Congratulations on choosing to participate in the Virtual Internship Program on Data Science with Open Weaver! This 4-week program is designed to be an interactive & practical internship and will help you gain industry-ready skills through project-based learning. The internship will consist of 3 Bootcamps (1 per week) , a few coding exercises, a project, and a final assessment.
WEEK 2 - DATA SCIENCE
Today, 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 today’s Bootcamp and this week’s exercises, we will learn how to build a movie recommendation system from scratch, empowering us to provide personalized movie suggestions to users.
Learning Objectives
After completing this course 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.
10-Min Tutorial
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 tutorial 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. Revisit the concepts discussed during the live bootcamp session in this 10-min tutorial video. If you would like to watch the recording of the entire bootcamp please click HERE.
Practical Exercise
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.
Week 2 Coding Exercises
Complete interesting coding exercises and receive your second level badge for this internship! Submit your solutions by adding a screenshot of the code and the output in the form.
It is mandatory for you to complete your weekly Coding Exercises in order to receive your Internship Completion Certificate.
Below are three sample coding exercise 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.
Support
Reach out to us by clicking on the reply button below for any help you may need with this course. You may also use the chat feature for support. To access the reply and chat feature, please sign-in to the the Community.
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