Data_projects_TripleTen

Data Cleaning & Missing Data Visualization

Goal:
Prepare a raw dataset for reliable analysis by identifying, handling, and visualizing missing or inconsistent data.

Steps:

• Detected missing values, duplicates, and outliers
• Applied imputation techniques and transformations
• Visualized missing data patterns and post-cleaning results
• Verified data integrity before downstream analysis

Outcome:
Produced a clean, consistent dataset ready for analysis and modeling, improving data quality and accuracy.

Skills Used: pandas, NumPy, data preprocessing, visualization, exploratory data analysis

Files:

• data_cleaning.ipynb

View Project: Notebook

Future Improvements

• Automate the data cleaning workflow using Python scripts or a reproducible pipeline (e.g., Prefect or Airflow) to handle larger datasets efficiently.
• Integrate automated quality checks and data validation (using libraries like pandera or great_expectations) to catch missing values or outliers before analysis.
• Build a simple dashboard that visualizes cleaning metrics (e.g., missing data percentage or duplicates over time) to monitor data health interactively.