LAPTOP PRICING DATASET

This dataset is a collection of features related to various laptops, such as brand, processor type, RAM, storage capacity, and other specifications. The dataset also includes the corresponding prices of these laptops. This dataset can be used for regression analysis to predict the prices of laptops based on their features.

Project goal: cleaning the dataset.

Table of Contents

1. Download libraries

2. Import dataset

3. Identify and handle missing values

4. Correct data format

5. Data Standardization

6. Data Normalization

7. Binning

8. Indicator variables

Parameters

The parameters used in the dataset are:

1. Manufacturer. The company that manufactured the laptop

2. Category. The category to which the laptop belongs: This parameter is mapped to numerical values in the following way:

Category - Assigned Value

• Gaming - 1
• Netbook - 2
• Notebook - 3
• Ultrabook - 4
• Workstation - 5

3. GPU. The manufacturer of the GPU. This parameter is mapped to numerical values in the following way:

GPU - Assigned Value

• AMD - 1
• Intel - 2
• NVidia - 3

4. OS. The operating system type (Windows or Linux): This parameter is mapped to numerical values in the following way:

OS - Assigned Value

• Windows - 1
• Linux - 2

5. CPU_core. The type of processor used in the laptop: This parameter is mapped to numerical values in the following way:

CPU_core - Assigned Value

• Intel Pentium i3 - 3
• Intel Pentium i5 - 5
• Intel Pentium i7 - 7

6. Screen_Size_cm. The size of the laptop screen is recorded in cm.

7. CPU_frequency. The frequency at which the CPU operates, in GHz.

8. RAM_GB. The size of the RAM of the system in GB.

9. Storage_GB_SSD. The size of the SSD storage in GB is installed in the laptop.

10. Weight_kg. The weight of the laptop is in kgs.

11. Price. The price of the laptop is in USD.