pratiksha = {
    "role"       : "Aspiring Data Analyst & AI Explorer",
    "status"     : "Actively seeking Data Analyst / AI roles 🔍",
    "superpower" : "Turning messy data into clean, useful insights",
    "approach"   : "Build projects. Don't just talk about them.",
    "currently"  : "Deep-diving into Data Analysis & Visualization 📊",
    "fun_fact"   : "I clean data like it personally offended me 🧹"
}

Data & Analytics

Programming & Web

Tools & Environments

• What it does: End-to-end IT support operations dashboard analyzing 8,000 tickets across 8 departments and 20 agents — tracking SLA compliance, agent workload, resolution times, and peak load patterns over 26 months (Jan 2024 – Feb 2026)
• Tools used: Python · SQL Server · Power BI · DAX
• Why it stands out: Designed specifically for IT services companies (TCS, Infosys, Wipro). Includes a Day × Hour peak load heatmap and an Agent Workload vs Breach Rate scatter chart — two visuals rarely seen in fresher portfolios
• Key insights: 23.49% overall SLA breach rate with no improvement trend over 26 months — a systemic staffing issue; Network category breaches at 37.5%; 3 agents carry 35% of total ticket volume; Marketing dept generates highest tickets per employee at 22.97
• 🔗 View Project

• What it does: Analyzes user behaviour, feature adoption, churn patterns, and revenue metrics for a B2B SaaS company across 5,000 users, 6,566 sessions, and 14,996 feature interactions
• Tools used: Python · SQL Server · Power BI · DAX · Power Query
• Why it stands out: Goes beyond surface-level dashboards — tracks the full B2B SaaS funnel from activation to churn with revenue impact quantified in dollars. Feature adoption correlation analysis (5 features = 55.3% conversion) shows product analytics thinking, not just reporting.
• Key insight: Only 25.5% of users are paid & active; users adopting 5 features convert at 55.3% vs 46.9% for single-feature users; $90,548 MRR lost to churn monthly
• 🔗 View Project

• What it does: Analyzes AI's impact on 13,700+ job records across 9 countries covering automation risk, salary changes, and reskilling urgency (2020–2026)
• Tools used: Python · SQL Server · Power BI · DAX · Power Query
• Why it stands out: Deliberately chosen to avoid oversaturated e-commerce datasets — addresses a topic every hiring manager in tech is thinking about right now
• Key insight: 27.78% of jobs face high automation risk; Energy & Finance most disrupted; avg salary increased by $3,500 post AI adoption
• 🔗 View Project

• What it does: Analyzes 10,000+ banking records covering customer demographics, transaction trends, account balances, and inactive account detection
• Tools used: SQL Server · Power BI · DAX · Power Query Editor
• Key insight: Identified Top N high-value customers, 90-day inactive accounts, and monthly transaction trends across Credit & Debit types
• 🔗 View Project

"Without data, you're just another person with an opinion." — W. Edwards Deming

Open to opportunities • Let's connect and build something meaningful!