Upload _1952.py

_1952.py

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+# -*- coding: utf-8 -*-
+""".1952
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Gw-RJg5Bp__ayBzDb0HsHaj9uYYfQ_nI
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings('ignore')
+# %matplotlib inline
+
+file_path = '/content/Key_Economic_Indicators.csv'
+df = pd.read_csv(file_path)
+
+df.head()
+
+df.isnull().sum()
+
+df.fillna(df.mean(), inplace=True)
+
+df['Date'] = pd.to_datetime(df[['Year', 'Month']].assign(DAY=1))
+
+df.drop(['Year', 'Month'], axis=1, inplace=True)
+
+df.head()
+
+plt.figure(figsize=(12, 6))
+sns.lineplot(data=df, x='Date', y='Consumer Confidence Index TX', label='TX')
+plt.title('Consumer Confidence Index Over Time')
+plt.xlabel('Date')
+plt.ylabel('Consumer Confidence Index')
+plt.legend()
+plt.show()
+
+plt.figure(figsize=(12, 6))
+sns.histplot(df['Unemployment TX'], kde=True, color='blue', label='TX')
+sns.histplot(df['Unemployment U.S.'], kde=True, color='red', label='US')
+plt.title('Distribution of Unemployment Rates')
+plt.xlabel('Unemployment Rate')
+plt.ylabel('Frequency')
+plt.legend()
+plt.show()
+
+numeric_df = df.select_dtypes(include=[np.number])
+
+plt.figure(figsize=(14, 10))
+sns.heatmap(numeric_df.corr(), annot=True, fmt='.2f', cmap='coolwarm')
+plt.title('Correlation Matrix of Economic Indicators')
+plt.show()
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+
+X = numeric_df.drop(columns=['Consumer Confidence Index TX'])
+y = numeric_df['Consumer Confidence Index TX']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = RandomForestRegressor(random_state=42)
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+
+mse = mean_squared_error(y_test, y_pred)
+rmse = np.sqrt(mse)
+rmse