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Dataset Structure

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LitapAI edited this page May 25, 2026 · 1 revision

📊 Dataset Structure

Overview

The PRISM Framework relies on structured datasets containing product-related metadata, market indicators, and engineered analytical features.

The dataset serves as the foundational intelligence source for ANN training, scoring generation, and strategic product prioritization.

Dataset Objectives

The dataset is designed to:

  • represent product characteristics
  • capture market relevance signals
  • support ANN learning
  • enable product ranking
  • improve scoring accuracy

Dataset Categories

The PRISM dataset contains multiple categories of information:

Category Purpose
Product Metadata Core product identification
Market Features Demand and competition indicators
Customer Signals User relevance insights
Trend Metrics Growth opportunity detection
Financial Indicators Pricing and feasibility analysis
Strategic Features Long-term product value

Example Dataset Schema

Column Name Description
Product_ID Unique product identifier
Product_Name Product title
Category Product category
Market_Demand Demand score
Customer_Relevance User alignment score
Innovation_Score Novelty evaluation
Competition_Level Market competition indicator
Pricing_Score Pricing feasibility
Trend_Score Future trend prediction
Strategic_Value Long-term opportunity score
Final_Label Product relevance classification

Data Sources

The PRISM framework may integrate data from:

  • product databases
  • e-commerce platforms
  • market reports
  • trend analytics
  • customer reviews
  • external APIs
  • structured metadata repositories

Data Preprocessing Pipeline

Before ANN training, the dataset undergoes preprocessing.

Steps Include

1. Missing Value Handling

Methods:

  • mean replacement
  • median replacement
  • row elimination

2. Feature Encoding

Categorical values are transformed using:

  • label encoding
  • one-hot encoding

3. Feature Normalization

Normalization improves ANN learning stability.

Common methods:

  • Min-Max Scaling
  • Standard Scaling

4. Outlier Detection

Removes abnormal data points that may distort predictions.

Techniques:

  • Z-score analysis
  • IQR filtering

Dataset Flow

Raw Product Data
        ↓
Cleaning & Validation
        ↓
Feature Engineering
        ↓
Normalization
        ↓
ANN Training Dataset
        ↓
Scoring Pipeline

Feature Engineering

Feature engineering enhances dataset intelligence.

Examples:

Engineered Feature Purpose
Demand Ratio Relative popularity
Market Saturation Competition analysis
Trend Velocity Growth acceleration
Strategic Weight Long-term viability

Training Dataset Split

Typical split configuration:

Dataset Percentage
Training Set 70%
Validation Set 20%
Testing Set 10%

Challenges in Dataset Design

Data Imbalance

Some product categories may dominate training data.

Noise

External market data may contain inconsistencies.

Dynamic Market Conditions

Market trends change over time.

Feature Drift

Feature importance may evolve.

Future Dataset Enhancements

Planned improvements include:

  • real-time data ingestion
  • API-connected market intelligence
  • vector embeddings
  • sentiment analysis integration
  • behavioral analytics
  • multimodal datasets

Metadata Intelligence

Metadata plays a critical role in PRISM.

The system uses metadata to:

  • identify hidden relationships
  • improve contextual understanding
  • support ANN predictions
  • refine scoring accuracy

Summary

The dataset architecture of the PRISM Framework is designed to provide structured, scalable, and intelligence-rich inputs for machine learning-driven product evaluation and prioritization.

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