Data-Mining-Complete-Pipeline

Healthcare Data Mining Pipeline

Complete implementation of a comprehensive data mining solution for healthcare analytics, covering statistical analysis, machine learning, and deep learning techniques.

Overview

This project implements a full data mining pipeline for healthcare data analysis, including exploratory data analysis, preprocessing, clustering, classification, and deep learning-based medical image classification.

Features

Part 1: Exploratory Data Analysis

• Custom statistical measures (mean, median, mode, variance, standard deviation)
• Distribution analysis and probability fitting
• Comprehensive visualization dashboard with histograms, box plots, scatter plots, Q-Q plots, and Chernoff faces

Part 2: Data Preprocessing

• Missing value analysis with multiple imputation strategies (mean/median/mode, KNN, MICE)
• Outlier detection using Z-score, IQR, Isolation Forest, and Local Outlier Factor
• Data transformation and normalization (log, Box-Cox, Min-Max, Z-score scaling)
• Feature engineering and encoding

Part 3: Unsupervised Learning

• Custom K-Means clustering implementation
• Hierarchical clustering (agglomerative and divisive)
• DBSCAN for density-based clustering
• Cluster validation with Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index

Part 4: Supervised Learning

• Custom implementations of Decision Tree, Naive Bayes, Logistic Regression, and k-NN
• Cross-validation from scratch
• Comprehensive model evaluation metrics
• Feature importance analysis

Part 5: Deep Learning

• ResNet implementation using PyTorch Lightning
• Transfer learning with pre-trained models
• Multi-class medical image classification
• ROC curve analysis and model comparison

Part 6: Advanced Analytics

• Association rule mining using Apriori algorithm
• Temporal analysis and risk scoring
• Comprehensive reporting and clinical insights

Requirements

numpy
pandas
matplotlib
seaborn
scipy
torch
torchvision
pytorch-lightning
timm
PIL
opencv-python

Installation

pip install numpy pandas matplotlib seaborn scipy torch torchvision pytorch-lightning timm pillow opencv-python

Usage

Run the complete pipeline:

from healthcare_pipeline import HealthcareDataMiningPipeline

pipeline = HealthcareDataMiningPipeline()
pipeline.run_complete_pipeline()

Implementation Details

All core algorithms are implemented from scratch without using scikit-learn for traditional machine learning tasks. Deep learning components use PyTorch and PyTorch Lightning.