SC1015Project - Telco Churn

We are Team 5 from Tutorial A127. Our team's goal is to predict features that contribute to customer churn in the field of Telecommunications.

Tech Stack

• Data Retrieval: Pandas read_csv()
• Data Wrangling: Python
• Data Visualization: Python
• Presentation: PowerPoint

Libraries/Algorithms

General

• Pandas
• Numpy

Plotting

• Matplotlib
• Seaborn
• Holoview

Machine Learning/Deep Learning

• Sklearn
• Gradient Boost
• K-Nearest Neighbour
• Support Vector Machine
• Random Forest Classifier
• eXtreme Gradient Boost
• TensorFlow Keras

Dataset

The dataset we used is obtained from Kaggle: https://www.kaggle.com/datasets/johnflag/jb-link-telco-customer-churn , saved as "telco_churn_data.csv" in this Repo

Contributors

• Loo Si Hui @Cebelle1 - EDA & Deep Learning
• Luyun Sean Gabriel De La Cruz @seelism - Initial testing of Machine Learning Models
• Nicole Kaira Almonte Imatong @nicolekaira - Random Forest Classifer, XGBoost Model

References

1. https://www.datacamp.com/tutorial/random-forests-classifier-python
2. https://www.datacamp.com/tutorial/xgboost-in-python
3. https://blog.devgenius.io/develop-a-logistic-regression-machine-learning-model-64d2be403ba3
4. https://www.javatpoint.com/k-nearest-neighbor-algorithm-for-machine-learning
5. https://en.wikipedia.org/wiki/Random_forest
6. https://www.datacamp.com/tutorial/svm-classification-scikit-learn-python
7. https://stats.stackexchange.com/questions/128880/number-of-feature-maps-in-convolutional-neural-networks