Mobile User Data Analysis Dashboard

Overview

This project analyzes user behavior and mobile data usage patterns to uncover trends, insights, and actionable recommendations. The analysis utilizes a combination of Power BI dashboards and exploratory data analysis in Python to present data-driven insights effectively.

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Components

1. Power BI Dashboard

• File: Mobile_User_Data_Analysis.pbix • Provides an interactive dashboard with key visualizations on user behavior and mobile usage. • Highlights: o Key metrics such as average app usage time, data consumption, and battery efficiency. o Comparative analysis by device type, operating system, and user demographics. o Interactive filters for deeper exploration of specific user segments.

2. Exploratory Data Analysis (EDA)

• File: User_data_EDA.ipynb • Python-based notebook for detailed analysis of the dataset. • Includes: o Data cleaning and preprocessing steps. o Visualization of key patterns and distributions. o Statistical summaries of user behavior metrics.

3. User Behavior Dataset

• File: User_behaviour.csv • Contains the raw data used for analysis. • Key Columns: o User ID: Unique identifier for users. o Device Model, Operating System: User device information. o App Usage Time (min/day), Data Usage (MB/day): Behavioral metrics. o Battery Drain (mAh/day), Screen On Time (hours/day): Device performance metrics. o Age, Gender, User Behavior Class: Demographics and classifications.

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Features

Power BI Dashboard

• KPI Highlights: o Average app usage time per user. o Daily data usage trends. o Device efficiency metrics. • Visualizations: o Bar charts for user demographics. o Line charts for time series trends. o Pie charts for device distribution. • Interactive Slicers: o Filter by operating system, device model, and user demographics.

EDA Insights

• Correlations:

o Relationships between app usage, data consumption, and device performance.

• Clustering:

o Identification of user segments based on behavior metrics.

• Anomalies:

o Detection of outliers in battery usage and data consumption.

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Python to MySQL Connection

This project also includes a Python script to connect to a MySQL database using Pandas. This allows for seamless data transfer and storage.

Code Snippet:

import pandas as pd import mysql.connector

connection = mysql.connector.connect( host='your_host', user='your_username', password='your_password', database='your_database' )

data = pd.read_csv('User_behaviour.csv')

data.to_sql('user_behavior', con=connection, if_exists='replace', index=False)

connection.close()

Benefits:

• Enables centralized data storage. • Facilitates further analysis and integration with other tools.

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How to Use

1. Power BI Dashboard:

o Open the Mobile_User_Data_Analysis.pbix file in Power BI Desktop. o Interact with filters to customize the view.

2. EDA Notebook:

o Open User_data_EDA.ipynb in Jupyter Notebook or a compatible IDE. o Run the notebook cells to replicate the analysis.

3. Dataset:

o Review User_behaviour.csv for raw data. o Use it as input for custom analyses or extensions.

4. MySQL Integration:

o Use the provided Python script to transfer data to a MySQL database.

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Benefits

• Understand user behavior patterns and optimize app performance. • Identify high and low-efficiency devices. • Discover trends in data usage for better infrastructure planning.

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Technical Requirements

• Power BI Desktop • Python 3.x (with libraries: Pandas, Matplotlib, Seaborn, mysql-connector-python) • A text editor or IDE for CSV and JSON files.

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Future Enhancements

• Real-time dashboard integration. • Advanced predictive models for user behavior forecasting. • Comprehensive device performance benchmarking.