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"""1957_249_949 |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/1q6DU2jTXfNY0uMxaBV2w2niCrYcsW86S |
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""" |
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import numpy as np |
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import pandas as pd |
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import os |
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for dirname, _, filenames in os.walk('/kaggle/input'): |
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for filename in filenames: |
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print(os.path.join(dirname, filename)) |
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import pandas as pd |
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import numpy as np |
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from sklearn.model_selection import train_test_split |
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from sklearn.ensemble import RandomForestRegressor |
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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data = pd.read_csv('/content/internet_usage.csv') |
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data.head() |
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data.tail() |
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data.describe() |
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numeric_cols = data.columns[2:] |
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data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors='coerce') |
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data = data.dropna(subset=numeric_cols, how='all') |
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data = data.fillna(data.mean(numeric_only=True)) |
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years = [int(col) for col in numeric_cols] |
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data['avg_usage'] = data[numeric_cols].mean(axis=1) |
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data['usage_change'] = data[numeric_cols].iloc[:, -1] - data[numeric_cols].iloc[:, 0] |
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data['rate_change'] = data['usage_change'] / (years[-1] - years [0]) |
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features = ['avg_usage', 'usage_change', 'rate_change'] |
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target_year = 2023 |
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target = str(target_year) |
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X = data[features] |
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y= data[target] |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) |
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model = RandomForestRegressor(n_estimators=100, random_state=42) |
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model.fit(X_train, y_train) |
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y_pred = model.predict(X_test) |
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mse = mean_squared_error(y_test, y_pred) |
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mae = mean_absolute_error(y_test, y_pred) |
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r2 = r2_score(y_test, y_pred) |
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print(f"Mean Squared Error: {mse}") |
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print(f"Mean Absolute Error: {mae}") |
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print(f"R-squared: {r2}") |
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plt.figure(figsize=(10, 6)) |
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plt.scatter(y_test, y_pred) |
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plt.xlabel("Actual Values") |
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plt.ylabel("Predicted Values") |
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plt.title("Actual vs. Predicted Values") |
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plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red') |
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plt.show() |
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feature_importance = model.feature_importances_ |
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feature_names = X.columns |
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plt.figure(figsize=(10, 6)) |
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sns.barplot(x=feature_importance, y=feature_names) |
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plt.title("Feature Importance") |
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plt.show() |
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def predict_future_usage(model, data, features, future_years): |
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predictions = {} |
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for year in future_years: |
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new_data = data.copy() |
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new_data[str(year)] = model.predict(new_data[features]) |
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predictions[year] = new_data[str(year)] |
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data[str(year)] = new_data[str(year)] |
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return predictions |
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future_years = [2024, 2025] |
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future_predictions = predict_future_usage(model, data, features, future_years) |
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print("\nFuture Predictions:") |
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for year, predictions in future_predictions.items(): |
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print(f"Predictions for {year}:") |
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print(predictions.head()) |
