Build a Machine Learning model to identify the habitability score of the property based on the property's basic information and location-based information.

Regression techniques

In this project, I have used Random forest Regressor and done a lot of data preprocessing on raw data finally get an accuracy of approximately 81 percent. This project mainly focuses on data preprocessing.

Chapman University CS-510 Computing For Scientists Final Project

Microservicio que predice la temperatura y humedad usando Arima y RandomForestRegressor

This project focuses on developing a Machine Learning model to predict housing prices in California.

Predicting House Prices with Random Forest Regression

Generated automated weekly sales forecast by departments in 45 stores using a dataset of weekly sales data spanning over 2 years, by employing the Random Forest Regressor model with achieving the Mean Absolute Error (MAE) of less than 10% of the forecast.

Cryptocurrency price prediction using Machine Learning, , aimed at aiding investors in making well-informed decisions by forecasting cryptocurrency prices across different timeframes in the dynamic and volatile market.

En este repositorio se almacenan los diferentes cuadernos utilizados a la hora de programar los algoritmos de ML utilizados para el estudio y realización del TFG

Car price prediction and its deployment

Analytics Vidhya hosts "JOB-A-THON" where over 7000+ enthusiasts got the opportunity to showcase their skills.

This model predicts the flight fare given the conditions and built using randomforest

This research is based on previous research related to Optimization of Airbnb Dynamic Pricing. This research analytical purposes was to create a model that was as flexible as possible by determining price at the scale of the smallest possible rental period at daily basis.

Regression - Bulldozer Sales Price - Kaggle Competition

A project to predict the average prices of Avocado in USA.

This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.

Supervised Machine Learning