mobile-price-prediction

This project aims to predict the price of mobile phones using machine learning. It uses a dataset of mobile phone specifications, including brand, RAM, ROM, camera, battery, and processor, to train a Decision Tree Regressor model. Key Features:

• Data cleaning and preprocessing
• Feature engineering
• Model training and evaluation
• Price prediction for new mobile phones How to use:

1. Clone the repository.
2. Install the required libraries.
3. Run the Jupyter Notebook mobile_price_prediction.ipynb.
4. Enter the specifications of the mobile phone you want to predict the price for.
5. The predicted price will be displayed. Dataset: The dataset used in this project is mobile_prices_2023.csv. It contains information about various mobile phones, including their specifications and prices. Model: The model used in this project is a Decision Tree Regressor. It is a supervised learning algorithm that uses a decision tree to predict the target variable. Evaluation: The model's performance is evaluated using the R-squared metric. The R-squared value of the model is high, indicating that it is a good predictor of mobile phone prices.