Analyzing data and creating visuals using Matplotlib
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Updated
May 4, 2023 - Jupyter Notebook
Analyzing data and creating visuals using Matplotlib
Exploratory Data Analysis (EDA) on a dataset from Kaggle
Modeling of strength of high performance concrete using Machine Learning
Classification model to classify whether a customer is going to churn or not. Using the dataset EDA is done.
This Repository Consists the exam Problems and solutions conducted on September - 2021
Data Anlysis project created with the use of VLOOKUP, pivot tables, and graphing in Excel to visualize data results.
This repository shows how outliers affect best fit linear regression line and how we can overcome this outlier problem with regularization.
Clustering on gene expression array.
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.
EDA is a must to do step in the data science workflow. Working on data, wrangling & transforming them is time consuming, and it determine the success degree of the next steps (pre preocessing, modelling, communicating outputs & decision making). This repo will show you how to perform EDA in R using the tidyverse ecosystem, and will introduce a c…
This repo contains EDA of red and white wine and how it relates to quality.
Dixon's Q Test calculator package for Dart
Data preprocessing is a data mining technique that is used to transform the raw data into a useful and efficient format.
This repo contains my work for Codeup's Anomaly Detection module.
Compare the performance of Pymaceuticals’ drug of interest, Capomulin, versus the other treatment regimens.
This project applies data wrangling techniques to a retailer's data set of online orders. These techniques include determining and removing syntactical as well as semantic anomalies, removing outliers and imputing missing values using basic machine learning.
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