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ML Model Repository

This repository contains machine learning models for various business use cases including sales forecasting and customer behavior prediction.

๐Ÿ“ Model Files

1. Ice Cream Sales Forecasting Models

ML-Job-1-child1_model.pkl

  • Type: Linear Regression
  • Purpose: Predicts ice cream sales based on weather and temporal factors
  • Features: Date, DayOfWeek, Month, Temperature, Rainfall
  • Target: IceCreamsSold
  • Algorithm: Linear Regression
  • Serialization Date: 2026-01-29T18:19:47.681000
  • Scikit-learn Version: 1.3.0

ML-Job-2-child3_model.pkl

  • Type: Lasso Regression
  • Purpose: Ice cream sales prediction with regularization
  • Features: Date, DayOfWeek, Month, Temperature, Rainfall
  • Target: IceCreamsSold
  • Algorithm: Lasso Regression (alpha=1.0)
  • Serialization Date: 2026-01-29T18:33:09.378000
  • Scikit-learn Version: 1.3.0

ML-Job-2-child3-1_model.pkl

  • Type: Lasso Regression
  • Purpose: Updated ice cream sales prediction model
  • Features: Date, DayOfWeek, Month, Temperature, Rainfall
  • Target: IceCreamsSold
  • Algorithm: Lasso Regression (alpha=1.0)
  • Serialization Date: 2026-01-29T18:42:52.078000
  • Scikit-learn Version: 1.3.0

2. Customer Spending Prediction Model

ML-Job-3-child3-1_model.pkl

  • Type: Lasso Regression
  • Purpose: Predicts customer average spending based on purchase frequency
  • Features: Name, AverageFrequency
  • Target: AverageSpend
  • Algorithm: Lasso Regression (alpha=1.0)
  • Serialization Date: 2026-01-29T18:58:15.438000
  • Scikit-learn Version: 1.3.0

๐Ÿ› ๏ธ Model Pipeline Structure

All models use a scikit-learn Pipeline with the following preprocessing steps:

Preprocessing Pipeline

  1. ColumnTransformer for feature-specific transformations:

    • Numerical Features: Imputation + Scaling
      • SimpleImputer (median strategy)
      • StandardScaler (for Lasso models) or passthrough (for Linear Regression)
    • Categorical Features: Imputation + Encoding
      • SimpleImputer (constant strategy with "missing")
      • OneHotEncoder (first category drop, handle_unknown='ignore')
  2. Regressor: Linear or Lasso regression

๐Ÿ“Š Model Input Formats

For Ice Cream Sales Models:

{
  "input_data": {
    "columns": ["Date", "DayOfWeek", "Month", "Temperature", "Rainfall"],
    "index": [0, 1, 2],
    "data": [
      ["2025-06-15", "Sunday", "June", 75.5, 0.0],
      ["2025-06-16", "Monday", "June", 72.0, 0.1],
      ["2025-06-17", "Tuesday", "June", 78.2, 0.0]
    ]
  }
}

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