This project was assigned during my internship in focusync. This is about using sql for querying and extracting meaningful banking data and then importing to power bi to create dashboard in visualising and tracking the transactions of customers, performances of branches, mostly used accounts and many more. It was difficult to take all such information in just spreadsheet so that's why this tool was used to track banking KPIs quickly.

This is about the performance of Hotel which illustrates revenue of each of their types, daily reservation status, demand for night stays and many more.

This is about how much transactions are taken place in every city, from which gender mostly and which customers has been loyal to their services

Focusync had banking partners who wanted to have view about their financial performances to help in making future decisions. The data was extracted from their websites respectively, then Extract Transform Load (ETL) process took place where we started cleaning data in Excel, made calculations of different accounting ratios, then exported results to power bi where dashboard of financial ratios such as current ratio, lending ratio, debt to asset ratio of each bank was shown visually. These results were then compared with each other and recommendations were made on basis of each financial ratio

This is machine learning project in which model predicts that which crops to grow on basis of season, place etc.

This program was created in python in which whatever users pick the option, the system will going recommend on basis of users' preference on its own.

This project was only done in Structure Query Language (SQL) for creating multiple Banking tables, Indexes, Stored Procedures, Views for joining different tables with each other based on common columns and logical relationship between them and then running other queries in order to extract and retrieve essential information that helped in interpreting and evaluating the data.

Developed a predictive model using Clustering and Logistic Regressions.
Forecasted suitable crops based on climate and soil conditions, achieving an accuracy rate exceeding 90%.

Employed SQL and Power BI tools for data cleaning, querying, and
calculations. Presented Market Revenue, Sales Trends, Profit Margins, Top Customers, and Products through insightful
visualisations on a dashboard.

Utilised SQL for data extraction, manipulation, and Common Table Expressions (CTEs) to find
pertinent information. Integrated tables with Power BI, creating visually impactful dashboards. Provided insights on
Total Revenue, Top-rated Products, and Products with Highest Profit Margins.