CS 6140: Machine Learning Assignments
-
Updated
Dec 13, 2017 - Python
CS 6140: Machine Learning Assignments
Bangalore House Price Predictor: A web app using Flask and scikit-learn to predict house prices in Bangalore based on location, area, bedrooms, and bathrooms.
Linear Regression, Logistic Regression, Neural Networks, Convolutional Neural networks, Auto Encoders
Multivariate time series forecasting(MLTS) has been a mainstream tool for forecasting in economics, traffic modelling, economics, future shipments, temperature forecasts(temperature forecast solely on previous year data(as shown in "Lugano temperature forecast", it requires weather modelling too). The basic assumption in multivariate time series…
As part of the UCSanDiego online course "Machine Learning Fundamentals"
Development of a predictive model that selects the most cost efficient supplier for a given task
Implementation of some Machine Learning Algorithms from scratch
Test task for the position of data analyst in the BIOCAD Corporation
Laptop price prediction model using XGB, Ridge, Lasso and SciKit-learn's Linear Regression. In the end, I deployed the best one using Joblib and Gradio.
This Model Build using Linear Regression + Ridge for Fire weather index on Algerian Data set
Gradient descent algorithm from scratch for linear and logistic regression with feature scaling and regularization.
This model trains according to the data and makes a Polynomial Regression curve of degree 16. The model is regularized using Ridge regression. It also compares the predicted values with original outputs and for different alphas.
Fantasy Football Machine Learning Prediction Tasks
A tool to jsonify some sklearn and xgboost models. A high level serialization that works across programming languages. Used for mllite tests.
Trying to build a repository that can be used to analyze & forecast commodities prices
Machine learning using python
Implementation of linear regression with L2 regularization (ridge regression) using numpy.
Data-Driven Decision Making: Selecting the Best Regression Model for E-commerce Sales
Add a description, image, and links to the ridge-regression topic page so that developers can more easily learn about it.
To associate your repository with the ridge-regression topic, visit your repo's landing page and select "manage topics."