# 📱 Phone Price Prediction

## 🚀 Introduction
The **Phone Price Detector** project aims to develop a machine learning model capable of predicting smartphone prices based on various features. By leveraging **Object-Oriented Programming (OOP)** principles, we will create a modular and maintainable codebase. This project includes **data preprocessing, implementing the K-Nearest Neighbors (KNN) algorithm, and utilizing classifiers** to achieve accurate price predictions.

## 🎯 Objectives
1. **🛠 Utilize OOP Concepts**
   - Design the system using **object-oriented programming** to encapsulate data and methods related to phone features and pricing.
2. **📊 Data Preprocessing**
   - ✅ Clean and analyze the dataset to ensure high-quality input for the model.
   - ✅ Handle missing values, outliers, and categorical data encoding.
3. **📈 KNN Implementation**
   - Implement the **K-Nearest Neighbors (KNN)** algorithm for price prediction.
   - Optimize hyperparameters to improve model performance.
4. **📉 Evaluation Metrics**
   - Display **accuracy, precision, recall, and F1 score** to evaluate model performance.
   - Provide insights into model reliability.
5. **📲 User Input Features**
   - Enable users to input smartphone features for price prediction.
   - Present prediction results along with evaluation metrics.
# Screen shots
## Home
![Home](https://github.com/user-attachments/assets/91614c81-4c4e-4906-8991-fd0ce9fae488)
## Predicting Price
![Predicting](https://github.com/user-attachments/assets/d210bc65-39bc-49aa-a84a-45f34d842e9d)
### Result
![Result](https://github.com/user-attachments/assets/32a8cb42-dd70-4e11-83c5-a308b64d6aff)

## View Phones list
![Phoneslist](https://github.com/user-attachments/assets/097c9391-3c96-4741-a878-b7a131234b9c)

## 🔍 Methodology
1. **📥 Data Collection**
   - Gather a **comprehensive dataset** containing smartphone features such as brand, model, RAM, storage, camera specifications, and their corresponding prices.
2. **🛠 Data Preprocessing**
   - Use **Pandas** and **NumPy** for data manipulation.
   - Clean the dataset by removing duplicates and handling missing values.
   - Analyze feature relationships using visualizations with **Matplotlib** and **Seaborn**.
3. **🤖 Model Development**
   - Implement **KNN, ANN, or Naïve Bayes** algorithms using **scikit-learn** based on requirements.
   - Train the model on the preprocessed dataset.
   - Use cross-validation techniques for better performance assessment.
4. **📊 Model Evaluation**
   - Calculate and display **accuracy, precision, recall, and F1 score** using **scikit-learn's metrics module**.
   - Provide insights into model effectiveness.
5. **💻 User Interface**
   - Develop an **interactive Jupyter Notebook** environment for users to input features and view predictions.

## 🛠 Tools and Technologies
- **💻 Programming Language:** Python
- **🛠 Development Environment:** Jupyter Notebook
- **📚 Libraries Used:**
  - 📊 `pandas` for data manipulation
  - 🔢 `numpy` for numerical operations
  - 📉 `matplotlib` and `seaborn` for data visualization
  - 🤖 `scikit-learn` for implementing KNN and evaluation metrics
  - ➕ Additional libraries as needed

## 🎯 Expected Outcomes
- ✅ A **robust price prediction model** for smartphones based on user-input features.
- 📈 **Detailed evaluation metrics** to assess model performance.
- 💡 An **interactive Jupyter Notebook** for users to explore predictions.

## 🎤 Conclusion
The **Phone Price Detector** project provides valuable insights into smartphone pricing trends, helping users make informed purchasing decisions. By implementing **OOP principles**, we ensure a structured and maintainable codebase, facilitating future improvements and scalability.

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👨‍💻 **Developed by:** Abdullah Bilal Yousif Student of AI
at SZABIST-ISB