NOTE: I created this repository for educational purposes. It will host a number of projects as part of the process and some exercises that we created, just purely a learning process. Not perfectly done.
With an upsurge in cybercrimes related to Sim Card Swap fraud in developing countries, making fraud detection is a top priority. If we are able to estimate whether someone is going to commit Sim Card Fraud we can surely try to prevent it earlier.
Predicting the likelihood of Sim Card Swap Fraud Occurrence.
- Train and test the data samples
- Normalize and summarize the data
- Define Problem
- Prepare Data
- Evaluate Algorithms
- Improve Results
- Present Results
Sim Card Swap Fraud Detection.
- Logistic Regression. Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis.
There can be many factors as to why someone would want to swap his/her sim card, I will just use few. The swap will be represented by
0 will represent not swapped. I created this data for this exercise.
Sample Output Representation:
- Sample Fake Data taken from Nairobi Data is not given in this case so I decided to create my own, I will identify Locations here though I will not use Location since we can have many customers living in the same Location.
|ID||Location||Age||Subscriber Complaints||Monthly Payments KSH||Contacts||Swap Agent|
Preview of Data
- Graphing the features in a pair plot
0.625 Not very bad since the data is Random.
Machine learning algorithms:
- Algorithm 1: Linear Regression
- Algorithm 2: Logistic Regression
- Algorithm 3: Linear Discriminant Analysis
- Algorithm 4: Classification and Regression Trees
- Algorithm 5: Naive Bayes
- Algorithm 6: K-Nearest Neighbors
- Algorithm 7: Learning Vector Quantization
- Algorithm 8: Support Vector Machines
- Algorithm 9: Bagged Decision Trees and Random Forest
- Algorithm 10: Boosting and AdaBoost
Copyright  [Madonah Syombua]
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