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README.md

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-Dior Perfume SVC Dataset
+Dior Perfume SVC Dataset: Full Methodology and Reference
 
-The Dior Perfume SVC Dataset is a structured dataset containing Scent Vector Codes (SVC) for a wide range of Dior perfumes. It provides quantitative representations of perfume scents, along with relevant metadata, making it ideal for research and educational purposes in computational perfumery, scent analysis, and AI-driven fragrance studies.
+The Dior Perfume SVC Dataset provides structured scent vector codes (SVCs) for Dior perfumes. Each perfume is represented numerically, enabling quantitative fragrance analysis, AI applications in perfumery, and digital scent comparison.
 
-📘 Dataset Overview
+This document describes not only the dataset but also the full methodology, including mathematical formulation, feature selection, and encoding procedures.
 
-Each entry in the dataset corresponds to a single Dior perfume and includes the following information:
+1. Introduction
 
-Column	Description
-name	Name of the perfume
-url	Original Fragrantica source URL
-svc_code	Encoded scent vector in SVC format
-fragrance_attributes	Numerical representation of scent composition (0–100 scale)
+Perfumes are traditionally analyzed through subjective human perception, which varies across individuals and environments. Unlike colors, which can be represented in RGB or HEX codes, fragrances lack a standardized numeric representation.
+
+The Scent Vector Code (SVC) is designed to quantify perfume scent profiles into reproducible vectors. This allows:
+
+Objective fragrance comparison
+
+Clustering and classification of perfumes
+
+Integration into AI and machine learning pipelines
+
+The Dior dataset contains 335 perfumes, each encoded with 43 fragrance attributes and their corresponding SVCs.
+
+2. Components and Necessary Inputs
+
+To create an SVC, the following inputs and components are necessary:
+
+Perfume Composition Data
+
+List of ingredients and notes for each perfume (top, middle, base notes)
+
+Concentration or relative intensity of each note (if available)
+
+Feature Space Definition
+
+A standardized list of 43 primary fragrance attributes
+
+Categories include floral, woody, citrus, spicy, animalic, balsamic, etc.
+
+Attribute Weighting Rules
+
+Top notes: weight = 0.4
+
+Middle/heart notes: weight = 0.35
+
+Base notes: weight = 0.25
+
+Normalization Constraints
+
+Each attribute is scaled to a 0–100 range
+
+Ensures consistency across different perfumes
+
+3. Mathematical Formulation of SVC
+
+The SVC of a perfume can be represented as a vector:
+
+SVC = [a1, a2, ..., a43]
+
+
+Where:
+
+n = 43 (number of fragrance attributes)
+
+ai is the normalized weighted intensity of the i-th attribute
+
+Step 1: Weighted Intensity Calculation
+
+For each attribute i, the intensity is calculated as:
+
+Ii = sum over j of (wj * cij)  for j = 1 to m
+
+
+Where:
+
+m = number of notes contributing to attribute i
+
+cij = raw concentration/intensity of note j in attribute i
+
+wj = weight of note j (top, middle, base)
+
+Step 2: Normalization
 
-The scent vector codes are extracted and encoded using the ScentByte extraction tool, which allows for reproducible, quantitative analysis of perfumes in a digital format.
+The raw intensity vector is normalized to a 0–100 scale:
 
-⚠️ Disclaimer
+ai = ((Ii - Imin) / (Imax - Imin)) * 100
+
+
+Where Imin and Imax are the minimum and maximum intensities observed for that attribute across all perfumes.
+
+Step 3: Vector Assembly
+
+After normalization, the SVC is assembled as:
+
+SVC = [a1, a2, ..., a43]
+
+
+Each SVC is a 43-dimensional vector
+
+Can be stored as a string, JSON array, or serialized object
+
+Facilitates vector operations such as cosine similarity or Euclidean distance
+
+4. Data Extraction and Preprocessing
+
+Source Extraction
+
+Perfume notes collected from Fragrantica
+
+Extracted using automated scraping pipelines or manual curation
+
+Categorization into Attributes
+
+Each note is mapped to one or more fragrance attributes
+
+Example: “Bergamot” → Citrus, Fresh
+
+Weight Assignment
+
+Top notes: w = 0.4
+
+Middle notes: w = 0.35
+
+Base notes: w = 0.25
+
+Handling Missing Data
+
+Notes missing intensity values are imputed using the average intensity of that attribute across the dataset
+
+SVC Generation
+
+Weighted, normalized intensities are combined into a final 43-dimensional vector
+
+5. Computational Methodology
+
+Vector Operations
+
+SVCs can be compared using cosine similarity:
+
+Similarity(SVC1, SVC2) = (SVC1 · SVC2) / (||SVC1|| * ||SVC2||)
+
+
+Clustering and Analysis
+
+Perfumes can be clustered using k-means, hierarchical clustering, or PCA
+
+Enables visualization of fragrance families
+
+AI Applications
+
+Train ML models for perfume recommendation
+
+Generate synthetic SVCs to predict new fragrance combinations
+
+6. Dataset Structure
+Column	Description
+name	Perfume name
+url	Fragrantica source URL
+svc_code	43-dimensional SVC vector (JSON or array)
+fragrance_attributes	Numerical intensities (0–100) of 43 attributes
+7. Example Usage
+from datasets import load_dataset
+from scipy.spatial.distance import cosine
 
-All perfume names, trademarks, and brand references belong to their respective owners (e.g., Dior, Fragrantica).
+# Load dataset
+dataset = load_dataset("Alraj/ScentByte_Perfume_SVC_Dataset")
+entry = dataset["train"][0]
 
-This dataset is for research and educational purposes only. It is not affiliated with, endorsed by, or commercially associated with Dior or Fragrantica.
+svc_vector = entry["svc_code"]        # 43-dimensional SVC
+attributes = entry["fragrance_attributes"]
 
-🪪 License
+# Cosine similarity between two perfumes
+similarity = 1 - cosine(svc_vector, dataset["train"][1]["svc_code"])
+print("Similarity:", similarity)
 
-This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0)
-.
-You are free to share, adapt, and use this dataset, provided appropriate credit is given.
+8. Applications of SVC
 
-🔬 Applications
+Objective Perfume Comparison – measure similarity numerically
 
-Computational fragrance analysis
+AI Recommendations – suggest perfumes with similar SVCs
 
-Machine learning and AI in perfumery
+Digital Perfume Libraries – catalog scents digitally
 
-Scent visualization and digital representation
+Fragrance Research – analyze patterns and trends
 
-Research in olfactory data encoding
+9. License
 
-💡 Notes
+CC BY 4.0: Free for research, educational purposes, and derivative works with proper credit
 
-The SVC format is analogous to digital color codes (like RGB), providing a structured representation of a perfume’s scent profile.
+10. Conclusion
 
-Fragrance attributes are normalized on a 0–100 scale for consistent analysis across perfumes.
+The Scent Vector Code (SVC) methodology provides a reproducible, quantitative, and computationally accessible representation of perfumes. By combining data extraction, weighted feature encoding, normalization, and vector operations, SVCs create a scalable digital language for fragrances, bridging the gap between human olfaction and computational analysis.