Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.

Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.

End-to-end marketing and business analysis projects utilizing machine learning and statistical analysis techniques using the R programming language.

My Python learning experience 📚🖥📳📴💻🖱✏

about statistical techniques for Data Science

Perform a STEP by STEP multiple mean comparison analysis on R

Residual analysis in Linear regression is based on examination of graphical plots which are as follows :: 1. Residual plot against independent variable (x). 2. Residual plot against independent variable()y. 3. Standardize or studentized residual plot 4. Normal probability plot

Projet pour une banque présente dans plusieurs pays, l'objectif est de cibler les prospects les plus susceptibles d'avoir, plus tard dans leur vie, de hauts revenus.

This problem concludes which factor is significantly effecting the CAT Score out of College type,program type,and interaction factor type for sample data. Here factorial Experiment design and Two Way Anova is used.

For this Project, I first applied an analysis of variance (ANOVA) model to the Pymaceutical dataset and then did a post-hoc analysis of the results by using Tukey Honest Significant Difference (HSD) to determine which drug treatments in the dataset significantly reduce tumor volume and metastasis. I then wrote a summary of my findings.

A few statistical methods appropriate for applications in the biological and social sciences.

This repository contains a project I completed for an NTU course titled CB4247 Statistics & Computational Inference to Big Data. In this project, I applied regression and machine learning techniques to predict house prices in India.

Tutorials for BSE classes.

Regression models for predicting customer acquisition costs (CAC) and the effectiveness of univariate and lasso feature selection techniques in improving the accuracy.

projet d'analyse exploratoire et de visualisation

Regression | Analysis | Modelling | Bayesian Search CV | Data Leakage | Overfit/Underfit | End-to-End Project

MA4094*-應用統計