This project is a Machine Learning application designed to predict the price range of mobile phones based on their hardware specifications (like RAM, Battery Power, and CPU cores). It uses a Support Vector Machine (SVM) model to classify devices into four distinct price categories with high accuracy.

Supervised Machine Learning Analysis Using Classification Models

This repository contains two classification projects: credit card default prediction and mobile price classification along with streamlit deployment .

SVM on mobile prices data set

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.

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.

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A machine learning project to classify mobile phone price ranges (low, medium, high, very high) using Decision Tree, Random Forest, and SVM. The models are compared on performance, and the best model is applied to unseen test data.

A Mini-project developed for a college assignment. Involves the use of popular packages to solve a classification problem of the Mobile Price Classification Dataset from Kaggle

Data analysis + Data preprocessing + Modeling with (decision tree , random forest , SVM)

Trying different classification models on mobile price data.

the final project of the Data Science Bootcamp with Coding Dojo Academy & Saudi Digital Academy

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

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First project with deep learning methods to learning...

Price Classification of Mobile Phone using Different Machine learning Model

Dataritz Phone Price Classification

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A classification model to predict whether price range of mobile based on certain specifications

Mobile phone purchase prediction project. Classification model based on phone features to recommend the best price range/category for customers.

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.

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Mobile Price Classification

An entrepreneur has started his own mobile company. He wants to give a tough fight to big companies like Apple, Samsung etc. He does not know how to estimate price of mobiles his company creates. In this competitive mobile phone market, one cannot simply assume things. To solve this problem, he collects sales data of mobile phones of various companies. He wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory etc) and its selling price. But he is not so good at Machine Learning. So, he needs your help to solve this problem. In this problem you do not have to predict actual price but a price range indicating how high the price is

the predictions may change.

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This repository includes the Mobile Price Classification model by using the dataset from Kaggle (https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data).