{"id":30584982,"url":"https://github.com/georgehanymilad/mobile-usage-behavior-analysis","last_synced_at":"2026-02-11T02:45:23.966Z","repository":{"id":311392517,"uuid":"1043561464","full_name":"GeorgeHanyMilad/Mobile-Usage-Behavior-Analysis","owner":"GeorgeHanyMilad","description":"Excel Project for Data 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-visualization","dataanalyst","dataanalytics","excel-dashboard","pivot-tables","powerquery","storytelling"],"created_at":"2025-08-29T10:18:38.052Z","updated_at":"2026-02-11T02:45:23.951Z","avatar_url":"https://github.com/GeorgeHanyMilad.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExcXFycDVxenZqaDl4enRkNzIzMTdjZXJnenVkcXc2OGF3azlyMXR1NCZlcD12MV9zdGlja2Vyc19zZWFyY2gmY3Q9cw/8f3nS1Gajtv70qNicE/giphy.gif\" width=\"200px\" height=\"200px\"\u003e\n\u003c/p\u003e\n\n# 📱 Mobile Usage Behavior Analysis (Excel Dashboard)\n\n\u003cdiv align=\"center\"\u003e\n\n\u003cimg src=\"https://github.com/GeorgeHanyMilad/Mobile-Usage-Behavior-Analysis/blob/master/Project%20Image.png?raw=true\" alt=\"Project Image\" width=\"80%\"/\u003e\n\n\u003c/div\u003e\n\n---\n\n## 📌 Table of Contents\n- [📖 Project Overview](#-project-overview)\n- [📊 Dataset](#-dataset)\n- [🛠️ Data Preparation \u0026 Transformation](#️-data-preparation--transformation)\n- [🧮 Pivot Tables](#-pivot-tables)\n- [📈 Dashboard](#-dashboard)\n- [🔍 Insights](#-insights)\n- [💡 Possible Reasons](#-possible-reasons)\n- [✅ Recommendations](#-recommendations)\n- [🧰 Tools \u0026 Technologies](#-tools--technologies)\n- [🙌 Acknowledgments](#-acknowledgments)\n\n---\n\n## 📖 Project Overview\nThis project focuses on analyzing **mobile usage behavior** using **Excel only**.  \nThe main goal is to study **user behavior**, identify **potential signs of mobile addiction**, and explore how **age and gender** affect:\n- Mobile screen time\n- Data usage\n- Number of installed applications\n- App usage patterns\n\nThe output is an **interactive dashboard** that helps decision-makers understand trends and take action.\n\n---\n\n## 📊 Dataset\n- **Source:** [Kaggle - Mobile Device Usage and User Behavior Dataset](https://www.kaggle.com/datasets/valakhorasani/mobile-device-usage-and-user-behavior-dataset)  \n- **Size:** 700 rows × 11 columns  \n- **Format:** CSV file  \n\n### 📑 Columns Description\n| Column | Description |\n|--------|-------------|\n| User ID | Unique identifier for each user |\n| Device Model | Model of the smartphone |\n| Operating System | OS of the device (iOS / Android) |\n| App Usage Time (min/day) | Daily app usage in minutes |\n| Screen On Time (hours/day) | Average screen time per day |\n| Battery Drain (mAh/day) | Daily battery consumption |\n| Number of Apps Installed | Total installed apps |\n| Data Usage (MB/day) | Daily data consumption |\n| Age | Age of the user |\n| Gender | Male / Female |\n| User Behavior Class | Classification of user usage behavior (1–5) |\n\n---\n\n## 🛠️ Data Preparation \u0026 Transformation\nPerformed in **Power Query**:\n1. **Data Cleaning**  \n   - No missing values  \n   - No duplicates  \n   - No outliers (validated using Box Plot)\n\n2. **Transformations**  \n   - Converted `User ID` \u0026 `User Behavior Class` to *Text*  \n   - Created **Age Groups** instead of raw ages  \n   - Replaced numeric **Behavior Classes (1–5)** with labels:  \n     - `1 → Uses Rarely`  \n     - `2 → Uses Sometimes`  \n     - `3 → Uses Normally`  \n     - `4 → Uses Often`  \n     - `5 → Uses Always`  \n   - Converted `App Usage Time` from minutes → hours\n\n## 📷 Power Query Preview:\n\u003cimg src=\"https://github.com/GeorgeHanyMilad/Mobile-Usage-Behavior-Analysis/blob/master/Power%20Query.jpg?raw=true\" alt=\"Pivot Table 1\" width=\"80%\"/\u003e\n\n---\n\n## 🧮 Pivot Tables\nThe analysis is powered by **Pivot Tables**, which summarize and structure the dataset before visualization.  \n\n📷 Pivot Table Previews:  \n\n\u003cdiv align=\"center\"\u003e\n\n## Pivot Tables - V1\n\u003cimg src=\"https://github.com/GeorgeHanyMilad/Mobile-Usage-Behavior-Analysis/blob/master/PivotTables_V1.jpg?raw=true\" alt=\"Pivot Table 1\" width=\"80%\"/\u003e\n\n## Pivot Tables - V2\n\u003cimg src=\"https://github.com/GeorgeHanyMilad/Mobile-Usage-Behavior-Analysis/blob/master/PivotTables_V2.jpg?raw=true\" alt=\"Pivot Table 2\" width=\"80%\"/\u003e\n\n## Pivot Tables - V3\n\u003cimg src=\"https://github.com/GeorgeHanyMilad/Mobile-Usage-Behavior-Analysis/blob/master/PivotTables_V3.jpg?raw=true\" alt=\"Pivot Table 3\" width=\"80%\"/\u003e\n\n## Pivot Tables - V4: KPIs\n\u003cimg src=\"https://github.com/GeorgeHanyMilad/Mobile-Usage-Behavior-Analysis/blob/master/KPIs.jpg?raw=true\" alt=\"Pivot Table 3\" width=\"80%\"/\u003e\n\n\u003c/div\u003e\n\n\u003e All slicers are connected to these Pivot Tables using **Report Connections** for a fully synchronized experience.\n\n---\n\n## 📈 Dashboard\nThe interactive dashboard was built using **Pivot Tables, Charts, KPIs, and Slicers**.  \nAll slicers are connected via **Report Connections** for a fully dynamic experience.  \n\n## 📷 Dashboard Preview:\n\n\u003cdiv align=\"center\"\u003e\n\n\u003cimg src=\"https://github.com/GeorgeHanyMilad/Mobile-Usage-Behavior-Analysis/blob/master/Dashboard.jpg?raw=true\" alt=\"Dashboard\" width=\"80%\"/\u003e\n\n\u003c/div\u003e\n\n### 🔑 KPIs:\n- **Users:** 700  \n- **Avg App Usage (Hr):** 4.5  \n- **Avg Screen Time (Hr):** 5.3  \n- **Avg Data Usage (MB):** 929.7  \n- **Avg Apps Installed:** 51  \n\n### 📊 Charts:\n1. App Usage (Hr) by Age  \n2. Screen Time (Hr) by Age  \n3. Data Usage (MB) by Age \u0026 Gender  \n4. App Usage (Hr) vs Data Usage (MB)  \n5. Apps Installed vs App Usage (Hr)  \n6. App Usage (Hr) by Gender  \n\n### 🎛️ Slicers:\n- Gender  \n- Age  \n- User Behavior Class  \n\n---\n\n## 🔍 Insights\n- **18–25** spend the most time on apps (avg. 4.8 hrs/day)  \n- **18–25** install the most apps (avg. 54 apps)  \n- **46–59** have the highest screen time (avg. 5.5 hrs/day)  \n- **36–45** show the lowest usage in all metrics  \n- Highest data consumption is among **46–59** (avg. 1022.4 MB for males, 929.2 MB for females)  \n- **Males vs Females:** Nearly identical app usage (~4.5 hrs/day)  \n- **Positive correlations:**  \n  - App Usage ⬆️ → Data Usage ⬆️  \n  - Apps Installed ⬆️ → App Usage ⬆️  \n\n---\n\n## 💡 Possible Reasons\n- **36–45:** Busy with work/family → lower usage  \n- **18–25:** Curious, more free time → higher usage \u0026 app installs  \n- **46–59:** Depend heavily on phones for news \u0026 entertainment → high data \u0026 screen time  \n- **Gender similarity:** Social apps (WhatsApp, Instagram, TikTok) used equally by both  \n- **Correlations:** More apps = more time, more time = higher data usage  \n\n---\n\n## ✅ Recommendations\n- Raise awareness among **18–25** on time management  \n- Encourage **46–59** to use data-saving tools  \n- Run awareness campaigns on **TikTok, Instagram, Facebook** for 18–25 age group  \n- Motivate **26–35** to balance entertainment with educational/utility apps  \n- Introduce **in-app notifications** reminding users to take breaks after 3 hours  \n\n---\n\n## 🧰 Tools \u0026 Technologies\n- **Microsoft Excel**: Data Analysis, Pivot Tables, Charts, Slicers  \n- **Power Query**: Data Cleaning \u0026 Transformation  \n- **Box Plot**: Outlier detection  \n\n---\n\n## 🙌 Acknowledgments\n- Dataset provided by [Kaggle](https://www.kaggle.com/datasets/valakhorasani/mobile-device-usage-and-user-behavior-dataset)  \n- Project developed as part of **Data Analysis practice using Excel**  \n\n---\n🚀 *Crafted with passion for Data Analytics \u0026 Visualization.*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeorgehanymilad%2Fmobile-usage-behavior-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgeorgehanymilad%2Fmobile-usage-behavior-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeorgehanymilad%2Fmobile-usage-behavior-analysis/lists"}