Machine learning is endlessly fascinating and intriguing, but it is important to recognize that not all problems are suited for machine learning. Once the use case has been analyzed, sometimes proper data wrangling and data analysis are the only services the client needs. Sometimes, the situation is best suited for traditional machine learning, and if you do not have data scientists or the required experts, automated machine learning can be your best friend. Identifying the problem and the best solution is the key. In most of our machine learning projects, spending time understanding, cleaning, and preparing the data is nearly 70% of the machine learning process.
We follow a 7-step process to create a model based on our client’s data.
Our 7 Step Process
1. Data Collection
The first step of the process is data collection. We collect the data from various sources, including generating synthetic data when required. The quality and quantity of the data will dictate how accurate the model and its outcome will be. Therefore, the next step, data preparation, is crucial.
2. Data Preparation
In this step we first must cleanse the data by using data wrangling to remove duplicates, correct errors, find missing values, convert data types, etc. We then use data visualization to help detect relevant relationships between variables or class imbalances. In this step we also discover relevant statistical properties like non-linearity, non-stationarity, multicollinearity, seasonality, etc. and then normalize and label the data. Finally, we split the data into training and test sets.
3. Feature Engineering
During feature engineering, certain attributes and variables are selected to develop a predictive model. It is best to use as few variables as possible in this step to improve the performance of the model. Machine learning will not reach its potential without human intervention. This is where domain expertise and experience are key in being able to analyze the cleansed and labeled data and to extract all relevant ‘features’. The “Factors of Influence” make the data set relevant for ‘Clustering’ and ‘Classification’ and allows the data to be explanatory and meaningful to run machine learning models.
4. Choosing a Model to Train
To find the right algorithm to predict your end results, we must consider the size and type of data, accuracy of the data, and the number of features selected. Different algorithms are for different tasks, so choosing the right one is of utmost importance. The goal of training advanced machine learning models is to answer a question or make a prediction correctly as often as possible. Think about what you are trying to achieve with this model.
5. Evaluating the Model
Dependent on the machine learning model used, we use various ways to evaluate a model’s performance:
- Confusion matrix
- F1 score
- Precision-Recall or PR curve
- ROC (Receiver Operating Characteristics) curve
We use a metric or combination of metrics to “measure” the objective performance of the model. We then test the model against previously unseen data. A good training/evaluation split could be 80/20, 70/30, or similar, depending on domain, data availability, and dataset particulars.
6. Parameter Tuning
In this step, we adjust the parameters to get a more accurate result. A hyper-parameter is a parameter whose value is set before the learning process begins. In this step we must define the number of iterations, that is the number of possible combinations that the search algorithm tests. Since the selection of combinations is random, we use distributions instead of a fixed set of values.
Continuous processes in machine learning execution and monitoring are:
7. Making Predictions
- Model tuning, back testing, regularizing & updating
- Tuning – cross-validation (CV) hyper-parameter optimization
The last step in our machine learning process is prediction. Prediction is the output of our model after it has been trained on the initial given data and then applied to new data to predict the likelihood of a specific outcome. Using the test data set, which has until this point been withheld from the model (and for which class labels are known) are used to test the model; a better approximation of how the model will perform in the real world. It is important to remember, “overfitting” the model generally takes the form of making an overly complex model to explain idiosyncrasies in the data under study. Cross validation is a powerful technique to avoid overfitting.
Our Successes and Experience with Machine Learning
- The Azure Cloud is an integral part of our efforts and has enabled our team to develop and deploy well-governed, accurate ML models to high-volume production environments using MLOPs.
- We have increased the pace of model development and deployment via monitoring, validation and operationalization of machine learning models through MLOps.
- In our Machine Learning projects, we have automated end-to-end pipelines from auto data management to auto ML and auto deployment of models.
- Data-Core has Machine Learning experience in various domains such as Retail, Manufacturing, Healthcare, Stock Market Trading and Legal.
- Experienced on various visualization tools to represent our Machine Learning output in user-friendly and interactive dashboards.
Data preprocessing and machine learning can be time consuming. Let Data-Core help you with your next machine learning project so you can save time and money. Contact us
today to get started.