Data Science

Data Quality Considerations for Machine Learning Models

Reducing the Impacts of Garbage In Garbage Out on Machine Learning Models Ensuring you have good data quality prior to running machine learning algorithms is a crucial step within the overall data science and machine learning workflow. The use of poor-quality data can lead to severe degradation in the results and have further consequences when

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Pandas Profiling — Easy Exploratory Data Analysis in Python

Exploratory Data Analysis (EDA) is an important and essential part of the data science and machine learning workflow. It allows us to become familiar with our data by exploring it, from multiple angles, through statistics, data visualisations, and data summaries. This helps discover patterns in the data, spot outliers, and gain a solid understanding of the data we are working with.

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