A comprehensive repository featuring practical applications of data structures, algorithms, and machine learning techniques. This project demonstrates key concepts through real-world data analysis, customer segmentation, and time series forecasting.
This repository contains implementations and applications of fundamental computer science concepts including:
- Data Analysis & Visualization: Exploratory data analysis using Python's scientific stack
- Machine Learning: Regression models, clustering, and classification algorithms
- Time Series Forecasting: ARIMA models for predictive analytics
- Data Processing: Working with real-world datasets (Customer Loyalty Card Data, Walmart data)
Perfect for learning and demonstrating practical applications of data science and machine learning.
Data-Structures_and_Algorithms/
├── README.md # This file
├── LICENSE # Apache 2.0 License
├── CustomerLoyaltyCardData.csv # Dataset: Customer loyalty/shopping data
│
├── walmart_customer_analysis..py # Walmart customer analysis & visualization
├── customer_segmentation_K-Means_Clustering.py # K-Means clustering for customer segments
├── DataAnalysisWithRegression_and_arima.py # Regression and ARIMA forecasting
│
└── venv/ # Python virtual environment
File: walmart_customer_analysis..py
Comprehensive exploratory data analysis of customer loyalty card data.
Features:
- Display and explore dataset structure
- Basic statistical summary
- Gender distribution analysis
- Age distribution visualization
- Annual income analysis
- Spending score distribution
Libraries Used: pandas, seaborn, matplotlib
Output:
- Statistical summaries
- Count plots for categorical data
- Histograms for numerical distributions
Example Usage:
python walmart_customer_analysis..pyFile: customer_segmentation_K-Means_Clustering.py
Unsupervised machine learning approach to segment customers into distinct groups.
Features:
- K-Means clustering algorithm implementation
- Elbow method for optimal cluster selection
- Cluster centers identification
- Cluster label assignment
- Visualization of customer segments
Libraries Used: pandas, scikit-learn, matplotlib
Key Metrics:
- Within-Cluster Sum of Squares (WCSS)
- Cluster centers
- Cluster labels
Algorithm Details:
1. Load customer data
2. Select features: Annual Income, Spending Score
3. Apply K-Means with k=4 clusters
4. Use Elbow Method to find optimal k (1-10)
5. Visualize clusters in 2D space
Example Usage:
python customer_segmentation_K-Means_Clustering.pyOutput:
- Cluster centers
- Elbow graph for optimal cluster determination
- 2D scatter plot with color-coded clusters
File: DataAnalysisWithRegression_and_arima.py
Advanced time series and predictive modeling combining regression and forecasting techniques.
Features:
A. Linear Regression
- Supervised learning for continuous prediction
- Train-test split (80-20)
- Data standardization/scaling
- Coefficient and intercept analysis
- Scatter plot: Original vs. Predicted values
B. Logistic Regression
- Classification algorithm
- Multi-class classification support
- Confusion matrix generation
- Model performance visualization
C. ARIMA (AutoRegressive Integrated Moving Average)
- Time series forecasting
- ARIMA(1,1,1) model configuration
- 10-step ahead forecasting
- Original vs. forecasted values visualization
Libraries Used: pandas, scikit-learn, statsmodels, matplotlib, seaborn, numpy
Process:
1. Load CustomerLoyaltyCardData.csv
2. Data preparation and feature engineering
3. Train-test split with scaling
4. Fit Linear Regression model
5. Fit Logistic Regression model
6. Generate ARIMA forecast
7. Visualize results with multiple plots
Example Usage:
python DataAnalysisWithRegression_and_arima.pyOutput:
- Linear regression coefficients and intercept
- Scatter plot comparison
- Logistic regression confusion matrix heatmap
- ARIMA forecast values
- Time series plot with forecasted trend
Customer shopping and demographic data used across all projects.
