- 📖 Project Overview
- 📊 Dataset
- 🛠️ Data Preparation & Transformation
- 🧮 Pivot Tables
- 📈 Dashboard
- 🔍 Insights
- 💡 Possible Reasons
- ✅ Recommendations
- 🧰 Tools & Technologies
- 🙌 Acknowledgments
This project focuses on analyzing mobile usage behavior using Excel only.
The main goal is to study user behavior, identify potential signs of mobile addiction, and explore how age and gender affect:
- Mobile screen time
- Data usage
- Number of installed applications
- App usage patterns
The output is an interactive dashboard that helps decision-makers understand trends and take action.
- Source: Kaggle - Mobile Device Usage and User Behavior Dataset
- Size: 700 rows × 11 columns
- Format: CSV file
| Column | Description |
|---|---|
| User ID | Unique identifier for each user |
| Device Model | Model of the smartphone |
| Operating System | OS of the device (iOS / Android) |
| App Usage Time (min/day) | Daily app usage in minutes |
| Screen On Time (hours/day) | Average screen time per day |
| Battery Drain (mAh/day) | Daily battery consumption |
| Number of Apps Installed | Total installed apps |
| Data Usage (MB/day) | Daily data consumption |
| Age | Age of the user |
| Gender | Male / Female |
| User Behavior Class | Classification of user usage behavior (1–5) |
Performed in Power Query:
-
Data Cleaning
- No missing values
- No duplicates
- No outliers (validated using Box Plot)
-
Transformations
- Converted
User ID&User Behavior Classto Text - Created Age Groups instead of raw ages
- Replaced numeric Behavior Classes (1–5) with labels:
1 → Uses Rarely2 → Uses Sometimes3 → Uses Normally4 → Uses Often5 → Uses Always
- Converted
App Usage Timefrom minutes → hours
- Converted
The analysis is powered by Pivot Tables, which summarize and structure the dataset before visualization.
📷 Pivot Table Previews:
All slicers are connected to these Pivot Tables using Report Connections for a fully synchronized experience.
The interactive dashboard was built using Pivot Tables, Charts, KPIs, and Slicers.
All slicers are connected via Report Connections for a fully dynamic experience.
- Users: 700
- Avg App Usage (Hr): 4.5
- Avg Screen Time (Hr): 5.3
- Avg Data Usage (MB): 929.7
- Avg Apps Installed: 51
- App Usage (Hr) by Age
- Screen Time (Hr) by Age
- Data Usage (MB) by Age & Gender
- App Usage (Hr) vs Data Usage (MB)
- Apps Installed vs App Usage (Hr)
- App Usage (Hr) by Gender
- Gender
- Age
- User Behavior Class
- 18–25 spend the most time on apps (avg. 4.8 hrs/day)
- 18–25 install the most apps (avg. 54 apps)
- 46–59 have the highest screen time (avg. 5.5 hrs/day)
- 36–45 show the lowest usage in all metrics
- Highest data consumption is among 46–59 (avg. 1022.4 MB for males, 929.2 MB for females)
- Males vs Females: Nearly identical app usage (~4.5 hrs/day)
- Positive correlations:
- App Usage ⬆️ → Data Usage ⬆️
- Apps Installed ⬆️ → App Usage ⬆️
- 36–45: Busy with work/family → lower usage
- 18–25: Curious, more free time → higher usage & app installs
- 46–59: Depend heavily on phones for news & entertainment → high data & screen time
- Gender similarity: Social apps (WhatsApp, Instagram, TikTok) used equally by both
- Correlations: More apps = more time, more time = higher data usage
- Raise awareness among 18–25 on time management
- Encourage 46–59 to use data-saving tools
- Run awareness campaigns on TikTok, Instagram, Facebook for 18–25 age group
- Motivate 26–35 to balance entertainment with educational/utility apps
- Introduce in-app notifications reminding users to take breaks after 3 hours
- Microsoft Excel: Data Analysis, Pivot Tables, Charts, Slicers
- Power Query: Data Cleaning & Transformation
- Box Plot: Outlier detection
- Dataset provided by Kaggle
- Project developed as part of Data Analysis practice using Excel
🚀 Crafted with passion for Data Analytics & Visualization.