A tool for simple data analysis. A rip-off of R's dlookr package (https://github.com/choonghyunryu/dlookr)

Projects of Business Analyst Nanodegree Program

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

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.

1-Outlier detection and removal of the outlier by Using IQR The Data points consider outliers if it's below the first quartile or above the third quartile 2-Remove the Outliers by using the percentile 3-Remove the outliers by using zscore and standard deviation

This repository contains my learning path of python for data-science essential training(part-1). Here, I have included chapter-wise topics and my practice problems. Also, feel free to checkout for better understanding.

Techniques to Explore the Data

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)

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

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.

Rowwise outliers detection is the most common action most spectroscopists/chemometricians take to deal with discordant reading. However, an alternative method such as MacroPCA enables to account for cellwise outliers in spectroscopic analysis.

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

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.

Prediction of Miles per gallon (MPG) Using Cars Dataset

Consider only the below columns and prepare a prediction model for predicting Price. Corolla<-Corolla[c("Price","Age_08_04","KM","HP","cc","Doors","Gears","Quarterly_Tax","Weight")]

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

This repository contains clustering techniques applied to minute weather data. It contains K-Means, Heirarchical Agglomerative clustering. I have applied various feature scaling techniques and explored the best one for our dataset

Obstructive Sleep Apnea classification with help of numerical data set which having the physical body characteristics with the help of machine learing

Exercises on Timeseries Decompositions, Monte Carlo Simulations, and Outlier Detection

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.