https://github.com/vipinchaudhary31122002/bytephone
A data science project that explores, visualizes, and models smartphone features from a futuristic 2025 dataset to predict mobile prices using machine learning.
https://github.com/vipinchaudhary31122002/bytephone
machine-learning matplotlib numpy pandas python3 scikit-learn seaborn streamlit
Last synced: 3 months ago
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A data science project that explores, visualizes, and models smartphone features from a futuristic 2025 dataset to predict mobile prices using machine learning.
- Host: GitHub
- URL: https://github.com/vipinchaudhary31122002/bytephone
- Owner: Vipinchaudhary31122002
- Created: 2025-03-25T06:09:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-07T22:22:07.000Z (over 1 year ago)
- Last Synced: 2025-04-12T15:18:45.830Z (over 1 year ago)
- Topics: machine-learning, matplotlib, numpy, pandas, python3, scikit-learn, seaborn, streamlit
- Language: Jupyter Notebook
- Homepage: https://bytephone-dmfvsu2zgckhnthkhhpbaq.streamlit.app/
- Size: 7.58 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📱 BytePhone 2025: Mobile Price Prediction
A data science project that explores, visualizes, and models smartphone features from a futuristic 2025 dataset to predict mobile prices using machine learning.
## 📊 Project Overview
This notebook focuses on a comprehensive workflow involving:
- Exploratory Data Analysis (EDA)
- Data preprocessing and feature engineering
- Model training using `RandomForestRegressor`
- Evaluation using MAE, RMSE, and R² metrics
The goal is to understand which features most influence mobile pricing and build a predictive model for estimating smartphone prices.
## 📁 Dataset
The dataset used is named **"Mobiles Dataset (2025)"**, which includes the following features:
- RAM (GB)
- Storage (GB)
- Battery Capacity
- Display Size
- Camera Specs (Front & Back)
- Mobile Weight
- Price (Target variable)
## 🚀 How to Run
1. Clone this repo:
```bash
git clone https://github.com/yourusername/bytephone-2025.git
cd bytephone-2025
```
2. Install required packages:
```bash
pip install -r requirements.txt
```
3. Run the notebook:
```bash
jupyter notebook bytephone-2025.ipynb
```