!pip install cpi
import pandas as pd
import matplotlib.pyplot as plt
import cpi
!pip install requests
import requests
!pip install openpyxl


# Assuming your file is in "Meltdown_Data.xlsx"
url = 'https://raw.githubusercontent.com/jeradmelgard/Supporting_Docs/main/Meltdown_Data.xlsx'
response = requests.get(url)

with open('document.xlsx', 'wb') as file:
    file.write(response.content) # Update with your file path

# Assign 'document.xlsx' to the file_path variable
file_path = 'document.xlsx'
df = pd.read_excel(file_path, sheet_name='Sheet1')

# Convert 'Year' column to datetime objects
df['Year'] = pd.to_datetime(df['Year'], format='%Y')

# Add CPI data to the DataFrame
df['CPI'] = df['Year'].apply(lambda x: cpi.inflate(1, x.year))

# Create a figure and axes
fig, ax1 = plt.subplots()

# Plot Average Unemployment Rate, Average Mortgage Rate, and CPI on the same axis
ax1.plot(df['Year'], df['Unemployment'], label='Unemployment Rate', color='blue')
ax1.plot(df['Year'], df['Mortgage_Rates'], label='Mortgage Rate', color='red')
ax1.plot(df['Year'], df['CPI'], label='CPI', color='purple')
ax1.set_xlabel('Year')
ax1.set_ylabel('Percentage (%) / CPI')
ax1.tick_params(axis='y')
ax1.legend(loc='upper left')

# Create a second y-axis for GDP
ax2 = ax1.twinx()
ax2.plot(df['Year'], df['GDP'], label='GDP', color='green')
ax2.set_ylabel('GDP (in billions USD)')
ax2.tick_params(axis='y')
ax2.legend(loc='upper right')

# Set the title
plt.title('Economic Indicators Over Time')

# Show the plot
plt.show()

# Calculate the correlation matrix
correlation_matrix = df.corr()
print(correlation_matrix)

# Further analysis and visualization can be done based on these initial explorations.

# Further analysis and visualization can be done based on these initial explorations.

!pip install cpi
import pandas as pd
import matplotlib.pyplot as plt
import cpi
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from matplotlib.ticker import FormatStrFormatter
!pip install requests
import requests

# Load the dataset
# Assuming your file is in "Meltdown_Data.xlsx"
url = 'https://raw.githubusercontent.com/jeradmelgard/Supporting_Docs/main/Meltdown_Data.xlsx'
response = requests.get(url)
df = pd.read_excel(file_path, sheet_name='Sheet1')

# Convert 'Year' column to datetime objects and extract year as integer
df['Year'] = pd.to_datetime(df['Year'], format='%Y')
df['Year'] = df['Year'].dt.year

# Add CPI data to the DataFrame (optional - not used in this plot)
df['CPI'] = df['Year'].apply(lambda x: cpi.inflate(1, x))

# Create a figure and axes
fig = plt.figure(figsize=(15, 13))
ax = fig.add_subplot(111, projection='3d')

# Scatter plot for Mortgage Rates and GDP
ax.scatter(df['Year'], df['Mortgage_Rates'], df['GDP'], c='blue', label='Mortgage Rates & GDP')

# Linear Regression
X = df[['Year', 'Mortgage_Rates']].values.reshape(-1, 2)
Y = df['GDP']
reg = np.poly1d(np.polyfit(X[:,0], Y, 1))

# Create the regression line
x_range = np.linspace(df['Year'].min(), df['Year'].max(), 100)
y_range = np.linspace(df['Mortgage_Rates'].min(), df['Mortgage_Rates'].max(), 100)
X_reg = np.array([x_range, y_range])
Z_reg = reg(x_range)
ax.plot(x_range, y_range, Z_reg, c='red', label='Regression Line')

# Set labels and title
ax.set_xlabel('Year')
ax.set_ylabel('Mortgage Rates')
ax.set_zlabel('GDP', rotation=90) # Make GDP label horizontal
plt.title('Mortgage Rates and GDP Over Time')

# Format year ticks to show as whole numbers
ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))

# Add a legend
ax.legend()

# Show the plot
plt.show()

!pip install cpi
import pandas as pd
import matplotlib.pyplot as plt
import cpi
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from matplotlib.ticker import FormatStrFormatter
!pip install requests
import requests

# Load the dataset
# Load the dataset
# Assuming your file is in "Meltdown_Data.xlsx"
url = 'https://raw.githubusercontent.com/jeradmelgard/Supporting_Docs/main/Meltdown_Data.xlsx'
response = requests.get(url)
df = pd.read_excel(file_path, sheet_name='Sheet1')

# Convert 'Year' column to datetime objects and extract year as integer
df['Year'] = pd.to_datetime(df['Year'], format='%Y')
df['Year'] = df['Year'].dt.year

# Add CPI data to the DataFrame (optional - not used in this plot)
df['CPI'] = df['Year'].apply(lambda x: cpi.inflate(1, x))

