Laptop Prices Analysis and Prediction

A Machine Learning project analyzing a dataset of laptops to understand price determinants and predict laptop prices and types. This project involves data analysis, visualization, and machine learning using Python.

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Project Description

This project explores a dataset containing information on various laptop models, including:

• Brand, Product, and Type
• Screen size and resolution
• RAM, storage, and CPU/GPU specifications
• Operating system, weight, and price in euros

The goal is to analyze the factors affecting laptop prices, build predictive models, and visualize data patterns.

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Dataset

The dataset laptop_prices.csv contains the following columns:

• Company, Product, TypeName, Inches, Ram, OS, Weight
• Price_euros, Screen, ScreenW, ScreenH, RetinaDisplay
• CPU_company, CPU_freq, CPU_model
• PrimaryStorage, SecondaryStorage, PrimaryStorageType, SecondaryStorageType
• GPU_company, GPU_model

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Project Goals

The project addresses the following tasks:

1. Brand Analysis: Identify the top 5 laptop brands by the number of products.
2. Price Analysis: Calculate average laptop prices per brand and find the highest/lowest average.
3. Correlation Analysis: Find correlations between Price_euros and numeric features like CPU_freq, Ram, Inches, and Weight.
4. Feature Engineering: Create a new feature StorageTotal = PrimaryStorage + SecondaryStorage.
5. Regression Modeling: Predict Price_euros using features like Ram, Inches, CPU_freq, PrimaryStorage, and GPU_company. Suggest the best regression model.
6. Classification Modeling: Predict TypeName using features like Inches, Ram, PrimaryStorage, and Weight. Identify important features and the best classification model.

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Usage

1. Load the dataset in a Jupyter Notebook or Google Colab:

import pandas as pd

df = pd.read_csv("laptop_prices.csv")

2. Perform analysis and modeling using Python libraries like pandas, numpy, matplotlib, seaborn, and scikit-learn.

3. Follow the project goals to answer all analysis and modeling questions.

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Libraries Required

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

Install via pip if needed:

pip install pandas numpy matplotlib seaborn scikit-learn