## Building a Segmentation Model

Segmentation, often referred to as clustering in the realm of data science, is a method used to divide a large set of data into smaller groups or clusters based on similarity. Instead of viewing data as one massive chunk, segmentation allows us to categorize these data points into meaningful structures,…

## Building a Recommendation System

A recommendation system is a type of software that provides suggestions or recommendations to users based on various types of data, such as user behavior, user preferences, or item characteristics. These systems are commonly used in applications like online shopping platforms to suggest products to users, streaming services to recommend…

## Time series analysis

Time series forecasting is a statistical technique used to predict future values based on historically observed data points ordered by time. Widely used in finance, economics, and business, it helps stakeholders anticipate future trends and make informed decisions. A time series is a sequence of data points, measured typically at…

## Triple Exponential Smoothing

Triple Exponential Smoothing, commonly known as the Holt-Winters Method, extends upon Double Exponential Smoothing to address time series data that contains both a trend and a seasonal component. It incorporates three equations to capture the level, trend, and seasonality of a dataset, making it particularly useful for predicting values in…

## Double Exponential Smoothing

Double Exponential Smoothing, also known as Holt’s Linear Exponential Smoothing, is a time series forecasting method that extends Simple Exponential Smoothing. While Simple Exponential Smoothing is best suited for time series without a trend, Double Exponential Smoothing can handle time series data with a trend but no seasonality. The primary…

## Exponential Smoothing

Simple Exponential Smoothing (SES) is a time series forecasting method that is especially suitable for univariate data without a trend or seasonal pattern. It uses weighted averages of past observations to forecast future points. The method is ‘exponential’ because the weights decrease exponentially as observations get older. Key Concept: Smoothing…

## Scoring Script

Preparing a scoring script is a crucial step in deploying the machine learning model. The scoring script is a standalone script (or application) that loads the trained ML model, performs any necessary preprocessing on new input data, runs this data through the model to get predictions, and then outputs these…

## Reinforcement Learning

Reinforcement Learning (RL) is a bit unique. It’s not like supervised learning where we have labeled data to guide the learning. But it’s also not unsupervised learning where the algorithm is left to find patterns on its own. In RL, we don’t give direct answers, but we do give feedback…

## Deployment of an ML model

Deploying a machine learning model as a REST API involves making the model available as a web service that can be accessed via HTTP requests. Here is a step-by-step guide to deploying a machine-learning model as a REST API: Step 1: Train Your Model Before we can deploy our model,…

## REST API

REST (Representational State Transfer) is an architectural style for designing networked applications. A REST API (Application Programming Interface) is a set of rules and conventions for building and interacting with web services. It uses HTTP requests to access and use data, allowing different software applications to communicate with each other…