Laptop Pricing
Laptop Pricing
AI Intermediate Level
- Submission
- Requirements
- Target
- Dataset
- Overview
🔒 Submission must include
Data cleaning steps ✔️
Data preparation and preprocessing ✔️
Data visualization (graphs/charts) ✔️
ML Life Cycle application ✔️
Applying a suitable ML or DL algorithm ✔️
Brief report (methodology + results + insights) ✔️
Key Takeaways🔹
Dataset
Laptop Price – Kaggle
Goal
Develop a machine learning model to predict laptop prices (Euro) based on technical specifications.
Evaluation
Evaluate the model using regression metrics
Task Requirements 📝
ML Lifecycle
Follow end-to-end process
Understanding → Preprocessing → EDA → Feature Engineering → Modeling → Evaluation → Reporting
Algorithm Selection
Evaluation Metrics
Deliverables
Cleaned notebook (code + explanations + visualizations)
Report (PDF or Word) detailing methodology and insights
PowerPoint presentation prepared by students to present their work
Target
The target variable of the dataset is
Price_euros
This represents the price of each laptop in Euros—a continuous value suited for regression modeling
Dataset Description
Source: Kaggle – “Laptop Price” dataset by Muhammet Varlı
Format: CSV
Records: ~1,303 (varies slightly based on cleaning)
Features: Includes categorical and numerical attributes like company, model, type, screen size, resolution, CPU, RAM, memory, GPU, operating system, weight, and price
Note
Categorical features need encoding (Label / One‑Hot)
The dataset may contain outliers (e.g., extremely high prices)
Numeric features should be scaled for regression
Feature extraction may include parsing screen resolution or splitting memory details
Dataset downloaded
Energy Consumption
Problem Statement
With the wide range of laptop brands, specifications, and form factors available in the market, pricing a laptop accurately can be complex. Many customers and sellers rely on intuition or brand reputation rather than data when estimating fair market prices.
The objective of this project is to build a machine learning model that can predict the price of a laptop (in Euros) based on its technical specifications. These include screen size, resolution, processor type, RAM, storage, GPU, operating system, and weight.
By leveraging historical data of laptops and their features, the model should learn the relationship between a laptop’s configuration and its price, enabling better decision-making for consumers, retailers, and manufacturers.
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