Yun.Bun

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Paws, Claws & Calculations

Customers, emails, payment…

Let’s explore the conceptual idea of a new brick-and-mortar pet shop to understand how techniques such as classification and clustering, feature selection, A/B testing, and anomaly detection can be applied from a practical business and operational perspective – without necessarily relying on technical AI models.

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1# Classification & Clustering

-> Preparation steps

• List all pet types you sell (dogs, cats, birds, fish, reptiles)
• Group products into clear categories (food, toys, grooming, accessories)
• Segment customers by buying behavior (frequent, occasional, first-time)
• Group products or pets by common features (size, price range, breed)
• Create labeled sections in store or website for each group

Real-world:

Consider customer segmentation in marketing.

• Classification: Assigns customers into categories like “High-Value” or “Low-Value” based on past purchase behavior.
• Clustering: Groups customers based on spending habits or preferences using statistics similarity measures.

scikit-learn
Widely used, easy-to-use library with many classification (e.g., decision trees, logistic regression) and clustering algorithms (e.g., KMeans, DBSCAN).
→ Good for building basic models and data grouping.

pandas & numpy
For data manipulation and preparation before classification/clustering.

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2# Feature Selection

• Identify key features for pets (age, breed, size, temperament)
• Identify key features for products (price, popularity, expiry date)
• Identify important customer traits (preferred pet type, purchase frequency)
• Prioritize these features to focus marketing and stocking decisions
• Remove or ignore irrelevant features (e.g., brand colors if not important)

Real-world:

Consider feature selection in customer churn prediction.

• Feature selection: An online subscription service might analyze user behavior. This might include information such as login frequency, time spend on the platform, subscription renewal patterns, and customer support complaints.
• A drop in engagement combine with more frequent complaints might indicate a high churn risk.

scikit-learn feature selection module
Has tools like SelectKBest, Recursive Feature Elimination (RFE), and feature importance from models.

boruta
An advanced library for feature selection based on random forests.

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#3 A/B Testing

• Choose one variable to test (e.g., product placement, discount offer)
• Create two versions for testing (A and B)
• Run each version for a set time (e.g., 2 weeks)
• Record sales, customer feedback, or engagement for each version
• Compare results to decide which version works better
• Implement the winning option store-wide

Real-world:

Consider a email marketing campaign.

• A/B testing will allow for data-drive decisions. Take the example of a company testing two email subject lines. Version A and Version B.
• After collecting enough data, a t-test determines whether Version B has significantly higher click-through rate. If the result is statistically significant, marketing teams confidently switch to Version B.

sciPy (stats module)
For performing statistical significance tests (t-tests, chi-square tests) to analyze A/B results.

statsmodels
Offers more advanced statistical modeling and hypothesis testing.

planOut (Python SDK)
For designing and running online experiments (A/B testing framework).

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4# Anomaly Detection

• Regularly check inventory for unusual changes (sudden shortages or surpluses)
• Monitor sales data for unexpected spikes or drops
• Track customer returns and complaints for unusual increases
• Watch for inconsistent data or record mismatches
• Investigate any anomalies promptly and take corrective action

Real-world:

Consider retail store card payments.

• Anomaly Detection might be in the form of in-store fraud detection. Applying this technology would enable secure transactions while minimizing false positives which may inconvenience genuine shoppers.
• Retailers and banks apply this to flag unusual transactions in brick-and-mortar stores.

scikit-learn
Contains algorithms like Isolation Forest, One-Class SVM for anomaly detection.

PyOD
Specialized Python toolkit for outlier detection with many algorithms.

statsmodels
For time-series anomaly detection via statistical methods.

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Probability and statistics are essential for helping individuals make informed decisions based on data rather than guesswork. They guide market research by identifying customer preferences, estimating demand, and supporting inventory planning through forecasting. These methods also assist in choosing the best location by analyzing foot traffic. Overall, they reduce uncertainty, optimize operations, and increase the likelihood of a successful and profitable launch!

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