Overview Welcome to the repository for my capstone project titled "Mobile Price Range Classification." This project focuses on building a classification model that can predict the price range of mobile phones based on various features. By analyzing mobile phone specifications and leveraging machine learning, we aim to develop a model that can accurately classify mobile phones into different price ranges.
Table of Contents
Introduction The mobile phone market is highly competitive, with phones available at various price ranges. This project aims to build a predictive model for classifying mobile phones into different price categories based on their specifications. Such a model can assist consumers, retailers, and manufacturers in making informed decisions.
Dataset The dataset used for this project consists of mobile phone specifications, including features like RAM, internal storage, battery capacity, camera quality, and more. Each mobile phone entry is labeled with its corresponding price range, making it suitable for classification tasks.
Project Goals The primary objectives of this capstone project are:
- To preprocess and prepare the mobile phone data for classification analysis.
- To perform feature engineering to create relevant features for modeling.
- To implement and evaluate different classification models for price range prediction.
- To select the best-performing model and provide classification results for mobile phones.
Methods Used
1. Data Preprocessing Data preprocessing includes handling missing values, encoding categorical features, and ensuring data quality. Additionally, we will split the data into training and testing sets for model development and evaluation.
2. Feature Engineering Feature engineering is a crucial step in creating relevant features that can influence the classification of mobile phones into price ranges. This involves selecting important features, scaling data, and creating new features when necessary.
3. Classification Models Multiple classification models will be implemented and evaluated, including Logistic Regression, XG Boost, Random Forest, and KNN. Model performance will be assessed using various classification metrics.
4. Evaluation Model evaluation will be based on classification metrics such as accuracy, precision, recall, F1-score, and confusion matrices. These metrics will help assess the model's accuracy in predicting mobile phone price ranges.
Results The results section will present the findings of the classification analysis, including the performance of different models and their classification results for mobile phone price ranges.
Conclusion This capstone project aims to provide an accurate classification model for categorizing mobile phones into price ranges. It can be valuable for consumers, retailers, and manufacturers in understanding the pricing structure of mobile phones. The conclusion section will summarize the project's outcomes and potential future work.