Techniques to Explore the Data
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Updated
Dec 1, 2020 - Jupyter Notebook
Techniques to Explore the Data
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.
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.
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.
The ConfidenceEllipse package provides functions for computing the coordinate points of confidence ellipses and ellipsoids for a given bivariate and trivariate dataset, at user-defined confidence level.
Exercises on Timeseries Decompositions, Monte Carlo Simulations, and Outlier Detection
👨💻 Learn how to implement a model of machine learning to solve a real problem
The dataset is about past loans. The loan_train.csv data set includes details of 346 customers whose loans are already paid off or defaulted.
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 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.
Files created to the Identificazione dei Sistemi Incerti project. Implemented Kalman Filter, EKF, UKF and a smoother. The Matlab files contain also the white-noise charaterzation of the signal and the outliers identification.
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.
[APSIPA ASC 2022] "Robust Online Tucker Dictionary Learning from Multidimensional Data Streams". In Proc. 14th APSIPA Annual Summit and Conference, 2022.
Демонстрация применения различных методов очистки данных
Toolkit to assist life science researchers in detecting outliers
A tool for simple data analysis. A rip-off of R's dlookr package (https://github.com/choonghyunryu/dlookr)
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.
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