# data[].attributes.body
information what we used daily

# data[].attributes.body
List of Robi subscribers

# data[].attributes.body
Facts or statistics which can be use for further analysis.

# data[].attributes.body
Elements that describes an object. Example: sales figure, revenue, date, age

# data[].attributes.body
Data is a set of information. Example: Temperature, Student List, Exam Result etc

# data[].attributes.body
Data is something from which we extract
meaningful information to do further analysis

# data[].attributes.body
Any numbers, or text , or image . Like sales numbers

# data[].attributes.body
Data means the characters for reference or analysis .

# data[].attributes.headline
Faisal Mahmud - information and statistics. Customer comments, queries and feedback - these are data

# data[].attributes.subject
Faisal Mahmud - information and statistics. Customer comments, queries and feedback - these are data

# data[].attributes.body
Numbers, Volume or any symbol

# data[].attributes.body
trend which can be used for further analysis

# data[].attributes.body
Data is some short of information

# data[].attributes.body
Information to use further action

# data[].attributes.body
Data is something from where we can conclude/extract something. Example of data like text, audio, video etc.

# data[].attributes.body
Target and achievement

# data[].attributes.body
No of 4G customer in Robi

# data[].attributes.body
Data is refer to any numeric information and facts. Revenue update is a kind of Data.

# data[].attributes.body
Data is the building block of information. Example- refill/revenue numbers, subscriber past purchases etc

# data[].attributes.body
Quantitative / Qualitative info

# data[].attributes.body
No Of EL Pos & Data Pos

# data[].attributes.body
Daily system performance

# data[].attributes.body
Data is meaning imposed to information. For example I got 85 in match is a piece of data where is 85 is only a number.

# data[].attributes.body
Data is kind of scattered information in numeric or qualitative manner like- name, numbers, size and etc.

# data[].attributes.body
Data refers to the required information needed for any analysis. For instance : no of link clicks from any digital media campaign / audience profile etc

# data[].attributes.body
A set of numbers and value with segment.

# data[].attributes.body
every bit of information/facts. example: hourly location data

# data[].attributes.body
Data means distinct pieces of information.....
examples of data are employee names, product names, addresses, registration marks, weights, prices etc.

# data[].attributes.body
Data is revenue target vs achievement and active user base of the services n find out the reason behind growth and de-growth

# data[].attributes.body
Data is information that has been translated into a form that is efficient for processing

# data[].attributes.body
Data represents the facts e.g. network performance

# data[].attributes.body
Data refers to characters/information/symbol that can be used to describe something.
Robi RGB
Data Sales
Bundle sales
GA count

# data[].attributes.body
collection of number & facts

# data[].attributes.body
descriptive indicators.
Example: training hours, attrition, step count

# data[].attributes.body
Data is actually information which has been translated or described in a form that is efficient for processing and analysis.
As example, I work with electrical consumption data from past years and use it for upcoming year predictive consumption.

# data[].attributes.body
In Computing Number movement or processing

# data[].attributes.body
Network fault prediction

# data[].attributes.body
End of year region wise 4G user throughput  need to be predicted where our inputs would be our network rollout and upgradation.

# data[].attributes.body
Site profitability forecasting for roll out.

# data[].attributes.body
Winback/Addback prediction model

# data[].attributes.body
HUB site down prediction during disaster

# data[].attributes.body
Team wise rollout performance (Acceptance)

# data[].attributes.body
From Finance point of you view following prediction we can do using ML
1) Interest rate in future
2) FX rate in future
3) Share Market
4) Fund forecast
5) Inflation rate
But do not how to do

# data[].attributes.body
To analyze region wise fiber (impact/nonimpact) and predict future cut probability and corresponding outages.

# data[].attributes.body
Revenue impact prediction for drive

# data[].attributes.body
From Finance point of view we can predict following area using ML
1) Revenue prediction
2) Interest Rate risk
3) FX rate risk
4) Inflation risk
5) Share Market
6) Fund Forecast

# data[].attributes.body
New acquisition churn rate after 2 or 3 months through ML ( Specific Area).

# data[].attributes.body
Asif
Bio Metric Device productivity prediction.

# data[].attributes.body
Revenue Prediction
People Productivity

# data[].attributes.body
1. DH & DSR Productivity based on market served
2. DH ROI profitability [+/- ROI]
3. Revenue Forecasting [Regular vs Pandemic situation]
4. DH & DSR incentive cost effective program [based on the competition movement]
5. New Sites Capitalization / Utilization

# data[].attributes.body
1. Recharge traffic forecast during EID
2. Daily Max TPS forecast
3. prediction of probability of incident
4. Daily traffic trend
5. retalers daily recharge amount prediction
6. determine whether retailer will churn or not

# data[].attributes.body
1. Biometric device productivity
2. New site monetization
3. Drive impact in overall revenue

# data[].attributes.body
Few use cases:
1. Relationship of Chatbot Interaction and Chatbot Revenue.
2. Network problem query forecast in social media.
3. Query volume forecast on social media
4. Chatbot User Forecast

# data[].attributes.body
Churn Prediction, Site revenue Prediction

# data[].attributes.body
Low Balance POS Prediction , Network problem area identification