Dimensionality reduction on automotive data for meaningful insights

Short description

Exploring an automotive data set using principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) to gain valuable understanding

Background

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Objective

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Client Name

MIT Applied Data Science Program

Release Date

February 8, 2022

Project Types

Dimensionality Reduction, Data Science

Skills

Data Preprocessing, Exploratory Data Analysis, Correlation Analysis, Dimensionality Reduction, Principal Component Analysis (PCA), t-distributed stochastic neighborhood embedding (t-SNE)

Tools

Python 3, Jupyter Notebooks, JetBrains DataSpell, Anaconda

Data set

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Results

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Dimensionality reduction on automotive data for meaningful insights