This repository contains a collection of Jupyter Notebooks for conducting Exploratory Data Analysis (EDA) and Statistical Analysis on various datasets.

This repository contains an implementation of the Pyramidal Lucas-Kanade optical flow algorithm

Treating noise and anomalies in the vehicle time-series data captured by drones

C++ version of qlibs, a collection of useful libraries for embedded systems : signal smoothing, PID control, Fuzzy Logic, fixed-point math and more...

Feature Engineering with Python

VisualQC : assistive tool to ease the quality control workflow of neuroimaging data.

This Project involved an Exploratory Data Analysis (EDA) and pre-processing on a 10K Play Store app dataset for analyzing the Android market.

Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. Consider only the below columns and prepare a prediction model for predicting Price. Corolla<-Corolla[c("Price","Age_

Leverage data analytics to identify "Hot Leads" and sculpt personalized strategies for maximum conversion potential, propelling X Education to new heights of success.

Time series data, prevalent across diverse domains like economics, finance, meteorology, and science, encompasses various phenomena such as daily sales, stock prices, temperatures, and population growth. We will analyze different datasets to discern patterns and forecast future trends or extract pertinent insights.

The official implementation code of Paper "PointCVaR: Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification" in AAAI 2024 (Oral)

A collection of useful libraries for embedded systems : signal smoothing, PID control, Fuzzy Logic, fixed-point math and more...

Hampel filter implemented in Julia

EDA including for the New York Airbnb data from Kaggle. Step-by-step process and conclusions of each step including the results on the relationships between the variables.

Dixon's Q Test calculator package for Dart

Using a insurance data to build a linear regression model , assumption checks, ANOVA , Post-hoc test, Mini=project

This project focuses on analyzing patient feedback regarding the treatment provided by home healthcare service agencies.

The Credit Card Fraud Detection project uses statistical techniques and machine learning for identifying fraudulent transactions. It includes data preprocessing, outlier detection using Boxplots and Z-scores, and a decision tree model. Evaluation goes beyond accuracy, considering precision, recall, F1-score, and ROC AUC.

a c++ library with statistical machine learning algorithms for linear and non-linear robust regression that can be used with python.

Exploratory data analysis (EDA) NOTES