Using multiple linear regression model to predict customer demand in order to make business decision

project to predict smartphone sales based on the marketing budget spent on advertising using three platforms involves collecting data on marketing spending and smartphone sales, and using statistical and machine learning techniques to build a model that can predict future smartphone sales based on changes in marketing budget.

Predicting annual highest of sneakers on StockX

Statistical analysis to predict the importance of various manufacturing parameters on fuel economy of a prototype car.

The comparison of multiple Machine Learning models refers to training, evaluating, and analyzing the performance of different algorithms on the same dataset to identify which model performs best for a specific predictive task.

This project aims to enhance the accuracy and efficiency of stock market predictions by employing a sophisticated machine learning methodology. This project leverages the power of PySpark, a robust framework for distributed data processing, to handle large datasets and perform complex computations.

Functional specification to calculate per country's happiness score

Gold Price Prediction

Learn how to use R and statistics in order to analyze vehicle data

Heart Risk Level Predicting Regression Model 💔

Probability and Statistics for Machine Learning

Business Goal: To model the demand for shared bikes 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.

Hosts Python content associated with Linear Regression series on YouTube

Creates a ML Pipeline leveraging PySpark SQL and PySpark MLib to predict sound level

An introduction to machine learning

The project involves the multivariate regression analysis of a dataset.

Hosts R content associated with Linear Regression series on YouTube

To increase efficiency of a cotton mill. I set up an ANOVA 3 factor analysis model in R to determine best spindle & position that produces the longest roving. The only significant difference in roving length was observed when position was 3 and spindle was 1 or 2. (ANOVA Model in R)

Performing multiple linear regression on a simple dataset.

Feature transformation is a technique in machine learning that is used to modify the original features of a dataset in order to improve the performance of machine learning algorithms.