Upload 1957_249_949.py

1957_249_949.py

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+# -*- coding: utf-8 -*-
+"""1957_249_949
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1q6DU2jTXfNY0uMxaBV2w2niCrYcsW86S
+"""
+
+import numpy as np
+import pandas as pd
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+  for filename in filenames:
+    print(os.path.join(dirname, filename))
+
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+data = pd.read_csv('/content/internet_usage.csv')
+
+data.head()
+
+data.tail()
+
+data.describe()
+
+numeric_cols = data.columns[2:]
+data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors='coerce')
+data = data.dropna(subset=numeric_cols, how='all')
+data = data.fillna(data.mean(numeric_only=True))
+
+years = [int(col) for col in numeric_cols]
+data['avg_usage'] = data[numeric_cols].mean(axis=1)
+data['usage_change'] = data[numeric_cols].iloc[:, -1] - data[numeric_cols].iloc[:, 0]
+data['rate_change'] = data['usage_change'] / (years[-1] - years [0])
+
+features = ['avg_usage', 'usage_change', 'rate_change']
+ target_year = 2023
+ target = str(target_year)
+
+ X = data[features]
+ y= data[target]
+
+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)
+
+y_pred = model.predict(X_test)
+
+mse = mean_squared_error(y_test, y_pred)
+mae = mean_absolute_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+
+print(f"Mean Squared Error: {mse}")
+print(f"Mean Absolute Error: {mae}")
+print(f"R-squared: {r2}")
+
+plt.figure(figsize=(10, 6))
+plt.scatter(y_test, y_pred)
+plt.xlabel("Actual Values")
+plt.ylabel("Predicted Values")
+plt.title("Actual vs. Predicted Values")
+plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red')
+plt.show()
+
+feature_importance = model.feature_importances_
+feature_names = X.columns
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x=feature_importance, y=feature_names)
+plt.title("Feature Importance")
+plt.show()
+
+def predict_future_usage(model, data, features, future_years):
+  predictions = {}
+  for year in future_years:
+    new_data = data.copy()
+    new_data[str(year)] = model.predict(new_data[features])
+    predictions[year] = new_data[str(year)]
+    data[str(year)] = new_data[str(year)]
+
+    return predictions
+
+future_years = [2024, 2025]
+future_predictions = predict_future_usage(model, data, features, future_years)
+
+print("\nFuture Predictions:")
+for year, predictions in future_predictions.items():
+  print(f"Predictions for {year}:")
+  print(predictions.head())