ASSIGNMENT - 08¶

This project is about the Data of internet usage [in kb] by graduate student at an indian university.¶

Answer for the following questions usign Data Analysis.

What is the most frequent internet activity time of the day ? How often the ip changes ? How often the device changed. What is the average usage per hour , per day and per month ?

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
In [6]:
data = pd.read_csv(r"C:\Users\ahlad\Downloads\ML_DL_Assignment_1.zip")
data.head()
Out[6]:
name start_time usage_time IP MAC upload download total_transfer seession_break_reason
0 user1 2022-05-10 02:59:32 00:00:36:28 10.55.14.222 48:E7:DA:58:22:E9 15861.76 333168.64 349030.40 Idle-Timeout
1 user1 2022-05-10 18:53:27 00:01:49:56 10.55.2.253 48:E7:DA:58:22:E9 16957.44 212152.32 229109.76 Idle-Timeout
2 user1 2022-05-10 21:20:44 00:01:35:00 10.55.2.253 48:E7:DA:58:22:E9 14080.0 195153.92 209233.92 Idle-Timeout
3 user1 2022-05-11 00:37:42 00:00:26:00 10.55.2.253 48:E7:DA:58:22:E9 5242.88 40806.4 46049.28 Idle-Timeout
4 user1 2022-05-11 02:59:38 00:00:11:52 10.55.2.253 48:E7:DA:58:22:E9 22067.2 10772.48 32839.68 Idle-Timeout
In [17]:
import datetime
from IPython.display import display
In [20]:
# Data cleaning and preprocessing
data.dropna(inplace=True) # Drop any missing values
data.drop_duplicates(inplace=True) # Drop any duplicate values
data['start_time'] = pd.to_datetime(data['start_time']) # Convert timestamp column to datetime
In [21]:
# What is the most frequent internet activity time of the day?
data['hour'] = data['start_time'].dt.hour
freq_activity_time = data['hour'].mode()[0]
print("The most frequent internet activity time of the day is: ", freq_activity_time, "hours.")

The most frequent internet activity time of the day is:  22 hours.
In [23]:
# How often the IP changes?
num_ip_changes = (data['IP'] != data['IP'].shift()).sum()
print("The IP changes ", num_ip_changes, "times.")

The IP changes  2304 times.
In [25]:
# How often the device changed?
num_device_changes = (data['MAC'] != data['MAC'].shift()).sum()
print("The MAC changes ", num_device_changes, "times.")

The MAC changes  1224 times.
In [30]:
# What is the average usage per hour, per day and per month?
data[' date'] = data['start_time'].dt.date
data[' month'] = data['start_time'].dt.month
data['total_transfer'] = pd.to_numeric(data['total_transfer'])
In [32]:
avg_usage_hour = data.groupby('hour')['total_transfer'].mean()
avg_usage_day = data.groupby('date')['total_transfer'].mean()
avg_usage_month = data.groupby('month')['total_transfer'].mean()
In [33]:
print("The average internet usage per hour is:\n", avg_usage_hour)
print("The average internet usage per day is:\n", avg_usage_day)
print("The average internet usage per month is:\n", avg_usage_month)

The average internet usage per hour is:
 hour
0     464530.443023
1     530880.856788
2     431576.112743
3     345303.341176
4     359809.443333
5     275960.910769
6     468959.586757
7     292886.830164
8     366681.918762
9     377480.638954
10    393259.119955
11    309492.445992
12    310137.981415
13    335270.579648
14    472403.712765
15    517005.111506
16    403919.401872
17    525423.692116
18    666590.764187
19    389841.785382
20    355862.804027
21    474038.339233
22    449600.499185
23    407785.083903
Name: total_transfer, dtype: float64
The average internet usage per day is:
 date
2022-05-09    109844.480000
2022-05-10    151600.782667
2022-05-11    411055.589200
2022-05-12    340207.616000
2022-05-13    297072.926250
                  ...      
2022-11-01    374462.644706
2022-11-02    463347.552895
2022-11-03    348276.877241
2022-11-04    424498.885517
2022-11-05    362904.137143
Name: total_transfer, Length: 154, dtype: float64
The average internet usage per month is:
 month
5     311177.156960
6     338418.082988
7     418583.993765
8     479042.438202
9     482955.522841
10    549467.626233
11    399804.112119
Name: total_transfer, dtype: float64