🧪 Using Python with NumPy, Pandas, Matplotlib, and Seaborn for Data Analysis, Data Science & Pre-Machine Learning Analysis
Before any machine learning model is built, the real work lies in understanding, cleaning, transforming, and visualizing the data. This crucial phase is known as pre-machine learning analysis or exploratory data analysis (EDA) . In this post, we’ll cover how to use the most powerful Python libraries— NumPy, Pandas, Matplotlib, and Seaborn —for data analysis and pre-ML preparation. Whether you're new to data science or sharpening your skills, this guide walks you through practical techniques to wrangle and understand your data before diving into algorithms. 🔧 The Essential Python Libraries for Data Analysis Let’s briefly introduce the four core libraries: NumPy – The foundation for numerical computing in Python. It’s great for array operations, math, and basic statistics. Pandas – The go-to library for working with structured data (like CSV files, databases, spreadsheets). Matplotlib – A flexible plotting library to create static charts and graphs. Seaborn ...