Features:
- CustomerID: Unique identifier
- Gender: Customer gender (M/F)
- Age: Customer age
- Annual Income (k$): Annual income in thousands of dollars
- Spending Score (1-100): Spending behavior score
Size: 3,780 bytes Records: ~200-500 customer entries
- Python 3.7+
- pip (Python package manager)
pip install pandas scikit-learn statsmodels matplotlib seaborn numpyCreate requirements.txt:
pandas>=1.3.0
scikit-learn>=0.24.0
statsmodels>=0.13.0
matplotlib>=3.4.0
seaborn>=0.11.0
numpy>=1.21.0
Install all dependencies:
pip install -r requirements.txt# Create virtual environment
python -m venv venv
# Activate virtual environment
# On Linux/Mac:
source venv/bin/activate
# On Windows:
venv\Scripts\activate
# Install dependencies
pip install pandas scikit-learn statsmodels matplotlib seaborn numpy# Make sure you're in the repository directory
python walmart_customer_analysis..py
python customer_segmentation_K-Means_Clustering.py
python DataAnalysisWithRegression_and_arima.pyAnalyze customer demographics:
python walmart_customer_analysis..pyExpected output: Visualizations of gender, age, income, and spending distributions
Identify customer segments:
python customer_segmentation_K-Means_Clustering.pyExpected output: Elbow curve, optimal cluster visualization, and cluster assignments
Predict and forecast:
python DataAnalysisWithRegression_and_arima.pyExpected output: Regression performance metrics, confusion matrix, and time series forecast
Purpose: Unsupervised learning to group customers into k clusters
Algorithm:
- Initialize k random centroids
- Assign each point to nearest centroid
- Calculate new centroids based on cluster members
- Repeat until convergence
Use Case: Customer segmentation for targeted marketing
Purpose: Predict continuous values based on input features
Formula: y = mx + b
Use Case: Predicting customer spending based on demographics
Purpose: Binary/multi-class classification
Use Case: Classifying customer behavior patterns
Purpose: Time series forecasting
Parameters:
- p: AutoRegressive order
- d: Differencing order (for stationarity)
- q: Moving Average order
Use Case: Predicting future spending trends
- ✅ Data loading and exploration
- ✅ Feature engineering and selection
- ✅ Data preprocessing and scaling
- ✅ Train-test data splitting
- ✅ Model evaluation and validation
- ✅ Supervised learning (Regression, Classification)
- ✅ Unsupervised learning (Clustering)
- ✅ Model fitting and prediction
- ✅ Hyperparameter tuning (Elbow method)
- ✅ Scatter plots
- ✅ Histograms
- ✅ Count plots
- ✅ Heatmaps (Confusion matrices)
- ✅ Time series plots
- ✅ Descriptive statistics
- ✅ Distribution analysis
- ✅ Correlation analysis
- ✅ Forecasting
- Optimal Clusters: 4 (determined by Elbow method)
- Features Used: Annual Income, Spending Score
- WCSS Range: Varies based on k value (1-10)
- Train-Test Split: 80-20
- Scaling Method: StandardScaler
- Test Size: 20% of data
- Model: ARIMA(1,1,1)
- Forecast Horizon: 10 steps
- Data Source: Customer Age
This repository is excellent for learning:
- Python data manipulation with Pandas
- Data visualization with Matplotlib and Seaborn
- Basic machine learning concepts
- Working with CSV datasets
- Scikit-learn model implementation
- Multiple algorithm comparison
- Cluster analysis and interpretation
- Time series analysis with ARIMA
- Real-world data science workflows
- Model evaluation strategies
- Business analytics applications
- Predictive modeling pipelines
Future improvements could include:
- Cross-validation for model robustness
- Feature importance analysis
- GridSearchCV for hyperparameter optimization
- Additional clustering algorithms (DBSCAN, Hierarchical)
- Advanced ARIMA diagnostics
- Deep learning models (Neural Networks)
- Dashboard/web interface
- Real-time data processing
- A/B testing framework
Solution: Install missing packages
pip install pandas scikit-learn statsmodels matplotlib seabornSolution: Ensure CustomerLoyaltyCardData.csv is in the same directory as scripts
Solution: Already handled with warnings.filterwarnings("ignore") in scripts
Solution: Add plt.show() is called; may need to configure matplotlib backend
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Copyright 2022 CodeDroid999.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
CodeDroid999
Contributions are welcome! Feel free to:
- Report bugs and issues
- Suggest improvements and enhancements
- Submit pull requests with new features
- Add additional algorithms and techniques
- Pandas Documentation
- Scikit-Learn Documentation
- Statsmodels Documentation
- Matplotlib Documentation
- Seaborn Documentation
- Language: Python
- Size: ~114 KB
- Last Updated: May 2026
- Stars: 4
- License: Apache 2.0
Happy Learning & Coding! 🚀