These libraries will automate our EDA process and reduce our efforts and time.
Exploratory Data Analysis and Modeling with Dataprep, PyCaret, and Klib
This repository contains code and examples of using three powerful Python libraries for data preparation, exploratory data analysis (EDA), and machine learning: dataprep, pycaret, and klib.
In this project, we perform data preparation, EDA, and machine learning modeling using:
Dataprep: For quick and efficient data cleaning and exploration. PyCaret: For simplified machine learning workflows. Klib: For advanced data cleaning and EDA. Featuretools: Enhances model performance through automated feature engineering.
Ensure you have Python 3.6+ installed. You'll need the following Python packages: dataprep pycaret klib featuretools pandas numpy
You can install these packages using pip:
Dataprep simplifies data cleaning and exploration with minimal code. It provides: Easy-to-use functions for data cleaning and validation. Interactive and comprehensive EDA reports. Integration with popular data analysis libraries.
Accelerates the data cleaning process. Generates insightful visualizations quickly. Reduces the need for repetitive code.
PyCaret is an open-source, low-code machine learning library that simplifies the process of training and deploying machine learning models. It provides: Automated machine learning workflows. Simple and consistent API for various machine learning tasks. Built-in hyperparameter tuning and model evaluation.
Reduces the time required for model development. Simplifies comparison of multiple models. Facilitates deployment of trained models.
Klib is a data cleaning and visualization library that helps in understanding data distributions and relationships. It provides:
Functions for cleaning data, including handling missing values and duplicates. Visualizations to understand correlations and distributions. Tools for data transformation and preprocessing.
Enhances data quality before analysis. Provides clear visual insights into data relationships. Simplifies preprocessing tasks.
Data Preparation and Cleaning: Use dataprep and klib to clean and explore your dataset. Exploratory Data Analysis: Generate EDA reports and visualize data distributions and correlations. Modeling: Use pycaret to build, compare, and deploy machine learning models.
The Featuretools package is valuable for automated feature engineering, creating new features from raw data to improve model performance. Both libraries are especially useful if you are working with small datasets.
Enhances model performance through automated feature engineering.