Predicting Customer Transaction Values in Bank

This notebook is focused on the santander-value-prediction-challenge published to Kaggle a few years back.

Mission Statement

My mission should I chose to accept it, is to predict transaction values for potential customers to the Santander bank in order to provide personalised ads and services to said potential customers. The bank believes that providing personalised services to the customer is vital in this digitized world. Here are some of the benefits for providing personalised financial services to the customers:

Enhanced Customer Experience and trust: Personalization tailors the banking experience to individual preferences and needs. This leads to higher customer satisfaction.
Improved Engagement: When customers receive services that are relevant to their financial goals and lifestyles, they are more likely to actively engage with the bank
Targeted Offerings: By analyzing customer data, banks can offer tailored product recommendations, such as credit cards, loans, or investment options
Competitive Advantage: In a competitive market, banks that can offer personalized services stand out. Customers are more likely to choose a bank that understands and caters to their individual financial situations and aspirations.
Revenue Growth: By offering personalized services and products, banks can cross-sell and upsell effectively. When customers see the value in the offerings, they are more inclined to invest and expand their relationship with the bank

A Happy Customer, A Happy Bank :)

Evaluation

The evaluation metric for this competition is Root Mean Squared Logarithmic Error. It’s a variation of the Root Mean Squared Error (RMSE) that takes the logarithm of the predicted and actual values before calculating the squared differences

The formula for RMSLE is represented as follows:

RMSLE = \(\sqrt{\frac{1}{n} \sum_{i=1}^n ((\log(p_i + 1) - \log(a_i+1))^2 }\)

Where:

\(n\) is the total number of observations in the (public/private) data set,

\(p_i\) is your prediction of target, and

\(a_i\) is the actual target for \(i\).

\(log(x)\) is the natural logarithm of \(x\) (\(log_e(x)\).

RMSE Vs RMSLE

The major difference between the two lies in the concept of overestimation and underestimation.

Overestimation: The model or estimate is too optimistic and predicts a value that is greater than what actually occurs. For E.g., overestimating value of a customer, and thus bank providing some additional benefits or services to that customer.

Underestimation: The model or estimate is too conservative and predicts a value that is lower than what actually happens. For E.g., underestimating the value of a customer, and thus bank misses out on providing some additional benefits or services to that customer.

RMSLE works better when underestimation can’t be tolerated, as it scales the data to logarithmic value. Thereby heavily reducing the chances of being affected by outliers (with very high numbers).

Importing the Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os

Setting up the pandas dataframe

# Declaring file paths
train_file_path = "train.csv"
test_file_path = "test.csv"

# For Kaggle...
# train_file_path = "/kaggle/input/santander-value-prediction-challenge/train.csv"
# test_file_path = "/kaggle/input/santander-value-prediction-challenge/test.csv"

# Reading the train and test csv file and storing into pandas dataframe
train_df = pd.read_csv(train_file_path)
test_df = pd.read_csv(test_file_path)

def display_all(df):
    with pd.option_context("display.max_rows", 1000): 
        with pd.option_context("display.max_columns", 1000): 
            display(df)

A high glance look at the data

display_all(train_df)

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4458	ffeb15d25	20000000.0	0.0	0	0.0	0	0	0	0	0	20000000.0	0.0	0.0	0	0	0	0	0.0	0	0.0	0	0	0.0	0	0	0.0	0	0	0	0.0	0	0.0	0	0.0	0.0	0	0.0	0	0	0.0	0.0	0	17800000.0	0	0	0.0	0.0	0.0	0	0	0.0	0.0	0	0	0	0.0	0.0	0	0	0	0	0.0	0.0	0	0	0	0	0	0.0	0	0	0	0	0	0	0	0	0	0	0.0	0.0	0	0	0	0.0	0.0	0.0	0.0	0	0.0	0	0.0	0	0	0.0	0.0	0	0	0.0	0	0.0	0	0	0	0.0	0	0.0	0	0	0.0	0.00	0.0	0	0.0	0	0.0	0	0	0.0	0	0.0	0	0	0	0.0	0.0	0.0	0	0	0	0	0	0	0	0	0.0	0	0	0	0.0	0	0.0	0	0	0	0	0.0	0	0.0	0	0.0	0	0	0.0	0	0.0	0	0.0	0.0	0	0	0	0	0	0	0	0	0	0.0	0	0.0	0	0.0	0.0	0	0	0.0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0	0	0	0	0.0	0.0	0	0	0	0	0.0	0.0	0	0.0	0.0	0	0	0	0	0.0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	0	0.0	20000000.0	0.0	0	0	0.0	0	0	0.0	0.0	0	0	0.0	0	0.0	0	0.0	0.0	0	0	0.0	0	0.0	0	0	0	0	0.0	0	0	0.0	0	0	0	0.0	0	0.0	0	0.0	0	0	0	0.0	0	0	0	0	0	0	0.0	0	0	0.0	0	0.0	0	0	0	0.0	0	0	0	0	0	0	0	0	0	0	0	0.0	0.0	0	0	0	0.0	20000000	0.0	0	0.0	0.0	0	0.0	12000000.0	0	0.0	0.0	0.0	0	0.0	22600000.0	0.0	0	0.0	0.0	0.0	0	0	0.0	0.0	0	0	0	0.0	0	0	0.0	0	0	0.0	0	0	0.0	0	0.0	0	0	0.0	0.0	0	30000000	0	0	0	0.0	0.0	0.0	0.0	0	0	0.0	0	0	0	0	0.0	0	0	0	0.0	0	0.0	0	0	0	0	0	0	0	0	0	6400000.0	0.0	0	0	0.0	0	0.0	0	0	0.0	0.0	0	0	0.0	0	0	0.0	0	0	0.0	0	0.0	0.0	0.0	0.0	0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0	0.0	0	0.0	0	0	0	0.0	0.0	0	0	0.0	0.0	0	0	0	0	0	0.0	22600000.0	0	0.0	0.0	0	0.0	0	0	0.0	0	0.0	0	0.0	0.0	0	10000000	0	0	5000000.0	0	0	0	0.0	0	0.0	0	0	0.0	0	0	0	14500000.00	0.0	0.0	0	0	0	0	0.0	0	0.0	0	0	0	0	0	0	64000000	20000000.0	0	0	0	0	0	0	0	0	0	0	0.0	0	0	0	0	0.0	20000000.0	0.0	0	0.0	0	0	...	0	0	0.0	0.0	0.0	0	0.0	0	0.0	0.0	0	0	0	0	0.0	0.0	0.0	0	0	0.0	0	0.0	0.0	0.0	0	0	0.0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	11333333.34	0.0	0.0	0.0	0.0	0	0	0	0	0	0	0.0	0	0	0.0	0	0.0	0	0.0	0	0	0	0.0	0	0.0	0	0.00	0	0.0	0.0	0.0	0	20000000.0	0	0	0	0	0	0.0	0.0	0	0	0	0	0.0	0	0	0	0.0	0.0	0	0.0	0	18000000.0	0	0	0.0	0	0	25850000.0	0	0	0.0	0	0	0	0.0	0.0	0	0	0.0	0	0	0	0.0	0.0	0	0.0	0	0.0	0	0.00	0.0	0.0	0	0.0	0.0	0	0.0	0	0.0	0	0	0	0	0	0.0	0.0	0	0	0.0	0.0	0	0	8000000.0	0.0	0	0	0	0	0	0	0	0	0.0	0	0	0.0	0	0.0	22500000.0	0	0	0.0	0	0.0	0	0	0	0	0	0.0	11200000.0	0.0	0.0	0	30000000	0	0.0	0.0	0.0	0	0	0.0	0	0	0.0	0	0.0	0	0	0	0	0	0	0	0.0	0.0	0.0	0	0	0	0	0	0	0	0	0	0.0	0.0	0	14000000.0	0.0	0	0.0	0.0	0	0	0	0	0	0.0	0	0.0	0	0	0	0.0	0.0	0	16000000.0	0	0.0	0	0	0	0	0	0.0	0	0	0	0.0	0.0	0	0	0	0	0	0	0.0	0	0.0	0.0	0	0	0	0	0.0	0	0.0	0.0	0	0.0	0	0	0	0	0	0	0.0	0.0	0	0	0	0	0	0	23600000.0	0	0	0	0.0	0	0.0	0	0	0	0	0	0.0	0	0.0	0.0	0.0	0	0	0	0.0	0	0.0	0	0	0.0	0	0	0	0.0	0	0	0	0.0	0	0	8000000.0	0	0.0	0	0	0	0.0	0	0.0	0.0	0	0	0.0	0.0	0	0	0.0	0	0	0	0	0	0	0	5800000.0	0	0.0	0.0	0	0.0	0	0	0	0	0	0.0	0.0	0	0.0	0	0	0	0	0	0	0	0	0.0	0	0	0.0	0.0	0	0	0.0	0.0	0	0	0	0	0.0	0.0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0	0	0	26000000.0	0	0.0	0	0.0	0	0	0.0	0	0	0	0	0	0	0	0	0.0	0	0	8600000.00	0	0	0	0	0.0	0	0	0.0	0	0	0	0.0	0.0	0	0	0.0	0	0	0	0.0	0.0	0	0	0	0.0	0.0	18000000.0	0.0	0	0.0	0	0.0	0	0.0	0	0	0.0	0.0	0.0	0.0	0.0	10000000.0	0.0	0	0	0	0	0	0.0	0	0	0	0.0	0	0	0	0.0	0.0	0	0.0	0.0	0.0	0.0	0	0	0	0	0.0	0	0	0.0	0	0	0.0	0	0	0.0	0	0	0.0	0.0	0.0	0.0	0	0	0	0	0	0	0

