This repo covers the basic machine learning regression projects/problems using various techniques through scikit learn library
- The file rgex1.ipynb was the very first attempt/beginner problem solution I worked on. It does basic linear regression through both statistics and ML techniques
- The file Boston_Housing_Price_ML.ipynb is the latest edition, applying complex functions and creating a framework where data can go through few models and train test, predict, and evaluate which I learned as I progressed through the intermediate and advanced training and applied it to the very first regression problem which is the Boston House Price: Using Linear Regression, Lasso Regression, Ridge Regression, Stochastic Gradient Descent, and Multilayer Preceptron MLP Regressor to predict the median value of home.
- The file DiamondPrice.ipynb is the latest edition, applying complex functions and creating a framework where data can go through few models and train test, predict, and evaluate which I learned as I progressed through the intermediate and advanced training and applied it this regression problem which is predicting the Price Diamond based on given set attribute: Using Linear Regression, Lasso Regression, Ridge Regression, Stochastic Gradient Descent, and Multilayer Preceptron MLP Regressor to predict the price of diamond based on numerical and categorical variables.
- The file Advertising_Sales_ML.ipynb is the latest edition, applying complex functions and creating a framework where data can go through few models and train test, predict, and evaluate which I learned as I progressed through the intermediate and advanced training and applied it this regression problem which is predicting the sales based on given set attributes (methods of advertising): Using Linear Regression, Lasso Regression, Ridge Regression, Stochastic Gradient Descent, and Multilayer Preceptron MLP Regressor to predict the sales based on numerical and categorical variables.