Dataconomy

MARCH 4, 2025

Classification Classification techniques, including decision trees, categorize data into predefined classes. Clustering Clustering groups similar data points based on their attributes. One common example is k-means clustering, which segments data into distinct groups for analysis.

Data Mining 91

Data Mining Data Mining Data Mining Decision Trees 91

Dataconomy

MARCH 17, 2025

Unsupervised models Unsupervised models typically use traditional statistical methods such as logistic regression, time series analysis, and decision trees. They often play a crucial role in clustering and segmenting data, helping businesses identify trends without prior knowledge of the outcome.

Decision Trees 91

Decision Trees Predictive Analytics Data Preparation Machine Learning 91

Dataconomy

JULY 24, 2023

It involves developing algorithms that can learn from and make predictions or decisions based on data. Familiarity with regression techniques, decision trees, clustering, neural networks, and other data-driven problem-solving methods is vital. Machine learning Machine learning is a key part of data science.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

Dataconomy

JUNE 13, 2023

These algorithms are carefully selected based on the specific decision problem and are trained using the prepared data. Machine learning algorithms, such as neural networks or decision trees, learn from the data to make predictions or generate recommendations.

Power BI 103

Power BI Artificial Intelligence Artificial Intelligence Data Analysis 103

Pickl AI

OCTOBER 15, 2024

Techniques like linear regression, time series analysis, and decision trees are examples of predictive models. These models enable businesses to anticipate customer behaviour, forecast sales, or predict risks. Popular clustering algorithms include k-means and hierarchical clustering.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

Pickl AI

SEPTEMBER 12, 2024

Importance of Data Science Data Science is crucial in decision-making and business intelligence across various industries. By leveraging data-driven insights, organisations can make more informed decisions, optimise processes, and gain a competitive edge in the market.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

APRIL 3, 2025

Modeling & Algorithms: Applying statistical models (like regression, classification, clustering) or Machine Learning algorithms to identify deeper patterns, make predictions, or classify data points. Modeling: Build a logistic regression or decision tree model to predict the likelihood of a customer churning based on various factors.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52