4459 rows × 4993 columns

train_df.describe(include='all').T

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
ID	4459	4459	000d6aaf2	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
target	4459.0	NaN	NaN	NaN	5944923.322036	8234311.50163	30000.0	600000.0	2260000.0	8000000.0	40000000.0
48df886f9	4459.0	NaN	NaN	NaN	14654.930101	389329.809351	0.0	0.0	0.0	0.0	20000000.0
0deb4b6a8	4459.0	NaN	NaN	NaN	1390.894819	64283.024499	0.0	0.0	0.0	0.0	4000000.0
34b15f335	4459.0	NaN	NaN	NaN	26722.450922	569965.183046	0.0	0.0	0.0	0.0	20000000.0
...	...	...	...	...	...	...	...	...	...	...	...
71b203550	4459.0	NaN	NaN	NaN	121380.933393	4720708.533279	0.0	0.0	0.0	0.0	301312000.0
137efaa80	4459.0	NaN	NaN	NaN	35734.514465	1614622.197535	0.0	0.0	0.0	0.0	106420000.0
fb36b89d9	4459.0	NaN	NaN	NaN	312374.074905	4318500.708335	0.0	0.0	0.0	0.0	140000000.0
7e293fbaf	4459.0	NaN	NaN	NaN	92199.596322	1635993.425336	0.0	0.0	0.0	0.0	61768000.0
9fc776466	4459.0	NaN	NaN	NaN	227909.956044	1811139.277424	0.0	0.0	0.0	0.0	43200000.0

4737 rows × 11 columns

train_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4459 entries, 0 to 4458
Columns: 4993 entries, ID to 9fc776466
dtypes: float64(1845), int64(3147), object(1)
memory usage: 169.9+ MB

test_df.head()

	ID	...
0	000137c73	...
1	00021489f	...
2	0004d7953	...
3	00056a333	...
4	00056d8eb	...

5 rows × 4992 columns

test_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 49342 entries, 0 to 49341
Columns: 4992 entries, ID to 9fc776466
dtypes: float64(4991), object(1)
memory usage: 1.8+ GB

print(f'Shape of Training dataset is: {train_df.shape}')
print(f'Shape of Testing dataset is: {test_df.shape}')

Shape of Training dataset is: (4459, 4993)
Shape of Testing dataset is: (49342, 4992)

49342/4459

11.065709800403678

Observations

There are 4992 independant variables and 1 target variable. The target variable is a continuous column containing transaction values.
Test data is ~11 times larger as compared to training data.
The dataset is hugely sparse. For multitude of columns, value for Q3 (75th Percentile) is 0.
The first column of data is ID field, and is highly unlikely to contribute to data modelling, as this is a primary key of the data and is unique for each rows.
The number of columns in training dataset is > the number of rows in the training data. This is unusual behaviour.
Training data contains dtypes: float64(1845), int64(3147), object(1) and test data contains dtypes: float64(4991), object(1)
The columns are obfuscated, i.e., not human readable. To keep data confidential adhering to customer trust and legal obligations, companies tend to obfuscate data. This makes it relatively tough to extract human readable meaningful observation out of the features.

Exploratory Data Analysis

np.max(train_df['target'].values), range(train_df.shape[0])

(40000000.0, range(0, 4459))

# Scatter-Plot for target variable

plt.figure(figsize=(21,7))
plt.scatter(range(train_df.shape[0]), np.sort(train_df['target'].values))

plt.xlabel('size', fontsize = 14)
plt.ylabel('target', fontsize = 14)
plt.title('Dependant variable distribution', fontsize = 18)
plt.show()

# Histogram of target feature

plt.figure(figsize=(21,7))
sns.histplot(train_df['target'].values, bins = 50, kde=False)

plt.xlabel('Target', fontsize = 14)
plt.title('Histogram of Target', fontsize = 18)
plt.show()

Observations

Target feature is in 1e7 range. np.max(train_df['target'].values) is 40000000 (i.e, 4e7)
From the histogram it is evident, that the target data is right skewed¹.
This indicates there’s high range of target values, and it’s best to scale or take log of the data. Thus, the evaluation metric is also RMSLE (instead of RMSE)

DREADED TERMINOLOGY ALERT

Right Skewed: Majority of the data points cluster towards the lower values, while a few stretch out to higher values thus making a long tail towards the right.

