CHEARS
Prediction Model
<p class="font_8">Task under Dept. of Information Technology, CHEARS</p>
<ul class="font_8">
<li><p class="font_8">Time limit for Video - Maximum 5 Minutes</p></li>
<li><p class="font_8">No. of maximum slides in presentations (if any): 15</p></li>
</ul>
Dear Intern, You need to complete following task within the duration of 1 Month.
Please follow the instructions given below on Prediction Mode.
Prepare the PowerPoint presentation of your code explaining its functionality. (Optional)
Record the video of code file with output, yourself explaining the steps.
Submit the video and presentation / Code file (Link will appear on your profile in last week of your internship)
Your video will be uploaded by our official account on the preferred social media of yours ( LinkedIn / Facebook / Instagram ). You will be tagged and the response of your connection and real world people on your presentation will be analyzed. This step is required to get the wide range feedback in real world. It is responsibility of the intern to divert the connection and advice them to put the feedback on your submission.
Evaluation grades - 40 points ( Way of presentation ) ; 40 points ( Quality of work ) ; 20 points ( Social Response ) . Grade will appear on your certificate.
Highest score holder will be declared as Intern of Month (In each domain IT / Public Health/ Public Relations).
Prediction Model:
Perform EDA on given data. Dataset Link
Create model Linear/Logistic/Decision Tree whatever you feel is best for the dataset given to you.
Clean code is a must.
About Dataset :
Each row represents a customer; each column contains customer’s attributes. The datasets have the following attributes or features:
State: string
Account length: integer
Area code: integer
International plan: string
Voice mail plan: string
Number vmail messages: integer
Total day minutes: double
Total day calls: integer
Total day charge: double
Total eve minutes: double
Total eve calls: integer
Total eve charge: double
Total night minutes: double
Total night calls: integer
Total night charge: double
Total intl minutes: double
Total intl calls: integer
Total intl charge: double
Customer service calls: integer
Churn: string
The "churn-bigml-20" dataset contains 667 rows (customers) and 20 columns (features).
The "Churn" column is the target to predict.
First slide should have following format :
<Title>
<Your Name>
CHEARS Internship
Department of Information Technology
<Month> <Year>