回归预测

Predict the quantity of turbidity, the level of product traces or suspended solids in the effluent, that will be returned in last rinsing phase during the cleaning of food and beverage industrial equipment

The objective of this project is to model the prices of Airbnb appartments in London.The aim is to build a model to estimate what should be the correct price of their rental given different features and their property.

Predicting the sales of a store

Examples of Supervised ML

Kaggle Competition

Udacity DataScience nanodegree 4th project: pick a dataset, explore it and write a blog post

Try sentiment score prediction :p

ML projects coded during Matrix 2 by DataWorkshop - car prices prediction

Nonlinear Regression Models

Predicting Disease Spread

This repository focuses on building several Regression Models-Linear Regression, XGBoost Regressor, Ridge Regression, Lasso Regression, Polynomial Regression that predicts the continuous outcome (House Prices) along with several Data Preparation Techniques (Transformations/Scaling, Imputation, Filtering of Outliers, Handling of correlated featur…

I participated in a Kaggle competition where I used a random forest regressor and hyper-parameter tuned my model to most accurately predict the sale price of a home based on multiple characteristics and features.

In this project, the selling price of the houses have been predicted using various Regressors, and comparison charts have been shown that depict the performance of each model. This submission was ranked 107 out of 45651 in first attempt on Kaggle leader-board which can be accessed from here : https://www.kaggle.com/c/home-data-for-ml-course/lead…

Goal is to predict the concrete compressive strength using collected data

This repository will work around solving the problem of food demand forecasting using machine learning.

House Prices: Advanced Regression Techniques

Strategizing to maximize Customer Retention in Telecom Company

Xgboost Hyperparameter Tunning Using Optuna

This repository contains Building, Training, Saving and deployment code for the model built on Boston Housing Dataset to predict Median Value of owner-specified homes in $1000s (MEDV).