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I have built a Model using Random Forest Regressor of California Housing Prices Dataset to predict the price of the Houses in California.
In this project, I applied different regression models for rmse and mae on antenna dataset for predict signal strength.
Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties
Adjusting the prices of a product or service based on various factors in real time
Reducing the cost & increasing efficiency of Merchant Ships
Developed a price prediction model using Random Forest Regression algorithm. Different graphs were created as a part of Exploratory Data Analysis. Feature Engineering was performed to make the data ready for building the model.Built an interactive dashboard using dash and plotly libraries
My Python learning experience 📚🖥📳📴💻🖱✏
Diabetes mellitus, commonly known as diabetes is a metabolic disease that causes high blood sugar. The hormone insulin moves sugar from the blood into your cells to be stored or used for energy. With diabetes, your body either doesn’t make enough insulin or can’t effectively use its insulin.
RandomForest Regressor Model ML for predicting Price of House.
Simple Application for predicting price of the flight. It uses sklearn pipeline to perform preprocessing , feature selection and feature engineering and model building .The pipeline object is saved in a pickle file and used in the flask application for prediction
Prediction of car prices using data from sahibinden.com
A model for predicting the selling price of a used car using machine learning algorithms. This model is deployed in the Heroku Cloud Platform.
This repository contains my final project for UT Austin's Data Analytics Bootcamp. My teammates and I explored a Wine Reviews dataset and built an interactive Tableau dashboard to recommend wines for a novice based on price, rating, variety, and country. We also built a machine learning model to train it to rate wine like an experienced sommelier.
Regression Machine Learning Project
Este trabajo se enfoca en la implementación de Limpieza, Análisis Exploratorio de Datos y Visualización de Datos para obtener conclusiones acerca del COVID-19 en Alemania.
Airline Fare Prediction using Regression
Prepr's Machine Learning Challenge
Note : This Repository consists files of the ML Project - Robust Yield Prediction on Farm Units for a new food chain company , It's my Final academic project - PHD for the PG Program pursued in Data Science & Analytics @ Insofe.
Car Price Prediction: Machine Learning (Data Science) Project using CarDekho.com dataset predicting prices of cars
In this project, we will predict the price for AMES House and learn Machine Learning Algorithms, different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model.
Data science project on Housing Prices Dataset regression analysis
In this project we will predict the time taken by NYC taxis to complete their trips using regression.
Data Science Project that awarded me with a certificate of Data Scientist Associate.
This project repository is a combination of all R and Python files that have led up to create the Sentiment Gummy Worm scoring model, which is a predictive randomForest model that calculates the mean sentiment score of a sentence based on buffer ratios, decay factors and input length.
A Regression model that predicts the price of bulldozers based on their features.
Built a regression model for house price prediction of New Taipei city of Xindian district, Taiwan. which can help urban design and urban policies, as it could help identify what factors have the most impact on property prices.
This repository contains my final project for UT Austin's Data Analytics Bootcamp. My teammates and I explored a Wine Reviews dataset and built an interactive Tableau dashboard to recommend wines for a novice based on price, rating, variety and country. We also built a machine learning model to train it to rate wine like an experienced sommelier.
This is the curated pile of notebooks/small projects which contains linear and non-linear regression models.
Gold Price Prediction || Stock Price Prediction || Portfolio Optimisation
The Housing Price Prediction Accuracy Improvement project is a data-driven initiative focused on enhancing the precision and reliability of housing price predictions. This project encompasses a multidisciplinary approach, combining data science, machine learning, and real estate insights to optimize the accuracy of forecasts in the housing market.
Steps to deploy a local spark cluster w/ Docker. Bonus: a ready-to-use notebook for model prediction on Pyspark using spark.ml Pipeline() on a well known dataset
A web application to predict the occurrence and confidence of wildfires using machine learning
This case study is to predict the taxi fare for a taxi ride in New York City from a given pickup point to the agreed dropoff location. Decision tree and Random Forest regressor is used for the fare prediction.
Predict Prices for Indian Flights
Development of an AutoML System to Predict the Compressive Strength of Concrete