This repository contains three data science projects demonstrating unsupervised learning techniques, including clustering, dimensionality reduction, and recommendation systems. These projects were completed as part of advanced machine learning coursework and showcase practical applications of data science in business contexts.

• Projects Overview
• Technologies Used
• Project Details
  • 1. Wholesale Customer Segmentation
  • 2. Restaurant Recommendation System
  • 3. Employee Retention Analysis
• Key Skills Demonstrated

• Python 3.x

• Core Libraries:

  • pandas - Data manipulation and analysis
  • NumPy - Numerical computing
  • scikit-learn - Machine learning algorithms
  • Matplotlib & Seaborn - Data visualization
  • scipy - Statistical analysis and hierarchical clustering

• Machine Learning Techniques:

  • K-Means Clustering
  • Hierarchical Clustering (Agglomerative)
  • Principal Component Analysis (PCA)
  • Collaborative Filtering
  • Standardization & Normalization
  • Silhouette Analysis
  • Elbow Method

File: section05_clustering_project-working.ipynb

Objective: Segment wholesale customers based on their annual spending across six product categories to enable targeted marketing strategies.

Dataset: Wholesale customer data containing annual spending on Fresh products, Milk, Grocery, Frozen goods, Detergents & Paper, and Delicatessen items for 440 customers.

Methodology:

• Data preprocessing and standardization using StandardScaler
• Exploratory data analysis to understand spending patterns
• K-Means clustering with optimal cluster determination via elbow method
• Hierarchical clustering with dendrogram visualization
• Cluster validation using silhouette scores
• Cluster profiling and interpretation

Key Findings:

• Successfully identified distinct customer segments based on purchasing behavior
• Different customer types show clear preferences for specific product categories
• Segments can be targeted with customized marketing and inventory strategies

Business Impact: Enables the wholesale distributor to develop segment-specific marketing campaigns, optimize inventory management, and improve customer relationship management.

File: section09_recommender_project_working.ipynb

Objective: Build a collaborative filtering recommendation system to suggest restaurants to users based on historical rating patterns.

Dataset: Restaurant ratings dataset containing 1,161 ratings from multiple consumers across various restaurants, with ratings on a 0-2 scale.

Methodology:

• Created user-item rating matrix with consumers as rows and restaurants as columns
• Implemented collaborative filtering approach
• Handled sparse matrix with mean imputation strategy
• Generated personalized restaurant recommendations based on user similarity
• Evaluated recommendation quality through rating predictions

Key Findings:

• Successfully built a recommendation engine that identifies similar users
• System can predict ratings for unvisited restaurants
• Mean-centering improves recommendation accuracy by accounting for user rating tendencies

Business Impact: Enhances customer experience through personalized restaurant suggestions, potentially increasing customer engagement and satisfaction.

File: section11_final_project_working.ipynb

Objective: Analyze employee data to identify distinct workforce segments and develop targeted retention strategies to reduce attrition.

Dataset: Employee dataset including demographic information (Age, Gender), job characteristics (JobLevel, Department, MonthlyIncome, PerformanceRating, JobSatisfaction), and attrition status.

Methodology:

1. Data Preparation & EDA

   • Data cleaning and type validation
   • Exploratory analysis of employee characteristics
   • Encoding of categorical variables

2. Initial Clustering (Round 1)

   • K-Means clustering on raw features
   • Optimal cluster determination using elbow method and silhouette analysis

3. PCA for Visualization (Round 1)

   • Dimensionality reduction to 2D for cluster visualization
   • Assessment of explained variance

4. Refined Clustering (Round 2)

   • Feature standardization
   • Re-application of K-Means clustering
   • Improved cluster separation

5. Enhanced Visualization (Round 2)

   • PCA-based 2D visualization of refined clusters
   • Cluster interpretation and profiling

6. Cluster Analysis & Recommendations

   • Deep dive into each cluster's characteristics
   • Correlation of cluster membership with attrition rates
   • Development of segment-specific retention strategies

Key Findings:

• Identified distinct employee segments with varying attrition risks
• Uncovered relationships between job satisfaction, income, performance, and retention
• Different employee segments require tailored retention approaches

Business Impact: Provides HR with actionable insights to reduce turnover through targeted interventions, potentially saving significant recruitment and training costs.

• Machine Learning: K-Means, Hierarchical Clustering, PCA, Collaborative Filtering
• Data Analysis: EDA, statistical analysis, pattern recognition
• Data Preprocessing: Standardization, normalization, encoding, missing value handling
• Python Programming: Efficient use of pandas, NumPy, scikit-learn
• Visualization: Creating insightful plots with Matplotlib and Seaborn

• Feature selection and engineering
• Cluster validation and interpretation
• Model evaluation and optimization
• Hyperparameter tuning (determining optimal k)
• Business insight generation from technical analysis

• Customer segmentation strategies
• Recommendation system design
• HR analytics and workforce management
• Understanding of business metrics and KPIs

Charles - Data Scientist.

MIT License - See LICENSE file for details.

• Projects completed as part of Maven Data Science in Python: Unsupervised Learning coursework