Found 2,326 repositories(showing 30)
Supervised Machine Learning Analysis Using Classification Models
g-random-a
This is car price prediction mobile app developed using Flutter framework as frontend and Django on backend.
ditekunov
A set of different models, that can be used to predict price range of a mobile phone.
Pratik94229
This repository contains two classification projects: credit card default prediction and mobile price classification along with streamlit deployment .
zohrehTofighi
No description available
Machine learning models are used to predict the prices of mobile phones based on their specifications, due to the increasing demand for smartphones and the vast number of models available. Logistic regression, decision trees, random forest, and XGBoosting algorithms are commonly used to make these predictions.
apoorvaKR12695
Supervised ML- Built a Multi-Class classification model to find the relation between features of a mobile phone(RAM, Internal Memory etc) and its selling price. Model will predict the price range indicating how high the price is.
JatinSadhwani02
I have implemented a Mobile Price Prediction using different Machine Learning Algorithms. This project will classify the price range of the mobile price. The price ranges from 0-3. We’ll discuss the price range in the dataset. Now I have trained a mobile price classification using 3 ML algorithms. This model classifies the range of the mobile based on the different parameters like from camera, touch screen, cores, battery, clock speed, internal memory, battery capacity, etc. After training the model using 3 algorithms, I compared all the models using the graph.
YousrHejy
Predict the price of Mobile using Supervised Machine Learning Algorithms
victorpreston
The app helps farmers optimize crop production through, AI-powered early disease detection, market analytics and market price predictions, precision farming and, maximize profits through smart technology. | Flutter-mobile app | Nodejs TypeScript-Backend
vikram-bhati
No description available
mbsoroush
This project it about predicting the price range of a mobile phone by building a model that takes into account various features provided in the dataset.
Tanwar-12
THE AIM IS TO PREDICT THE PRICE OF CAR USING ALL THE GIVEN FEATURES.
zealptekin
Applied ML pipeline using various classifiers and made prediction in Python
Mobile Price Prediction
saadafp
In this project, I am going to work on Mobile Dataset and predict their prices.
emirkaanozdemr
No description available
SarathiManikandan0
No description available
samousavizade
No description available
devsuvendu
No description available
SurajKad
(Alma_Better Capston Projrct-3)
This repository contains the files related to our project "Smart Mobile Phone Price Prediction Using Machine Learning".
Mobile Price Range Prediction: Use sales data to build a classification model for mobile phone price ranges. Features include battery power, camera, memory, and connectivity. Split data, apply logistic regression, KNN, SVM (linear and rbf), and evaluate using confusion matrices. Select the most accurate model.
tandrimasingha
This is a price prediction system where we can predict the current price of certain items. The items include Car, Gold, Bitcoin, Mobile and Avocado price.
HashemIlI
Mobile phone purchase prediction project. Classification model based on phone features to recommend the best price range/category for customers.
PratishMashankar
An end to end mobile price prediction model deployed using AWS SageMaker
im45145v
No description available
PremPatil0129
No description available
sowmyakommepalli1-wq
Mobile Price Prediction uses machine learning to estimate smartphone price ranges based on features like RAM, battery, camera, storage, and connectivity, helping users and businesses make informed pricing decisions.
SumangalKhatua
A dataset of mobile phone information such as brand, model, specs, and historical prices is used to train a machine learning model for mobile price prediction. The model predicts future prices based on patterns found in the data. A large and diverse dataset is important for accurate predictions.