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Overview

The SmartEat AI ML pipeline processes recipe data from Food.com to create a personalized recommendation system. The pipeline transforms raw recipe data into a trained K-Nearest Neighbors (KNN) model that powers nutritional recommendations.

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Data Source

The pipeline uses the Food.com - Recipes and Reviews dataset from Kaggle, containing over 522,000 recipes with comprehensive nutritional information.

Dataset Size

522,517 recipes with 28 columns

Features

Nutritional data, ingredients, categories, and ratings

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Pipeline Stages

1

Data Acquisition

Download the dataset from Google Drive and load it into a pandas DataFrame using the Python engine for error tolerance.
df = pd.read_csv("recipes.csv", engine='python')
The dataset includes:
  • Recipe metadata (name, author, category)
  • Time information (prep time, cook time)
  • Nutritional content (calories, macronutrients)
  • Ingredients and instructions
  • User ratings and reviews
2

Data Exploration

Analyze the dataset structure, distributions, and data quality:Key Findings:
  • Complete nutritional data for all recipes
  • Missing values in optional fields (ratings, servings)
  • 32,600 duplicate recipe names requiring deduplication
Nutritional Statistics:
  • Average calories: 484.4 per serving
  • Average protein: 17.5g
  • Average carbohydrates: 49.1g
  • Average fat: 24.6g
3

Data Cleaning

Clean and prepare the dataset for model training:
  • Remove duplicate recipes (same name)
  • Handle missing values in non-critical fields
  • Parse and normalize ingredient lists
  • Convert categorical data to appropriate formats
  • Filter outliers in nutritional values
The cleaning process reduces the dataset to unique, high-quality recipes suitable for recommendations.
4

Feature Engineering

Extract and engineer features for the recommendation model:Primary Features:
FEATURES = [
    'calories',
    'fat_content',
    'carbohydrate_content',
    'protein_content'
]
Additional Processing:
  • Text vectorization of ingredients using TF-IDF
  • Category encoding for meal types and diet types
  • Normalization of nutritional values
  • Creation of derived features (macronutrient ratios)
5

Model Training

Train the K-Nearest Neighbors model:
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import NearestNeighbors

# Scale features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(df[FEATURES])

# Train KNN model
knn = NearestNeighbors(n_neighbors=550, metric='euclidean')
knn.fit(X_scaled)
The model uses Euclidean distance to find nutritionally similar recipes.
6

Model Persistence

Save the trained model and required artifacts:
from joblib import dump

dump(df_cleaned, 'df_recetas.joblib')
dump(scaler, 'scaler.joblib')
dump(knn, 'knn.joblib')
These files are stored in backend/app/files/ for production use.

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Notebook Reference

The complete pipeline is documented in:

ML Training Notebook

notebooks/Cuaderno_SmartEatAI.ipynb contains the full implementation with visualizations and analysis

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Key Technologies

import pandas as pd
import numpy as np
import regex as re
import unicodedata

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Data Quality Metrics

MetricValue
Total Recipes522,517
Complete Nutritional Data100%
Duplicate Names32,600
Average Rating4.63/5.0
Recipes with Reviews51.5%
The pipeline emphasizes data quality over quantity, ensuring that only recipes with complete, accurate nutritional information are used for recommendations.

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Visualizations

The notebook includes comprehensive visualizations:
  • Nutritional distribution histograms
  • Missing data heatmaps
  • Word clouds of ingredients
  • Correlation matrices
  • Recipe category distributions

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Next Steps

After the pipeline completes:
  1. The trained model is loaded into the backend (backend/app/core/ml_model.py)
  2. The KNN model powers recipe similarity searches
  3. The scaler ensures consistent feature normalization
  4. The recipe dataframe enables fast lookups

Related Documentation

See KNN Recommender for details on how the model is used in production.

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