Let’s create a histogram for log of target features

plt.figure(figsize=(21, 7))
sns.histplot(np.log1p(train_df['target'].values), bins = 50, kde = False)
plt.title('Histogram of Log of Target Feature', fontsize = 18)
plt.xlabel('log of Target', fontsize = 14)
plt.show()

Observations

Taking the log of data, gives a relatively balanced distribution

Data Pre-Processing

Check for Null Values

We check for Null values in the dataset. If there are any null values, we impute them with (1) median or using (2) KNN-Imputer.

columns_with_null_values = train_df.columns[train_df.isnull().sum()!=0]
columns_with_null_values

Index([], dtype='object')

print(f'Number of features in data having null values = {columns_with_null_values.size}')

Number of features in data having null values = 0

Observations:

Since there are no Null Values, we don’t need to perform any imputation. Hence, we move forward to the next step in data pre-processing.

Constant Variance Check

Variance describes how the data in your distribution varies w.r.t the mean. According to normal statistical intuition, feature having very low variance doesn’t contribute to the prediction much, i.e., these aren’t much important. However, such is not always the case and deep learning models tend to take care about those features. Albiet, for the current dataset, let’s remove the features having constant variance, i.e., variance of 0; implying they don’t change at all and don’t contribute towards predictions.

columns_to_remove_constant = []
for col in train_df.drop(columns=['ID', 'target']).columns:
    if train_df[col].std()==0:
        columns_to_remove_constant.append(col)

print("***************Columns to remove:***************\n")
print(columns_to_remove_constant)
print()
print(f'Number of columns to be removed = {len(columns_to_remove_constant)}')

