Data Science - Machine Learning Helper-AI-powered Data Preparation

Example

Automatically generating visualizations and statistics for understanding the distribution, count, and correlation of features within a dataset.

Scenario

Before modeling, a data scientist wants to understand the characteristics of the dataset to determine the preprocessing steps. The tool provides insights into missing values, outliers, and the distribution of data.

Example

Offering solutions for handling missing values, encoding categorical variables, standardizing numerical data, and outlier treatment.

Scenario

A user is preparing data for a machine learning model and needs to clean and transform the data efficiently. The tool suggests methods for dealing with missing data, normalizing data, and converting categorical variables into a format that can be used by machine learning algorithms.

Example

Recommending and applying Principal Component Analysis (PCA) for datasets with high dimensionality and generating code for splitting datasets into training, validation, and test sets.

Scenario

An analyst working on a high-dimensional dataset wants to reduce its complexity without losing critical information. The tool recommends PCA to retain significant features and provides a method to split the data for model training and evaluation.

Example

Suggesting appropriate machine learning models based on the target feature and offering strategies to balance imbalanced datasets.

Scenario

In a project aiming to predict customer churn, the dataset is heavily imbalanced. The tool suggests resampling techniques to balance the dataset and recommends suitable classification models for the project.