Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/xeneta/LeadQualifier

:dart: Qualify sales leads with machine learning
https://github.com/xeneta/LeadQualifier

Last synced: 18 days ago
JSON representation

:dart: Qualify sales leads with machine learning

Awesome Lists containing this project

README 

        
# 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



![](https://raw.githubusercontent.com/xeneta/LeadQualifier/master/img/sheet.png)



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*:



![](https://raw.githubusercontent.com/xeneta/LeadQualifier/master/img/predictions_sheet.png)



Got questions? Email me at [email protected].