This is an Exploratory Data Analysis (EDA) in 12 Steps with an easy going dataset for beginners. The goal is to understand the correlation between variables step by step. For advance practionners you can use the profiling package in Python

In this repository I have performed Exploratory Data Analysis on the dataset student_performance.csv. In which i have tried to detect outliers,missing values,relationship among features and across features,Categorical data and continuous/numerical data.

An Apache Spark (Scala) workflow for outlier detection, using K-means clustering.

In this repository, using the statistical software R, are been analyzed robust techniques to estimate multivariate linear regression in presence of outliers, using the Bootstrap, a simulation method where the construction of sample distribution of given statistics occurring through resampling the same observed sample.

Localization processes for functional data analysis. Software companion for the paper “Localization processes for functional data analysis” by Elías, A., Jiménez, R., and Yukich, J. (2020)

This was my first project ever on Python. It's also my first attempt at EDA for my Executive PGP Course, with IIIT-B and UpGrad.

This project focuses on analyzing app data from the Google Play Store to derive insights and identify patterns that can help app developers, marketers, and users make informed decisions. The dataset includes information about various app attributes like ratings, reviews, installs, size, category, content rating, and more.

This repository contain all the file related to Feature Scaling,Label Encoding and corelation,Outliers Removal etc.in short it contain all files related to data preprocessing.

Simple heap and running median (min/max heaps) implementation for small dev. boards like Arduino.

A Descriptive Data Analysis using Microsoft Excel's advanced data analysis tools.

R-based statistical analysis of Boston Housing Data. Explored feature scales, computed descriptive stats, visualized data, and identified outliers (e.g., higher crime rates in specific areas). Examined variable relationships, calculated correlation coefficients, and presented findings via cross-classifications.

Classification model to classify whether a customer is going to churn or not. Using the dataset EDA is done.

A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection

In this repository I have performed Exploratory data analysis on the dataset famously known as House Price Prediction.

🎯 Database optimization and sales performance analysis for a fine wine company seeking to improve their data management practices and data maturity level - use of Python and JupyterLab (Business insights, Data collection, Cleaning, EDA, and Data Visualization)

Outliers Analysis project done as part of MSc Artificial Intelligence Research

This project analyzes road accident data using MS Excel to identify trends, patterns, and contributing factors to accidents. Through data visualization techniques and statistical analysis, it provides insights that can inform safety measures and policy decisions, aiming to enhance road safety and reduce accident rates.

Techniques to Explore the Data

Predict laptop prices using machine learning. This project leverages multiple linear regression to achieve an 82% prediction precision. Explore the influence of features like brand, specs, and more on laptop prices.

[APSIPA ASC 2022] "Robust Online Tucker Dictionary Learning from Multidimensional Data Streams". In Proc. 14th APSIPA Annual Summit and Conference, 2022.