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.

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

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.

Determining uncorrelated returns

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.

Forecasting Admission using Deep Learning Regression

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

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.

MLR assignment

ML MODEL CONCEPTS

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.

Performed predictive analysis on Advertising budget data set.

No description provided.

Building a predictive model for Salary hike based on YearExperience

Predicting the salary using Polynomial Regression

Assignment for building a linear regression model for predicting the demand for bike sharing

Creating customer segments using unsupervised learning algorithms

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.

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.

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

This project demonstrates the use of Multiple Linear Regression to predict the profits of startups based on investment in R&D, Administration, and Marketing of dataset (50_Startups.csv)

A regressor project trained to predict the output of a windmill

Used libraries and functions as follows: