Topic: residual-analysis Goto Github
Some thing interesting about residual-analysis
Some thing interesting about residual-analysis
residual-analysis,Forecast Bitcoin daily closing prices using a Python repository featuring regression and time series models. From Linear and Polynomial Regression to ARIMA, gain insights into cryptocurrency trends. Visualize historical data, evaluate models with key metrics, and analyze residuals for validation
User: areeba0
residual-analysis,Residual analysis in Linear regression is based on examination of graphical plots which are as follows :: 1. Residual plot against independent variable (x). 2. Residual plot against independent variable()y. 3. Standardize or studentized residual plot 4. Normal probability plot
User: ashishyadav24092000
Home Page: https://github.com/ashishyadav24092000/Residual-Analysis-in-Linear-Regression
residual-analysis,The goal of this project is to build multiple linear regression models for the prediction of car prices.
User: chaitanyac22
residual-analysis,To model the demand for shared bikes with the available independent variables
User: chinmayeeguru
residual-analysis,Generate Data with Hidden Images in Residual Plots
Organization: coatless-rpkg
Home Page: https://r-pkg.thecoatlessprofessor.com/surreal/
residual-analysis,This repository contains implementations of regression models on the Starbucks stock market. The goal is to provide a comprehensive understanding of the performance of these models. Also, implement metrics without relying on external machine learning libraries. ☕️📈
User: edgar-la
residual-analysis,📊⚙️ Using 7 years of my sleep data, this project predicts Sleep Quality using a linear regression model based on predictors such as time in bed, time asleep, temperature, alarm, and steps.
User: ericksonl
residual-analysis,Anomaly detection for building HVAC data.
Organization: hamk-uas
residual-analysis,Practices and Assignments from the Data Analysis and Regression Class
User: jaewonson37
residual-analysis,Topic : Predicting Medal Counts by Countries in Upcoming Olympics Games // # Integrated historical Olympic data with demographic, health, and economic datasets to generate a large dataset # Developed a linear regression model displaying prediction accuracy close to 70%, within the margin of error
User: jaewonson37
residual-analysis,The "Advertising Impact Analysis" project aims to analyze the relationship between advertising expenditure across different channels (such as TV, radio, online) and its impact on sales or revenue.
User: jhay001
Home Page: https://www.kaggle.com/code/jibrilyahayajibril/eda-sales-prediction-using-slr
residual-analysis,The goal of the project is to predict Life Expectancy using various factors and to determine the relationship that exists between them.
User: keshavelangods
residual-analysis,Feasibility of staring a Sunday edition for a large Metroplitan newsapaper
User: manasik29
residual-analysis,Intended to implement multiple-indicator, multiple-cause (MIMIC) modelling with discrete indicators in R.
User: mariakamran86
residual-analysis,Statistical Modelling and Data Visualization of a Climate Change Dataset (January 1984 to December 2008 ) Sourced from Kaggle
User: maxxhvo
residual-analysis,Prediction of Miles per gallon (MPG) Using Cars Dataset
User: moindalvs
residual-analysis,This repository contains a project I completed for an NTU course titled CB4247 Statistics & Computational Inference to Big Data. In this project, I applied regression and machine learning techniques to predict house prices in India.
User: nlawira
residual-analysis,Time Series Forecasting
User: nsb700
residual-analysis,Used libraries and functions as follows:
User: patilsukanya
residual-analysis,A boat-sharing system is a service in which boats are made available for city tour. Required to model the demand for open boats with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market.
User: priya-aggarwal27
residual-analysis,Prediction Car prices using a Linear Regression model
User: saranya-ponnarasu
residual-analysis,Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in t
User: shwetapardhi
residual-analysis,The goal of the report was to fit the linear regression model to the data and check whether the data met the assumptions of the model. The results were used to make predictions.
User: szymex49
residual-analysis,A MATLAB program related to Regression Models
User: tommykwok722
residual-analysis,Predicting the Likelihood of Diabetes Using Common Signs and Symptoms - About one-third of patients with diabetes do not know that they have diabetes according to the findings published by many diabetes institutes around the world. Detecting and treating diabetes patients at early stages is critical in order to keep them healthy and to ensure their quality of life is not compromised. Early detection will also help to mitigate the risk of serious complications like heart disease & stroke, blindness, limb amputations, and kidney failures as a result of diabetes. The data set consists of signs and symptoms of 516 newly diabetic or would be diabetic patients, who presented at Sylhet Diabetes Hospital in Sylhet, Bangladesh. The data had been collected using the direct questionnaires method at the hospital under the supervisor of Doctors. The Source for the data set is the UCI Machine Learning Repository at, https://archive.ics.uci.edu/ml/datasets/Early+stage+diabetes+risk+prediction+dataset. The data set has 16 descriptive features and one target feature. This study intends to build a logistic regression model to predict the likelihood of having diabetes using common signs and symptoms presented by patients. A successful model will enable early detection of diabetes through signs and symptoms shown by possible patients. This study consists of two phases: 1) Phase I - preprocess and explore the data set in order to make it ready to consume for model development. 2) Phase II - build a logistic regression model to predict the likelihood of having diabetes based on signs and symptoms. The Phase I part has already been completed under previous work/submission and this report intends to cover the work carried out for Phase II. All the activities have been performed in the R package and the report has been compiled using R-Markdown.
User: udeshikadissa
residual-analysis,Assignment-05-Multiple-Linear-Regression-2. Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in the past few years State -- states from which data is collected Profit -- profit of each state in the past few years.
User: vaitybharati
residual-analysis,Multi-Linear-Reg
User: vaitybharati
residual-analysis,Supervised-ML---Multiple-Linear-Regression---Cars-dataset. Model MPG of a car based on other variables. EDA, Correlation Analysis, Model Building, Model Testing, Model Validation Techniques, Collinearity Problem Check, Residual Analysis, Model Deletion Diagnostics (checking Outliers or Influencers) Two Techniques : 1. Cook's Distance & 2. Leverage value, Improving the Model, Model - Re-build, Re-check and Re-improve - 2, Model - Re-build, Re-check and Re-improve - 3, Final Model, Model Predictions.
User: vaitybharati
residual-analysis,Supervised-ML---Multiple-Linear-Regression---Toyota-Cars. EDA, Correlation Analysis, Model Building, Model Testing, Model Validation Techniques, Collinearity Problem Check, Residual Analysis, Model Deletion Diagnostics (checking Outliers or Influencers) Two Techniques : 1. Cook's Distance & 2. Leverage value, Improving the Model, Model - Re-build, Re-check and Re-improve - 2, Model - Re-build, Re-check and Re-improve - 3, Final Model, Model Predictions.
User: vaitybharati
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