GSMarena_PhoneAnalysis

Analyzing smartphone specifications data extracted from GSMarena for insights into market trends, brand preferences, and technological advancements. Includes data extraction scripts, database storage, statistical analysis, machine learning, and visualization tools.

Components

1. Data Extraction Scripts: Utilizes scripts to extract smartphone specifications data from GSMarena website.
2. Database Storage: Designs and implements a database schema to store the extracted data efficiently.
3. Statistical Analysis: Conducts statistical analysis on the dataset to derive meaningful insights and trends.
4. Visualization Tools: Utilizes visualization tools such as Power BI to create interactive dashboards and visual representations of the analyzed data.
5. Machine Learning: Applies machine learning techniques to derive insights and predictions from the smartphone specifications dataset.

Phase 1: Data Extraction

• Utilizing GSM Arena website to extract smartphone data.
• Extracted information includes network compatibility, launch date, body dimensions, display specifications, platform details, memory capacity, camera features, sound output, communication capabilities, additional features, battery type, and miscellaneous details.

Phase 2: Database Design

Database Design:

• Identified main entities such as brands, models, and specifications.
• Determined relationships between entities.
• Defined specific data attributes to be stored for each entity.
• Employed normalization techniques to eliminate data redundancy and ensure data integrity.

Database Technology Selection:

• Selected a suitable Database Management System (DBMS) like MySQL based on scalability and performance requirements.

Phase 3: Statistical Analysis

• Conducted statistical analysis to address various questions and hypotheses using the extracted data.
• Utilized descriptive statistics to gain insights into the dataset.
• Formulated and tested hypotheses related to smartphone characteristics.

Phase 4: Power BI Dashboard

• Developed visualizations and a logical dashboard using the constructed database.
• Designed meaningful charts and graphs to represent key insights from the data.

Phase 5: Machine Learning

In this phase, we apply machine learning to the smartphone specifications dataset from GSMarena to derive insights and predictions. We focus on three key questions:

1. Market Segmentation: Utilize clustering to segment the smartphone market based on specifications, revealing emerging trends and consumer segments.

2. Brand Classification: Classify smartphones into brands using classification algorithms, analyzing brand preferences and market dynamics.

3. Price Prediction: Develop a model to predict smartphone prices based on specifications, exploring regression algorithms and feature importance analysis.

These tasks aim to provide actionable insights for stakeholders in product development and marketing strategies.

Contributions

Contributions to GSMarena_PhoneAnalysis are welcome! Please feel free to submit pull requests, report issues, or suggest improvements.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute the code for your purposes.