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:dart: Qualify sales leads with machine learning
| Date | Stars |
|---|---|
| 2026-07-31 | 655 |
| 2026-08-03 | 655 |
| 2026-08-06 | 656 |
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# LeadQualifier
This repo is a collection of scripts we use at Xeneta to **qualify sales leads** with machine learning. Read more about this project in the Medium article [Boosting Sales With Machine Learning](https://medium.com/xeneta/boosting-sales-with-machine-learning-fbcf2e618be3).
You can use this repo for **two things:**
1. Try to beat our predictions using **our data** and your own algorithm
2. Create a lead qualifier for your company, using **your own data**
## Setup
Start off by running the following command:
pip install -r requirements.txt
You'll also need to download the stopword from the [nltk](http://www.nltk.org/index.html) package. Run the Python interpreter and type the following:
import nltk
nltk.download('stopwords')
# 1. Experiment with your own algorithms
We'd love to see more algorithms on the leaderboard, so send us a pull request once you've implemented one.
## [Xeneta Qualifier](https://github.com/xeneta/LeadQualifier/tree/master/xeneta_qualifier)
We've provided you with our vectorized and transformed data [here](https://github.com/xeneta/LeadQualifier/tree/master/xeneta_qualifier/data). We can unfortunately not share the raw text data, as it contains sensitive company information (who our customers are).
To test our your own algorithm, simply add it the [run.py](https://github.com/xeneta/LeadQualifier/blob/master/xeneta_qualifier/run.py) file and run the script:
python run.py
Thanks to [lampts](https://github.com/lampts) for implementing the best performing algorithm so far, the [SGDClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html).
### Leaderboard:
| Algorithm | Precision | Recall | F1 Score|
| ------------- |:--------------| :------|:--------|
| SGD Classifier| 0.872 | 0.940 | 0.905 |
| Random Forest | 0.845 | 0.915 | 0.878 |
**PS:** We're also experimenting with a neural net (in TensorFlow) in the nn.py file.
# 2. Create your own lead qualifier
To create your own lead qualifier, you'll need to get hold of company descriptions (to create your dataset). We currently use [FullContact](https://www.fullcontact.com/developer) for this.
**Note:** We've added dummy data, so that you can run both scripts without getting errors, and to give you examples on how the sheets should look like.
## [Train Algorithm](https://github.com/xeneta/LeadQualifier/tree/master/train_algorithm)
This script trains an algorithm on **your own** input data. It expects two excel sheets named **qualified** and **disqualified** in the [input](https://github.com/xeneta/LeadQualifier/tree/master/train_algorithm/input) folder. These sheets need to contain two columns:
- URL
- Description
Run the script:
python run.py
It'll dump three files into the [qualify_leads](https://github.com/xeneta/LeadQualifier/tree/master/qualify_leads) project:
- algorithm
- vectorizer
- tfidf_vectorizer
You're now ready to start classifying your sales leads!
## [Qualify Leads](https://github.com/xeneta/LeadQualifier/tree/master/qualify_leads)
This is the script that actually predicts the quality of your leads. Add an excel sheet named **data** in the [input](https://github.com/xeneta/LeadQualifier/tree/master/qualify_leads/input) folder. Use the same format as the example file that's already there.
Run the script:
python run.py
It'll output an excel sheet with a column named **Prediction**, where 1 equals *qualified* and 0 equals *disqualified*:
Got questions? Email me at [email protected].
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Per Harald Borgen · Co-founder at Scrimba · Norway
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1ec7d59177c94b5c, llm:Repository description: 'Qualify sales leads with machine learning' (Python). No topics listed.
matched fp:1ec7d59177c94b5c, llm:Repository description: 'Qualify sales leads with machine learning' (Python). No topics listed.
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