# 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(

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 […]

In our previous exploration of penalized regression models such as Lasso, Ridge, and ElasticNet, we demonstrated how effectively these models manage multicollinearity, allowing us to utilize a broader array of features to enhance model performance. Building on this foundation, we now address another crucial aspect of data preprocessing—handling missing values. Missing data can significantly compromise the accuracy and reliability of models if not appropriately managed. This post explores various imputation strategies to address missing data and embed them into our […]

Automating Data Cleaning Processes with Pandas Few data science projects are exempt from the necessity of cleaning data. Data cleaning encompasses the initial steps of preparing data. Its specific purpose is that only the relevant and useful information underlying the data is retained, be it for its posterior analysis, to use as inputs to an AI or machine learning model, and so on. Unifying or converting data types, dealing with missing values, eliminating noisy values stemming from erroneous measurements, and […]

This post will demonstrate the usage of Lasso, Ridge, and ElasticNet models using the Ames housing dataset. These models are particularly valuable when dealing with data that may suffer from multicollinearity. We leverage these advanced regression techniques to show how feature scaling and hyperparameter tuning can improve model performance. In this post, we’ll provide a step-by-step walkthrough on setting up preprocessing pipelines, implementing each model with scikit-learn, and fine-tuning them to achieve optimal results. This comprehensive approach not only aids […]

One of the significant challenges statisticians and data scientists face is multicollinearity, particularly its most severe form, perfect multicollinearity. This issue often lurks undetected in large datasets with many features, potentially disguising itself and skewing the results of statistical models. In this post, we explore the methods for detecting, addressing, and refining models affected by perfect multicollinearity. Through practical analysis and examples, we aim to equip you with the tools necessary to enhance your models’ robustness and interpretability, ensuring that […]

Machine learning projects often require the execution of a sequence of data preprocessing steps followed by a learning algorithm. Managing these steps individually can be cumbersome and error-prone. This is where sklearn pipelines come into play. This post will explore how pipelines automate critical aspects of machine learning workflows, such as data preprocessing, feature engineering, and the incorporation of machine learning algorithms. Let’s get started. The Power of PipelinesPhoto by Quinten de Graaf. Some rights reserved. Overview This post is […]

When we analyze relationships between variables in machine learning, we often find that a straight line doesn’t tell the whole story. That’s where polynomial transformations come in, adding layers to our regression models without complicating the calculation process. By transforming our features into their polynomial counterparts—squares, cubes, and other higher-degree terms—we give linear models the flexibility to curve and twist, fitting snugly to the underlying trends of our data. This blog post will explore how we can move beyond simple […]

Linear regression models are foundational in machine learning. Merely fitting a straight line and reading the coefficient tells a lot. But how do we extract and interpret the coefficients from these models to understand their impact on predicted outcomes? This post will demonstrate how one can interpret coefficients by exploring various scenarios. We’ll explore the analysis of a single numerical feature, examine the role of categorical variables, and unravel the complexities introduced when these features are combined. Through this exploration, […]

Image by Author After the highly successful launch of Gemma 1, the Google team introduced an even more advanced model series called Gemma 2. This new family of Large Language Models (LLMs) includes models with 9 billion (9B) and 27 billion (27B) parameters. Gemma 2 offers higher performance and greater inference efficiency than its predecessor, with significant safety advancements built in. Both models outperform the Llama 3 and Gork 1 models. In this tutorial, we will learn about the three […]