shwetapardhi/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

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Shipra-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.

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shanthiachar18/Project-on-Simple-Linear-Regression-2

Building a predictive model for Salary hike based on YearExperience

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shanthiachar18/Project-on-Polynomial-Regression

Predicting the salary using Polynomial Regression

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shanthiachar18/Project-onMultiple-Linear-Regression-2

Multiple Linear Regression model for ToyotaCorolla data

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Prem-98/Multi-linear-regression

MLR assignment

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vaitybharati/P24.-Supervised-ML---Simple-Linear-Regression---Newspaper-data

Supervised-ML---Simple-Linear-Regression---Newspaper-data. EDA and Visualization, Correlation Analysis, Model Building, Model Testing, Model predictions.

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vaitybharati/Assignment-05-Multiple-Linear-Regression-2

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.

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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.

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vaitybharati/P27.-Supervised-ML---Multiple-Linear-Regression---Toyoto-Cars

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.

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Jalalbaim/Predicting-Admission-with-Deep-Learning

Forecasting Admission using Deep Learning Regression

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ZJW-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

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franklinjtan/Portfolio-Diversification-Correlation-Risk-Management-with-Python

Determining uncorrelated returns

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SukanyaPa/Assignment-05.-Multiple-Linear-regression-Q2

Used libraries and functions as follows:

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Abdullah2020/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.

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Ashlesha8421/R-square-Adj-R-Square

Size: 2.93 KB - Last synced: about 1 year ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

Vasatika/Predictive_Analysis_Advertising_Data

Performed predictive analysis on Advertising budget data set.

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ORNL-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.

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