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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
Bayesian Statistics: Linear Regression and multi-linear models and related concepts (multicollinearity, correlation coefficient etc) on iris dataset in pymc3
Focusing on regression analysis, understanding its principles, and applying it to real-world datasets to predict outcomes and understand relationships between variables.
Project conducted in STAT 4355.001.S22. Utilized the R Programming Language to determine a multi-linear model fitting to predict the number of bike rentals. Determined the appropriate attributes that significantly influenced the number of bike rentals. Collaboration with three other classmates.
Implementation of a gradient descent algorithm for a multi-linear regression problem based on multiple predictors, and which takes in the value of the desired learning rate and the number of iterations.
Know about the Multi Linear Regression and calculate the model accuracy using various techniques. Performed EDA and identified null values and outliers and removed collinearity. Visualize using different charts and made accurate model by measuring R2 score.
"Linear Regression Step by Step" is a repository that provides a comprehensive notebook with step-by-step examples, exercises and libraries to understand and implement Linear Regression easily.
This code includes reading the data file, data visualization, variable splitting, model building, prediction and different metrics calculation using multi linear regression.