# Create a figure and axes
fig = plt.figure(figsize=(15, 13))
ax = fig.add_subplot(111, projection='3d')

# Scatter plot for Unemployment and GDP
ax.scatter(df['Year'], df['Unemployment'], df['GDP'], c='blue', label='Unemployment & GDP')

# Linear Regression
X = df[['Year', 'Unemployment']].values.reshape(-1, 2)
Y = df['GDP']
reg = np.poly1d(np.polyfit(X[:,0], Y, 1))

# Create the regression line
x_range = np.linspace(df['Year'].min(), df['Year'].max(), 100)
y_range = np.linspace(df['Unemployment'].min(), df['Unemployment'].max(), 100)
X_reg = np.array([x_range, y_range])
Z_reg = reg(x_range)
ax.plot(x_range, y_range, Z_reg, c='red', label='Regression Line')

# Set labels and title
ax.set_xlabel('Year')
ax.set_ylabel('Unemployment')
ax.set_zlabel('GDP', rotation=90) # Make GDP label horizontal
plt.title('Unemployment and GDP Over Time')

# Format year ticks to show as whole numbers
ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))

# Add a legend
ax.legend()

# Show the plot
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from matplotlib.ticker import FormatStrFormatter
!pip install requests
import requests

# Load the dataset
# Load the dataset
# Assuming your file is in "Meltdown_Data.xlsx"
url = 'https://raw.githubusercontent.com/jeradmelgard/Supporting_Docs/main/Meltdown_Data.xlsx'
response = requests.get(url)
df = pd.read_excel(file_path, sheet_name='Sheet1')

# Convert 'Year' column to datetime objects and extract year as integer
df['Year'] = pd.to_datetime(df['Year'], format='%Y')
df['Year'] = df['Year'].dt.year

# Create a figure and axes
fig = plt.figure(figsize=(12, 10))  # Increased figure size
ax = fig.add_subplot(111, projection='3d')

# Scatter plot for Default, Unemployment, and Mortgage Rates
ax.scatter(df['Unemployment'], df['Mortgage_Rates'],
           df['Default'], c='blue',
           label='Default Rate vs. Unemployment & Mortgage Rates')

# Linear Regression
X = df[['Unemployment', 'Mortgage_Rates']].values.reshape(-1, 2)
Y = df['Default']
reg = np.poly1d(np.polyfit(X[:, 0], Y, 1))

# Create the regression line
x_range = np.linspace(df['Unemployment'].min(), df['Unemployment'].max(), 100)
y_range = np.linspace(df['Mortgage_Rates'].min(),
                     df['Mortgage_Rates'].max(), 100)
X_reg = np.array([x_range, y_range])
Z_reg = reg(x_range)
ax.plot(x_range, y_range, Z_reg, c='red', label='Regression Line')

# Set labels and title
ax.set_xlabel('Unemployment Rate')
ax.set_ylabel('Mortgage Rates')
ax.set_zlabel('Default Rate', rotation=90, labelpad=20)  # Added labelpad
plt.title('Mortgage Default Rate vs. Unemployment and Mortgage Rates')

# Add a legend
ax.legend()

# Adjust layout to prevent labels from being cut off
plt.tight_layout()

# Show the plot
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
!pip install requests
import requests

# Load the dataset
# Assuming your file is in "Meltdown_Data.xlsx"
url = 'https://raw.githubusercontent.com/jeradmelgard/Supporting_Docs/main/Meltdown_Data.xlsx'
response = requests.get(url)
df = pd.read_excel(file_path, sheet_name='Sheet1')

# Convert 'Year' column to datetime objects and extract year
df['Year'] = pd.to_datetime(df['Year'], format='%Y')
df['Year'] = df['Year'].dt.year

# Calculate the correlation matrix
correlation_matrix = df.corr()

# Create the heatmap using seaborn
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)
plt.title('Correlation Matrix of Economic Indicators')
plt.show()

!pip install plotly
import plotly.express as px
import pandas as pd
!pip install requests
import requests
!pip install statsmodels


# Load the dataset
# Load the dataset
# Assuming your file is in "Meltdown_Data.xlsx"
url = 'https://raw.githubusercontent.com/jeradmelgard/Supporting_Docs/main/Meltdown_Data.xlsx'
response = requests.get(url)  # Update with your file path
df = pd.read_excel(file_path, sheet_name='Sheet1')

# Convert 'Year' column to datetime objects and extract year
df['Year'] = pd.to_datetime(df['Year'], format='%Y')
df['Year'] = df['Year'].dt.year

# Create a list of columns to plot (excluding Year)
columns_to_plot = df.columns.tolist()[1:]

# Create scatter plots for each variable
for column in columns_to_plot:
    fig = px.scatter(df,
                     x='Year',
                     y=column,
                     title=f'{column} Over Time',
                     trendline="ols")  # Add a trendline
    fig.show()