https://github.com/clamytoe/investments-vc
Capstone project for CareerERA Post Graduate Program in Data Science
https://github.com/clamytoe/investments-vc
Last synced: 11 months ago
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Capstone project for CareerERA Post Graduate Program in Data Science
- Host: GitHub
- URL: https://github.com/clamytoe/investments-vc
- Owner: clamytoe
- License: mit
- Created: 2022-12-23T01:30:58.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-01-31T04:29:13.000Z (over 3 years ago)
- Last Synced: 2025-03-01T21:48:16.928Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 29 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Investments Venture Capitalists (*investments-vc*)
> *Capstone project that I created as part of CareerERA's Post Graduate Program*
![Python version][python-version]
![Latest version][latest-version]
[![GitHub issues][issues-image]][issues-url]
[![GitHub forks][fork-image]][fork-url]
[![GitHub Stars][stars-image]][stars-url]
[![License][license-image]][license-url]
NOTE: This project was generated with [Cookiecutter](https://github.com/audreyr/cookiecutter) along with [@clamytoe's](https://github.com/clamytoe) [toepack](https://github.com/clamytoe/toepack) project template.
**Author**: [Martin Uribe](clamytoe@gmail.com)
## Purpose
This project is part of my first capstone project for the [Post Graduate Program in Data Science](https://www.careerera.com/data-science/post-graduate-program-in-data-science) for [CareerERA](https://www.careerera.com/).
I was given a random dataset with no description of what each feature meant or what to do with it. I cleaned it up, performed some EDA, ran some extensive parameter tuning on different classifiers, and created a model that was able to generate the probability of a startup being acquired.
This was a challenge because the target classes were highly imbalanced.
> In the end, my model achieved an **86%** accuracy.
Further details can be found in the [Jupyter notebook](MartinUribe_InvestmentsVC_Capstone.ipynb). I've included the [html export](MartinUribe_InvestmentsVC_Capstone.html) of the notebook so that the interactive plots would be available offline without having to recreate my environment and running the notebook.
**Project structure:**
```bash
.
├── LICENSE
├── MartinUribe_InvestmentsVC_Capstone.html
├── MartinUribe_InvestmentsVC_Capstone.ipynb
├── README.md
├── cleanup_data.py
├── create_model.py
├── data
│ ├── cleaned_data.csv
│ └── investments_VC.csv
├── requirements.txt
└── rf-up-86.pkl
```
## Initial setup
```zsh
cd Projects
git clone https://github.com/clamytoe/investments-vc.git
cd investments-vc
```
### Anaconda setup
If you are an Anaconda user, this command will get you up to speed with the base installation.
```zsh
conda create --name vc --file requirements.txt
conda activate vc
```
### Regular Python setup
If you are just using normal Python, this will get you ready, but I highly recommend that you do this in a virtual environment.
There are many ways to do this, the simplest using *venv*.
```zsh
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
## Creating a cleaned dataset
The initial dataset required a lot of tweaking in order to get it ready. The cleaned dataset exceeds the maximum size requirements for GitHub, so it was not provided. Not to worry though, I have provided a script to generate it.
Simply run the follwing command:
```bash
python cleanup_data.py
Created data/cleaned_data.csv
```
## Generating the model
To generate the model, simply run the following command:
```bash
python create_model.py
Importing dataset: data/cleaned_data.csv
Processing data...
Resampling imbalanced classes...
Splitting the data into train, validation, and test sets...
Scaling numeric features...
One Hot Encoding categorical features...
Creating model...
Fitting the model...
Testing the model...
Classification Report
precision recall f1-score support
0 0.89 0.81 0.85 988
1 0.83 0.90 0.87 1012
accuracy 0.86 2000
macro avg 0.86 0.86 0.86 2000
weighted avg 0.86 0.86 0.86 2000
Confusion Matrix
[[805 183]
[ 98 914]]
Saving the model...
[DONE] Model saved to rf-up-86.pkl
```
## TODO
* [X] Cleaned up the data
* [X] Analyzed the data
* [X] Extracted some meaningful insights from the data
* [X] Feature engineered 988 new features
* [X] Successfully handled the imbalance of the target classes
* [X] Performed some extensive model tuning to find optimal parameters
* [X] Selected the best model
* [X] Saved the model for deployment
* [X] Create python script to prepare the data
* [X] Create python script to create and save the model
* [ ] Deploy the model with FastAPI and gunicorn or Flask and nginx
* [ ] Containerize the model with Docker
* [ ] Deploy the model on the cloud
## License
Distributed under the terms of the [MIT](https://opensource.org/licenses/MIT) license, "investments-vc" is free and open source software.
## Issues
If you encounter any problems, please [file an issue](https://github.com/clamytoe/toepack/issues) along with a detailed description.
[python-version]:https://img.shields.io/badge/python-3.9.15-brightgreen.svg
[latest-version]:https://img.shields.io/badge/version-0.1.0-blue.svg
[issues-image]:https://img.shields.io/github/issues/clamytoe/investments-vc.svg
[issues-url]:https://github.com/clamytoe/investments-vc/issues
[fork-image]:https://img.shields.io/github/forks/clamytoe/investments-vc.svg
[fork-url]:https://github.com/clamytoe/investments-vc/network
[stars-image]:https://img.shields.io/github/stars/clamytoe/investments-vc.svg
[stars-url]:https://github.com/clamytoe/investments-vc/stargazers
[license-image]:https://img.shields.io/github/license/clamytoe/investments-vc.svg
[license-url]:https://github.com/clamytoe/investments-vc/blob/master/LICENSE