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Data Science Project?—?Build a Decision Tree Model with Healthcare Data

Mlearning.ai

Data Science Project — Build a Decision Tree Model with Healthcare Data Using Decision Trees to Categorize Adverse Drug Reactions from Mild to Severe Photo by Maksim Goncharenok Decision trees are a powerful and popular machine learning technique for classification tasks.

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Understanding Data Science and Data Analysis Life Cycle

Pickl AI

Overview of Typical Tasks and Responsibilities in Data Science As a Data Scientist, your daily tasks and responsibilities will encompass many activities. You will collect and clean data from multiple sources, ensuring it is suitable for analysis. Sources of Data Data can come from multiple sources.

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Five machine learning types to know

IBM Journey to AI blog

Naïve Bayes algorithms include decision trees , which can actually accommodate both regression and classification algorithms. Random forest algorithms —predict a value or category by combining the results from a number of decision trees.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Web Scraping : Extracting data from websites and online sources. Sensor Data: Capturing real-time data from IoT devices or sensors. Public Datasets: Utilising publicly available datasets from repositories like Kaggle or government databases. classification, regression) and data characteristics.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. The SELECT statement retrieves data from a database, while SELECT DISTINCT eliminates duplicate rows from the result set. What are the advantages and disadvantages of decision trees ?