Data Analysis Platform Data Analysis Platform - Streamlit Application Project Overview Data Analysis Platform is a comprehensive web-based tool for performing end-to-end data analysis tasks. Built with Python and Streamlit, it provides an intuitive interface for data preprocessing, clustering, classification, and visualization without requiring any coding knowledge.
Key Features Core Capabilities 📊 Data Management: Upload, view, and edit datasets
🧹 Data Preprocessing: Filter, clean, and prepare data for analysis
🧩 Clustering: K-means clustering with visualization
🧠Classification: Multiple algorithms with detailed evaluation metrics
📈 Visualization: Interactive charts and dashboards
Technical Highlights Machine Learning Integration: Scikit-learn for clustering and classification
Comprehensive Metrics: 8+ evaluation metrics for classification models
Interactive UI: Streamlit-based interface with real-time updates
Data Exploration: Dynamic filtering and visualization tools
Technology Stack Backend Python Data Ecosystem:
Pandas (Data manipulation)
NumPy (Numerical computing)
Scikit-learn (Machine learning)
Visualization:
Matplotlib
Seaborn
Streamlit native charts
Frontend Streamlit: Interactive web interface
Dynamic Components:
Data editors
Interactive sliders
Multi-select filters
Installation Guide Prerequisites Python 3.8+
pip package manager
Setup Steps Clone repository:
bash git clone https://github.com/yourrepo/data-analysis-platform.git cd data-analysis-platform Create virtual environment:
bash python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate Install dependencies:
bash pip install -r requirements.txt Run application:
bash streamlit run app.py Usage Guide Workflow Upload Data: Start by uploading your CSV file
Preprocess: Filter and clean your data
Analyze:
Perform clustering to find patterns
Build classification models
Visualize: Explore through interactive dashboards
Export: Save your processed dataset
Key Functions Data Filtering: Slider controls for numeric columns, multi-select for categorical
Clustering: Adjustable number of clusters with visual output
Classification: 4 algorithm choices with detailed performance metrics
Dashboard: Multiple chart types for data exploration
Architecture text [Data Input] │ ▼ [Preprocessing]───▶[Clustering]───▶[Visualization] │ │ ▼ ▼ [Classification] [Dashboard] │ ▼ [Evaluation Metrics] Configuration The application requires no configuration files. All settings are adjustable through the UI:
Clustering: Adjust number of clusters (2-10)
Classification: Choose from 4 algorithms
Visualization: Select columns and chart types
Performance Typical performance metrics:
Operation Time Complexity Notes Data Loading O(n) Scales with file size Clustering O(nki) n=samples, k=clusters, i=iterations Classification Varies by algorithm Random Forest typically slowest License MIT License
Roadmap Planned Features Additional clustering algorithms (DBSCAN, Hierarchical)
Regression analysis capabilities
Dimensionality reduction (PCA, t-SNE)
Automated feature engineering
Export capabilities for models and visualizations
Research Areas Integration with AutoML solutions
Custom model deployment
Big data handling optimizations