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🛠 Projects

Hands-on mini-projects and capstones in Python and R. Most core algorithms are implemented from scratch so their mechanics stay visible; the pattern-mining library lab also demonstrates pandas/mlxtend in Python and arules in R.

Choose a Starting Point

If you want to… Start with Why
Run a first script with no extra packages Attribute Auditor Small, readable Python program with a built-in demo dataset
Strengthen statistical reasoning Location & Dispersion Explorer Connects formulas to a complete descriptive report
Practice a real cleaning workflow Data Quality Auditor Detects missing values, duplicates, and outliers before analysis
Compare unsupervised methods Hierarchical & Density-Based Clustering Contrasts linkage-based clusters with DBSCAN noise handling
Build a predictive workflow Model Evaluation Centers the confusion matrix, metrics, and cross-validation
Create a portfolio case study Student Performance Predictor Combines preparation, exploration, modeling, and interpretation

How to Work Through a Lab

  1. Read the project's concept and expected output before opening the code.
  2. Predict what the built-in dataset should reveal.
  3. Run the Python or R version and compare the result with your prediction.
  4. Change one assumption, parameter, or input column and explain what changes.
  5. Finish by writing a short conclusion: What decision can this analysis support, and what can it not prove?

Note

The Python projects emphasize readable implementations of the underlying algorithms. They avoid pandas, NumPy, and scikit-learn except where a project explicitly compares the from-scratch approach with common library tooling.

🛠 Project Dashboard

Module 01 — Data & Attribute Types

Project Focus Languages
Attribute Auditor Infers each column's measurement scale from data characteristics Python
Dataset Type Explorer Classifies a dataset as Record/Matrix/Transaction/Graph/Ordered Python
Attribute Scale Exercises Solves the chapter exercise on scale identification Python, R

Module 02 — Descriptive Statistics

Project Focus Languages
Frequency Table Builder Absolute, relative, and cumulative frequency tables Python
Location & Dispersion Explorer Full location & dispersion stats with ASCII box plot Python
Distribution Visualizer Histograms, bar charts, frequency distributions Python
Bivariate Quantitative Analyzer Covariance, Pearson r, Spearman ρ step-by-step Python
Categorical Relationship Analyzer Contingency tables, grouped box plots, chi-square hint Python
Chapter 2 Exercise Solutions Verified solutions for the chapter exercise set Python, R

Module 03 — Descriptive Multivariate Analysis

Project Focus Languages
Multivariate Statistics Explorer Location, dispersion, covariance, and correlation matrices Python, R
Multivariate Visualization Parallel coordinates, star plots, bubble charts, 3D scatter, Chernoff faces Python, R
Correlation & Heatmap Analysis Scatter plot matrix, correlogram, heatmap with dendrograms Python, R
Frequency Tables & Mosaic Plots Joint frequency tables and mosaic plots for qualitative attributes Python, R

Module 04 — Data Quality and Preprocessing

Project Focus Languages
Data Quality Auditor Detects and fixes missing values, duplicates, and outliers Python, R
Discretization & Encoding Equal-width/depth bins, one-hot, gray code, thermometer code Python, R
Normalization, Distance & Sampling Min-max, z-score, Euclidean distance, stratified sampling Python, R
Data Transformation Log transform, absolute value, skewness reduction Python, R

Module 05 — Clustering

Project Focus Languages
Distance Measures Minkowski L1/L2, Hamming, and edit distance from scratch Python, R
K-Means Clustering K-means + K-means++ + elbow curve Python, R
Hierarchical & Density-Based Clustering All 4 linkage methods with ASCII dendrogram, BFS-based DBSCAN Python, R

Module 06 — Frequent Pattern Mining

Project Focus Languages
Itemsets and Association Rules Apriori from scratch + rule generation sorted by lift Python, R
FP-Growth and Pattern Types FP-tree construction, recursive mining, maximal/closed detection Python, R
Pattern Mining with mlxtend / arules Practical mining with library tooling Python, R

Module 07 — Classification

Project Focus Languages
Decision Trees Entropy, information gain, gain ratio, Gini Python, R
k-NN and Naive Bayes Distance-based k-NN, step-by-step Gaussian Naive Bayes Python, R
Model Evaluation Confusion matrix, precision/recall/F1, 5-fold cross-validation Python, R

Module 08 — Final Projects (Capstones)

Project Theme Languages
End-to-End Data Pipeline Full pipeline on the Friends dataset Python, R
Market Basket & Customer Segmentation Purchase patterns + customer clusters Python, R
Predictive Analytics Classification Study Compares 3 classifiers with evaluation Python, R
Iris Complete Walkthrough Every technique applied to the Iris dataset Python, R
Student Performance Predictor Grade patterns → early warning system Python, R
Retail Sales Analytics Identifies customers likely to churn Python, R
Comprehensive Course Capstone "Know Your Data" — 8-phase clinical analysis Python, R

💡 Prerequisites

Tool Used For
Python 3 All projects; standard library for most labs, with optional pandas/mlxtend in the library comparison
R Visualization, statistics, and library-based comparisons
Excel Formula walkthroughs for select exercises

A Five-Project Portfolio Route

For a compact but representative portfolio, complete these in order:

  1. Location & Dispersion Explorer — communicate a clear descriptive profile.
  2. Data Quality Auditor — document cleaning choices and their consequences.
  3. K-Means Clustering — explain feature scaling, initialization, and cluster selection.
  4. Itemsets and Association Rules — interpret support, confidence, and lift in context.
  5. Comprehensive Course Capstone — connect the full workflow in one defensible case study.

Use the learning toolkit for setup, dataset preparation, and a project write-up checklist.