UPI Transaction Analysis with Python & SQL

@murtuza90

With over a decade of in-depth experience, I am Murtuza, your ideal candidate for this UPI Transaction Analytics project. As a proactive Python and SQL expert, I have spent years navigating the intricacies of these specific technologies to deliver client-focused results. I am confident in my ability to meet all the requirements you've outlined - from validating and cleaning your data using pandas, numpy, and matplotlib to crafting DDL scripts with PostgreSQL or MySQL for database design. My insights will help you gain a comprehensive perspective of your data through statistical analysis using scipy/statmodels. Moreover, throughout my career, I have adeptly handled missing foreign keys and inconsistent categorical data during the cleaning phase using meticulous documentation that clarifies the process. This ensures not only accurate results but also effective-communicability of my actions. In regards to visualization, I excel at transforming raw data into interactive and decisive Power BI dashboards that will surely fulfill your executive as well as fraud analyst views. Additionally, my ability to distill complex statistical analyses into essential insights will be valuable when providing you with a succinct2-page executive summary backed by actionable recommendations that can drive your strategic planning moving forward. With every line of code I write to bolster not just features but scalable & high-performing solutions that yield superior ROI.

@that1beast

Hello, I can deliver this as a complete end-to-end analytics project—from raw UPI data to executive-ready insights—covering cleaning, SQL design, statistical validation, and an interactive Power BI dashboard. My approach: • Data Validation & Cleaning (Python): Identify missing values, orphaned foreign keys, duplicates, and inconsistent categories. I standardize categorical fields using mapping dictionaries, resolve FK gaps via reference lookups or flagging for exclusion, and document every fix in a structured Data Quality Log. • Database Design (PostgreSQL/MySQL): Create normalized schema (Customer, Device, Merchant, Transaction) with PK/FK constraints, indexes, and ingestion scripts. • EDA & Metrics: Build derived KPIs such as Fraud Rate, Failure Rate, Retention, and cohort trends using pandas, matplotlib, seaborn. • Statistical Testing: Validate hypotheses using T-tests, ANOVA, Chi-square, and correlation (scipy/statsmodels) with interpretation in business terms. • Power BI Dashboard: Executive + Fraud Analyst views, DAX measures, clean model, drill-through and interactive filtering. • Executive Summary: 2-page insight brief with top 5 findings and strategic recommendations. Deliverables include notebooks, SQL scripts, .pbix file, data log, and summary report. I’m ready to begin immediately and can share relevant dashboard samples upon request. Best regards, Aniket Singh

@ayushmaanksahu4

Hi there, You’re absolutely in the RIGHT PLACE. I’ve delivered SIMILAR PROJECTS multiple times and know EXACTLY how to execute this efficiently and correctly from day one. To lock down the SCOPE, TIMELINE, AND PRICING, I’ll need to ask you a few key questions. Unfortunately, Freelancer’s 1500 CHARACTER LIMIT doesn’t allow me to break everything down properly here. Let’s jump on CHAT so I can show you my PROVEN PAST WORK, walk you through the REAL RESULTS I’ve delivered, and outline a CLEAR ACTION PLAN for your project. You’ll immediately see why my approach is DIFFERENT and EFFECTIVE. If you’re serious about getting this done RIGHT, I’m ready to move forward. Looking forward to CONNECTING and WINNING TOGETHER. Cheers, Aayushaman Sahu

@Amarpratap89

Having extensively worked in the sphere of automation tools, I am well-versed in utilizing Python to create streamlined systems that can handle large volumes of data - a crucial aspect for this project given the nature of UPI transactions. With an understanding of data cleaning practices, I am more than capable of managing missing foreign keys and inconsistent categorical data effectively. While carrying out the rigorous task of cleaning and validating data, I ensure detailed documentation, which you have specifically mentioned as a requirement for this project. My proficiency in SQL is a direct result of successfully designing and implementing databases like MySQL and PostgreSQL in numerous projects. This ensures that your desired DDL scripts are accurate and efficient, allowing for seamless data ingestion from cleaned datasets. My skills in the statistical analysis using libraries such as scipy or statsmodels will play a pivotal role in validating business hypotheses with tests like T-tests, ANOVA, Chi-square, Correlation – guaranteeing reliable results that can aid your strategic decisions.

@Wizztron

I recently delivered a project with this exact scope, focusing on clean, professional, and user-friendly data transformation and visualization processes. I understand the importance of seamless integration between data cleaning, SQL database design, statistical analysis, and an automated Power BI dashboard that offers executive and analyst views. I handle missing foreign keys by investigating their origin and deciding on deletion or imputation, while inconsistent categorical data is standardized through mapping and validation. My expertise spans Python (pandas, seaborn), SQL (PostgreSQL), and Power BI, ensuring end-to-end analytics delivery. While I am new to freelancer, I have tons of experience and have done other projects off site. I would love to chat more about your project! Regards, MN Williams

@rajk271

Hello, I can complete this entire end-to-end UPI analytics project within 5 days, covering data validation, SQL schema design, Python EDA, statistical testing, and a fully interactive Power BI dashboard. I’ll begin with a structured data quality assessment where I document missing values, broken foreign keys, duplicates, and inconsistent categorical entries in a clear log. Missing foreign keys will be validated against master tables and either remapped logically or flagged with justification, while inconsistent categories will be standardized using rule-based cleaning before loading everything into a properly constrained relational schema (Customer, Device, Merchant, Transaction). After database setup, I’ll perform detailed EDA using pandas, matplotlib, and seaborn, build derived KPIs like fraud rate, failure rate, and retention metrics, and validate business hypotheses using T-tests, ANOVA, Chi-square, and correlation analysis with scipy/statsmodels. The final Power BI dashboard will include both Executive and Fraud Analyst views with a clean data model and optimized DAX measures. All deliverables — data quality log, SQL scripts, Jupyter notebooks, .pbix file, and a concise executive summary with strategic recommendations — will be provided within the 5-day timeline.