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Laptop Price Modelling Dataset
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About
This dataset is designed for machine learning regression problems, particularly focusing on laptop price prediction. It serves as a valuable resource for data scientists, machine learning enthusiasts, and researchers keen to explore and analyse various laptop specifications. The dataset provides an extensive collection of laptop attributes and features, making it an ideal tool for diverse analytical and modelling tasks. It includes information on thousands of laptops, covering a wide array of brands, models, and configurations, from entry-level to high-end devices. Each entry offers a plethora of attributes, such as processor details, memory capacity, storage size, display characteristics, graphics capabilities, battery life, and operating system.
Columns
- Company: The name of the laptop's manufacturer.
- TypeName: Describes the specific type or category of the laptop (e.g., Notebook, Gaming).
- Ram: The amount of RAM (Random Access Memory) in GB.
- Weight: The weight of the laptop, likely in kg.
- Price: The price of the laptop, dependent on all other features.
- TouchScreen: A binary indicator (0 or 1) showing whether the laptop has a touchscreen display.
- Ips: A binary indicator (0 or 1) indicating if the laptop screen uses IPS (In-Plane Switching) technology.
- Ppi: Pixels Per Inch, representing the pixel density of the display.
- Cpu_brand: The brand of the Central Processing Unit (CPU) in the laptop.
- HDD: The size of the Hard Disk Drive (HDD) in GB.
- SSD: The size of the Solid State Drive (SSD) in GB.
- Gpu_brand: The brand of the Graphics Processing Unit (GPU) in the laptop.
- Os: The operating system installed on the laptop.
Distribution
The dataset is provided as a data file, typically in CSV format. The specific file is
laptop_data_cleaned.csv, with a size of approximately 126.64 kB. It contains 1273 valid records across all 13 columns, offering a detailed collection of laptop specifications.Usage
This dataset is suitable for various analytical and machine learning applications, including:
- Exploratory data analysis: Uncovering interesting trends, patterns, and correlations among different laptop specifications.
- Predictive modelling: Building models to estimate laptop prices, assess performance benchmarks, or predict user preferences based on specific features.
- Machine learning tasks: Utilisation for classification, regression, clustering, and recommendation systems.
- Model training: Training models to predict laptop performance, user ratings, or identify key factors influencing customer satisfaction.
Coverage
This dataset focuses solely on laptop specifications and attributes. Geographic, time range, and demographic scopes are not specified. Please note that this dataset is a fictional creation intended purely for illustrative purposes in a response.
License
CC0: Public Domain
Who Can Use It
This dataset is primarily intended for:
- Data Scientists: For in-depth analysis and model development.
- Machine Learning Enthusiasts: To practice and apply various machine learning algorithms.
- Researchers: To explore relationships between laptop features, performance, and user preferences, and to identify influencing factors.
Dataset Name Suggestions
- Laptop Specification Data
- Laptop Price Modelling Dataset
- Notebook Feature Collection
- Device Price Prediction Data
- Laptop Attribute Analysis Dataset
Attributes
Original Data Source: Laptop Price Modelling Dataset
QUALITY