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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.
Web scraping, EDA, and statistical modeling (ANOVA & OLS Regression) of 2025 Steam game sales data using Python.
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.
Tutorials for BSE classes.
A collection of INF2178H course projects showcasing the full Data Science lifecycle, including experimental planning, data cleaning, exploratory analysis, modeling, and evaluation. Projects apply qualitative and quantitative methods for knowledge discovery and decision-making.
Hyperparameter tuning of a Convolutional Neural Network (CNN) for CIFAR-10 image classification using fractional factorial Design of Experiments (DOE) and regression modeling.
Statistical analysis and visualizations was written in R programming, Load, clean up, reshape datasets using Tidyverse. visualize datasets with basic plots such as line, bar, scatter plots using ggplot2, Implemented and evaluated one-sample t-Tests, two-sample t-Tests, and analysis of variance (ANOVA) models for the dataset.
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.
Welcome to the Statistics repository! Here, you'll find files that cover various statistical topics. The repository is organized into different categories to help you navigate through the content!
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.
A few statistical methods appropriate for applications in the biological and social sciences.
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