- Description_of_data
- Overview
- Installation
- Technologies Used
- Bug / Feature Request
- Directory Tree
- Future scope of project
- id:ID
- battery_power:Total energy a battery can store in one time measured in mAh
- blue:Has bluetooth or not
- clock_speed:speed at which microprocessor executes instructions
- dual_sim:Has dual sim support or not
- fc:Front Camera mega pixels
- four_g:Has 4G or not
- int_memory:Internal Memory in Gigabytes
- m_dep:Mobile Depth in cm
- mobile_wt:Weight of mobile phone
- n_cores:Number of cores of processor
- pc:Primary Camera mega pixels
- px_height:Pixel Resolution Height
- px_width:Pixel Resolution Width
- ram:Random Access Memory in Megabytes
- sc_h:Screen Height of mobile in cm
- sc_w:Screen Width of mobile in cm
- talk_time:longest time that a single battery charge will last when you are
- three_g:Has 3G or not
- touch_screen:Has touch screen or not
- wifi:Has wifi or not
In this Project,On the basis of the mobile Specification like Battery power, 3G enabled , wifi ,Bluetooth, Ram etc we are predicting Price range of the mobile
After Learning all the concepts it is important to work on application (real world application) to actually make a difference.This is aim of this project
The Code is written in Python 3.7 If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository.
├── Mobile Price Range Prediction.ipynb
├── test.csv
├── train.csv
If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result
- Try Still with dif Algorithms with some validations
- Deploy this project
- Front-End
- Use deep learning