Module 3 - Project 2 (ML) - Churn Analysis

Victor, Susanna, Vero & Anna

Data Analytics-Part Time Course | Barcelona June 2020

Content

• Project Description and Workflow
• Organization
• Links

Project Description and Workflow

Description

This project is to practice Machine Learning.

Dataset The dataset chosen is data of 10k clients from Bank - Direct Link to kaggle. Note: the dataset contained in this repository (the one you must use) differs from the original kaggle dataset.

Metadata

- RowNumber: corresponds to the record (row) number and has no effect on the output. This column will be removed.
- CustomerId: contains random values and has no effect on customer leaving the bank. This column will be removed.
- Surname: the surname of a customer has no impact on their decision to leave the bank. This column will be removed.
- CreditScore: can have an effect on customer churn, since a customer with a higher credit score is less likely to leave the bank.
- Geography: a customer’s location can affect their decision to leave the bank. We’ll keep this column.
- Gender: it’s interesting to explore whether gender plays a role in a customer leaving the bank. We’ll include this column, too.
- Age: this is certainly relevant, since older customers are less likely to leave their bank than younger ones.
- Tenure: refers to the number of years that the customer has been a client of the bank. Normally, older clients are more loyal and less likely to leave a bank.
- Balance: also a very good indicator of customer churn, as people with a higher balance in their accounts are less likely to leave the bank compared to those with lower balances.
- NumOfProducts: refers to the number of products that a customer has purchased through the bank.
- HasCrCard: denotes whether or not a customer has a credit card. This column is also relevant, since people with a credit card are less likely to leave the bank. (0=No,1=Yes)
- IsActiveMember: active customers are less likely to leave the bank, so we’ll keep this. (0=No,1=Yes)
- EstimatedSalary: as with balance, people with lower salaries are more likely to leave the bank compared to those with higher salaries.
- Exited: whether or not the customer left the bank. This is what we have to predict. (0=No,1=Yes)

Goal

Classify if a customer from a Bank is going to churn or not. What if would be possible to predict if a customer will leave our company?

Workflow

1. Environment

2. Data Preparation

   2.1 Data Wrangling

    - Explore Data
    - Clean Data
    - Transform Data
   

   2.2 Feature Selection

    - Delete not useful variables
    - Correlation      
   

   2.3 Feature Engineering

    - Log transformation
    - MinMaxScaler
    - Creat new columns
   

3. Select From Model (Classification Models)

    - LogisticRegression
    - SVM
    - KNeighborsClassifier
    - DecisionTreeClassifier
    - LinearDiscriminantAnalysis
    - GaussianNB
    - RandomForestClassifier
    - MLPClassifier
   

4. Train Models (Machine Learning Classification Models)

    - LogisticRegression
    - SVM
    - RFC
    - NeuralNetworks
   

5. Ensemble Methods

    - XGBoost
    - Stacking
   

Organization

Main Organization of the project thought GitHub Project board:

1. Organize a Project GitHub board with main tasks and timings
2. Create a repository including a .gitignore file and a Readme.md
3. Data Preparation
4. Modeling
5. Tuning
6. Write conclusions
7. Complete Readme.md

Inside this repository you can find:

CSVs raw data:

- Churn_Modelling.csv

CSVs clean data:

- Churn_Clean.csv
- Churn_Norm.csv
- Churn_norm_LDA.csv
- Churn_LDA.csv

Jupyter notebook of the data wrangling:

- DataPreparation.ipynb
- SelectFromModel.ipynb
- PCA-LDA.ipynb
- ML_logistic_regression_regularized_models.ipynb
- ML_RFC.ipynb
- MODEL_SVM.ipynb
- ML_MLPC.ipynb
- EnsembleLearning.ipynb

Other:

- Gitignore.txt
- README.md

Links

Repository

PowerPoint Presentation

GitHub Project