We started building our own company for mobile phones (phoneky). We want to fight a tough battle with big companies like Apple, Samsung, etc. We don't know how to estimate the price of cell phones made by our company. In this competitive market for mobile phones, it is not enough to make random predictions and assumptions. To solve this problem, we collect mobile phone sales data from different companies.
phoneky want to discover some relationship between the features of a mobile phone (eg: - RAM, internal memory, etc.) and the selling price. In this problem, you don't have to predict the actual price but the price range indicates how high the price will be
In this Project, based on the mobile Specification like Battery power, 3G enabled, wifi, Bluetooth, Ram, etc we are predicting the Price range of the mobile. This kind of prediction will help us estimate the price of mobiles to give tough competition to manufacturers Also it will be useful for Consumers to verify that they are paying the best price for mobile.
Dataset as 21 features and 2000 entries, we will use data mobile phone from Kaggle.
Here are the features of the dataset:
| Field Name | Description |
|---|---|
| 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 |
These are the technologies and libraries that I will be using for this project:
- Technologies: Python, Jupyter Notebook.
- Libraries: NumPy, Pandas, Matplotlib, Seaborn, plotly, Scikit-learn.
Please feel free to let me know if you have any questions. Email: [email protected] and [email protected]