Learn how Santander Bank used ML to predict customer transactions in this student project blog. See the code and results! #MachineLearning #Banking #DataScience

Santander Customer Transaction Prediction using ML

Dataset used: Santander Customer Transcation Dataset
In this challenge, Santander invites Kagglers to help them identify which customers will make a specific transaction in the future, irrespective of the amount of money transacted
The data provided in this dataset has the same structure as the real data they have available to solve this problem.
Dataset consists of 200k+ rows and 200+ columns
The data is anonimyzed, each row containing 200 numerical values identified just with a number
To run this notebook, select "Run" > "Run on Colab" and connect your Google Drive account with Jovian. Make sure to use the GPU runtime if you plan on using a GPU.

Download the Dataset

Steps:

Install required libraries
Download data from Kaggle
View dataset files
Load training set with Pandas
Load test set with Pandas

Install Required Libraries

!pip install opendatasets numpy pandas jovian matplotlib  scikit-learn  --quiet

import jovian
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns