Web 3.0 and the metaverse are building up lots of hype and expectations but it is important to keep in mind that almost every project
Welcome! This is the first publication of our Data Scientist posts in which we will be working on Data Science Topics (Data Visualisation, Clustering Techniques, Regression/Classification/Time
The linear regression minimize an Error function (also called loss function or cost function) using a coefficient a for each feature variable, plus b. We
When we try to train a classification model to predict fraudulant bank transactions, 99% of transactions are legitimate and 1% are fraudulent. If we build
This article is about Preprocessing and building Pipeline. For a better understanding it is recommended to read this article before: Fine-Tuning Your Model (Classification Metrics, Logistic
Welcome in this article about Clustering for dataset exploration. Soon we will post another article related Visualization with hierarchical clustering and t-SNE, stay tuned! Always
This blogpost is to help you identify why to use a specific model instead of another by understand the pros and cons of each one.
An important part in the job of a data scientist is the communication of insights to other people. Visualizations are an excellent way to share
Welcome in this article about Clustering for dataset exploration. Soon we will post another article related Visualization with hierarchical clustering and t-SNE, stay tuned! Always
This article is about Preprocessing and building Pipeline. For a better understanding it is recommended to read this article before: Fine-Tuning Your Model (Classification Metrics, Logistic
When we try to train a classification model to predict fraudulant bank transactions, 99% of transactions are legitimate and 1% are fraudulent. If we build
The linear regression minimize an Error function (also called loss function or cost function) using a coefficient a for each feature variable, plus b. We
Welcome! This is the first publication of our Data Scientist posts in which we will be working on Data Science Topics (Data Visualisation, Clustering Techniques, Regression/Classification/Time
This blogpost is to help you identify why to use a specific model instead of another by understand the pros and cons of each one.
An important part in the job of a data scientist is the communication of insights to other people. Visualizations are an excellent way to share
Welcome in this article about Clustering for dataset exploration. Soon we will post another article related Visualization with hierarchical clustering and t-SNE, stay tuned! Always
This article is about Preprocessing and building Pipeline. For a better understanding it is recommended to read this article before: Fine-Tuning Your Model (Classification Metrics, Logistic
When we try to train a classification model to predict fraudulant bank transactions, 99% of transactions are legitimate and 1% are fraudulent. If we build
The linear regression minimize an Error function (also called loss function or cost function) using a coefficient a for each feature variable, plus b. We
Welcome! This is the first publication of our Data Scientist posts in which we will be working on Data Science Topics (Data Visualisation, Clustering Techniques, Regression/Classification/Time