Freelancers using MySQL in Maharashtra

Freelancers using MySQL in MaharashtraFreelancers using MySQL in Maharashtra

Projects People

Abu Aasif Ansari

Bhiwandi, India

I build AI agents & internal tools that act on data

12: Followers

I build AI agents & internal tools that act on data

Cover image for I specialize in data cleaning and preprocessing, ensuring yo...

I specialize in data cleaning and preprocessing, ensuring your spreadsheets or databases are accurate, well-formatted, and ready for analysis or reporting. What Ill do for you: Remove duplicates, nulls, and inconsistencies Standardize date, text, and numeric formats Fix spelling/case issues Split/merge columns (e.g., names, addresses) Convert file formats (CSV Excel SQL) Python or SQL-based advanced cleaning (if required) Supported formats: Excel, CSV, Google Sheets, SQL dumps Clean, fast & confidential service 100% satisfaction guaranteed Message me before ordering so I can understand your dataset and goals!

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Cover image for Data Analytics Project – Subscription Model Analysis (SQL & ...

Data Analytics Project – Subscription Model Analysis (SQL & Power BI) Analyzed a subscription-based business model for Foodie-Fi (TastyStream), a digital platform offering on-demand global cuisine content. The objective was to understand customer journeys, subscription transitions, and revenue performance using SQL and Power BI. Key Contributions & Insights: Segmented customers based on trial-to-paid and plan transitions. Analyzed free trial behavior and conversion patterns. Evaluated revenue performance across subscription plans. Built interactive dashboards in Power BI for actionable insights. Tools Used: MYSQL, Power BI Outcome: Delivered a clear, insight-driven dashboard supporting data-driven decisions in a subscription-based business.

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Cover image for 🚀 SQL & Tableau Project | Sales Analytics Dashboard To prac...

🚀 SQL & Tableau Project | Sales Analytics Dashboard To practice real-world analytics, I built an interactive Sales Analytics Dashboard using SQL & Tableau to analyze sales performance and customer behavior. 🔹 Dataset Highlights OrderID, CustomerID, Product, Category, Sales, Quantity, Discount, Profit, Region, OrderDate 🔹 Dashboard Coverage • Sales overview: Total Sales, Profit & Orders • Product performance: Top-selling products & category insights • Monthly sales trends 🔹 Tools Used • SQL • Tableau 🔹 Key Insights • A small customer segment drives most revenue • Certain product categories consistently outperform others • Clear seasonal sales patterns 🔹 What I Learned • Writing efficient SQL queries for analysis • Translating numbers into business-friendly visuals • Designing dashboards that support data-driven decisions

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Cover image for Ask My Docs — RAG-Based AI Chat Agent

Ask My Docs — RAG-Based AI Chat Agent

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Full Stack Developer | Custom web & mobile app solution

Full Stack Developer | Custom web & mobile app solution

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Tracking Solution

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BestLoots: WordPress Plugins

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Cover image for Estate Property: Real Estate Management

Estate Property: Real Estate Management

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Cover image for Golden Chow: Smart & Seamless Food Delivery Management

Golden Chow: Smart & Seamless Food Delivery Management

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Data Analyst & BI Dev | Dashboards, SQL, Python & ETL

New to Contra

Data Analyst & BI Dev | Dashboards, SQL, Python & ETL

Cover image for Blinkit-Sales-Dashboard
This project is an interactive

Blinkit-Sales-Dashboard This project is an interactive Power BI dashboard developed to analyse Blinkit's sales performance, outlet operations, product distribution, and customer-related metrics. The dashboard converts raw retail sales data into meaningful business insights through visualisation and KPI tracking. The objective of this project is to monitor business performance, identify sales trends, evaluate outlet efficiency, and support data-driven decision-making. Key KPIs Total Sales: $1.20M Average Sales: $140.99 Number of Items: 8,523 Average Rating: 3.9 Dashboard Features Sales Analysis Tracked total revenue and average sales performance to understand overall business growth. Outlet Performance Analysed outlet sales based on the following: Outlet Size Outlet Location Type Outlet Establishment Year Outlet Type Product Category Analysis Identified top-performing product categories contributing the highest revenue. Customer & Product Insights Compared low-fat and regular products to understand purchasing trends and monitored customer ratings for performance evaluation. Interactive Filters Added slicers for: Outlet Location Type Outlet Size Item Type Outlet Type Outlet Identifier These filters allow users to dynamically explore data and generate business insights. Project Outcome This project demonstrates practical skills in business intelligence, KPI reporting, analytical thinking, and dashboard development using Power BI. It highlights the ability to transform raw business data into business insights for decision-making. Developed By Sunny Mehta

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Cover image for Marketplace Profitability & P&L Engine
Overview
This

