Datathon Telenor Solution - Game of Prediction (GoP)

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Objective:

The objective of this analysis is to make the predictions for upcoming 4 days based on the functioning of the ravens that are not reaching the destination on time.
This kind of exploration would help us to scrutinize and apprehend the towers who would require our consideration, in order to demonstrate the reasons which are playing a major role in the interruptions.

Let’s start by understanding the dataset.

Introduction:

The dataset talks about the communication between the towers (ravens). The land based communication happens with the help of signals. A cellular network or mobile network is a communication network where the last link is wireless. This wireless transmission is done by a tower which comprises of a transmitter and a receiver (for the wireless transmission). The channel provides transmission for both data as well as Voice transmission.

Every cellular network has different set of frequencies, to avoid any kind of overlapping and interference.

Despite of many precautions, for maintaining the setup, there are few parameters that are still impacting the transmission. Those parameters can be classified as:

Infrastructure
Interference between the different frequencies
Climatic conditions
External Factors (Predators etc.)

Business Understanding

In this we need to analyse the dataset provided to us and derive a “Decision Model”. Before starting to move in the direction of Business understanding let’s get a good hold on “Decision Model”.

Managing the basic Business logic, which can lead to visible conclusions and outcomes. The raw input given to the decision model should contain all the facts and the logics on which our Decision model will work. Ultimately, it is the logic that derives the entire process. This logic gives the correct shape and leads to the conclusion in order to provide two things:

Meaning to the Decision Model
Value to the Business

After understanding the “Decision Model” let’s know that why on the first page we require that concept here in analysing.

The major reason that led us towards the direction of “Decision Model” is the requirement to understand the background of the dataset.

Background

This data is derived from the total number of times the raven was sent for communication purpose but was not able to reach the destination on time. Hence, there was a quick need of a perfect yet effective plan to end up these communication crisis.

Data Understanding

Under this we would be covering the below mentioned points:

Initial Data Collection: Data is for about a month starting from 6th July’2018 – 4th Aug’2018.
Familiar with Data: The dataset consists of columns which describes the delay due to which the communication failed or was delayed, that require our utmost attention.

Understanding of the Columns in the Dataset:

“DATETIME”: This column records the date and time of each transmission.
“Raven Name”: This column records from where the transmission of communication is taking place. The data passes through many phrases, such as obtaining the IP address of the website name, Domain Name System. After this the connection request from sender is sent to the receiver. When acceptance acknowledgement is sent from the server to the client connection is successfully setup. Delays an happen in this process.

We have divided the delays in three categories such as:

DNS Delay:

The DNS delay mainly concerns with local internet service provider (ISP). The ISP catches the DNS record queried through the server. This delay from ISP will prevent us from viewing the website. This is known as DNS propagation delay. We have two columns for the delays:

“FIRST_DNS_RESPONSE_SUCCESS_D”:
“DNS_RESPONSE_SUCCESS_DELAY”:
Connection Delay:

The Delay in establishing the connection. The maximum number of time a TCP Packet is retransmitted in established state before giving up. The default value is 15, which corresponds to a duration of 13-30 minutes, depending upon the retransmission time out

“TCP_SETUP_TOTAL_DELAY”
“FIRST_TCP_RESPONSE_SUCCESS_D”
“TCP_CONNECT_DELAY”
“SYN_SYN_DELAY”
Page Delay:
“FIRST_GET_RESPONSE_SUCCESS_D”
“PAGE_CONTENT_DOWNLOAD_TOTAL_D”
“PAGE_SR_DELAYS”
“PAGE_BROWSING_DELAYS”
“PAGE_BROWSING_DELAY”
SR=> Selective response

Selective repeat attempts to retransmit only those

Packets are lost (due to errors).

Data Preparation

We have filtered the data according to the columns that are mentioned in the dataset.

The data set contains approximately 10,819 rows that have the value as “Zero”. We have to exclude those rows in order to get the appropriate values for our analysis and forecast. Moreover, the dataset has one column that has time and date included in them. Using that column, we have extracted the date and the time which we would be using in the time series analysis ahead. The data that we are using is already clean and doesn’t require more cleaning.

Modelling

The model that we are using in order to analyse the failure rate of ravens is the Auto- regressive Integrated Moving Average (ARIMA) Model. This helps us to forecast the parameters that led to the delay or failure rate of the ravens.

The reason we have opted for this model because ARIMA models provide another approach to time series forecasting. ARIMA models is the most widely used approach to time series forecasting, and provides complementary approaches to the problem. ARIMA models aim to describe the autocorrelations in the data.

Data Analysis

In this section our major focus is on analysing the data provide to us of the basic business prospective. Before we proceed any further we need to get clear with the basic set of questions which can be analysed by this data.

TOP TEN RAVEN WITH FAILS

In this we found the top ten raven that have taken maximum time to reach their destination with respect to time. We have taken those delay columns in consideration in which the columns have some values.