Advanced Python import Techniques

In Python, you use the import keyword to make code in one module available in another. Imports in Python are important for structuring your code effectively. Using imports properly will make you more productive, allowing you to reuse code while keeping your projects maintainable. This video course provides a comprehensive overview of Python’s import statement and how it works. The import system is powerful, and this course will teach you how to harness this power. While you’ll cover many of […]

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Branching Out: Exploring Tree-Based Models for Regression

Our discussion so far has been anchored around the family of linear models. Each approach, from simple linear regression to penalized techniques like Lasso and Ridge, has offered invaluable insights into predicting continuous outcomes based on linear relationships. As we begin our exploration of tree-based models, it’s important to reiterate that our focus remains on regression. While tree-based models are versatile, how they handle, evaluate, and optimize outcomes differs significantly between classification and regression tasks. Tree-based regression models are powerful […]

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Decision Trees and Ordinal Encoding: A Practical Guide

Categorical variables are pivotal as they often carry essential information that influences the outcome of predictive models. However, their non-numeric nature presents unique challenges in model processing, necessitating specific strategies for encoding. This post will begin by discussing the different types of categorical data often encountered in datasets. We will explore ordinal encoding in-depth and how it can be leveraged when implementing a Decision Tree Regressor. Through practical Python examples using the OrdinalEncoder from sklearn and the Ames Housing dataset, […]

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From Single Trees to Forests: Enhancing Real Estate Predictions with Ensembles

# Import necessary libraries for preprocessing import pandas as pd from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, FunctionTransformer from sklearn.compose import ColumnTransformer   # Load the dataset Ames = pd.read_csv(‘Ames.csv’)   # Convert the below numeric features to categorical features Ames[‘MSSubClass’] = Ames[‘MSSubClass’].astype(‘object’) Ames[‘YrSold’] = Ames[‘YrSold’].astype(

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5 Free Courses to Master Deep Learning in 2024

5 Free Courses to Master Deep Learning in 2024Image by Author AI applications are everywhere. I use ChatGPT on a daily basis — to help me with work tasks, and planning, and even as an accountability partner. Generative AI hasn’t just transformed the way we work. It helps businesses streamline operations, cut costs, and improve efficiency. As companies rush to implement generative AI solutions, there has been an unprecedented surge in the demand for AI skills. There hasn’t been a better time to […]

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The Concise Guide to Feature Engineering for Better Model Performance

The Complete Guide to Feature Engineering for Better Model Performance Feature engineering helps make models work better. It involves selecting and modifying data to improve predictions. This article explains feature engineering and how to use it to get better results. What is Feature Engineering? Raw data is often messy and not ready for predictions. Features are important details in your data. They help the model understand and make predictions. Feature engineering improves these features to make them more useful. Modeling […]

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5 Real-World Machine Learning Projects You Can Build This Weekend

5 Real-World Machine Learning Projects You Can Build This WeekendImage by Author | Created on Canva Building machine learning projects using real-world datasets is an effective way to apply what you’ve learned. Working with real-world datasets will help you learn a great deal about cleaning and analyzing messy data, handling class imbalance, and much more. But to build truly helpful machine learning models, it’s also important to go beyond training and evaluating models and build APIs and dashboards as needed. […]

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From Data to Insights: A Beginner’s Journey in Exploratory Data Analysis

From Data to Insights: A Beginner’s Journey in Exploratory Data AnalysisImage by Editor | Ideogram Every industry uses data to make smarter decisions. But raw data can be messy and hard to understand. EDA allows you to explore and understand your data better. In this article, we’ll walk you through the basics of EDA with simple steps and examples to make it easy to follow. What is Exploratory Data Analysis? Exploratory Data Analysis (EDA) is the process of examining your […]

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Quiz: Using Python’s pip to Manage Your Projects’ Dependencies

Interactive Quiz ⋅ 8 QuestionsBy Philipp Acsany Share In this quiz, you’ll test your understanding of Python’s standard package manager, pip. You’ll revisit the concepts behind pip, important commands, and how to install packages. The quiz contains 8 questions and there is no time limit. You’ll get 1 point for each correct answer. At the end of the quiz, you’ll receive a total score. The maximum score is 100%. Good luck! « Browse All Python Quizzes    

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Python 3.13 Preview: Free Threading and a JIT Compiler

Although the final release of Python 3.13 is scheduled for October 2024, you can download and install a preview version today to explore the new features. Notably, the introduction of free threading and a just-in-time (JIT) compiler are among the most exciting enhancements, both designed to give your code a significant performance boost. Check out what’s new in the Python changelog for a complete list of the upcoming features and improvements. This document contains a quick summary of the release […]

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