Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Python’s simplicity, versatility, and extensive library support make it the go-to language for AI development.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Mlearning.ai

MARCH 26, 2023

Pre-requisite: Programming Language To begin, it’s crucial to have a basic understanding of a programming language, and Python is the perfect choice due to its simplicity and extensive libraries. Explore the data (EDA) and spot patterns and missing values. In this project: First, get the gist of the problem and the data.

Data Science 52

Data Science EDA Decision Trees Machine Learning 52

Pickl AI

MAY 30, 2024

Also Read: Explore data effortlessly with Python Libraries for (Partial) EDA: Unleashing the Power of Data Exploration. Must Check Out: How to Use ChatGPT APIs in Python: A Comprehensive Guide. By checking patterns, distributions, and anomalies, EDA unveils insights crucial for informed decision-making.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

APRIL 3, 2025

Exploratory Data Analysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!) EDA: Calculate overall churn rate. 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

Pickl AI

SEPTEMBER 12, 2024

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Exploratory Data Analysis (EDA): Analysing and visualising data to discover patterns, identify anomalies, and test hypotheses.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

PyImageSearch

DECEMBER 12, 2022

We went through the core essentials required to understand XGBoost, namely decision trees and ensemble learners. Since we have been dealing with trees, we will assume that our adaptive boosting technique is being applied to decision trees. Looking for the source code to this post? Table 1: The Dataset.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Exploratory Data Analysis 52

Heartbeat

SEPTEMBER 28, 2023

Import Libraries First, import the required Python libraries, such as Comet ML, Optuna, and scikit-learn. Load and Explore Data We load the Telco Customer Churn dataset and perform exploratory data analysis (EDA). They help you understand the data’s characteristics and make informed decisions to optimize customer retention strategies.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines ML 52