Financial Analysis in Python: A Beginner’s Guide for Finance Professionals Part 2

All of us in the financial world are constantly seeking innovative ways to make informed decisions.

Python, a versatile and powerful programming language, has emerged as a valuable tool for conducting financial analysis.

In this article, we’ll explore four essential financial models that can be implemented using Python, assuming you’re using Google Colab and have minimal coding experience.

If you haven’t read part 1 of this article, go here first.

Financial Analysis in Python: A Beginner’s Guide for Finance Professionals Part 1

1. Forecasting Sales

Forecasting sales is crucial for businesses to plan their strategies effectively. Python provides various libraries and tools to help financial analysts predict future sales accurately.

# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Sample sales data
data = {'Year': [2015, 2016, 2017, 2018, 2019],
        'Sales': [1000, 1200, 1400, 1600, 1800]}

df = pd.DataFrame(data)

# Create a linear regression model
model = LinearRegression()
model.fit(df[['Year']], df['Sales'])

# Predict sales for 2020
predicted_sales =…