Data cleaning and preprocessing are critical steps in data analysis as they ensure the data is of high quality and ready for analysis. Here are some basic steps for data…

Aggregation, grouping, and filtering of data are essential operations in data analysis. Pandas provide several functions to perform these operations. Here are some examples Aggregation # load the data into…

Matplotlib and Seaborn are two popular Python libraries for data visualization. Matplotlib is a low-level library that provides basic plotting functionality, while Seaborn is built on top of Matplotlib and…

Matplotlib is a powerful data visualization library in Python. It provides a variety of tools for creating a wide range of visualizations, from simple line plots and scatter plots to…

Seaborn is a Python library that provides a high-level interface for creating informative and attractive statistical graphics. It's built on top of matplotlib and provides additional functionality and flexibility for…

Plotly is a popular library for creating interactive visualizations in Python. Here's an example of how to create an interactive scatter plot using Plotly import plotly.graph_objs as go import pandas…

Scikit-Learn (also known as sklearn) is a popular machine-learning library in Python. It provides a wide range of algorithms and tools for building machine learning models, including classification, regression, clustering,…

Scikit-Learn supports both supervised and unsupervised learning, which are two of the main categories of machine learning. Supervised learning involves building a model to predict an output variable (also known…

Model selection and evaluation are essential components of machine learning. The goal of model selection is to find the best algorithm and hyperparameters that can achieve high accuracy on the…

Feature engineering and selection are essential steps in the process of building predictive models with machine learning. Feature engineering involves transforming raw data into features that can be used by…