Modeling of strength of high performance concrete using Machine Learning
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Updated
May 29, 2020 - Jupyter Notebook
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.
Perform EDA on an air quality dataset. Identify relationships in the data and discover trends in pollutant levels over time
Analyzing data and creating visuals using Matplotlib
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.
This repo contains EDA of red and white wine and how it relates to quality.
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.
There are lot of things that need to be done on the given dataset before we feed it to the machine, these things come under data preprocessing. In this repository I have tried to explain those things with some examples.
To perform exploratory data analysis and visualization on a dataset containing customer information, to identify potential target customers for the bank’s future marketing campaign.
Identify missing values, outliers and trends in medical data. Create bar charts, heatmaps and other visualizations to understand how the features impact the target column of the data set
To explore the given dataset for all basic statistics such as the distributions, correlations, outliers, missing values, etc.
Sprint 9, Task 1
Add a description, image, and links to the outliers topic page so that developers can more easily learn about it.
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