Data Engineer

Md. Imrul  • Peerlist

Framework: Laravel V-7 Frontend: jQuery, Ajax, Bootstrap 4 Database: MySQL Description: This is an online betting platform with four user roles: Super Admin, Admin, Club Admin, and Normal User. Super Admin, Admin, and Club Admin have access to a dashboard, with role-based permissions governing their actions. Super Admin has exclusive rights to create new Admin accounts. Admin users can manage tasks such as creating users or clubs, and setting up new games with questions and potential answers for betting. The platform includes an Auto Stack Management feature, allowing admins to quickly generate game questions with possible answers for faster betting setup. This system also organizes questions under stack names. Admins can communicate with clubs and users via messaging, handle mobile SMS services, and manage deposits, withdrawals, and site settings. Super Admin has exclusive access to view session users along with their IP addresses, recharge the company's main account, and update referral user commission rates. Club Admins are responsible for managing their respective clubs. Normal Users can only participate in matches if they are part of a club. They can also have referral users. When a user wins a match, both their club and their referral user receive commissions. Users can track all transactions on their accounts, including deposits, withdrawals, and other activities.

3inBD

Bangladesh Army Database

Calculator

Framework: Laravel v-7 Frontend: Ajax, Jquery, Bootstrap4 Database: MySQL Description: This project is developing for multi seller purpose with pure coding. Implemented features are (i) Shopping Cart (ii) Coupon System on Order (iii) Order placement with email notification to users. (iv) Payment Gateway Integration with Bkash, Rocket and many more Banking agents. (v) Admin Dashboard for Admins and Sellers with custom middlewere. (vii) Shop, Coupon, Product, Order etc Panels in Admin Dashboard. (viii) Multiple Authentication System for permissions. (ix) Search by product category (x) Search product with name. (xi) User can rate on a product upto 5 star. (xi) User can comment as well. [NOTE: NO PACKAGE IS USED. USING PURE CODING]

Deal Ocean

Deep Learning-Based Violence Detection System

Digital Employee Attendance System

Education Board Result

Home Automation System

CMS: Wordpress Back-End: PHP Front-End: HTML, CSS, JavaScript, JQuery Database:MySql

Online E-Commerce Website

Back-End: PHP Front-End: HTML, CSS, Bootstrap Database: MySQLi

Online Food Ordering System

I’ve delved into AI-enhanced email data analysis to uncover crucial insights about internal communication flows within a company. Here are some highlights: 🔍 Key Insights: >>> The Customer Service Department receives the most reports via email. >>> Morly Finey from Customer Service has the highest percentage (38%) of report emails. >>> Six employees across different departments have received no reports and are performing well. >>> Over 19% of emails are sent after working hours, and 5.825% are sent outside of office days, indicating a high workload. >>> Weekend email sending (Saturday & Sunday) is less than 4%, which may indicate incomplete work or other issues. >>> The Executive department sends 50% more emails than the second-highest sender. >>> The email open rate is 91.34%, indicating that emails are being read. >>> The Executive Management sends the most warning messages to employees. >>> Most negative emails are related to legal matters and operational issues. 📊 Key Graphs Included: - Weekly Email Open Rates - Top Employee (No Warning) - Warning Distribution by Employee - Warning Distribution by Department - Email Journey using Decomposition Tree - Trend of # Opened Emails Over Time - Comparison of Emails Over Time: Workdays vs. Non-Workdays - Trend of Opened Emails (%) Over Time - Comparison of Emails Over Time: Workdays vs. Non-Workdays using Waterfall Chart - Email Sent By Department - Open Rate (Email) using Stacked Bar Chart - Various Pie Charts for Ratio Analysis Check out the detailed insights and visualizations in the attached Image!

Onyx - DataDNA Dataset Challenge - Email Anal

I’ve explored employee performance and retention analysis to derive key insights about the workforce dynamics in a company. Here are some highlights: 🔍 Key Insights: >> Cohort Retention Rate: Retention is stable for the first 4-5 years but significantly drops after that. >> 2014 Cohort: Only 1% of employees hired in 2014 remain by 2023, highlighting significant long-term attrition. >> Recent Cohorts (2020-2023): Short-term retention is high, but a noticeable decline appears after 4 years, as seen in the 2019 cohort. >> Hiring & Resignations Trend: Hiring remained stable, with a slight decline recently, while resignations have remained steady, indicating a good retention rate overall. >> Turnover Rate Peaks: Turnover rates peaked notably in 2016 and 2023. >> Performance vs Salary: Employees earning between $5000-$6999 USD tend to perform the best, suggesting motivation is high in mid-range salary groups. >> Satisfaction vs Salary: Higher salaries ($7000-$9000 USD) correlate with higher employee satisfaction, indicating salary is a strong driver of satisfaction. >> Performance Across Age Groups: Employees aged 50-59 outperform others, while those under 30 show lower performance, indicating that experience drives better results. >> Department & Salary Overview: Sales, Operations, and IT departments have the largest workforce, with Engineering holding the highest average salary due to technical expertise requirements. 📊 Key Graphs Included: - Employee Retention Count & % Since Hire Date - Department Wise Resigned Count - Trend of Hires and Resignations Over Time - Employee Turnover Rate by Year - Employee Turnover Rate by Year, Quarter - Performance & Satisfaction Variation with Salary Group - Performance Distribution Across Age Groups - Employee Retention Count and Rate Since Hire Date - Salary by Overtime and Promotion Status - Department and Job Title Overview