Prediction. A simple Machine learning web app deployed which predict laptop prices according to laptop configuration defined by user.
Running the Application Start the application:
Copy code streamlit run app.py Open your web browser and go to http://localhost:5000 to access the application.
Enter the laptop specifications into the form. Click on the "Predict Price" button. The predicted price will be displayed on the screen. Batch Predictions Prepare a CSV file with the laptop specifications. Upload the CSV file through the application. Download the CSV file with the predicted prices.
The dataset used for training the model includes various features such as:
Brand Model Processor RAM Storage Graphics Card Screen Size Operating System Weight You can obtain the dataset from the data folder in this repository or use your own dataset with similar features.
The model training process involves the following steps:
Data Preprocessing: Cleaning and preparing the data for training. Feature Engineering: Creating new features or transforming existing ones. Model Selection: Choosing the appropriate machine learning algorithm. Training: Training the model using the prepared data. Evaluation: Evaluating the model's performance using metrics like RMSE, MAE, and R^2. Model Training The model training process involves the following steps:
Data Preprocessing: Cleaning and preparing the data for training. Feature Engineering: Creating new features or transforming existing ones. Model Selection: Choosing the appropriate machine learning algorithm. Training: Training the model using the prepared data. Evaluation: Evaluating the model's performance using metrics like RMSE, MAE, and R^2.
We welcome contributions to the Laptop Price Prediction System! Here are some ways you can help:
Reporting bugs and issues Suggesting new features Improving documentation Contributing code
Fork the repository. Create a new branch (git checkout -b feature-branch). Commit your changes (git commit -am 'Add new feature'). Push to the branch (git push origin feature-branch). Create a new Pull Request.