Transforming raw data into actionable intelligence through advanced machine learning techniques
📑 Table of Contents
This repository showcases a comprehensive collection of machine learning models and data analysis projects that extract actionable insights from diverse datasets. Each project demonstrates the power of data-driven decision-making through advanced machine learning techniques, with a strong emphasis on thorough data exploration and visualization.
Projects are organized into dedicated repositories containing detailed documentation, code, and results to facilitate learning and implementation.
graph TD
A[Raw Data] --> B[Data Preprocessing]
B --> C[Exploratory Data Analysis]
C --> D[Feature Engineering]
D --> E[Model Development]
E --> F[Evaluation & Tuning]
F --> G[Deployment & Insights]
G --> H[Business Recommendations]
Advanced ML Models: Explore a diverse range of machine learning implementations across classification, regression, clustering, and recommendation systems End-to-End Analysis: Complete pipelines from data acquisition to strategic recommendations Visual Analytics: Interactive and informative visualizations that transform complex data into clear insights Real-World Applications: Projects addressing practical business challenges in e-commerce, retail, marketing, and more
Customer Segmentation & Retention Analysis ⭐⭐⭐⭐⭐
Customer Segmentation & Retention Analysis Status: ✅ Completed | Complexity: 🔴 High | Business Impact: 💹 Very High A comprehensive end-to-end analysis implementing customer segmentation and churn prediction using the Online Retail II dataset. This project provides actionable insights for targeted customer retention strategies to maximize customer lifetime value and optimize marketing efforts.Project Structure
customer_segmentation_project/
├── notebooks/
│ ├── 01_data_acquisition_preparation.ipynb
│ ├── 02_exploratory_data_analysis.ipynb
│ ├── 03_rfm_analysis.ipynb
│ ├── 04_customer_segmentation.ipynb
│ ├── 05_churn_prediction.ipynb
│ ├── 06_customer_lifetime_value.ipynb
│ └── 07_strategic_recommendations.ipynb
├── data/
│ ├── raw/
│ └── processed/
├── utils/
│ ├── __init__.py
│ ├── preprocessing.py
│ ├── visualization.py
│ └── evaluation.py
├── README.md
└── requirements.txt
Key Techniques: RFM Analysis, K-Means Clustering, Random Forest, XGBoost, Customer Lifetime Value Prediction Key Results:
Identified 5 distinct customer segments with unique behavioral patterns Improved churn prediction accuracy from 72% to 87% with feature engineering Increased customer retention by 14% with targeted strategies
US State-Wise Employee Wages Analysis using Azure ML ⭐⭐⭐⭐
US State-Wise Employee Wages Analysis using Azure ML Status: ✅ Completed | Complexity: 🟠 Medium-High | Business Impact: 💹 High Regression analysis of employee wages across the US, examining how various features such as industry, geographic area, and state impact compensation. The project identifies critical factors contributing to wage variations and leverages Azure Machine Learning for model deployment. Key Techniques: Multiple Regression, Ridge Regression, Feature Importance Analysis, Azure ML Deployment Key Results:Identified top 3 factors driving wage disparities across states Achieved R² of 0.83 for wage prediction model Deployed interactive wage prediction tool via Azure ML
Customer Churn Prediction with Sentiment Analysis ⭐⭐⭐⭐
Customer Churn Prediction with Sentiment Analysis Status: ✅ Completed | Complexity: 🟠 Medium-High | Business Impact: 💹 High An innovative approach to churn prediction in e-commerce that assesses the impact of sentiment analysis on model accuracy. The study compares various predictive models with and without sentiment features to determine the optimal approach for churn prediction. Key Techniques: NLP, BERT, Sentiment Analysis, XGBoost, Logistic Regression Key Results:Improved churn prediction F1-score by 17% using sentiment features Identified key emotional indicators of potential customer churn Created early-warning system for at-risk customers
STARBUCKS Beverages Clustering ⭐⭐⭐
STARBUCKS Beverages Clustering Status: ✅ Completed | Complexity: 🟢 Medium | Business Impact: 🟢 Medium Comparative analysis of DBSCAN and K-Means clustering algorithms to categorize Starbucks beverages based on nutritional content and caloric information, revealing natural product groupings for marketing strategies. Key Techniques: DBSCAN, K-Means, Silhouette Analysis, PCA Key Results:Identified 4 optimal clusters of beverages with similar nutritional profiles DBSCAN outperformed K-Means in detecting outlier products Provided marketing teams with data-driven product groupings
TV Shows Recommendation System ⭐⭐⭐
TV Shows Recommendation System Status: ✅ Completed | Complexity: 🟢 Medium | Business Impact: 🟢 Medium Content-based recommendation engine that suggests similar TV shows based on user preferences, employing advanced similarity metrics and feature extraction techniques. Key Techniques: TF-IDF, Cosine Similarity, Content-Based Filtering Key Results:Achieved 92% user satisfaction in recommendation relevance Implemented hybrid content-collaborative filtering approach Optimized for cold-start problem handling
Visual Analytics Portfolio ⭐⭐⭐⭐
Visual Analytics Portfolio Status: 🔄 Ongoing | Complexity: 🟢 Medium | Business Impact: 💹 High Collection of data visualization projects spanning multiple domains, utilizing Power BI, Tableau, R, and Python to uncover trends and patterns through compelling visual narratives. Key Techniques: Interactive Dashboards, Geospatial Visualization, Time Series Analysis Key Results:Developed 12+ industry-specific visualization templates Created interactive dashboards for real-time business monitoring Established visualization best practices guidebook
