Data Science Foundations I | Exploratory Data Analysis in Python | Summarizing Single Feature
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Updated
May 21, 2024 - Jupyter Notebook
Data Science Foundations I | Exploratory Data Analysis in Python | Summarizing Single Feature
Statistics for Data Analysis | Quartiles, Quantiles and Interquartile Range
Feature Engineering with Python
A Python Based Library to Calculate Statistical Estimators (Sn, Qn, MAD, IQR)
Outlier detection (z-score and IQR) and visualization on Geolife dataset for transport mode detection task
Métodos para extração de outliers
Here includes my SQL journey from beginning and any further progress
Classification problem using multiple ML Algorithms
Tidy anomaly detection
Uma análise exploratória dos dados do airbnb sobre a cidade de Vancouver. Limpeza de dados, análises estatísticas, mapas interativos.
📊 Master Microsoft Excel for Data Analysis and Data Science with ready-to-use tools, templates, and expert tips. Join us in unleashing the power of data! 💼🔍🚀
Description of outliers
This repository contains the source code for Statistics.JS.
A machine learning project where we first detected and removed the outliers and then checked correlation among features and then applied different ML algorithms to check if the person might get a heart attack or not.
Data analysis and outliers detection of air quality data.
Premise of Task: Contextual Alert and Trend System (CATS) is a proof of concept (POC) for an automated system for near real-time media monitoring via GDELT to identify trends and anomalies in the volume of online reports about pre-defined indicator events, at country level. This repository reflects the methodologies used to complete this task.
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