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.

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.

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.

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.

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.

The Phone Kart project is designed to develop a machine learning-based predictive system capable of classifying mobile phones into specific price categories based on their technical specifications. The primary aim is to assist online marketplaces, retailers, and customers in estimating the appropriate price range for a given phone configuration.

The Phone Kart project is designed to develop a machine learning-based predictive system capable of classifying mobile phones into specific price categories based on their technical specifications. The primary aim is to assist online marketplaces, retailers, and customers in estimating the appropriate price range for a given phone configuration.

Mobile Price Classification classify mobile price range

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

A case study to classify mobile price ranges based on given specifications

Here we need to classify the price range of a mobile based on given features

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.

Discover my GitHub repository for mobile price classification. Explore projects, code samples, and machine learning algorithms to accurately classify mobile phones based on their price ranges. Ideal for data enthusiasts and mobile industry professionals. Start exploring now!

This project focuses on classifying mobile phones into price ranges based on hardware specifications and screen dimensions. The dataset includes 2,000 rows and 21 features, with the target variable price_range divided into four classes.

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

Mobile Price Range Prediction A machine learning project that classifies mobile phones into price categories (low, medium, high, very high) based on their specifications. It uses multiple models, compares their performance, applies hyperparameter tuning, and visualizes results to identify the most accurate model for price prediction.

This project aims to develop a predictive model to classify mobile phones into different price ranges based on their features. By leveraging machine learning algorithms, specifically Logistic Regression, the project analyzes various technical specifications of mobile phones to predict their corresponding price category.

The Mobile Price Prediction project aims to build a learning model that predicts the price range of a mobile phone based on its technical specifications. Using features such as battery power, RAM, screen size, camera quality, and more, the model classifies a phone into predefined price categories (e.g., low, medium, high, premium).

The task is to build a predictive model that can accurately classify mobile phones into predefined price ranges based on various attributes such as battery power, camera features, memory, connectivity options, and more.

The project uses a Machine Learning model to accurately classify the price range of a mobile, given its features. We use a dataset that contains the features of a mobile that affect its price, namely processor clock speed, RAM capacity, front camera, primary camera, internal memory and so on.

The dataset contains specifications of 2000 mobile phones with 20 numerical features such as RAM, battery power, camera, screen resolution, and connectivity. The target variable `price_range` classifies phones into four categories: low, medium, high, and very high cost.

This project builds machine learning models to classify mobile phones into different price ranges based on their specifications. It includes data preprocessing, feature engineering, Exploratory Data Analysis (EDA), and the application of classification algorithms with hyperparameter tuning for better accuracy.

Classify Mobile Price Range

classify mobile price range

Classify Mobile Price Range

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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.