Marketplace Profitability & P&L Engine Overview This project is an end-to-end E-commerce Marketplace Analytics solution built using MySQL, SQL, Power BI, and Excel. The objective of this project is to analyze marketplace profitability across multiple sales channels by calculating revenue, product costs, marketplace commissions, shipping expenses, and net profit. The project simulates a real-world marketplace operations environment where business teams need visibility into product performance, marketplace profitability, and operational KPIs. Business Problem E-commerce businesses often track revenue but fail to understand true profitability. This project helps answer key business questions: Which marketplace generates the highest profit? Which products are most profitable? How do commissions impact margins? What is the overall profit margin? How does shipping cost affect profitability? Tech Stack MySQL SQL Power BI Microsoft Excel Data Sources Products Contains SKU-level product information. Fields: SKU Product Name Category Cost Price Selling Price Orders Contains marketplace order transactions. Fields: Order ID Order Date Marketplace SKU Quantity Marketplace Charges Marketplace commission structure. Fields: Marketplace Commission Percentage Shipping Cost Shipping cost by marketplace. Fields: Marketplace Shipping Cost Returns Order return information. Fields: Order ID Return Status SQL Implementation The project uses multiple SQL tables and joins to create a unified profitability dataset. Key SQL Concepts Used: CREATE TABLE INNER JOIN SQL Views Aggregations Calculated Metrics A consolidated analytical view was created: vw_marketplace_pnl This view combines all business logic and serves as the primary source for Power BI reporting. Profitability Metrics Revenue Revenue = Quantity × Selling Price Product Cost Product Cost = Quantity × Cost Price Commission Cost Commission Cost = Revenue × Marketplace Commission % Shipping Cost Shipping Cost = Quantity × Shipping Cost Profit Profit = Revenue − Product Cost − Commission Cost − Shipping Cost Profit Margin % Profit Margin % = Profit / Revenue Power BI Dashboard Executive Summary KPIs: Total Revenue Total Profit Total Orders Profit Margin % Marketplace Analysis Revenue by Marketplace Profit by Marketplace Marketplace Performance Comparison Product Analysis Top Profitable Products Product Revenue Analysis Product Profitability Ranking Trend Analysis Revenue Trend Profit Trend Order Trend Project Architecture Excel → MySQL → SQL View → Power BI Dashboard Key Outcomes Built a scalable marketplace profitability model. Centralized business logic using SQL Views. Automated profitability calculations. Delivered interactive executive dashboards for decision-making. Simulated a real-world marketplace operations analytics workflow. Skills Demonstrated SQL Data Modeling Business Intelligence Power BI E-commerce Analytics Marketplace Operations Profitability Analysis Data Visualization Author Sunny Mehta Open to opportunities in: E-commerce Operations Marketplace Management Business Analytics Operations Analytics Data Analytics

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Cover image for Netflix Movies Analysis Dashboard using

Netflix Movies Analysis Dashboard using Power BI Project Overview Interactive Power BI dashboard analyzing Netflix movie trends, popularity, ratings, genres, and language distribution. KPIs Total Movies Total Votes Total Popularity Average Rating Dashboard Features Top 10 Movies by Popularity Release Year Trend Movies Distribution by Language Genre Analysis Popularity vs Vote Average Tools Used Power BI Power Query DAX Data Modeling

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Cover image for Banking Data Analysis Dashboard

Develop a

Banking Data Analysis Dashboard Develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers Solution – With our dashboards which are created using Power BI latest tools helps the company to make a decision based on the applicant’s profile like if the applicant is likely to repay the loan then approving the loan otherwise not. About Dataset – This dataset basically contains information about bank details ,various client details which consists of multiple tables which are interlinked with each other through keys like primary key and foreign key. The various tables are Banking Relationship, Client-Banking, Gender, Investment Advisor and Period. KPI’S: In which followings KPIS are present : Total Clients : Total Clients KPI represents total number of clients in banking. Total Clients = DISTINCTCOUNT('Clients - Banking'[Client ID] ) Total Loan : Total Loan gives you information about the bank loan + Business lending + credit cards balance of particular investor , gender. Total Loan = [Bank Loan] + [Business Lending] + [Credit Cards Balance] Bank Loan : Bank Loan gives you information what is the loan amount of loan to be repaid by the client to bank. Bank Loan = SUM('Clients - Banking'[Bank Loans] ) Business Lending : Business lending gives you information about the loan amount given to small business. Business Lending = SUM('Clients - Banking'[Business Lending] ) Total Deposit Total Deposit gives you information about the amount deposited by particular investors in bank Total Deposit = [Bank Deposit] + [Savings Account] + [Foreign Currency Account] + [Checking Accounts] Total Fees : Total Fees is nothing but the amount charged by the bank for account set-up , maintenance charges etc. Total Fees = SUMX('Clients - Banking' , [Total Loan] * 'Clients - Banking'[Processing Fees] ) Bank Deposit : Bank deposit is the money put in the bank. Bank Deposit = SUM('Clients - Banking'[Bank Deposits] ) Checking Account Amount : Checking account amount is nothing but which offers easy access to your money for daily transactional needs. Checking Accounts = SUM('Clients - Banking'[Checking Accounts] ) Total CC Amount : Total CC Amount is a short-term source of financing for a company by a bank. Total CC Amount = SUM('Clients - Banking'[Amount of Credit Cards] ) Saving Account Amount : A savings account is an interest-bearing deposit account held at a bank. Savings Account = SUM('Clients - Banking'[Saving Accounts] ) Foreign Currency Amount : Foreign Currency Account means an account held in a currency that is not the currency of India or Bhutan or Nepal. Foreign Currency Account = SUM('Clients - Banking'[Foreign Currency Account] ) Engagement Account : Engagement Banking is nothing but puts the customer at the center and aims to deliver the digital experiences they expect. Engagment Length = SUM('Clients - Banking'[Engagment Days]) Credit Cards Balance : It is the total amount of money currently owned by a cardholder to their credit card bank. Credit Cards Balance = SUM('Clients - Banking'[Credit Card Balance] ) 📊 Tools Used Excel (Data Cleaning) SQL (Data Processing) Python (EDA) Power BI (Dashboard) 📈 Key Insights Private banks dominate loan distribution European segment shows highest deposits Low-income customers have high loan dependency

