Quantitative Booklet

A curated set of small, self-contained analytics / data science “booklets” in Python and R.

Each booklet folder contains:

• a short README (what the analysis does, where the data comes from, how to run)
• a runnable script (.py or .R)
• an assets/ folder where plots/figures are saved
• a link to the original PDF in booklets/ (optional reference)

Booklets included

Python

• Canadian Immigration Analysis (1980–2013) — exploratory visuals over time + decade aggregation.
• Payment Fraud Monitoring — feature engineering + visual diagnostics for fraud vs genuine transactions.
• Image Autoencoder (MNIST) — a dense autoencoder with reconstruction examples.
• Quantum Circuit (Qiskit) — 1-qubit Hadamard circuit on simulators (and optional hardware setup).
• Global Ocean Weather (1750–1850) — yearly pivots of reported ocean weather events.

R

• Climate Stations Profiling by Country — build a wide annual-peak table from station .mrc files.
• Nile River Flow Monitoring — ARIMA exploration, trend tests, and structural time-series models.
• Product Marketing Statistics — descriptive statistics + distribution plots for weekly product sales.
• Sea Ice Extent Prediction — seasonal decomposition + ARIMA forecasting.
• Sea Ice Extent Quick View — interactive 3D view of monthly average extent (Plotly).

How to run

Clone

git clone https://github.com/59alireza59/Quantitative-Booklet.git
cd Quantitative-Booklet