🎯 RELOAD'25 Task Submission - Teen Smartphone Usage Analysis - Harsh Giri

[giri-harsh]

🎯 RELOAD'25 Task Submission

👤 Participant Information

• Full Name: Harsh Giri
• Email: 2006.harshgiri@gmail.com
• Phone: 9871696166
• College Roll No: 24154015
• Uni Roll no. 2400271540048
• Year & Branch: 2rd Year, CSE (Data Science)

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🎯 Task Details

• Track: Developer 💻
• Task Completed: Teen Smartphone Usage Analysis

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🔗 Submission Links

• GitHub Repository: https://github.com/giri-harsh/Phone-Addiction-Pred/tree/main
• Notebook File : https://github.com/giri-harsh/Phone-Addiction-Pred/blob/main/Teen%20Addiction%20Prediction.py
• Clean Dataset (.csv): https://github.com/giri-harsh/Phone-Addiction-Pred/blob/main/teen_phone_addiction_cleaned.csv
• Visualizations Folder: https://github.com/giri-harsh/Phone-Addiction-Pred/tree/main/Data%20Visualization
• EDA https://github.com/giri-harsh/Phone-Addiction-Pred/tree/main/EDA

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🛠️ Technical Implementation

Tech Stack/Tools Used:

• Primary: Python 3.x
• Libraries: pandas, numpy, matplotlib, seaborn, plotly, scikit-learn
• Environment: VS Code Kernel
• Deployment: GitHub repository

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✨ Features Showcase

Core Features Implemented:

• Data Cleaning: handled missing values, duplicates, and outliers
• EDA: smartphone usage patterns, academic performance correlation, sleep and mental health analysis
• Visualizations: histograms, box plots, scatter plots, bar charts, heatmaps
• Machine Learning: Random Forest regression model to predict addiction level
• Insights: 6+ key findings and actionable recommendations

Bonus Features Added:

• Interactive Visualizations with Plotly
• Clean, professional notebook presentation with markdown storytelling

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✅ Checklist Confirmation

• Used proper Git workflow (no file uploads)
• Minimum 5 meaningful commits
• All features working as intended
• Repository is public and accessible

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Additional Comments:
This project gave me deep insights into smartphone usage patterns among teenagers and helped me strengthen my EDA + ML modeling skills 🚀

• I was unable to use Jupyter Notebook or Google Colab for this project, so I have provided a fully functional Python script instead.
  Please note that the graphs will not be displayed automatically unless you run the code. To address this, I have also included the generated graph outputs in the repository for reference, as mentioned earlier.