Mobile Phone Price Prediction with Feature Extraction This project aims to develop a machine learning model for predicting mobile phone prices based on key features. By understanding the features that most influence price, we can assist a prominent mobile phone seller in refining their pricing strategy.

Project Goals: Build a predictive model to estimate mobile phone prices. Identify the most influential features affecting price through feature extraction. Recommend impactful features for informed pricing and marketing decisions.

Data: The provided dataset contains detailed information on various mobile phones, including: Model, Color Memory, RAM, Battery Capacity Rear/Front Camera Specs AI Lens Presence, Mobile Height Processor Price (target variable)

Methodology: Data Exploration: Analyze dataset structure, data types, and feature value ranges.

Data Preprocessing: Handle missing values, outliers, and inconsistencies. Encode categorical variables (e.g., one-hot encoding).

Feature Extraction: Identify highly impactful features using: Statistical methods (correlation analysis) Feature importance techniques (selection or dimensionality reduction)

Model Building: Split the dataset into training and testing sets. Develop a machine learning model for price prediction (using linear regression, random forests).

Model Evaluation: Assess model performance using metrics like mean absolute error (MAE) and root mean squared error (RMSE).

Feature Importance Analysis: Validate feature importance identified during extraction using model insights.

Reporting & Visualization: Comprehensive report with visualizations summarizing key findings are derived to show the client.

Outcomes: A well-performing model for mobile phone price prediction. Identification of the most influential features affecting mobile phone prices. Actionable recommendations for optimizing pricing and marketing strategies based on feature importance.

Tools and Libraries: This project involves employing: Machine learning libraries (Scikit-learn, TensorFlow, etc.) Data visualization tools (Matplotlib, Seaborn) Data analysis techniques (correlation, feature selection)

Repository Structure Processed_Flipdata.csv/: Contains the dataset used for analysis. Feature_Extraction and price prediction of mobiles phones.ipynb/: Jupyter notebooks for each stage of the analysis. Feature_Extraction and price prediction of mobiles phones_PPT/: Presentation of Project done. Final_mobile_data.csv/: Data after cleaning and featuring engineering README.md: Overview of the project and instructions for replication.

Benefits: This project empowers the mobile phone seller with data-driven insights to: Set competitive and informed prices. Target marketing efforts based on impactful features. This project sets the stage for further exploration into advanced models and market dynamics, ultimately guiding a data-centric approach to mobile phone sales. Conclusion: This project successfully developed a machine learning model for predicting mobile phone prices based on key features. Through feature extraction techniques, we identified the most influential factors impacting price, providing valuable insights for informed decision-making.