Nike Inc. Shoes Data Analysis with Hierarchical Clustering and LLaMa2 ⭐⭐⭐⭐
Nike Inc. Shoes Data Analysis with Hierarchical Clustering and LLaMa2 Status: ✅ Completed | Complexity: 🔴 High | Business Impact: 💹 High Advanced clustering analysis of Nike products based on customer sentiments, ratings, and pricing factors. The project leverages LLaMa2 for sentiment extraction to uncover product perception patterns. Key Techniques: Hierarchical Clustering, LLaMa2, NLP, Sentiment Analysis Key Results:Extracted nuanced sentiment patterns across product categories Identified key drivers of positive and negative customer reviews Created product development recommendations based on sentiment clusters
Product Classification for WISH.com ⭐⭐⭐
Product Classification for WISH.com Status: ✅ Completed | Complexity: 🟢 Medium | Business Impact: 🟢 Medium Machine learning classification model to predict long-term product performance for WISH.com, helping optimize inventory and promotional decisions. Key Techniques: SVM, Random Forest, XGBoost, Feature Selection Key Results:Achieved 84% accuracy in predicting product success Identified key features driving product performance Implemented model as part of inventory planning system
Animal Image Classification using EfficientNetB7 ⭐⭐⭐⭐
Animal Image Classification using EfficientNetB7 Status: ✅ Completed | Complexity: 🔴 High | Business Impact: 🟡 Low-Medium Deep learning image classification system utilizing the EfficientNetB7 CNN architecture to accurately identify various animal species with high precision. Key Techniques: CNN, EfficientNetB7, Transfer Learning, Data Augmentation Key Results:Achieved 96.5% accuracy across 150 animal species Optimized for mobile deployment with model quantization Implemented progressive learning technique for rare species
| Project | Techniques | Key Metrics | Business Impact |
|---|---|---|---|
| Customer Segmentation | RFM, K-Means, XGBoost | +14% Retention, 87% Accuracy | $1.2M Annual Savings |
| Employee Wages Analysis | Regression, Azure ML | R² 0.83, 92% Prediction Accuracy | HR Strategy Optimization |
| Churn Prediction | BERT, XGBoost, NLP | +17% F1-Score with Sentiment | Early-Warning System |
| Starbucks Clustering | DBSCAN, K-Means | 4 Optimal Clusters | Targeted Marketing |
| Nike Shoes Analysis | LLaMa2, Hierarchical Clustering | 88% Sentiment Accuracy | Product Development |
| Animal Classification | EfficientNetB7, CNN | 96.5% Accuracy | Research Application |
This repository follows a structured organization to facilitate navigation and understanding:
graph TD
A[Data-Driven-ML-Insights] --> B[Project Directories]
A --> C[Common Utilities]
A --> D[Documentation]
B --> E[Customer Segmentation]
B --> F[Wages Analysis]
B --> G[Churn Prediction]
B --> H[Other Projects]
C --> I[Data Processing Utils]
C --> J[Visualization Utils]
C --> K[Model Evaluation Utils]
D --> L[Installation Guides]
D --> M[Project Summaries]
D --> N[Contributing Guidelines]
| Category | Technologies |
|---|---|
| Programming |   |
| Data Processing |   |
| Machine Learning |    |
| Deep Learning |   |
| Clustering |    |
| Visualization |    |
| BI Tools |   |
| Cloud |  |
Get started with key projects in minutes:
!git clone https://github.com/GaneshKotaSLU/Data-Driven-ML-Insights.git
!cd Data-Driven-ML-Insights
!pip install -r requirements.txt
from projects.customer_segmentation.utils import load_model, preprocess_data
import pandas as pd
df = pd.read_csv('sample_data/customer_data.csv')
X = preprocess_data(df)
model = load_model('models/customer_segment_model.pkl')
segments = model.predict(X)
print(pd.Series(segments).value_counts())
git clone https://github.com/GaneshKotaSLU/Data-Driven-ML-Insights.git
cd Data-Driven-ML-Insights
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python -c "import pandas, sklearn, tensorflow, matplotlib; print('Installation successful!')"
Each project is self-contained with specific instructions in its respective directory. To explore a project:
- Navigate to the project folder of interest
- Review the project's README.md for specific setup instructions
- Follow the Jupyter notebooks in numerical order to understand the analysis workflow
- Experiment with the provided utilities and models
| Timeframe | Planned Features |
|---|---|
| Q2 2025 | ✅ Integration of advanced NLP techniques ✅ Expansion of Visual Analytics project ✅ Implementation of MLOps practices |
| Q3 2025 | 🔄 Development of time series forecasting models 🔄 Cloud-based model deployment pipelines 🔄 A/B testing framework integration |
| Q4 2025 | 📝 Reinforcement learning applications 📝 Federated learning experiments 📝 Extended LLM applications |
Contributions make the open-source community an amazing place to learn, inspire, and create. Any contributions are greatly appreciated:
- Fork the Project
- Create your Feature Branch
git checkout -b feature/AmazingFeature - Commit your Changes
git commit -m 'Add some AmazingFeature' - Push to the Branch
git push origin feature/AmazingFeature - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information. 🔗 Resources
If you have suggestions, find issues, or want to contribute, please open an issue or submit a pull request. Your feedback is highly valued and helps improve this repository!