Knowledge Graph — Coursera Notes › Academic disciplines › Computer Science / Information Technology › Artificial Intelligence › Machine Learning & Data

Data preprocessing

concept · part of Machine Learning & Data

The step of transforming raw data into a format suitable for machine learning, including scaling and encoding.

Feature values are scaled to a range between 0 and 1 using MinMaxScaler to ensure optimal neural network performance. The dataset is split into 80% training and 20% testing sets.

Inside Data preprocessing (10)

• Data cleaning — The process of handling missing values, outliers, and inconsistencies in a dataset.
• Outliers — Data points that deviate significantly from other observations, identified via Z-score or IQR.
• CSV — CSV is a common file format for storing tabular data, often used in data preprocessing and analysis.
• Data normalization — TensorFlow normalizes data by dividing pixel values by 255.0 to get values in [0,1].
• fit — A method that learns preprocessing parameters from data, e.g., LabelEncoder learns the mapping from original to encoded labels.
• MinMaxScaler — A scaling method that transforms features to a fixed range, typically [0, 1].
• Missing values — Data points that are absent in a dataset, handled by removal or imputation.
• Normalization — Scaling data to a standard range, including Min-Max scaling and Z-score standardization.
• One-hot encoding — A technique to convert categorical variables into binary columns for machine learning models.
• transform — A method that applies a learned transformation to a DataFrame, returning a prepared DataFrame.

Connections

• Uses One-hot encoding
• Uses Scikit-learn
• Prerequisite of Amazon Neptune ML
• Related to Missing values
• Related to Outliers
• Related to Normalization
• Related to Data transformation

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