28 Aug :: Week 2 Live Bootcamp


In week 2 of the internship, our focus will be on how Netflix recommends movies based on your viewing history and how e-commerce websites generate suggestions like “Frequently Bought Together.” Despite seeming simple, these choices rely on advanced statistical methods to predict and provide accurate recommendations. Through the Week 2 Live Bootcamp and Coding Project, we will learn how to build a Movie Recommendation System from scratch, empowering us to provide personalized movie suggestions to users.


After completing this week’s Bootcamp & Coding Project you will:

  • Have a good working knowledge of the Fundamentals of Data Analysis.
  • Learn various concepts involved in building the Recommender such as use of a collaborative filtering mechanism using implementation Pandas and NumPy library.
  • Have a fully functional Movie Recommender prototype that you can customize and fine tune the model to enhance its performance


Movie Recommendation System using Pandas, work with the different types of dataset to analyze, merge, sort a dataframe and use the data analysis libraries like NumPy and Pandas to work through a simple data analysis solution using the concept of collaborative filtering.

Watch this Live Bootcamp (recording available below after live session) on Movie Recommendation System with Pandas. This includes an understanding of Python language, an IDE like Jupyter or PyCharm to write Python code and essential libraries like Pandas & NumPy.

NOTE :: You must sign in/sign up (top right corner of this page) to record your attendance while watching this live or recorded bootcamp. Just sign in and then watch - it’s as easy as that!


Click the below button to access the Movie Recommendation System kandi kit. This kit has all the required dependencies and resources you need to build your application.

Click on the 1-Click Installer button on the kandi kit page to install the Movie Recommendation System kit. On installing and running this kit, you will have a working model that you can customize and use in your project.

kandi 1-Click Kit - Dark

Below are three sample coding exercises that will help you advance in your journey in Data Science. To get started, use the relevant keywords to search for simple code snippets in the search bar on kandi.

Sample Exercise 1 - Pandas dataframe join: This exercise helps join/merge columns with other DataFrame either on index or on a column.

Sample Exercise 2 - Sort Pandas dataframe: In order to sort the data frame in pandas, function [sort_values() is used. Pandas sort_values() can sort the data frame in ascending or descending order.

Sample Exercise 3 - Removing duplicate rows in a dataframe using Pandas: Pandas drop_duplicates() method helps in removing duplicates from the Pandas Dataframe in python.


Complete and submit this interesting Coding Project and receive your Data Science Learner Badge!

It is mandatory for you to complete your weekly Coding Project in order to receive your final Internship Completion Certificate.

NOTE :: For questions 6-10, please ensure you upload screenshots of your code and outcome in PNG format ONLY. We will not be able to consider answers provided in text or any other format. You will receive your Badge #4 if you meet the minimum requirement of 60% correct answers. If not, you will receive an email from us requesting you to retake the Coding Project.

Coding Project - Week 2

Showcase your progress & win exciting prizes!

  1. Share your badge on social media. You can save the image and share.
  2. Add a short note on why you are excited to learn new digital skills.
  3. Ensure you tag #Ready2GoDigital @ICTAcademy @OpenWeaver.

Win goodies every week! Share badges every week and stand to win Airpods at the end of the internship!


Reach out to us by replying below for any questions! You must be signed in with your registered email address to use the ‘reply’ feature.


1. Which of the following is not true about DataFrame?

  • DataFrame object can be created by passing dictionaries
  • DataFrame size is immutable
  • DataFrame index can be string
  • Column of DataFrame can have data of different types
0 voters

2. To count the total no. of elements in a DataFrame we use ______.

  • size
  • len
  • count
  • values
0 voters
1 Like

3. Identify the correct statement:

  • A DataFrame can only store homogenous elements
  • Empty of DataFrame counts NaN or NA values
  • The index of DataFrame can be a number, letter, or string
  • Size of DataFrame returns the total no. of rows
0 voters
1 Like

4. To extract the first three rows and three columns of a DataFrame ‘exp’ which of the following is True?

  • exp.iloc[0:2,0:2]
  • exp.iloc[0:3,0:3]
  • exp.iloc[1:3,1:3]
  • exp.iloc[1:4,1:4]
0 voters
1 Like

5. A DataFrame object can be created by using?

  • Python Dictionary
  • Python List
  • Pandas Series
  • All of the above
0 voters
1 Like

I am unable to access my Kandi KIt. Kindly help me

the kit didnt run on my system

the kit is running on an older python version making it useless for systems with python 3.11 , how do we resolve it and access the kit?

Excited for the bootcamp!!


ig we will have to setup a venv for this project

Hello ma’am. I gave my assessment of week 1 coding project… I completed it on Thursday but I still didn’t receive any mail about my 3rd badge… Please can you help me regarding the badge mail

We have created 1-click kit (with python <3.10) such that all the dependencies required to run the solution are tested and comes as a single package across different versions of windows OS. You can still run the solution manually in upgraded Python by installing the dependencies manually.

I haven’t received my Week 1 Badge after submitting my week 1 project. What should I do?

Please share screenshot of your issue

Please share the error you are getting?

You will receive your badge by end of today!

how do i install them manually?

Sir will I receive today or something the week 1 badge?

Could anyone tell me how to check whether the my Week 1 Coding Project has been finished or not ?
I don’t know where to find week1 badges.