End-to-end machine learning regression model for predicting housing prices in Bengaluru, with Heroku deployment.
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Updated
Jan 1, 2022 - Jupyter Notebook
End-to-end machine learning regression model for predicting housing prices in Bengaluru, with Heroku deployment.
School exercise - Multivariate Statistical Methods subject
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.
Approach to some basic Machine Learning Techniques.
Predictive Analytics for Real Estate Investment: A Regression Model Approach for Surprise Housing in the Australian Market using Regularization methods (Ridge and Lasso)
Regression models(lasso, ridge, DT) using NumPy.
Exploring World Development Indicators: Identifying relationship between Health Indicators using Linear Regression & Classification of Income Group based on Health Indicators using Logistic Regression.
Building Advanced regression models (Lasso and Ridge) for house price prediction in the Australian market
As part of the UCSanDiego online course "Machine Learning Fundamentals"
In this project, I build 20+ models predicting Spotify song popularity. These include neural networks, Lasso and Ridge regression models. I also leverage OpenAI chat-completion API to engineer features from song lyrics.
This repository contains projects completed during during my Udacity Data Science Nanodegree course.
Sale trending
Regresión Lineal Múltiple con Modelos Regularizados (Lasso y Ridge) y Sin Regularizar
This is a sample ML Regression Project whose web part is created by Flask and it involves AWS Deployment
Developed regularization and tree-based machine learning models to predict remission status in a cohort of 5059 patients. Elastic net and Random Forest models were compared on F1 scores accuracy, sensitivity, specificity, and AUC ROC.
A collection of multiple projects involving tasks such as classification, time series forecasting , regression etc. on a number of datasets using different machine learning algorithms such as random forest, SVM, Naive Bayes, Ensemble, perceptron etc in addition to data cleaning and preparation.
Metis project 2/7
This is First Project of Machine Learning by me
In this series of notebooks, we will dive into each step of the data analysis process of a data set with some information about a list of cars and several attibutes, including their prices. So essentially we will develop a model to predict cars price.
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