How do machine learning algorithms affect your data analysis?

Framing the actual business problem is really important before jumping into choosing an algorithm. It is always good to check the business intent of what needs to be built and how you would it would be used. Questions fall under three categories descriptive, predictive or causal and this categorization can influence what we build. For example, A descriptive question: how many people churned in the past 1 year ( retrospective data analysis) ?; A predictive question: what is the likelihood of a person to churn overall (P(C))? ; A causal question: given a particular treatment is the person still likely to churn ( P(C|Treatment))? Each of these questions are meant to solve different parts of a business problem and have different solutions.

Additionally, machine learning enables predictive modeling, allowing you to make informed decisions based on future trends and patterns in your data. This can lead to enhanced efficiency, improved accuracy, and strategic decision-making in various domains.

Machine learning offers numerous benefits for data analysis. It automates complex tasks like data cleaning and feature engineering, as well as specialized tasks like anomaly detection and sentiment analysis. Machine learning excels in uncovering hidden patterns and trends in data, assisting in customer segmentation, identifying key factors, and optimizing outcomes. These capabilities enhance efficiency and provide deeper insights.

However, the world of machine learning can be quite complex. From supervised to unsupervised. If you don’t know which question you are trying to answer, then it might be hard to understand which problem the algorithms are solving. This is something that can also translate to working with customers that want to find patterns but don’t know what special pattern they want to find. That’s the reason why Data Engineering helps you solve that question before tackling any type of question. You need find who are you trying to discover first. Some people may debate that unsupervised machine learning helps with that, but I believe that it tends to help find groups within your main group. Thus, finding other groups after you know who to catch.

Machine learning algorithms revolutionize data analysis by automating labor-intensive tasks, increasing efficiency, and minimizing errors. They excel in complex pattern detection, revealing hidden insights and relationships in datasets. Anomaly detection ensures data integrity, vital in quality control and fraud detection. These algorithms empower predictive analytics, enhancing strategic planning, and resource allocation. Segmentation, feature engineering, and optimization provide precision, while sentiment analysis and scalability offer insights in marketing and big data. Continuous learning allows adaptability to evolving data, shaping a data-driven future.

The Most challanges you will face in machine Learning is data quality, insufficent data. Insufficient data may leads to irrelavent and you will not able get the desired output mainly while validating the model you need to check the bias and variance.

Some of the issues presented above (e.g. lack of data) can be overcome by leveraging pre-trained models. Such models will significantly accelerate the development time, skipping several data steps you would do otherwise. For many task, you will see good performance with off-the-shelf models with no need to fine tune them (at least to begin with).

Agree. Each ML algorithm has plus and minus. The developer should know how the algorithm works internal with math calculations and what assumptions it makes (For ex. Naive Bayes assumes independency of attributes and simplifies chain rule of conditional probability). Trying to improve the performance of ML models may lead to increase in the complexity of the model and thus leads to bias. So, achieving a trade-off between bias and variance is also a challenge while model building. Overfitting is another challenge - if we don't have enough data and/or right data. Improper sampling between train and test sets when there is an imbalanced class problem will definitely lead to a poor model.

The challenges do not end with developing a good model. One of the most unavoidable challenges is model degradation over time. The model might show satisfactory performance for some time which can degrade later. This can happen owing to issues in the incoming data or changes in the environment. The model which learned relationships from a specific dataset (on which it was trained) might not hold valid if the data itself shows drift or the relationship of features with the target variable changes.

3 more challenges: Computational Resources: Machine learning, especially deep learning, can require significant computational resources. Training complex models on large datasets can be time-consuming and may require specialized hardware, like GPUs. Interpretability: Many machine learning models, particularly complex ones like neural networks, are often seen as “black boxes”. Their decision-making process can be hard to interpret, which can be a problem in fields where explainability is important. Data Privacy and Security: Machine learning models are often trained on sensitive data, raising concerns about data privacy and security. It’s crucial to ensure that data is handled securely and that privacy is maintained.

One of the important steps in traditional ML is that after understanding and cleaning the data, we need to engineer relevant features. This would require sufficient domain knowledge and clarity on the what needs to be predicted. For eg- if a bank or a financial institution is developing a model to predict credit card payment defaulters, and the available data has variables like credit card limit, expenses made on card etc, it would be helpful to create meaningful features like (expenses made on card/ card limit) instead of taking the variables in their raw form. The person with x expenses might be less risky if he has a huge limit as compared to someone who has done same expenses but has a much lower card limit.

This is my top 10 for ML: 1) Data Quality: Clean and preprocess data to ensure it's diverse and representative. 2) Feature Engineering: Select impactful features and normalize data. 3) Model Selection: Start with simple models, then explore complex ones as needed. 4) Validation: Use cross-validation to gauge generalizability. 5) Regularization: Implement to prevent overfitting. 6) Hyperparameter Tuning: Optimize with grid or random search. 7) Evaluation Metrics: Choose metrics that match your goals. 8) Ensemble Methods: Boost performance with ensemble techniques. 9) Experimentation: Log experiments meticulously. 10) Explainability: Make models interpretable.

Algorithms are tools; they are not the analysis. First, define the questions your analysis needs to answer, then create visuals to understand the data, and finally look in your toolbox of models, algorithms, and software to identify methods appropriate to both the dataset and the questions being asked.

Other tools I used: TensorFlow and Keras: TensorFlow is a powerful library for numerical computation, particularly well-suited for large-scale Machine Learning. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow. It’s user-friendly and easy to prototype. PyTorch: Developed by Facebook’s artificial-intelligence research group, it’s another library for machine learning in Python. It’s more flexible and efficient for deep learning models. Tableau: While it’s primarily used for data visualization, Tableau also has some built-in machine learning capabilities.

In my early data science work, I underestimated the significance of data visualization, viewing it merely as an aesthetic factor. However, I soon realized its pivotal role in expediting processes and understanding the dataset better. Tools like wandb are instrumental in my work now, providing real-time visuals and ease of integration with machine learning models. Remember, patience in the data processing phase is crucial - it's an essential step.

- arxvig.org - scholars.google.com - important books like Deep Learning from Ian Goodfellow and others, An Introduction to Statistical Learning from Hastie and others. - paper review in youtube and other resources. - classic libraries documentation like scikit-learn and pytorch

Another important aspect to consider is how to make your ML model consumable by your end users. The end users might not understand Machine Learning or Data Science. In order to ensure adoption of your model/product, one should also focus on interpretability of model results.

In the hype of so many algorithms one key aspect to keep in mind is that understanding the data is far more important and a significant amount of time must be spent in just the EDA and understanding the features of your dataset. If you understand the data well, applying algorithms and interpreting the results will be very much impactful.

Machine learning is like a super tool that helps people who work with data understand things better or making smarter choices. It's like having a super car; you need to drive it responsibly to get where you want to go safely...