/MOBILE-PHONE-PRICE-RANGE-PREDICTION

This project aims to predict the price range of mobile phones based on various features such as specifications, brand, and other factors. By analyzing the data, the model can help users estimate the price range of a mobile phone before purchasing it.

Primary LanguageJupyter Notebook

Mobile Phone Price Range Prediction

This project aims to predict the price range of mobile phones based on various features such as specifications, brand, and other factors. By analyzing the data, the model can help users estimate the price range of a mobile phone before purchasing it.

Features

  • Data preprocessing and cleaning
  • Exploratory data analysis (EDA)
  • Model building and evaluation
  • Price range prediction for mobile phones

Technologies Used

  • Python
  • Pandas
  • NumPy
  • Scikit-learn
  • Matplotlib
  • Seaborn

Prerequisites

  • Python 3.x
  • Jupyter Notebook or any other preferred IDE

Installation

  1. Clone the repository:

    git clone https://github.com/iqbaljntra/MOBILE-PHONE-PRICE-RANGE-PREDICTION.git
cd MOBILE-PHONE-PRICE-RANGE-PREDICTION

  2. Create a virtual environment and activate it:

    python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

  3. Install the required packages:

    pip install -r requirements.txt

Usage

  1. Open the Jupyter Notebook:

    jupyter notebook

  2. Open the notebook file mobile_phone_price_prediction.ipynb and run the cells to execute the code step-by-step.

Data Description

The dataset contains various features related to mobile phones such as brand, specifications, and price range. Key features include:

  • battery_power: Battery power in mAh
  • ram: RAM capacity in GB
  • camera: Megapixels of the primary camera
  • price_range: Price range category (e.g., low, medium, high)

Model Building

The model is built using the following steps:

  1. Data preprocessing: Handling missing values, encoding categorical features, etc.
  2. Exploratory Data Analysis (EDA): Understanding the data distribution and relationships between features.
  3. Model training: Using various algorithms like Decision Trees, Random Forests, Gradient Boosting, etc.
  4. Model evaluation: Evaluating the performance of the models using metrics like accuracy, precision, recall, and F1-score.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

  • The dataset used for this project is sourced from [appropriate source, e.g., Kaggle, if applicable].
  • Thanks to the contributors of various Python libraries used in this project.

Contact

If you have any questions or suggestions, feel free to contact me at iqbaljntra@gmail.com.