rsquare-values
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vaitybharati / Assignment-04-Simple-Linear-Regression-1
Assignment-04-Simple-Linear-Regression-1. Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regression.
Language:Jupyter Notebook 3Assignment-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.
Language:Jupyter Notebook 3Supervised-ML---Simple-Linear-Regression---Newspaper-data. EDA and Visualization, Correlation Analysis, Model Building, Model Testing, Model predictions.
Language:Jupyter Notebook 3Supervised-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.
Language:Jupyter Notebook 3Jalalbaim / Predicting-Admission-with-Deep-Learning
Forecasting Admission using Deep Learning Regression
Language:Jupyter Notebook 2ZJW-92 / Ames_House_Price_Prediction
This is a house price prediction study which utilized Exploratory Data Analysis, Dealing with Missing Values, Linear Regression with LASSO and Ridge regularization to predict house prices in the Ames Housing Data Set
Language:Jupyter Notebook 2franklinjtan / Portfolio-Diversification-Correlation-Risk-Management-with-Python
Determining uncorrelated returns
Language:Jupyter Notebook 1ORNL-AMO / Sliding-Regression-Tool
This is a application used to preform linear regression based math on excel files of energy data to find rSquare values, savings percentages, fitted models, and p values (still in the works). Data is shown through two different ways, the first being a heatmap based on rSquare values, and the second being a graph of both rSquare values and savings percentage.
Language:CSS 1Prem-98 / Multi-linear-regression
MLR assignment
Language:Jupyter Notebook 1utsavchaudharygithub / MechaCar_statistical_analysis
MechaCar prototypes Collected summary statistics on the pounds per square inch (PSI) of the suspension coils from the manufacturing lots Ran t-tests to determine if the manufacturing lots are statistically different from the mean population Designed a statistical study to compare vehicle performance of the MechaCar vehicles against vehicles from other manufacturers.
Language:R 1PatilSukanya / Assignment-05.-Multiple-Linear-regression-Q2
Used libraries and functions as follows:
Language:Jupyter Notebook 0shanthiachar18 / Project-on-Polynomial-Regression
Predicting the salary using Polynomial Regression
Language:Jupyter Notebook 0Building a predictive model for Salary hike based on YearExperience
Language:Jupyter Notebook 0shwetapardhi / Assignment-04-Simple-Linear-Regression-1
Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regressi
Language:Jupyter Notebook 0Abdullah2020 / Hamoye_StageB
This is my Hamoye Stage B project. The project focuses on Predicting Energy Efficiency of Buildings. It implemented different Machine Learning algorithm technique that are not limited to Linear Regression, LASSO, Ridge etc.
Language:Jupyter Notebookjbp261 / Customer-Segment-Project
Creating customer segments using unsupervised learning algorithms
Language:Jupyter NotebookShipra-09 / ML-Project-Multiple-Linear-Regression
Exploring Insights/Inferences by performing EDA on the given project data (50_Startups and Toyota Corolla data) . Model fitting via linear regression by Importing sklearn package. Selecting the best fitted model via python programming.
Language:Jupyter NotebookVasatika / Predictive_Analysis_Advertising_Data
Performed predictive analysis on Advertising budget data set.
Language:R
