AI Lesson & Submodule
Data preprocessing
Missing values, outlier detection, categorical encoding, feature scaling, standardization, and data leakage.
Why This Matters
Garbage in, garbage out. Cleaning feature representations directly determines the model's accuracy limit.
What You Will Learn
- •Imputing missing values and dropping bad data
- •Detecting outliers and standardizing scales
- •Encoding categories (One-Hot, Label encoding)
- •Preventing features data leakage during transformations
Concepts Covered
Missing ImputationOutliersCategorical EncodingStandardizationData Leakage
Mapped Foundation Project: House Price Prediction
Train linear regression models predicting continuous outputs from numerical features.
Architecture Preview
System flow diagrams outlining client nodes, processing engines, and backing databases for House Price Prediction.
Client WorkspaceInference GatewayHouse Price Prediction Logic Engine
Tech Stack Planned
PythonScikit-LearnPandas
GitHub: Coming SoonLive Demo: Coming Soon
Coming SoonTechnical Interview Value
- ?Explain why preprocessing transformations must be fitted ONLY on training data to prevent leakage