Build a regularized regression model to understand the most important variables to predict housing prices.
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Updated
Sep 29, 2024 - Jupyter Notebook
Build a regularized regression model to understand the most important variables to predict housing prices.
Gradient descent algorithm from scratch for linear and logistic regression with feature scaling and regularization.
A tool for visualizing the coefficients of various regression models, taking into account empirical data distributions.
A project using the Ames Housing dataset to predict home prices based on their characteristics.
Deep Study of the Linear Regression Machine Learning Model, including its Regularization Techniques
Performed rigorous preprocessing, and data cleaning, and conducted exploratory data analysis to identify trends, patterns, and outliers, leading to valuable insights. Employed various statistical methods concepts to get insights about the data for prediction.
High Throughput Light Weight Regularized Regression Modeling for Molecular Data
This repository is dedicated to my participation in Datatalks Mlzoomcamp
Lasso/Elastic Net linear and generalized linear models
Analysis pipeline for modeling of illness perception one year after COVID-19
Built a regression model to predict bike demand on data from Seoul, South Korea. and employed one hot encoding to create dummy variables Benchmarked Cat Boost against Linear regression, Lasso and Ridge regression, Gradient Boost and performed feature engineering and tuned the hyperparameters for the optimum performance
Build a regularized regression model to predict the price of houses with the available independent variables
A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price.
Predict the vehicle price from the open source Auto data set using linear regression. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.
The course studies fundamentals of distributed machine learning algorithms and the fundamentals of deep learning. We will cover the basics of machine learning and introduce techniques and systems that enable machine learning algorithms to be efficiently parallelized.
Predict the vehicle price from the open source Auto data set using linear regression. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.
Regularized logistic regressions with computational graphs
In linear regression, regularization is a process of making the model more regular or simpler by shrinking the model coefficient to be closer to zero or absolute, ultimately to address over fitting.
A Mathematical Intuition behind Linear Regression Algorithm
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