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Café Sales: Data Cleaning & Revenue Analysis ☕📊 Project Overview This project focuses on the end-to-end data cleaning and exploratory analysis of a messy café sales dataset. The primary goal was to transform raw, inconsistent transaction logs into a reliable source of information for business decision-making, ensuring that data trends and statistical integrity remained intact after the cleaning process.
Problem Statement The original dataset contained several quality issues that prevented accurate analysis:
Missing Values: Significant gaps in "Payment Method" and "Location" columns.
Data Inconsistencies: Entries labeled as 'UNKNOWN' or 'ERROR' across multiple features.
Format Issues: Numeric columns and dates were stored as objects, requiring type conversion for calculation.
Technical Stack Language: Python
Libraries: Pandas (Data Manipulation), NumPy (Statistical Imputation), Seaborn & Matplotlib (Advanced Visualization).
Key Steps Performed Data Auditing: Utilized df.info() and df.isnull().sum() to identify the scale of missing and corrupted data.
Standardization: Converted "Quantity", "Price Per Unit", and "Total Spent" to numeric types using errors='coerce' to handle anomalies.
Smart Imputation:
Replaced 'UNKNOWN' and 'ERROR' strings with 'Others' or specific category defaults.
Filled missing numerical values using np.nanmean and median to maintain the central tendency of the data.
Statistical Validation: Created a Linear Regression Plot using sns.lmplot to verify the mathematical relationship between "Quantity" and "Total Spent" after cleaning, ensuring no outliers were distorting the revenue logic.
Key Insights Successfully reduced null values to zero across all critical columns.
Normalized "Payment Method" and "Location" data, enabling a clear view of customer preferences (e.g., In-Store vs. Takeaway).