Welcome to LeadGen Tool, a smart, modular lead generation dashboard built for the SaaSquatchLeads challenge.
This project focuses on quality-first development, offering actionable insights from company data enriched with news-based scoring and sentiment analysis to prioritize outreach targets effectively.
Built by Sahaj Gupta
- Visualize top industries, company sizes, and LinkedIn trends
- Analyze employee count vs followers
- Identify underserved company segments for SaaS outreach
- Search by company name or industry
- Instant filtering to find specific target groups
- Filter by company size (small, mid, large)
- Filter by country code
- Export selected company data to CSV or Excel
- Choose specific columns for better CRM integration
A custom-built AI module that scores companies based on their real-time intent and news sentiment, using scraping + NLP.
| Business Signal | Score |
|---|---|
| Funding Announcement | +3 |
| Product Launch | +2 |
| Executive Hire | +1 |
| Layoffs/Scandal | -2 |
| Sentiment (TextBlob) | ±0.5 |
Categorizes leads into:
High Potential • Mid Potential • Neutral • Cautionary Leads
- Python
- Streamlit – UI and interactivity
- BeautifulSoup & Selenium – Scraping
- Pandas – Data handling
- TextBlob – Sentiment analysis
- Kaggle Dataset + LinkedIn Scraping
├── app.py # Main Streamlit dashboard (UI)
├── lead_scout.py # Lead scoring logic based on company metadata
├── news_lead_scout.py # News scraping & sentiment-based scoring module
├── dataset-cleaning.ipynb # Notebook for data cleaning and preprocessing
├── gameplan.txt # Project outline / development notes
├── requirements.txt # Python dependencies
├── README.md # Project documentation
├── LICENSE # MIT License
├── .gitignore # Files/folders to ignore in Git
├── Dataset/ # Static company datasets
│ ├── LinkedIn company information datasets (Public web data).csv
│ └── LinkedIn people profiles datasets.csv
├── Cleaned_Dataset/ # Static company datasets
│ ├── cleaned_company_data.csv
│ └── news_leads.csv
├── Web Scrapper/ # Scripts & outputs for scraping LinkedIn data
│ ├── scrapper1.py # Scraper script for LinkedIn
│ ├── linkedin_scrapper.ipynb # Scraping notebook
│ ├── linkedin_jobs_page.html # Saved HTML for offline parsing
│ └── linkedin_jobs_extracted.csv
├── demo/ # Sample outputs or demo-ready data
│ └── linkedin_jobs_extracted.csv
├── news_leads.csv # Output: News-based scored leads