Jupyter notebooks and Python code for analyzing air quality (fine particle, PM2.5)
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Updated
Aug 15, 2021 - HTML
Jupyter notebooks and Python code for analyzing air quality (fine particle, PM2.5)
Tutorial on representational similarity analysis for MIND 2018
DFT simulation of He atom
A recommender engine based on Collaborative Filtering of the games available on the Steam Game Store
R Gallery Book
Udacity Data Analyst Nanodegree - Project III
A Python based movie recommender hosted using Flask
Vizualisation of the "correlation" between categorical variables using a cramer's v heatmap
by using a dataset , we are calculating and playing with distance and some features ,i used python ,jupyter note bookes and numpy and pandas.
Learned time series analysis from Quantstart
Archive: Data, scripts, and outputs for the paper "Sparse Estimation of Correlations among Microbiomes (SECOM)". Please check our ANCOMBC R package for the most up-to-date SECOM functions.
This Matlab code is used to find the Correlation of plain and cipher images.
All my DATA developer projects
A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these.
Kaggle challenge to predict if a customer is satisfied or dissatisfied with their banking experience.
Correlation between word segmentation on child directed speech and reported infants' word understanding in several languages
I conducted sentiment analysis by scrapping data from Glassdoor and augmented the insights to a prediction model to predict the attrition at IBM. I also used clustering on internal IBM HR data before prediction to evaluate the model accuracy.
Statistics on Ethereum tokens to get results from data. This is a study of various features of the BNB token over the time period 2017-18. Here we find out the the behaviour of users buying and selling the token. we also try to analyze the user activity from other ethereum tokens and find the relationship between influential buyers and token pri…
FIMUS imputes numerical and categorical missing values by using a data set’s existing patterns including co-appearances of attribute values, correlations among the attributes and similarity of values belonging to an attribute.
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