Project Title: Data Preprocessing Pipeline

Objective:

To design and implement an automated, reusable pipeline that transforms raw data into a clean, structured format suitable for machine learning and analysis.

Key Components:

Data Ingestion:

Load raw data from sources such as CSVs, databases, APIs, or cloud storage.

Support for batch or streaming data input.

Data Cleaning:

Handle missing values (e.g., imputation, removal).

Remove or correct duplicates and outliers.

Standardize formats (e.g., date parsing, string normalization).

Feature Engineering:

Encode categorical variables (One-Hot, Label Encoding).

Normalize or scale numerical features (StandardScaler, MinMaxScaler).

Create new features (e.g., time-based features, aggregations).

Data Transformation Pipelines:

Use tools like scikit-learn Pipelines, Pandas, or Spark to chain transformations.

Ensure transformations are reproducible and consistent between training and inference.

Validation & Logging:

Implement checks for schema validation, data types, and anomalies.

Log data statistics and pipeline steps for auditability.

Modularity & Reusability:

Design modular functions or classes for each step.

Package as a library or script that can be reused in different projects.

Integration & Versioning:

Version control data transformations using DVC, Git, or metadata tracking tools.

Integrate with ML pipelines for seamless transition from preprocessing to training.

Outcome:

A robust, automated data preprocessing pipeline that ensures clean, consistent, and high-quality input for machine learning models, reducing manual errors and accelerating experimentation.