✨ 💻 ✨
- In this project, a supervised machine learning model is built to predict tentative laptop price based on its specifications.
- This model is trained on dataset which is taken from kaggle.
- The dataset contains laptop specifications and corresponding prices.
- The description of this dataset is as following:
| Column name | Description |
|---|---|
| Company | Laptop manufcturing company names |
| TypeName | Type of laptop (Notebook, Ultrabook, 2in1, etc.) |
| Inches | Laptop screen size in inches |
| ScreenResolution | Screen resolutions with screen display type |
| Cpu | CPU name with speed in GHz |
| Ram | RAM size of laptop in GB |
| Memory | Memory type and size of memory in GB and TB |
| Gpu | GPU name with their series |
| OpSys | Operating System of laptop |
| Weight | Weight of laptop in kg |
| Price | Laptop price in ( ₹ ) Indian Rupee |
- Scikit-learn library is used to build the machine learning model.
- Streamlit is used to make a web application that allows users to select the laptop specifications and user gets tentative price of the laptop.
- numpy
- pandas
- matplotlib
- seaborn
- scikit-learn
- pickle
- streamlit
- Data preprocessing
- EDA
- Algorithm selection
- Training
- Evaluation
- Prediction
- Use the Command Prompt to open the Streamlit app in a browser:
- Menu:
- Preprocessed Data:
- Price for the default options chosen:
- Choosing options for price prediction:
Nehal Gund
Contributions and issues requests are welcome!
Give a ⭐ if you like this project!