Data pre-processing techniques — Data is very important and a need for predictive modeling. Data goes through various pre-processing techniques before being feed into machine learning model. Feature engineering is a vital part of data pre-processing that handles missing values, data scaling, data encoding from string categories to numerical, and other techniques. Each column…

Project management methodology — CRISP ML(Q) — CRoss Industry Standard Process for Machine learning with Quality Assurance, It comprises of 6 phases: Data and Business understanding Data preparation Model building and Tuning Evaluation Model Deployment Monitoring and Maintenance Data and Business understanding Business Understanding: Business understanding comes with the business problem that arises in…

Pre-trained models in machine and deep learning — In simple terms, it is a technique to use a trained model on the dataset that is run on a new, different dataset. The core idea is to take the knowledge of the trained model and apply it to a new but related application. …

Algorithm to improve the speed and performance — What is machine learning? It is a technique to learn patterns from the data and make predictions. The machine learning algorithms implementation is data-based. As with time, we see the evolution of algorithms and some algorithms like SVM, Random Forest, or Gradient Boosting gives better result mostly on every type…

Better understanding of skewed data in statistics and data science — In this article, we will study the interpretation of the Q-Q plots with different data distribution shapes i.e. in the case of normal distribution and skew distribution cases. In simple terms, the Q-Q plot is useful to interpret if the feature (column) follows a normal distribution or not. The full…

Regularization method in machine learning — In simple terms, the elastic net regression took the qualities of ridge and lasso regression to regularize the machine learning regression model. Where do we use elastic net regression? It helps to overcome the issues of over-fitting with ridge quality. Dealing with multi-collinearity issues in the data. Reducing features in…

Build machine learning models by using automation pipelines — EvalML library is an automation tool that builds machine learning models by using pipelines. It evaluates the feature engineering automatically making the work easier for data scientists. It also does hyper-parameter tuning inside this automation technique. To install the evalML library use the command shown below: pip install eval If…

Supervised multi-class classification in machine learning — Introduction In the logistic regression, we deal with binary class i.e., two classes in the output columns. However, in the real world, we get various types of data and sometimes have more than two classes in the output column. In that case, we can use soft-max regression is a multinomial…

Various types of outlier detection and removal techniques — Introduction Outliers detection and removal is an important feature engineering tool to manage/remove/treat the problematic data points in the entire data set. The data point that is very far from normal values either as low or high value, these data points are called point outliers. …