Tech_AI_Adoption_Dashboard 🌐

Cross-Country Technology Adoption & AI Analytics (1990–2030)

An end-to-end data analytics portfolio project analysing 35 years of technology evolution, AI adoption, media consumption, and digital transformation across 5 countries — India, USA, China, Japan, and Germany — with forecasting to 2030.

---

📊 Dashboard Preview

5-page interactive Power BI dashboard covering AI Adoption, Media Consumption, Digital Transformation, Country Competitiveness, and Forecasting to 2030.

---

---

📄 Dashboard PDF

View Full Dashboard PDF

🔍 Project Overview

Attribute	Detail
Domain	Technology & Media Analytics
Countries	India, USA, China, Japan, Germany
Time Period	1990 – 2030 (35 years historical + forecast)
Total Rows	~3.6 Million
Tables	4 fact tables
Dashboard Pages	5 pages
KPIs Calculated	27

---

🛠️ Tech Stack

Tool	Purpose
Python (Faker, Pandas, Matplotlib, Seaborn)	Data generation, EDA, KPI calculation
MySQL + MySQL Workbench	Data storage and SQL cleaning
SQLAlchemy + PyMySQL	Python-MySQL connection
Power BI Desktop	Dashboard and forecasting
Jupyter Notebook	EDA and KPI notebooks
GitHub	Version control and portfolio hosting

---

📁 Project Structure

Tech_AI_Adoption_Dashboard/
│
├── Data Generator/
│   └── 01_Data_generator_for_Tech_Ai.ipynb
│       — Synthetic data generation using Faker
│       — Country + sector specific bias injection
│       — Direct MySQL push via SQLAlchemy
│
├── Source/
│   ├── 02_EDA_Media.ipynb        — media_tech_usage analysis
│   ├── 03_EDA_App.ipynb          — app_download_comparison analysis
│   ├── 04_EDA_AI.ipynb           — ai_transformation_2026 analysis
│   ├── 05_EDA_Comp.ipynb         — company_adaptation analysis
│   ├── 06_KPI_Calculation_CCTA.ipynb
│   │   — 27 KPIs calculated across 4 dashboard pages
│   │   — MinMaxScaler composite competitiveness score
│   ├── 01_cleaning_media_tech_usage.sql
│   ├── 01_cleaning_app_download_comparison.sql
│   ├── 01_cleaning_ai_transformation_2026.sql
│   └── 01_cleaning_company_adaptation.sql
│
├── Visuals/
│   └── 31 EDA charts saved as PNG
│       ├── media_01 to media_07
│       ├── app_01 to app_08
│       ├── ai_01 to ai_08
│       └── comp_01 to comp_08
│
├── Tech_AI_Adoption_Dashboard.pbix
└── README.md

---

📊 EDA Visuals

Media Tech Usage — Avg Daily Usage Hours by Platform

App Download Comparison — Retention vs Uninstall Rate by Country

AI Transformation 2026 — AI Adoption Rate by Country

Company Adaptation — Avg Digital Maturity Score by Industry

---

🔄 Analyst Workflow

Raw Data Generation (Python Faker) ↓ MySQL Import (SQLAlchemy batched push) ↓ SQL Cleaning (4 scripts — NULLs, duplicates, standardisation) ↓ Python EDA (bias injection, rolling imputation, visuals) ↓ KPI Calculation (27 KPIs validated in Python) ↓ Power BI Dashboard (5 pages + forecasting to 2030)

---

📋 Data Tables

Table	Rows	Period	Key Metrics
media_tech_usage	~891K	1990–2025	Daily usage hrs, penetration rate, ad spend
app_download_comparison	~891K	1990–2025	Downloads, retention, uninstall rate, revenue
ai_transformation_2026	~891K	2026 only	AI adoption rate, productivity gain, investment
company_adaptation	~891K	1990–2025	Digital maturity, revenue growth, tech investment

---

📈 Key Findings

AI Adoption

• USA leads at 68% adoption vs India at 44.5%
• Finance sector highest at 70% vs Government at 47.9%
• Claude delivers highest productivity gain at 70%
• Jobs displaced (23K avg) outpace jobs created (15K avg) across all countries

Media Consumption

• Americans average 8 hrs/day vs Indians at 3.5 hrs
• Japan highest retention rate at 75%
• India highest uninstall rate at 42.5%
• Netflix dominates USA/Germany | WeChat owns China | Hotstar leads India

Digital Transformation

• Technology industry digital maturity 7.55/10 vs Manufacturing 5.67
• USA and Japan tied at top with 7.14 maturity score
• Technology companies show 67.5% revenue growth vs Other at -2.5%
• Technology digital revenue 89.5% vs Manufacturing 21.5%

Country Competitiveness

• USA and Japan lead on AI readiness and digital maturity
• India lowest customer satisfaction at 2.25/5
• China highest app downloads at 350M avg

Forecasting to 2030

• Daily media usage projected to reach 9+ hours
• Digital revenue % to accelerate sharply post-2025
• App downloads showing sustained upward trajectory

---

⚙️ Setup Instructions

Prerequisites

pip install pandas numpy faker sqlalchemy pymysql matplotlib seaborn scikit-learn

MySQL Setup

CREATE DATABASE IF NOT EXISTS tech_ai_1990_2030;

Configuration

Replace credentials in all notebooks before running:

password = quote_plus("YOUR_PASSWORD")
engine   = create_engine(
    f"mysql+pymysql://YOUR_USERNAME:{password}@127.0.0.1/tech_ai_1990_2030"
)
visuals_path   = r"YOUR_VISUALS_PATH"
finalized_path = r"YOUR_FINALIZED_PATH"

Run Order

1. 01_data_generation_and_cleaning.ipynb
2. Run SQL cleaning scripts (in Source folder)
3. 02_EDA_Media.ipynb
4. 03_EDA_App.ipynb
5. 04_EDA_AI.ipynb
6. 05_EDA_Comp.ipynb
7. 06_KPI_Calculation_CCTA.ipynb
8. Open Tech_AI_Adoption_Dashboard.pbix in Power BI
9. Refresh data from Finalized folder

---

📌 Data Note

Raw data is synthetically generated using Python Faker with realistic country-specific and sector-specific bias injection. Run 01_data_generation_and_cleaning.ipynb to reproduce the dataset. Finalized CSVs are not included in this repository due to file size.

---

👤 Author

Abishek | Aspiring Data Analyst |📍 Gurgaon, India

---

🏷️ Tags

#DataAnalytics #PowerBI #Python #MySQL #AIAdoption #DigitalTransformation #MediaConsumption #Forecasting #PortfolioProject #DataScience #BusinessIntelligence