https://github.com/georgehanymilad/mobile-usage-behavior-analysis
Excel Project for Data Analysis
https://github.com/georgehanymilad/mobile-usage-behavior-analysis
data-analysis data-visualization dataanalyst dataanalytics excel-dashboard pivot-tables powerquery storytelling
Last synced: 28 days ago
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Excel Project for Data Analysis
- Host: GitHub
- URL: https://github.com/georgehanymilad/mobile-usage-behavior-analysis
- Owner: GeorgeHanyMilad
- Created: 2025-08-24T05:58:11.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2025-08-24T06:20:12.000Z (7 months ago)
- Last Synced: 2025-08-24T13:40:34.308Z (7 months ago)
- Topics: data-analysis, data-visualization, dataanalyst, dataanalytics, excel-dashboard, pivot-tables, powerquery, storytelling
- Homepage: https://www.kaggle.com/datasets/valakhorasani/mobile-device-usage-and-user-behavior-dataset
- Size: 2.81 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 📱 Mobile Usage Behavior Analysis (Excel Dashboard)
---
## 📌 Table of Contents
- [📖 Project Overview](#-project-overview)
- [📊 Dataset](#-dataset)
- [🛠️ Data Preparation & Transformation](#️-data-preparation--transformation)
- [🧮 Pivot Tables](#-pivot-tables)
- [📈 Dashboard](#-dashboard)
- [🔍 Insights](#-insights)
- [💡 Possible Reasons](#-possible-reasons)
- [✅ Recommendations](#-recommendations)
- [🧰 Tools & Technologies](#-tools--technologies)
- [🙌 Acknowledgments](#-acknowledgments)
---
## 📖 Project Overview
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.
---
## 📊 Dataset
- **Source:** [Kaggle - Mobile Device Usage and User Behavior Dataset](https://www.kaggle.com/datasets/valakhorasani/mobile-device-usage-and-user-behavior-dataset)
- **Size:** 700 rows × 11 columns
- **Format:** CSV file
### 📑 Columns Description
| 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) |
---
## 🛠️ Data Preparation & Transformation
Performed in **Power Query**:
1. **Data Cleaning**
- No missing values
- No duplicates
- No outliers (validated using Box Plot)
2. **Transformations**
- Converted `User ID` & `User Behavior Class` to *Text*
- Created **Age Groups** instead of raw ages
- Replaced numeric **Behavior Classes (1–5)** with labels:
- `1 → Uses Rarely`
- `2 → Uses Sometimes`
- `3 → Uses Normally`
- `4 → Uses Often`
- `5 → Uses Always`
- Converted `App Usage Time` from minutes → hours
## 📷 Power Query Preview:
---
## 🧮 Pivot Tables
The analysis is powered by **Pivot Tables**, which summarize and structure the dataset before visualization.
📷 Pivot Table Previews:
## Pivot Tables - V1
## Pivot Tables - V2
## Pivot Tables - V3
## Pivot Tables - V4: KPIs
> All slicers are connected to these Pivot Tables using **Report Connections** for a fully synchronized experience.
---
## 📈 Dashboard
The interactive dashboard was built using **Pivot Tables, Charts, KPIs, and Slicers**.
All slicers are connected via **Report Connections** for a fully dynamic experience.
## 📷 Dashboard Preview:
### 🔑 KPIs:
- **Users:** 700
- **Avg App Usage (Hr):** 4.5
- **Avg Screen Time (Hr):** 5.3
- **Avg Data Usage (MB):** 929.7
- **Avg Apps Installed:** 51
### 📊 Charts:
1. App Usage (Hr) by Age
2. Screen Time (Hr) by Age
3. Data Usage (MB) by Age & Gender
4. App Usage (Hr) vs Data Usage (MB)
5. Apps Installed vs App Usage (Hr)
6. App Usage (Hr) by Gender
### 🎛️ Slicers:
- Gender
- Age
- User Behavior Class
---
## 🔍 Insights
- **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 ⬆️
---
## 💡 Possible Reasons
- **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
---
## ✅ Recommendations
- 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
---
## 🧰 Tools & Technologies
- **Microsoft Excel**: Data Analysis, Pivot Tables, Charts, Slicers
- **Power Query**: Data Cleaning & Transformation
- **Box Plot**: Outlier detection
---
## 🙌 Acknowledgments
- Dataset provided by [Kaggle](https://www.kaggle.com/datasets/valakhorasani/mobile-device-usage-and-user-behavior-dataset)
- Project developed as part of **Data Analysis practice using Excel**
---
🚀 *Crafted with passion for Data Analytics & Visualization.*