Python implementation of Decision Tree Regression and Random Forest Regression. Efficient algorithms for predictive modeling. Ideal for regression tasks in machine learning.
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Updated
Apr 13, 2024 - Jupyter Notebook
Python implementation of Decision Tree Regression and Random Forest Regression. Efficient algorithms for predictive modeling. Ideal for regression tasks in machine learning.
Intelligent systems Jupyter notebooks.
Exploring hybride learning prototype
Neural network library in C++
Gradient Boosting prediction for the profit of 50 american startups
This repository is comprised of the Exploratory Data Analysis of the Body Fat data set from Kaggle. The feature engineering, hyperparameter tuning, and model training of the model. With comparative outlooks on the prediction vs actual results to understand and determine model accuracy.
Machine Learning model to predict the price of a house taking a few parameters like crime rate, number of rooms etc. Uses a pre-existing dataset from the UCI ML repository for training the model.
Data-Driven Price Prediction and Market Segmentation of Air BnB so both host and guest know about the price in the market.
国内基金数据获取及回归排名
Application and Evaluation of Genetic Algorithms on Decision Trees. A project for the course of Software Dependability at University of Salerno.
Regression Analysis Utility
Python implementation of K-Nearest Neighbors (KNN) Regressor for regression tasks. Versatile algorithm for predicting continuous outcomes based on neighboring data points. Suitable for various machine learning applications.
Experimentation with Neural Networks, as well as recommender systems related to movies.
Multiple linear regression model implementation with automated backward elimination (with p-value and adjusted r-squared) in Python and R for showing the relationship among profit and types of expenditures and the states.
Weather prediction with Gaussian Process Regression
My Machine Learning course projects
It is an End to End Data Science project using Linear Regression Machine Learning model.
Predict sales prices and practice different machine learning regressors.
Solving Industry based and Solution based problems through Neural Networks
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