***************Columns to remove:***************

['d5308d8bc', 'c330f1a67', 'eeac16933', '7df8788e8', '5b91580ee', '6f29fbbc7', '46dafc868', 'ae41a98b6', 'f416800e9', '6d07828ca', '7ac332a1d', '70ee7950a', '833b35a7c', '2f9969eab', '8b1372217', '68322788b', '2288ac1a6', 'dc7f76962', '467044c26', '39ebfbfd9', '9a5ff8c23', 'f6fac27c8', '664e2800e', 'ae28689a2', 'd87dcac58', '4065efbb6', 'f944d9d43', 'c2c4491d5', 'a4346e2e2', '1af366d4f', 'cfff5b7c8', 'da215e99e', '5acd26139', '9be9c6cef', '1210d0271', '21b0a54cb', 'da35e792b', '754c502dd', '0b346adbd', '0f196b049', 'b603ed95d', '2a50e001c', '1e81432e7', '10350ea43', '3c7c7e24c', '7585fce2a', '64d036163', 'f25d9935c', 'd98484125', '95c85e227', '9a5273600', '746cdb817', '6377a6293', '7d944fb0c', '87eb21c50', '5ea313a8c', '0987a65a1', '2fb7c2443', 'f5dde409b', '1ae50d4c3', '2b21cd7d8', '0db8a9272', '804d8b55b', '76f135fa6', '7d7182143', 'f88e61ae6', '378ed28e0', 'ca4ba131e', '1352ddae5', '2b601ad67', '6e42ff7c7', '22196a84c', '0e410eb3d', '992e6d1d3', '90a742107', '08b9ec4ae', 'd95203ded', '58ad51def', '9f69ae59f', '863de8a31', 'be10df47c', 'f006d9618', 'a7e39d23d', '5ed0abe85', '6c578fe94', '7fa4fcee9', '5e0571f07', 'fd5659511', 'e06b9f40f', 'c506599c8', '99de8c2dc', 'b05f4b229', '5e0834175', 'eb1cc0d9c', 'b281a62b9', '00fcf67e4', 'e37b65992', '2308e2b29', 'c342e8709', '708471ebf', 'f614aac15', '15ecf7b68', '3bfe540f1', '7a0d98f3c', 'e642315a5', 'c16d456a7', '0c9b5bcfa', 'b778ab129', '2ace87cdd', '697a566f0', '97b1f84fc', '34eff114b', '5281333d7', 'c89f3ba7e', 'cd6d3c7e6', 'fc7c8f2e8', 'abbbf9f82', '24a233e8f', '8e26b560e', 'a28ac1049', '504502ce1', 'd9a8615f3', '4efd6d283', '34cc56e83', '93e98252a', '2b6cef19e', 'c7f70a49b', '0d29ab7eb', 'e4a0d39b7', 'a4d1a8409', 'bc694fc8f', '3a36fc3a2', '4ffba44d3', '9bfdec4bc', '66a866d2f', 'f941e9df7', 'e7af4dbf3', 'dc9a54a3e', '748168a04', 'bba8ce4bb', 'ff6f62aa4', 'b06fe66ba', 'ae87ebc42', 'f26589e57', '963bb53b1', 'a531a4bf0', '9fc79985d', '9350d55c1', 'de06e884c', 'fc10bdf18', 'e0907e883', 'c586d79a1', 'e15e1513d', 'a06067897', '643e42fcb', '217cd3838', '047ebc242', '9b6ce40cf', '3b2c972b3', '17a7bf25a', 'c9028d46b', '9e0473c91', '6b041d374', '783c50218', '19122191d', 'ce573744f', '1c4ea481e', 'fbd6e0a0b', '69831c049', 'b87e3036b', '54ba515ee', 'a09ba0b15', '90f77ec55', 'fb02ef0ea', '3b0cccd29', 'fe9ed417c', '589e8bd6f', '17b5a03fd', '80e16b49a', 'a3d5c2c2a', '1bd3a4e92', '611d81daa', '3d7780b1c', '113fd0206', '5e5894826', 'cb36204f9', 'bc4e3d600', 'c66e2deb0', 'c25851298', 'a7f6de992', '3f93a3272', 'c1b95c2ec', '6bda21fee', '4a64e56e7', '943743753', '20854f8bf', 'ac2e428a9', '5ee7de0be', '316423a21', '2e52b0c6a', '8bdf6bc7e', '8f523faf2', '4758340d5', '8411096ec', '9678b95b7', 'a185e35cc', 'fa980a778', 'c8d90f7d7', '080540c81', '32591c8b4', '5779da33c', 'bb425b41e', '01599af81', '1654ab770', 'd334a588e', 'b4353599c', '51b53eaec', '2cc0fbc52', '45ffef194', 'c15ac04ee', '5b055c8ea', 'd0466eb58', 'a80633823', 'a117a5409', '7ddac276f', '8c32df8b3', 'e5649663e', '6c16efbb8', '9118fd5ca', 'ca8d565f1', '16a5bb8d2', 'fd6347461', 'f5179fb9c', '97428b646', 'f684b0a96', 'e4b2caa9f', '2c2d9f267', '96eb14eaf', 'cb2cb460c', '86f843927', 'ecd16fc60', '801c6dc8e', 'f859a25b8', 'ae846f332', '2252c7403', 'fb9e07326', 'd196ca1fd', 'a8e562e8e', 'eb6bb7ce1', '5beff147e', '52b347cdc', '4600aadcf', '6fa0b9dab', '43d70cc4d', '408021ef8', 'e29d22b59']

Number of columns to be removed = 256

# Removing the constant columns
train_df.drop(columns_to_remove_constant, axis = 1, inplace = True)
test_df.drop(columns_to_remove_constant, axis = 1, inplace = True)

print(f'New Shape of training dataset = {train_df.shape}')
print(f'New Shape of testing dataset = {test_df.shape}')

New Shape of training dataset = (4459, 4737)
New Shape of testing dataset = (49342, 4736)

Duplicate Check

If there are any duplicated columns in the dataframe, we can remove those. We will use negate operation ~ to accomplish this. We check for duplicate column using pd.duplicated() which returns True if the column is duplicated. We then perform negate operation so that the the True columns become those which are not duplicated and can be kept in the dataframe.

train_df = train_df.loc[:,~train_df.columns.duplicated()]
print(f'Shape of training dataset after removing duplicate columns = {train_df.shape}')

Shape of training dataset after removing duplicate columns = (4459, 4737)

Sparse Data Check

Sparse data simply means, that most of the values in that dataframe/array, etc is 0. We will drop sparse data from training dataset and test dataset. There are other ways as well, but I didn’t try those yet.

def drop_sparse_data_from_df(df):
    exclude_cols = ['ID', 'Target']
    cols_to_drop_data = [x for x in df.columns if x not in exclude_cols]
    
    for col in cols_to_drop_data:
        if len(np.unique(df[col]))<=1:
            print(f'sparse col = {col}')
            df.drop(col, axix=1, inplace=True)
            
    return df

train_df = drop_sparse_data_from_df(train_df)
test_df = drop_sparse_data_from_df(test_df)

Splitting Data into Train and Valid

TBD