https://github.com/mmbazel/classifying-sales-calls

Turning salesforce lead, oppty, & sales activities data => Sales predictions using pandas, Scikit-learn, SQLAlchemy, Redshift, XGBoost Classifier
https://github.com/mmbazel/classifying-sales-calls

capstone classification classifying-sales-calls data-science data-science-notebook data-science-portfolio data-science-projects data-visualization gradient-boosting logistic-regression machine-learning saas sales springboard springboard-career-track springboard-data-science springboard-projects

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Turning salesforce lead, oppty, & sales activities data => Sales predictions using pandas, Scikit-learn, SQLAlchemy, Redshift, XGBoost Classifier

• Host: GitHub
• URL: https://github.com/mmbazel/classifying-sales-calls
• Owner: MMBazel
• Created: 2019-07-03T00:05:19.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2021-02-26T00:17:11.000Z (over 4 years ago)
• Last Synced: 2025-04-01T08:12:09.094Z (3 months ago)
• Topics: capstone, classification, classifying-sales-calls, data-science, data-science-notebook, data-science-portfolio, data-science-projects, data-visualization, gradient-boosting, logistic-regression, machine-learning, saas, sales, springboard, springboard-career-track, springboard-data-science, springboard-projects
• Language: Jupyter Notebook
• Homepage:
• Size: 6.95 MB
• Stars: 26
• Watchers: 0
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
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# 🕵️‍♀️ Classifying Sales Calls ☎️

👋 Hi!

My name is Mikiko Bazeley 👩🏻‍💻 and this is my first capstone project: 

💸 Predicting the Outcomes of Demo Calls for a SaaS Company. 📱

From Oct 2018 to April 2019 I completed a number of projects as part of the Springboard Data Science Track. 🧠 

For this project I implemented a gradient boosted tree 🌲 model (XGBoost) to classify qualification of sales demo calls in order to improve forecasting efforts. This was the first time machine learning (or data science) techniques had been used in the company on something other than the product. 

I attained ~80% accuracy & demonstrated the value of implementing machine learning to solve low-hanging fruit problems on the business side. This demonstration lead the BI teamt committing to leveraging machine learning and data science for future initiatives. 🎉💪🏻

To find out more about this project, check out the attached presentation below! 

☑️ Jupyter notebook for the project: [LINK](https://github.com/MMBazel/Classifying-Sales-Calls/blob/master/Model%2BAnalysis/Capstone1-FinalVersion-BazeleyMikiko-Springboard.ipynb)  

☑️ Final write up: [LINK](https://github.com/MMBazel/Classifying-Sales-Calls/blob/master/Capstone%20Project%201_%20Final%20Report.pdf) 

☑️ Slides presentation: [LINK](https://github.com/MMBazel/Classifying-Sales-Calls/tree/master/Slide%20JPGs) 

☑️ The nitty gritty detailed reports depicting the process: 

[Write Ups](https://github.com/MMBazel/Classifying-Sales-Calls/tree/master/Detailed%20Milestone%20Reports) | [Notebooks](https://github.com/MMBazel/Classifying-Sales-Calls/tree/master/Detailed%20Milestone%20Notebooks) 

For more information about my Springboard work: 

📝 All of the documentation, code, and notes can be found [here](https://github.com/MMBazel/springboard-program), as well as links to other resources I found helpful for successfully completing the program. 

💬 For questions or comments, please feel free to reach out on [LinkedIn](https://www.linkedin.com/in/mikikobazeley/). 

⚠️ If you find my repo useful, let me know OR ☕ consider buying me a coffee! https://www.buymeacoffee.com/mmbazel ☕.

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