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Machine Learning - SUPPORT VECTOR MACHINES

Customer-Retirement--Prediction

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Background

BANK CUSTOMERS RETIREMENT PREDICTIONS USING SUPPORT VECTOR MACHINES

You work as a data scientist at a major bank in NYC and you have been tasked to develop a model that can predict whether a customer is able to retire or not based on his/her features. Features are his/her age and net 401K savings (retirement savings in the U.S.). You though that Support Vector Machines can be a great candidate to solve the problem.

Goals

  • IMPORTING DATA
  • VISUALIZING THE DATA
  • MODEL TRAINING
  • EVALUATING THE MODEL
  • IMPROVING THE MODEL

How to run

Open Google Colab https://colab.research.google.com/ and in the Navbar go to:

  • File
  • Upload Notebook
  • Run the Cells

Proccess

Import the data set and visualizing the properties

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With Pairgrid from Seaborn library, vizualizing the data points

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With .count function, vizualing false and positives input

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Transforming the data andSplinting into train and test

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Using Confusion Matrix to Evaluating the Model

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Improving the model

  • Scalling X_train
  • Visualing points for X_train
  • Visualing points for X_train_Scalled

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Improving even more the model

  • Importing Gridsearch from Sklearn library
  • Match best combinations to find the best parameters to the model
  • Confusion Matrix with the result

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