Tag Archives: Missing Values

ML Zoomcamp 2023 – Decision Trees and Ensemble Learning– Part 3

Building upon the necessary preparation steps outlined in the previous article, this section is dedicated to two critical processes: re-encoding categorical variables and performing the train/validation/test split, a crucial step in preparing our data for modeling and evaluation. Data cleaning and preparation – Data Transformation and Splitting – Part 2/2 Re-encoding the categorical variables ToContinue reading “ML Zoomcamp 2023 – Decision Trees and Ensemble Learning– Part 3”

ML Zoomcamp 2023 – Machine Learning for Classification– Part 4

EDA – Exploratory Data Analysis The topics that we cover in this section are: Checking missing values The following snippet indicates that the dataset ‘df_full_train’ contains no missing values: Looking at the target variable (churn) First what we can check is the distribution of our target variable ‘churn’. How many customers are churning and howContinue reading “ML Zoomcamp 2023 – Machine Learning for Classification– Part 4”

ML Zoomcamp 2023 – Machine Learning for Regression – Part 2

Exploratory data analysis (EDA) – General Information Exploratory data analysis (EDA) is an essential step in the data analysis process. It involves summarizing and visualizing the main characteristics of a dataset to gain insights and identify patterns or trends. By exploring the data, researchers can uncover hidden relationships between variables and make informed decisions. OneContinue reading “ML Zoomcamp 2023 – Machine Learning for Regression – Part 2”