Inflation Rate Prediction added

Finacial Domain/Inflation Rate Prediction/app.py

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+
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import streamlit as st
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+
+# Load the dataset
+df = pd.read_csv('inflation_data.csv', parse_dates=['Date'])
+
+# EDA Section
+st.title("Inflation Rate Prediction")
+st.write("### Exploratory Data Analysis")
+
+st.write("#### Time Series of Economic Indicators")
+plt.figure(figsize=(14, 8))
+plt.plot(df['Date'].values, df['Inflation_Rate'], label='Inflation Rate')
+plt.plot(df['Date'].values, df['GDP_Growth_Rate'], label='GDP Growth Rate')
+plt.plot(df['Date'].values, df['Unemployment_Rate'], label='Unemployment Rate')
+plt.plot(df['Date'].values, df['Interest_Rate'], label='Interest Rate')
+plt.plot(df['Date'].values, df['Money_Supply'], label='Money Supply')
+plt.legend()
+st.pyplot(plt)
+
+st.write("#### Correlation Matrix")
+plt.figure(figsize=(10, 6))
+sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
+st.pyplot(plt)
+
+# ML Section
+st.write("### Inflation Rate Prediction")
+
+# Prepare the data
+X = df[['GDP_Growth_Rate', 'Unemployment_Rate', 'Interest_Rate', 'Money_Supply']]
+y = df['Inflation_Rate']
+
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train the model
+model = RandomForestRegressor(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Make predictions
+y_pred = model.predict(X_test)
+
+# Evaluate the model
+mse = mean_squared_error(y_test, y_pred)
+st.write(f"#### Mean Squared Error: {mse}")
+
+# Plot the predictions
+st.write("#### True vs Predicted Inflation Rate")
+plt.figure(figsize=(10, 6))
+plt.plot(y_test.values, label='True Inflation Rate')
+plt.plot(y_pred, label='Predicted Inflation Rate')
+plt.legend()
+st.pyplot(plt)