Predicting how much loan will be approved

Python-Based Statistical Graphical User Interface for Python

Stats, linear algebra and einops for xarray

A F&B manager wants to determine whether there is any significant difference in the diameter of the cutlet between two units. A randomly selected sample of cutlets was collected from both units and measured? Analyze the data and draw inferences at 5% significance level. Please state the assumptions and tests that you carried out to check validit…

Undergraduate thesis, Seoul National University Dept. of Economics — "Modeling Volatility and Risk Spillover Between the Financial Markets of US and China Using GARCH Value-at-Risk Forecasting and Granger Causality."

Minimalist Data Wrangling with Python (Open-Access Textbook)

Assignment Basic Stats

The aim is to develop an ML- based predictive classification model (logistic regression & decision trees) to predict which hotel booking is likely to be canceled. This is done by analysing different attributes of customer's booking details. Being able to predict accurately in advance if a booking is likely to be canceled will help formulate prof…

The objective is to build a ML-based solution (linear regression model) to develop a dynamic pricing strategy for used and refurbished smartphones, identifying factors that significantly influence it.

In this project, statistical analysis approach is employed to decide what hypothesis to be rejected. Scipy Stat library is used to find the pvalue that will determine if Hypothesis Null should or should not be rejected.

Learned how to find or scrape information from networks and manipulate that information to display trends. This was done in Python using Google Colab's .ipynb environment. Libraries used were Pandas, NumPy, Seaborn, Scipy, Matplotlib, SkLearn, and FacebookProphet.

Group training project involving analysis of various aspects involving students in Python (Pandas, Matplotlib etc.)

HISDAC-ES: Creating historical settlement data for Spain (1900-2020) based on cadastral building footprint data

Manuscript, source codes and data sets on estimating Singapore’s lower-bound SARS-CoV-2 Infection Trend In 2020.

Using scipy.stats to investigate some data from a sample of patients who were evaluated for heart disease.

https://gulinan.github.io/mat244e/

Assignment 2 Set 2

A Python based implementation of t-test to find relative effect of recession on university and non-university US regions.

Group Project: Top 1000 Movies by IMDB Rating Exploratory Data Analysis

data-analysis labs. 50/50 made with Oleh Tonkoshkur