Mobile Price Classification Project

This project focuses on classifying the estimated price range of mobile phones based on various features. We employed two machine learning algorithms, Naive Bayes and Support Vector Machine (SVM), for the classification task. Additionally, the project involved statistical analysis and feature standardization to improve the models' performance.

Project Overview

The goal of this project was to build a predictive model that can classify mobile phones into different price ranges based on their features. We used a dataset containing various attributes of mobile phones, such as RAM, battery capacity, camera quality, and more, along with their corresponding price ranges.

Dataset

The following dataset was used in this project: https://drive.google.com/file/d/1Ea7dmnS8GjyZHZHT6YEnH4rLAwMlU5hd/view?usp=sharing

Data Preprocessing

To prepare the data for modeling, we conducted several data preprocessing steps:

Feature Standardization:

To ensure fair treatment of features with different scales, we standardized the numerical features before feeding them to the models.

Feature Selection:

We performed exploratory data analysis and statistical analysis to identify the most significant features for the classification task.

Models

We trained and evaluated two machine learning algorithms for the classification task:

Naive Bayes Classifier:

A probabilistic algorithm based on Bayes' theorem, well-suited for classification tasks.

Support Vector Machine (SVM):

A powerful algorithm for both binary and multiclass classification, capable of handling complex decision boundaries.

Results

The models were able to effectively classify mobile phones into their respective price ranges with high accuracy. The performance of Naive Bayes and SVM was compared, and both algorithms showed promising results.

Conclusion

In conclusion, this project successfully utilized Naive Bayes and SVM algorithms for mobile price range classification based on various mobile features. By conducting statistical analysis and standardizing the features, we enhanced the models' predictive capabilities. The project demonstrates the potential of machine learning techniques in predicting price ranges for mobile phones, which can be valuable for various market analysis and decision-making processes.

How to Use

To replicate or extend this project, follow these steps:

• Clone the repository to your local machine.
• Open the Jupyter Notebook files to access the code and analysis.
• Execute the code cells to perform the classification task and analyze the results.