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Senior Data Analyst | Business Intelligence Consultant

10: Followers

Senior Data Analyst | Business Intelligence Consultant

Cover image for Power BI Templates for Self-Service Reporting

Power BI Templates for Self-Service Reporting

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Cover image for Inventory Optimization with ABC-XYZ Segmentation

Inventory Optimization with ABC-XYZ Segmentation

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Cover image for MRR & Churn Analysis Dashboard for SaaS Retention and Growth

MRR & Churn Analysis Dashboard for SaaS Retention and Growth

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Fullstack Engineer with NodeJS & Fintech Exp

Fullstack Engineer with NodeJS & Fintech Exp

Cover image for Fintech API Development with Django, PostgreSQL, and Redis

Fintech API Development with Django, PostgreSQL, and Redis

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Cover image for Digital Taxation System for National Government

Digital Taxation System for National Government

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Blockchain Developer and Smart Contract Architect

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Web Development

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Ariston IT Services

Ariston IT: Elevate Your Business with Custom Software

Ariston IT: Elevate Your Business with Custom Software

Cover image for Business Listing / Advertising Platform

Business Listing / Advertising Platform

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Sports Event Booking App

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Cover image for Tour & Guide Booking and Management

Tour & Guide Booking and Management

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Pimpri-Chinchwad, India

Power BI Expert | Turning Raw Data into Actionable Insights

Power BI Expert | Turning Raw Data into Actionable Insights

Cover image for Data Analytics Project | BlinkIT

Data Analytics Project | BlinkIT Grocery Sales Analysis Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈 📊 What I worked on: Analyzed sales performance across product categories, outlet types, sizes, and locations Identified top-performing item categories and customer preferences Studied the impact of outlet size, establishment year, and visibility on sales Converted raw data into actionable insights and business recommendations 💡 Key Insights: Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales Medium-sized outlets outperform others in overall revenue FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz ) Feedback and suggestions are always welcome!

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Cover image for Data Analytics Project | BlinkIT

Data Analytics Project | BlinkIT Grocery Sales Analysis Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈 📊 What I worked on: Analyzed sales performance across product categories, outlet types, sizes, and locations Identified top-performing item categories and customer preferences Studied the impact of outlet size, establishment year, and visibility on sales Converted raw data into actionable insights and business recommendations 💡 Key Insights: Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz )

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Cover image for I’m excited to share my

I’m excited to share my latest Data Analytics project where I analyzed Customer Shopping Behavior to uncover trends in sales, demographics, and purchasing habits.This project was a great exercise in building a complete data pipeline. The Tech Stack: Python (Pandas & NumPy): Used in Jupyter Notebooks for initial data exploration and statistical analysis. PostgreSQL: Utilized for rigorous data cleaning, querying, and structuring the dataset for analysis. Power BI: Built an interactive dashboard to visualize key metrics like seasonal trends, subscription impacts, and category performance. Project Highlights: ✅ Data Cleaning: Leveraged PostgreSQL and Pandas to handle missing values and standardize categorical data.

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Cover image for Venezuela’s Oil Reserves Analysis Dashboard

Venezuela’s Oil Reserves Analysis Dashboard 📊 I am excited to share my latest data analytics project focusing on the energy sector. Using a dataset covering 23 reservoirs, I designed a comprehensive dashboard to track production capacity and resource distribution in Venezuela. Key Features: ✅ Real-time Metrics: Tracking the 390.50 Billion total barrels in reserve. ✅ Granular Analysis: Production capacity breakdown by reservoir name and oil grade (Extra Heavy to Light). ✅ Stakeholder Mapping: Visualizing the production sum by major operators. Tools Used: [pandas(jupyter notebook)/mysql(for analysis and cleaning of data), Power BI / Excel(for building dashboard)] I’d love to hear your thoughts or feedback on the dashboard design! must visit my github for full project -( https://lnkd.in/dYKdZ8J2 )

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Nanded-Waghala, India

Power BI & SQL expert delivering actionable business insight

Power BI & SQL expert delivering actionable business insight

Cover image for Airline Data Analysis Using SQL

Airline Data Analysis Using SQL

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Cover image for Yelp User Engagement Analysis

Yelp User Engagement Analysis

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Cover image for Crunchy Corner Business Optimization & Financial Analysis

Crunchy Corner Business Optimization & Financial Analysis

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