Mobile Price Prediction

Project Overview

This project aims to predict the price of mobile phones based on various features such as brand, storage, RAM, screen size, camera specifications, and battery capacity. The dataset used consists of data about various mobile phones, including their specifications and prices. The objective is to apply machine learning algorithms to build a model that can predict the price of a mobile phone given its specifications.

Key Features

• Data Preprocessing: The dataset is cleaned and preprocessed, including removing spaces from column names, converting units, and handling missing values.
• Exploratory Data Analysis (EDA): Various visualizations are used to explore the relationship between mobile phone features and their price.
• Feature Engineering: Categorical features like brand and model are transformed using one-hot encoding, and numerical features are processed for analysis.
• Outlier Removal: Outliers in numerical features are removed to improve model performance.
• Model Training: Multiple machine learning models, such as Linear Regression, Ridge Regression, Random Forest, XGBoost, and others, are used to predict the price of the mobile phones.
• Model Evaluation: The models are evaluated based on their R-squared score, with the best model being selected for final predictions.

Purpose and Applications

The main purpose of this project is to predict mobile phone prices based on their features, which can be useful in:

• E-commerce platforms: Predicting mobile phone prices for online retailers.
• Price comparison websites: Helping consumers compare mobile phone prices.
• Market analysis: Understanding how different features of mobile phones affect their price.
• Sales forecasting: Helping mobile phone manufacturers or sellers predict the potential price of new models.

Installation

1. Clone the repository:

   git clone https://github.com/BhaveshBhakta/Mobile-Price-Prediction-Using-XgBoost.git

2. Navigate to the project folder:

   cd Mobile-Price-Prediction-Using-XgBoost

3. Run the Jupyter notebook

Collaboration

Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.