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.

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

Mobile_PriceRange_Classification(LR,DT,RF,SVM,NB)

Classification of a mobile phone's price range using SVM with PCA

A machine learning model that predicts mobile phone price ranges using Support Vector Machines (SVM). Features include comprehensive data analysis, interactive visualizations, and accurate price range classification.

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.

📊 Comparative performance analysis of various classification algorithms (Logistic Regression, Decision Tree, Random Forest, SVM, KNN, Naive Bayes) on a mobile price range dataset using Python and scikit-learn.

Multi-class classification project predicting mobile price ranges using ML models (DT, RF, SVM, Ensemble). Includes EDA, preprocessing, feature engineering, model evaluation, and a futuristic Streamlit dashboard for real-time predictions and analytics.

Mobile Price Classification is a machine learning project that predicts a smartphone’s price range using technical features. It involves EDA, feature analysis, model training, and evaluation using algorithms like KNN, SVM, and Random Forest, with cross-validation and performance comparison.

This mobile price classification project explores and evaluates various machine learning algorithms to predict phone price ranges based on technical specs like RAM and battery power. By comparing models like SVM and Logistic Regression, it provides insights for consumer decisions and business strategic planning

Machine learning project for predicting mobile price ranges using KNN, Decision Tree, Random Forest, SVM, and Logistic Regression. Includes data preprocessing, feature selection, training, and evaluation. Provides accuracy scores, classification reports, and confusion matrices for model performance comparison.

This project implements a robust ML classification pipeline to predict mobile phone price ranges. It features extensive EDA with Seaborn, strategic feature engineering, and a comparison of 5 models including SVM and XGBoost. Through hyperparameter tuning, the model identifies RAM and battery as key price drivers.

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.