Collect 200 leads from LinkedIn, organize in Excel sheet with name, email, and company

Collect 200 leads from LinkedIn, organize in Excel sheet with name, email, and companyCollect 200 leads from LinkedIn, organize in Excel sheet with name, email, and company

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SAJIB HOSSEN

• Mar 16

Collect 200 leads from LinkedIn, organize in Excel sheet with name, email, and company

Data Analysis Data Entry Lead Generation Google Docs Microsoft Excel Microsoft Word

Josiah Temitope

• Mar 16

Great job

Geethasree Naguboina

pro

• Mar 17

Lead generation + structured Excel organization is a great combo — having name, email, company and social accounts all in one clean sheet makes outreach so much faster. Did you build this with manual research or a scraping tool?

Marco Mastroeni

• Mar 18

Well done. If I remember correctly, webscraping on LinkedIn is difficult

Istiak Ahmed khan

• Mar 25

Great job Sajib

Muhammad Umar

• Nov 7

Which one do you think i should put as a FEATURED SECTION cover on linkedin? Help me decide!

1 voted

33%

2 voted

67%

3 votes

Closed

Benjamin C

max

• Nov 8

which one matches your headline better? thats usually the tiebreaker for featured covers

Mohit Patil

• Nov 8

This dashboard tracks headcount, attrition, open positions and time to hire, along with the hiring funnel and the age distribution of employees.

#Excel #MicrosoftExcel #DataAnalysis #DataAnalytics #Spreadsheets #ExcelExpert #Freelance#HRAnalytics #PeopleAnalytics

Microsoft Excel Accounting Account Management Excel VBA

Mahammadali Mammadov

• Nov 7

End-to-end retail analytics pipeline that turns raw, messy CSV exports into business decisions, built in Python and SQL and runnable with one command.

What it does:

Cleans and validates 6 relational tables, fixes 585 data-quality issues and logs every action

Loads a SQLite warehouse with keys, constraints and window-function SQL

Customer analytics: RFM segmentation, cohort retention, CAC / LTV by channel, product Pareto, market-basket analysis

Churn prediction model validated on future data (ROC-AUC 0.71, no data leakage)

6-month revenue forecast, best of 3 models by backtest (4.2% MAPE)

Auto-generated Excel report (live formulas, native charts) and an interactive Streamlit dashboard

18 automated tests

Data Analysis Data Visualization Statistics Matplotlib pandas Python

Eros C.

• Nov 7

Logging every cleaning action is the part I like most: when a number in the report looks strange, you can follow it back to the fix that changed it. With 585 issues, how did you decide which ones the script fixes alone and which ones it only reports?