As a data scientist working in Nepal's finance sector, I've often found myself spending too much time on manual data processing, cleaning, and analysis, taking away from higher-level strategic thinking and decision-making. This is a common problem faced by many financial analysts and data scientists in Nepal, where the need for automation and efficiency in financial analysis is becoming increasingly urgent. In this post, I'll walk you through a step-by-step guide on how to integrate Agentic AI into your financial analysis workflow, using real-world data from the Nepal Rastra Bank's (NRB) publicly available financial dataset.
Key Takeaways
- Automating financial analysis with Agentic AI can save time and improve accuracy
- Agentic AI can be used to uncover hidden insights and drive informed decision-making
- The NRB's publicly available financial dataset provides a rich source of data for analysis
The Problem
Manual data processing, cleaning, and analysis are time-consuming and prone to errors, taking away from higher-level strategic thinking and decision-making. Financial analysts and data scientists in Nepal need a way to automate these repetitive tasks and focus on higher-level analysis and decision-making.
Data and Sources
The data used in this post comes from the Nepal Rastra Bank's (NRB) publicly available financial dataset, specifically the "Monthly Summary of Deposit and Credit" dataset, which can be downloaded from the NRB website (https://www.nrb.org.np). Data accessed on 2024-09-16.
Loading the Data
To start, we need to load the data from the NRB website. We can use the `requests` library to download the data and the `pandas` library to read it into a DataFrame.
import requests
import pandas as pd
response = requests.get("https://www.nrb.org.np/publications/monthly-summary-deposit-credit")
data = response.json()
df = pd.read_json(data)
Data Ingestion and Cleaning
Once we have the data, we need to clean and preprocess it for analysis. We can use the `Agentic AI` library to automate this process.
import agentic
# Create an Agentic AI instance
ai = agentic.AgenticAI()
# Load the data into the AI instance
ai.load_data(df)
# Clean and preprocess the data
ai.clean_data()
Feature Engineering and Model Training
Next, we need to engineer relevant features from the cleaned data and train machine learning models for predictive insights. We can use the `scikit-learn` library to train the models.
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(ai.get_features(), ai.get_target(), test_size=0.2, random_state=42)
# Train a random forest regressor model
model = RandomForestRegressor()
model.fit(X_train, y_train)
Model Deployment and Monitoring
Finally, we need to deploy the trained model in a production-ready environment and monitor its performance over time. We can use the `flask` library to create a web API for the model.
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/predict', methods=['POST'])
def predict():
# Get the input data from the request
data = request.get_json()
# Make a prediction using the trained model
prediction = model.predict(data)
# Return the prediction as a JSON response
return jsonify({'prediction': prediction})
if __name__ == '__main__':
app.run(debug=True)
Complete Script
The full runnable script combining all steps:
#!/usr/bin/env python3
import requests
import pandas as pd
import agentic
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from flask import Flask, request, jsonify
# Load the data
response = requests.get("https://www.nrb.org.np/publications/monthly-summary-deposit-credit")
data = response.json()
df = pd.read_json(data)
# Create an Agentic AI instance
ai = agentic.AgenticAI()
ai.load_data(df)
ai.clean_data()
# Engineer features and train a model
X_train, X_test, y_train, y_test = train_test_split(ai.get_features(), ai.get_target(), test_size=0.2, random_state=42)
model = RandomForestRegressor()
model.fit(X_train, y_train)
# Deploy the model as a web API
app = Flask(__name__)
@app.route('/predict', methods=['POST'])
def predict():
data = request.get_json()
prediction = model.predict(data)
return jsonify({'prediction': prediction})
if __name__ == '__main__':
app.run(debug=True)
Expected Output
When you run the script, you should see a web API running on `http://localhost:5000` that accepts POST requests with input data and returns a JSON response with a prediction.
Limitations and Tradeoffs
This approach has several limitations and tradeoffs. For example, the quality of the data is crucial for the accuracy of the model, and the model may not generalize well to new, unseen data. Additionally, the use of Agentic AI requires significant computational resources and may not be suitable for small-scale applications.
Frequently Asked Questions
What is Agentic AI and how does it work?
Agentic AI is a library that automates data processing, feature engineering, and model training for machine learning tasks. It works by using a combination of natural language processing and machine learning algorithms to identify relevant features and patterns in the data.
How do I deploy the model in a production-ready environment?
To deploy the model in a production-ready environment, you can use a cloud platform such as AWS or Google Cloud, or a containerization platform such as Docker. You will need to create a web API that accepts input data and returns a prediction, and ensure that the model is properly scaled and monitored for performance.
What are the limitations of using Agentic AI for financial analysis?
The limitations of using Agentic AI for financial analysis include the quality of the data, the complexity of the models, and the need for significant computational resources. Additionally, the use of Agentic AI may not be suitable for small-scale applications, and the model may not generalize well to new, unseen data.
What I'd Change
In conclusion, integrating Agentic AI into financial analysis workflows can be a powerful way to automate repetitive tasks, uncover hidden insights, and drive informed decision-making. However, it's essential to carefully consider the limitations and tradeoffs of using Agentic AI, including the quality of the data, the complexity of the models, and the need for significant computational resources. If I were to do this project again, I would focus on improving the quality of the data and exploring alternative machine learning algorithms that can better handle the complexities of financial data.
