Machine Learning - SUPPORT VECTOR MACHINES

Customer-Retirement--Prediction

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

With Pairgrid from Seaborn library, vizualizing the data points

With .count function, vizualing false and positives input

Transforming the data andSplinting into train and test

Using Confusion Matrix to Evaluating the Model

Improving the model

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

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