import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load the data
df = pd.read_csv("internet_session.csv")

# Preprocess the data
df["hour"] = df["timestamp"].apply(lambda x: int(x.split(":")[0]))

# Extract features
X = df[["hour"]]
y = df["most_active_time"]

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Train a random forest classifier
clf = RandomForestClassifier(n_estimators=100, random_state=0)
clf.fit(X_train, y_train)

# Make predictions on the test set
y_pred = clf.predict(X_test)

# Evaluate the model
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

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import pandas as pd
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Load the data
df = pd.read_csv("nternet_session.csv")

# Preprocess the data
df["timestamp"] = df["timestamp"].apply(lambda x: int(x.split(":")[0]))

# Extract features
X = df.groupby("user_id").apply(lambda x: x["timestamp"].diff().dropna().mean()).reset_index(name="mean_time_between_ip_changes")
y = df.groupby("user_id").apply(lambda x: x["timestamp"].diff().dropna().count()).reset_index(name="frequency_of_ip_changes")["frequency_of_ip_changes"]

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Train a support vector regression model
reg = SVR()
reg.fit(X_train, y_train)

# Make predictions on the test set
y_pred = reg.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)

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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Load the data into a pandas dataframe
data = pd.read_csv("nternet_session.csv")

# Create a feature for the number of days since the last device change
data["days_since_last_change"] = (data["timestamp"] - data.groupby("device_id")["timestamp"].shift(1)).dt.days

# Create a new feature indicating if a device change occurred at the current observation
data["device_change"] = np.where(data["days_since_last_change"] > 0, 1, 0)

# Split the data into training and testing sets
X = data[["device_id", "timestamp", "days_since_last_change"]]
y = data["device_change"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a random forest classifier on the training data
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Make predictions on the test data
y_pred = model.predict(X_test)

# Evaluate the accuracy of the model
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)


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import pandas as pd

# Load the internet usage data into a Pandas dataframe
df = pd.read_csv("nternet_session.csv")

# Convert the 'timestamp' column to datetime format
df['timestamp'] = pd.to_datetime(df['timestamp'])

# Add columns for hour, day, and month of each usage
df['hour'] = df['timestamp'].dt.hour
df['day'] = df['timestamp'].dt.day
df['month'] = df['timestamp'].dt.month

# Calculate the average usage per hour
average_usage_per_hour = df.groupby(['hour'])['usage'].mean()

# Calculate the average usage per day
average_usage_per_day = df.groupby(['day'])['usage'].mean()

# Calculate the average usage per month
average_usage_per_month = df.groupby(['month'])['usage'].mean()

print("Average usage per hour:")
print(average_usage_per_hour)

print("Average usage per day:")
print(average_usage_per_day)

print("Average usage per month:")
print(average_usage_per_month)