Workshop on ML/AI 🧠 using Python 3 🐍 with introduction to Language Basics, Constructs, Linear Regression, Multi-Linear Regression, Logistic Regression, KNN and Neural Networks @ What After College 🎓.

Workshop on ML/AI 🧠 using Python 3 🐍 with introduction to Language Basics, Constructs, Linear Regression, Multi-Linear Regression, Logistic Regression, KNN and Neural Networks @ What After College 🎓.

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…

Workshop on ML/AI 🧠 using Python 3 🐍 with introduction to Language Basics, Constructs, Linear Regression, Multi-Linear Regression, Logistic Regression, KNN and Neural Networks @ What After College 🎓.

This code includes reading the data file, data visualization, variable splitting, model building, prediction and different metrics calculation using multi linear regression.

Find all ExcelR Data Science Assignment Solution Here

Multi-Linear and Binary Logistic Regression in C.

Week1-exercise

This code includes reading the data file, data visualization, variable splitting, model building, prediction and different metrics calculation using multi linear regression.

"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.

Supervised-ML---Multiple-Linear-Regression---Cars-dataset. Model MPG of a car based on other variables. 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. Levera…

Prediction of Miles per gallon (MPG) Using Cars Dataset

Ongoing practice with using Machine Learning models.

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.

Prepare a prediction model for profit of 50 startups data and Consider only the some columns and prepare a prediction model for predicting Price.

In this project I attempt to develop a quantitative portofolio hedging strategy based on multi - linear regression

Multiple Linear Regression Dataset Name - 50_startups data.

Used different ML Algorithms to predict the fares of Airline Tickets

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.

Analyzing best features affecting resell value and making predictions.