Launching a mobile company to rival giants like Apple & Samsung, we leverage sales data to discern price ranges for mobiles based on features via ML models like SVM, DT, & RF. Not aiming for exact prices, but a strategic price bracket.

This app leverages a Support Vector Machine (SVM) classifier with optimized hyperparameters to predict mobile phone price ranges based on specifications.

📱 Predict mobile phone price ranges using specifications with a Support Vector Machine (SVM) model. Includes an interactive Streamlit web app for single & batch predictions, model evaluation, and visualization.

To evaluate all attributes of a phone and develop an ML Classifier Model that can predict the price range.

This Study aims at accurately predicting in what price range a particular mobile falls into , by fitting the data into five classifiers (K-NearestNeighbour, Decision Tree, Random Forest Classifier, Naive Bayes Classifier, and Support Vector Machine Classifier) and identify the best classifier with highest accuracy.

This project demonstrates predicting mobile phone price categories based on technical specifications. A Random Forest Classifier is trained on the dataset to classify phones into four price ranges (low, medium, high, very high). The model achieved ~89.75% accuracy on validation data. Feature importance analysis shows which specifications contribute

A model that classifies mobiles in term of price ranges (In part of Mentorness internship program)

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Predicting mobile phone price ranges from hardware specs using Logistic Regression and Random Forest.

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we classify different mobiles into different price range based on its specifications

ML-based web app to classify mobile phones into price ranges using features like RAM, battery, and camera.

This project uses machine learning to predict mobile phone price categories based on features like RAM, battery, and camera. It is built using Streamlit and supports both single and batch predictions through a web interface. The model also provides cost estimation by mapping predicted categories to approximate price ranges in ₹.

Mobile Price Range Classifier predicts mobile price categories using machine learning based on given specifications.

Mobile Price Range Classifier - streamlit App

Classifies mobile phones into price ranges using features like RAM, battery, and memory.

Machine learning classifier project about Mobile price range prediction

self project - Mobile Price Range Prediction Preject 3 (Classifier)

Classifying mobile phone price ranges using a Support Vector Machine (SVM) classifier based on device features.

Machine learning project using AWS SageMaker to build, train, and deploy a Random Forest classifier that predicts mobile phone price ranges.

Mobile Price Range Prediction Model: Developed a Decision Tree Classifier to predict mobile phone price ranges based on various features. Conducted thorough exploratory data analysis and visualization to understand data patterns. Achieved high accuracy on the test set.

A comprehensive end-to-end machine learning operations (MLOps) project demonstrating mobile phone price range classification using AWS SageMaker, Scikit-Learn, and RandomForest classifier.

A machine learning model that predicts the price range of mobile phones based on hardware features like RAM, battery, and screen resolution using Random Forest Classifier.

Objective: To predict price range of the mobile for test data and to check the accuracy of the classifiers Decision tree, Logistic Regression classifier, K- Nearest Neighbor, Random forest model

This project demonstrates an end-to-end Machine Learning pipeline that predicts mobile phone price ranges using traditional ML models. The workflow includes data preprocessing, training a Random Forest Classifier on **AWS SageMaker**, deploying the model as an endpoint, and running live predictions.

A Jupyter notebook which applies a Gaussian Naive Bayes classifier to mobile phone specification data, performs classification according to price range, and then varies the training dataset size to assess performance by creating a learning curve manually. The learning curve is then compared to the sklearn learning_curve function.

A System that predicts a mobile phone's price range using a Random Forest Classifier. The project includes a fully integrated machine learning pipeline: data loading, preprocessing with StandardScaler, model training, and evaluation. It features a user-friendly interface for real-time predictions and model performance visualization. 📱🤖

This project demonstrates how a **Support Vector Machine (SVM)** classifier can be used to predict the **price range** of a mobile phone based on its specifications. The goal is not just to build a high-performing model, but to understand how SVM handles **multi-class classification** with structured tabular data.