaper from big consulting company, or with some of paper research. We must collected that all of things and making a resume about it.

Understanding the purpose and categorisation of data mining algorithms is crucial before delving into their intricacies. These algorithms are designed to address specific questions about data, which can be classified into several types of questions, as follows: 1) Descriptive: 2) Diagnostic 3) Prescriptive 4) Predictive 5) Inferential 6) Causal analysis. Subsequently, when exploring the algorithms in depth, it is advisable to implement them from scratch to gain a thorough understanding of their mechanisms, elucidate how input data should be structured and facilitate the interpretation of algorithmic outputs. By comprehending these study types and in depth knowledge of algorithms will aid their application.

What I found best was to understand firstly what the algorithm exactly does. What is it doing as a whole? What is its output? Once I understood that, I found it easier to understand the smaller pieces such as the mathematical concepts that make up the algorithm.

In my opinion, the basics of navigating your way around data mining algorithms is how to select the right model for your task. First know whether your task is classification or regression based on your target variable, and then choose the appropriate model that suits your data. The assumptions of each model must also be carefully considered.

As I commented in the previous step, once you have a better understanding of topics on a broader level, it becomes easier to identify the algorithms that are relevant to your use case, enabling you to choose the algorithm wisely.

I always consider two things. What is my data, and what is my goal? For example, am I trying to see if there is a linear correlation? Am I trying to categorize something, maybe even cluster?

In one of my initial data science projects, I learned the hard way how crucial data preparation is. Initially, I skipped some steps in the cleaning process, thinking they were trivial. However, the results were far from satisfactory. After going back and rigorously handling missing values and outliers, and applying PCA for dimensionality reduction, the performance of my algorithms improved drastically. This experience taught me that investing time in thorough data preparation is invaluable. It not only enhances algorithm performance but also provides more reliable and accurate outcomes.

Additionally, transforms are an important aspect of data preparation that was not explicitly mentioned in the original statement. To address this, I would add the following points about data transformation techniques: 1. Normalisation: 2. Standardisation: 3. Encoding categorical variables 4. Log transformation 5. Binning 6. Polynomial features 7. Power transforms 8. Signal transforms These transformation techniques are crucial in preparing data for mining algorithms, as they can significantly improve model performance and interpretability. They work alongside the cleaning and dimensionality reduction methods mentioned to create a comprehensive data preparation process.

During my first machine learning project, I had rushed the data preparation process because I was eager to dive into the actual machine learning process and see results. I began to receive very high cross validation scores and was thrilled. However, when I went to tell my mentor about my results, he told me that the correlations that I discovered are not normal and encouraged me to re-examine my data. I had later learned that my data was biased due to an additional column that should have been removed. Arguably, data processing is the most important step, as poorly processed data can produce poor results.

In a recent project, I realized the importance of algorithm tuning when my initial results didn’t meet expectations. I took a methodical approach, experimenting with various neural network parameters, including the number of hidden layers and the learning rate. Using grid search, I meticulously tested different combinations and monitored their impact on performance metrics. Each adjustment brought incremental improvements, and through this iterative process, I found the optimal settings that significantly enhanced the model's predictive accuracy. This experience reinforced that fine-tuning algorithms requires patience and a willingness to experiment to achieve the best results.

When tuning hyperparameters, it's essential to use cross-validation techniques such as k-fold cross-validation. This involves splitting the data into multiple subsets, training the model on different combinations of these subsets, and evaluating performance on held-out data. Cross-validation helps prevent overfitting to the specific dataset used for tuning and provides a more robust estimate of how well the tuned model will perform on new, unseen data. This approach ensures that the optimized hyperparameters lead to a model that generalizes well, rather than one that performs exceptionally on the training data but poorly on new data

Ensuring the dataset utilised in the train, validation, and testing of model development is a representative of the population that the model is applied to is an important point. For example using an old (10 year) website data for the current website would not be valid as habits and/or webpages would have changed leading to data and/or concept drift.

ChatGPT & Co können hier auch ein guter Lehrer sein, wenn man ihnen die richtigen Fragen stellt. Vor allem, wenn es um die Wissensbreite geht. Wohl weniger, wenn es um die allerneusten Entwicklungen geht.