statstics

A Lite & Memory Efficient PHP Library for Scientific Computing

Little python 3 script, take an export file of Revolut and draw some meaningfull plots using plot.ly API, print some informations about the spendings and write a report in Markdown format.

A complete NumPy learning repository containing fundamentals, mathematical functions, searching, sorting, statistics, CSV handling, array manipulation, practice problems, and a real-world MegaMart Retail Analytics project built entirely with NumPy.

Complete 1 Week to understand Role of Statistics to play with data.

Final Degree Project: Analyzing MANETs Routing Protocols emulated with Mininet WiFi

This is done as a project work in my data science course

Course Materials I taught/TA-ed/led at NYU. Courses are primarily about various topics of Statistics.

Utilizing R created and analyzed datasets to review and validate their statistical similarities

Predicting winter lake mixed-layer temperature on Lake Superior from ERA5-Land climate reanalysis using multiple linear regression in R. Full workflow with AIC/BIC/Lasso selection, diagnostics, and grouped cross-validation (adjusted R² ≈ 0.80).

Machine learning for credit card default. Precision-recalls are calculated due to imbalanced data. Confusion matrices and test statistics are compared with each other based on Logit over and under-sampling methods, decision tree, SVM, ensemble learning using Random Forest, Ada Boost and Gradient Boosting. Easy Ensemble AdaBoost classifier appear…

This project analyzes marketing campaign data to understand how customer demographics, lifestyle, and past purchases affect their likelihood to respond to marketing campaigns.The goal is to discover basic actionable insights to optimize sales funnels and marketing efforts.

Data science lecture for ENSMM engineering school 2022

EDA and regression analysis on wage data using Python

Dashboard for home devices

Generates plain-language narratives from R statistical objects, model output, ggplot figures, and datasets, via any LLM that the `ellmer` package supports.

This Python notebook demonstrates how to compute the five-number summary (minimum, Q1, median, Q3, maximum), calculate the interquartile range (IQR), detect outliers, and visualize data distributions using box plots with Seaborn and Matplotlib. It’s ideal for statistical analysis and data visualization tasks.

Collection of exercises related to data analysis

Statistics for numbers arranged in columns inside an Emacs buffer