Final Projects and Real-World Applications

# CHAPTER 20

Final Projects and Real-World Applications #

1. Chapter Introduction #

2. Project 1: Ecommerce Preprocessing System #

Goal: Create a daily pipeline that cleans raw Shopify/Stripe exports for the marketing team. The Dataset: A messy CSV containing daily transactions, user emails, and product IDs. Key Cleaning Steps:

1. 1. Datetime Parsing: Convert timestamps to local timezones. Extract DayOfWeek to analyze weekend vs weekday sales.

1. 2. String Normalization: Clean UTM parameters from marketing URLs. Standardize promo_code casing (SUMMER20 vs summer20).

1. 3. Data Type Correction: Strip $ from revenue columns and cast to float.

1. 4. Validation: Assert that discount_amount is never greater than subtotal.

3. Project 2: Healthcare Data Pipeline #

Goal: Prepare Electronic Health Records (EHR) for a hospital readmission prediction model. The Dataset: Demographics, lab results, and hospital stay durations. Key Cleaning Steps:

1. 1. Handling MNAR: Patients discharged early will have missing lab results. Create a new categorical variable: Test_Not_Administered instead of imputing with the mean.

1. 2. Outlier Winsorization: Cap extreme biological readings (e.g., Blood Pressure) at clinical maximums rather than deleting the patient record.

1. 3. Age Calculation: Calculate Age accurately using Admission_Date - Date_Of_Birth.

1. 4. Encoding: One-Hot Encode admission types (Emergency, Elective, Urgent).

4. Project 3: Financial Data Cleaner (Anti-Money Laundering) #

Goal: Clean banking transactions to detect structuring (smurfing) and fraud. The Dataset: Millions of transaction logs. Key Cleaning Steps:

1. 1. Chunk Processing: The file is 20GB. Use pd.read_csv(chunksize=500000) to process the data without crashing RAM.

1. 2. Regex Extraction: Extract Merchant Names from messy bank statements (e.g., POS DEBIT 12/04 AMAZON.COM WA -> AMAZON.COM).

1. 3. Feature Engineering: Calculate a rolling 7-day transaction velocity for each Account_ID to spot sudden spikes.

1. 4. Subsetting: Filter out internal bank transfers (Amount = 0.00 or description contains "FEE REVERSAL").

5. Project 4: CRM Data Repair System #

Goal: Deduplicate a massive Salesforce CRM database after a company merger. The Dataset: Customer names, phone numbers, emails, and physical addresses. Key Cleaning Steps:

1. 1. Regex Phone Cleaning: Strip all non-numeric characters from phone numbers. Ensure all numbers are exactly 10 digits (append/remove country codes as needed).

1. 2. Address Standardization: Map "St.", "Street", "ST" to a single standard.

1. 3. Fuzzy Deduplication: Group by Email (lowercase). Sort by Last_Login_Date. Use drop_duplicates(keep='last') to retain the most current user profile.

1. 4. SQL Integration: Write the cleaned data back to a PostgreSQL database using Pandas to_sql().

6. Project 5: Machine Learning Preprocessing Toolkit #

Goal: Build a reusable Python class that acts as a plug-and-play cleaner for any tabular dataset. The Architecture: Create a .py file with a Class containing methods for standard ML prep.

class MLDataPreprocessor:
    def __init__(self, df):
        self.df = df
        
    def handle_missing(self, strategy='median'):
        # Code to impute missing numeric with median, categoricals with mode
        return self
        
    def encode_categoricals(self, max_categories=10):
        # Code to drop high-cardinality columns and One-Hot encode the rest
        return self
        
    def scale_numeric(self, method='standard'):
        # Apply StandardScaler or MinMaxScaler
        return self

Output: A GitHub repository showcasing your object-oriented programming (OOP) skills applied to data engineering.

7. Course Conclusion and Next Steps #

You have completed Data Cleaning for Beginners to Advanced. You now possess the skills to transform raw, chaotic data into structured, valuable information.

What should you learn next?

1. 1. SQL Databases: Deepen your knowledge of PostgreSQL and database administration.

1. 2. Data Visualization: Learn Tableau, PowerBI, or Python's Plotly to visualize your newly cleaned data.

1. 3. Machine Learning: Now that you can prepare data, learn scikit-learn to build predictive models on top of it.

1. 4. Cloud Data Engineering: Learn AWS (S3, Glue, Athena) or Snowflake to apply your cleaning skills at massive cloud scale.

Happy Cleaning!

8. MCQs #

9. Interview Questions #

• Q: Describe a complex data cleaning project you have worked on. What were the challenges and how did you solve them?

• Q: If you are building an automated pipeline to clean daily sales data, what steps would you take to ensure the pipeline doesn't fail silently if the source data format suddenly changes?

10. Summary #