Python 3.13 Preview: A Modern REPL

One of Python’s strong points is its interactive capabilities. By running python you start the interactive interpreter, or REPL, which allows you to perform quick calculations or explore and experiment with your code. In Python 3.13, the interactive interpreter has been completely redesigned with new modern features. Python’s REPL has remained largely unchanged for decades. Instead, alternative interpreters like IPython, bpython, and ptpython have addressed some of the built-in REPL’s shortcomings, providing more convenient interactive workflows for developers. As you’re […]

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Research Focus: Week of September 23, 2024

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. NEW RESEARCH ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons Time-series forecasting is a technique used to predict future values based on previously observed data points over time. It has extensive applications for traffic flow, renewable energy, retail, finance, and climate, among other uses. For these applications,  

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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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