Upload 1094_871_252_511_.py

1094_871_252_511_.py

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+# -*- coding: utf-8 -*-
+"""1094_871_252_511_
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Mz5F8L08R3c_7vQ77xcKumhO9Q__9jAV
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.cluster import KMeans
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+import plotly.io as pio
+pio.renderers.default = 'notebook'
+pio.renderers.default = 'iframe_connected'
+
+import pandas as pd
+df=pd.read_csv("/content/unified_monthly_data_interpolated_1990_20250101 (1).csv")
+
+df.info()
+
+df.shape
+
+df.isna().sum()
+df.duplicated().sum()
+
+df['Date'] = pd.to_datetime(df['Date'])
+df.set_index('Date', inplace=True)
+df.drop(columns=['Region'], inplace=True)
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+plt.figure(figsize=(16, 12))
+corr = df.corr()
+sns.heatmap(corr, annot=True, fmt='.2f', cmap='Reds', linewidths=0.5)
+plt.title('Correlation Heatmap')
+plt.tight_layout()
+plt.show()
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.cluster import KMeans
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+import pandas as pd
+
+scaler = StandardScaler()
+scaled_data = scaler.fit_transform(df)
+pca = PCA(n_components=2)
+pca_result = pca.fit_transform(scaled_data)
+df['PCA1'] = pca_result[:, 0]
+df['PCA2'] = pca_result[:, 1]
+
+kmeans = KMeans(n_clusters=3, random_state=42)
+df['Cluster'] = kmeans.fit_predict(pca_result)
+
+fig = px.scatter(df, x='PCA1', y='PCA2', color=df['Cluster'].astype(str),
+                 title='KMeans Clustering on PCA Features')
+fig.show()
+
+plt.figure(figsize=(14, 6))
+for col in ['AverageSalesPrice', 'MedianSalesPriceofHousesSold', 'MedianListingPriceperSquareFeet']:
+  sns.lineplot(data=df[col], label=col)
+
+plt.title('House Price Trends Over Time')
+plt.xlabel('Date')
+plt.ylabel('Price')
+plt.legend()
+plt.tight_layout()
+plt.show()
+
+X = df.drop(columns=['MedianListingPriceperSquareFeet', 'Cluster', 'PCA1', 'PCA2'])
+y = df['MedianListingPriceperSquareFeet']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = RandomForestRegressor(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+preds = model.predict(X_test)
+
+mse = mean_squared_error(y_test, preds)
+r2 = r2_score(y_test, preds)
+print(f"Mean Squared Error: {mse:.3f}")
+print(f"R2 Score: {r2:.3f}")
+
+importances = model.feature_importances_
+feat_df = pd.DataFrame({'Feature': X.columns, 'Importance': importances}).sort_values(by='Importance', ascending=False)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(data=feat_df, x='Importance', y='Feature', palette='viridis')
+plt.title('Feature Importance')
+plt.tight_layout()
+plt.show()