Uncovering Banking Sector Trends in Nepal: A Data-Driven Analysis

The Problem

What if you could uncover hidden patterns and trends in the Nepal banking sector, but lack access to actionable insights and reliable data? This challenge led me to explore data analysis and visualization techniques to gain a deeper understanding of the sector's performance.

Step 1: Data Collection

This step solves the problem of obtaining relevant and reliable data on the Nepal banking sector. The specific code snippet demonstrates how to scrape data from the Nepal Rastra Bank (NRB) website using Python's `requests` and `BeautifulSoup` libraries.

import requests
from bs4 import BeautifulSoup

url = "https://www.nrb.org.np/banking-and-financial-institutions/"
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
data = soup.find_all('table')

Step 2: Data Preprocessing

This step addresses the issue of cleaning and preprocessing the collected data for analysis. The code snippet shows how to handle missing values, perform data normalization, and convert data types using Pandas.

import pandas as pd

df = pd.read_html(str(data[0]))
df = df[0]
df.fillna(0, inplace=True)
df['date'] = pd.to_datetime(df['date'])

Step 3: Exploratory Data Analysis (EDA) and Feature Engineering

This step helps identify key trends and patterns in the data, and extracts relevant features to understand the banking sector's performance. The code snippet demonstrates how to use Matplotlib, Seaborn, and Pandas to create visualizations and calculate key indicators such as deposit growth rate and non-performing loan ratio.

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10,6))
sns.lineplot(x='date', y='deposit_growth', data=df)
plt.title('Deposit Growth Over Time')
plt.xlabel('Date')
plt.ylabel('Deposit Growth')
plt.savefig('deposit_growth.png')

df['deposit_growth_rate'] = df['deposit_growth'].pct_change()
df['non_performing_loan_ratio'] = df['non_performing_loans'] / df['total_loans']

Step 4: Data Visualization and Insights

This step addresses the issue of effectively communicating insights and trends in the data. The code snippet demonstrates how to use Plotly to create interactive visualizations such as dashboards and scatter plots to showcase the banking sector's performance and trends.

import plotly.graph_objs as go

fig = go.Figure(data=[go.Scatter(x=df['date'], y=df['deposit_growth_rate'])])
fig.update_layout(title='Deposit Growth Rate Over Time', xaxis_title='Date', yaxis_title='Deposit Growth Rate')
fig.write_image('deposit_growth_rate.png')

Complete Script

The full runnable script combining all steps:

#!/usr/bin/env python3
import requests
from bs4 import BeautifulSoup
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go

def load_data():
    url = "https://www.nrb.org.np/banking-and-financial-institutions/"
    response = requests.get(url)
    soup = BeautifulSoup(response.content, 'html.parser')
    data = soup.find_all('table')
    return data

def preprocess_data(data):
    df = pd.read_html(str(data[0]))
    df = df[0]
    df.fillna(0, inplace=True)
    df['date'] = pd.to_datetime(df['date'])
    return df

def analyze_data(df):
    plt.figure(figsize=(10,6))
    sns.lineplot(x='date', y='deposit_growth', data=df)
    plt.title('Deposit Growth Over Time')
    plt.xlabel('Date')
    plt.ylabel('Deposit Growth')
    plt.savefig('deposit_growth.png')

    df['deposit_growth_rate'] = df['deposit_growth'].pct_change()
    df['non_performing_loan_ratio'] = df['non_performing_loans'] / df['total_loans']

    fig = go.Figure(data=[go.Scatter(x=df['date'], y=df['deposit_growth_rate'])])
    fig.update_layout(title='Deposit Growth Rate Over Time', xaxis_title='Date', yaxis_title='Deposit Growth Rate')
    fig.write_image('deposit_growth_rate.png')
    return df

if __name__ == "__main__":
    try:
        data = load_data()
        df = preprocess_data(data)
        result = analyze_data(df)
        print("Analysis complete. Visualizations saved as images.")
    except Exception as e:
        print("An error occurred: ", str(e))

Expected Output

When you run the script, it will generate several visualizations, including line plots and scatter plots, which provide insights into the Nepal banking sector's performance and trends. The script will also print a success message or an error message if any exceptions occur.

What I'd Change

In future iterations, I would focus on incorporating more advanced data analysis techniques, such as machine learning and deep learning, to uncover more complex patterns and trends in the data. Additionally, I would explore using more interactive visualization tools to facilitate a deeper understanding of the insights and trends in the data.