Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Repo containing code for Towards Data Science articles

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."

Stock prediction web app using flask, pandas, scikit-learn and am charts

Houses price prediction web app

PYTHON- Projects in my MAT-243 STATS for STEM I course at SNHU (HTML files and Python files with source code and reports)

Prediction of Cab Prices depending upon the distance and other factors.

Tutorial to deploy a ML Model to Heroku with Flask web application.

Using PyCaret to Predict Apple Stock Prices

Streamlining Application Processes: A Comprehensive Solution with Innovative Tracking and Notification System

Forecasting Exercises done in R

Boston Housing Prediction - 2nd project for Udacity's Machine Learning Nanodegree

Car Price Predicting using Machine Learning Model and flask implementation

In this assignment, we will tackle a regression problem. We will be working on a dataset consolidated from census data in the USA. The goal is to accurately predict cancer mortality based on information related to US counties.The dataset contains 33 different features (demography, medical information).

The goal was to perform predictive maintenance on commercial turbofan engine. The approach used here is a data-driven approach, meaning that data collected from the operational jet engine is used to perform predictive maintenance modeling. To be specific, to build a predictive model to estimate the Remaining Useful Life ( RUL) of a jet engine ba…

Materials for my SCS Short course, Visualizing Linear Models: An R Bag of Tricks, Oct. 2021

This repository aims towards Sports analytics by predicting the first inning Score Based on the previous data and by taking at least the first 5 overs data from the user to predict the final score that a particular team will make based on the prediction

Collection of end-to-end regression problems (in-depth: linear regression, logistic regression, poisson regression) 📈

In this project, I am performing A/B testing for the company’s new website. I performed hypothesis testing with Python and NumPy to determine p-value and used regression models to advise if the company should launch a new website. The result is robust statistical analysis and interpretation of results to ensure the right decision for the company.

project in Udacity Data Analysis nanodegree program. Focus on probability-distributions, A/B-testing and regression models.