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.

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

Classification Project: Mobile Price Range Prediction

Classification Project on Mobile Price Range Prediction

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In this Jupyter Notebook we discuss about the ML Classification Project on Mobile Price Range Prediction

A machine learning-powered web application that predicts the price range of mobile phones based on their specifications. Built with Python, Streamlit, and Scikit-learn, this project enables interactive price range predictions using a trained classification model.

This data science project aimed to predict mobile phone price ranges based on their specifications using machine learning algorithms. The dataset consisted of various features related to mobile phones such as battery capacity, RAM, internal memory, camera quality, and other hardware specifications.

The project aims to analyze sales data of mobile phones to uncover the relationship between various features of the phones (such as RAM, internal memory, etc.) and their selling prices. Rather than predicting the exact price of a phone, the goal is to categorize phones into price ranges, indicating their relative price points.

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This data science project aimed to predict mobile phone price ranges based on their specifications using machine learning algorithms.

No description available

No description available

No description available

ML Classification Project on Mobile Price Range Prediction

ML Classification Project on Mobile Price Range Prediction

In today's fiercely competitive mobile phone market, companies are constantly striving to gain a deeper understanding of the dynamics behind mobile phone sales data and the key factors that influence pricing.

Machine learning classification project

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 ba…

I have implemented a Mobile price prediction using different Machine Learning Algorithms. This project will classify the price range of the mobile price. The prices range 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 ba.

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 ba…

Mobile Phone Price Range Prediction is a machine learning project that classifies mobile phones into different price ranges based on technical specifications such as RAM, battery power, camera quality, and processor features. Multiple classification models were trained and evaluated to achieve accurate price prediction.

This project applies K-Nearest Neighbors (KNN) classification to predict mobile phone price ranges based on various technical features. It includes data preprocessing, model training, evaluation, and predictions on test data.

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 Phone Price Prediction In this project, we are going to explore and analyze a dataset which contains specifications of two thousand mobile phones and try to predict optimum price ranges for a list of mobile phones in the market by applying various machine learning algorithms such as logistic regression, decision tree, random forest and k-nearest neighbors(knn). ## Data We split our data into training and validation datasets and apply ML pipeline on these two. Than, run the most accurate model(knn) to predict target values of test dataset. You can reach the dataset from [documentation on ReadTheDocs](https://www.kaggle.com/iabhishekofficial/mobile-price-classification)