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The purpose of this analysis is to identify (and predict) customers who will stay 2 years or more. This is important since the company (Kin Security) must profit. A commercial campaign may be created based on this analysis.
The model used is Logistic Regression. The process followed is:
- Cleaning dataset according to desired population. See
notebooks/0.1-axel-exploration.ipynb
. - EDA in order to understand data. See
notebooks/0.2-axel-exploration.ipynb
andnotebooks/0.3-axel-exploration.ipynb
. - Feature engineering. Normalization and encoding. See
1.1-axel-modeling.ipynb
. - Modeling. Logistic regression model. See
1.2-axel-modeling.ipynb
.
Raw data were too large and you won't see them on GitHub. For your convenience, an auxiliar directory was created on which
cleaned and processed data is stored. See data_sent_github
.
On the other hand, if you launch the project in Deepnote, you will be able to see raw data.
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── processed <- The final, canonical data sets for modeling. (Too large for GitHub)
│ └── raw <- The original, immutable data dump. (Too large for GitHub)
│
├── data_sent_github <- Processed data sent to GitHub. Contains the same as data/processed
├── reports <- Project report
│ └── report_churn_model.pdf
│
├── notebooks <- Jupyter notebooks. Naming convention: number (for ordering),
│ ├── 0.1-axel-exploration.ipynb
│ ├── 0.2-axel-exploration.ipynb
│ ├── 0.3-axel-exploration.ipynb
│ ├── 1.1-axel-modeling.ipynb
│ └── 1.2-axel-modeling.ipynb
│
└── requirements.txt <- The requirements file for reproducing the analysis environment.