You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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 y…
Predicting the Likelihood of Diabetes Using Common Signs and Symptoms - About one-third of patients with diabetes do not know that they have diabetes according to the findings published by many diabetes institutes around the world. Detecting and treating diabetes patients at early stages is critical in order to keep them healthy and to ensure th…
Forecast Bitcoin daily closing prices using a Python repository featuring regression and time series models. From Linear and Polynomial Regression to ARIMA, gain insights into cryptocurrency trends. Visualize historical data, evaluate models with key metrics, and analyze residuals for validation
Residual analysis in Linear regression is based on examination of graphical plots which are as follows :: 1. Residual plot against independent variable (x). 2. Residual plot against independent variable()y. 3. Standardize or studentized residual plot 4. Normal probability plot
Topic : Predicting Medal Counts by Countries in Upcoming Olympics Games // # Integrated historical Olympic data with demographic, health, and economic datasets to generate a large dataset # Developed a linear regression model displaying prediction accuracy close to 70%, within the margin of error
This repository contains implementations of regression models on the Starbucks stock market. The goal is to provide a comprehensive understanding of the performance of these models. Also, implement metrics without relying on external machine learning libraries. ☕️📈
This repository contains a project I completed for an NTU course titled CB4247 Statistics & Computational Inference to Big Data. In this project, I applied regression and machine learning techniques to predict house prices in India.
The goal of the report was to fit the linear regression model to the data and check whether the data met the assumptions of the model. The results were used to make predictions.