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import pandas as pd | |
from sklearn.preprocessing import StandardScaler | |
from sklearn.linear_model import LinearRegression | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
# Example Monthly Balance Sheet and Income Statement Data | |
# Replace with your actual data | |
balance_sheet_data = { |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import pandas as pd | |
from sklearn.preprocessing import StandardScaler | |
from sklearn.linear_model import LinearRegression | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
# Example Balance Sheet and Income Statement Data | |
# Replace with your actual data | |
balance_sheet_data = { |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import pandas as pd | |
import numpy as np | |
from sklearn.preprocessing import StandardScaler | |
from sklearn.linear_model import LinearRegression | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
# Load data | |
# Replace 'your_data.csv' with your actual data file | |
# Make sure the CSV file is in the same directory as this script |