Phone Price Prediction Dataset

Dataset Columns:

• Phone Model: Details the phone’s name, brand, and specifications.
• User Rating: The average rating based on customer feedback.
• Review Count: The total number of customer reviews.
• Price (Target Column): The phone’s price, which serves as the target variable for predictive models.
• Image URL: Links to images of the phones, useful for image-based analysis.
• Product Page URL: A link to the product’s details for further exploration.

Use Cases:

This dataset can support several diverse machine learning tasks:

• Text-Based Price Prediction: Utilize features such as phone model, ratings, and reviews with Natural Language Processing (NLP) to estimate prices.
• Image-Based Price Prediction: Download phone images and use Convolutional Neural Networks (CNNs) to predict prices based on visual features.
• Hybrid Prediction Models: Combine textual and image data for more accurate price forecasting.
• Sentiment Analysis: Analyze user reviews to determine sentiment patterns and correlate them with price fluctuations or product quality.
• Market Trend Analysis: Examine patterns in phone characteristics to identify key drivers influencing price, such as brand reputation or technical specifications.

Applications:

• Feature Importance Analysis: Investigate which features (e.g., phone specifications, user reviews) contribute most to price variance.
• Deep Learning Techniques: Explore advanced deep learning methods using multimodal data (text + image) for enhanced predictions.
• Customer Behavior Analysis: Study how product reviews and ratings impact phone pricing dynamics over time.