This app leverages a Support Vector Machine (SVM) classifier with optimized hyperparameters to predict mobile phone price ranges based on specifications.
The Mobile Price Classification project aims to classify mobile phones into different price ranges using their specifications. The model is built using a Support Vector Machine (SVM) classifier, which has been optimized for better performance.
- Predicts mobile phone price ranges based on various specifications.
- Utilizes a Support Vector Machine (SVM) for classification.
- Optimized hyperparameters for improved accuracy.
The dataset used for this project includes various features of mobile phones, such as:
- Battery capacity
- Screen size
- RAM
- Internal memory
- Camera resolution
- Mobile weight
- Connectivity features (Bluetooth, Wi-Fi, 4G support)
To set up the project, follow these steps:
- Clone the repository:
git clone https://github.com/sumoondev/Mobile-Price-Classification.git cd Mobile-Price-Classification/FDS - Install the required dependencies:
pip install pandas numpy scikit-learn matplotlib seaborn
To use the application, run the main script or Jupyter Notebook provided in the repository. The steps include:
Preprocess the data (handle missing values, normalize features). Train the SVM model with the training data. Evaluate the model on the test data.
The model's performance is evaluated using metrics such as:
Accuracy Precision Recall F1-score Hyperparameter tuning is performed to optimize the SVM classifier for better results.
The SVM classifier achieved a high accuracy rate in predicting mobile phone price ranges. Detailed results, including confusion matrices and classification reports, are available in the results section of the notebook.