Supervised Learning Mobile Price Range Prediction Project

Welcome to the supervised learning mobile price range prediction project repository. This project focuses on predicting the price range of mobile phones using machine learning algorithms. By analyzing various features and attributes of mobile devices, this project assists consumers and retailers in making informed decisions about mobile phone purchases and pricing strategies.

Overview

Mobile price range prediction is essential for consumers to make informed decisions based on their budget and desired features, as well as for retailers to set competitive pricing strategies. This project employs supervised learning techniques to build predictive models capable of classifying mobile phones into different price ranges based on their specifications and features.

Dataset

The dataset used in this project consists of information regarding various mobile phone features such as battery capacity, RAM, camera quality, display size, and other hardware specifications. Additionally, it includes the price range category for each mobile phone. The dataset is preprocessed to handle missing values, encode categorical variables, and normalize numerical features for model development.

Approach

Data Preprocessing: The dataset undergoes preprocessing steps such as handling missing values, encoding categorical variables, and scaling numerical features to prepare it for model training.

Feature Engineering: Relevant features are selected or engineered to capture significant patterns and relationships within the data, enhancing the model's predictive performance.

Model Selection: Different classification algorithms such as logistic regression, decision trees, random forests, and support vector machines (SVM) are explored to identify the most suitable model for mobile price range prediction.

Model Training: The selected classification model is trained on the preprocessed dataset to learn patterns and relationships between mobile phone features and their respective price ranges.

Model Evaluation: The trained model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score to assess its classification performance.

Hyperparameter Tuning: Hyperparameters of the selected model are fine-tuned using techniques like grid search or random search to optimize classification performance further.

Prediction and Deployment: Once the model is trained and evaluated satisfactorily, it can be deployed to predict the price range category of new mobile phones based on their specifications.