Ml smart cart - Implement data preprocessing, feature engineering, and behavioral feature creation#204
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Sep 21, 2025
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- Data Preprocessing: Consolidated raw data sources, standardized dtypes, and handled missing values to deliver a clean, analysis-ready dataset.
- Seasonality & Promotions: Engineered a dynamic adjusted_spend feature by applying seasonal multipliers and synthetic promotional discounts.
- Preference Features: Created core ML features recency_days, freq_30d, and budget_alignment to model user purchase behavior.
Consolidate multiple product CSV files into a unified dataset. Drop irrelevant columns (like Unnamed: 0). Standardize column types: Handle missing values (NaN for prices, product categories).
2. Seasonality & Promotions Add seasonal_factor column based on months (e.g., 1.5x in Nov/Dec, 0.8x in Jan/Feb). Add promotion_discount column for selected products or categories. Compute adjusted_spend = transaction_price * seasonal_factor * (1 - promotion_discount). This functionlity required the simultaed transaction csv file.
Compute Recency: days since last purchase per user-product pair. Compute Frequency: number of purchases in last 30 days. Compute Budget Alignment: deviation of user spend vs. average spend.
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