Movie Recommendation System Test phase, srs, design phase and source code final deliverable

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Movie Recommendation System Test phase, srs, design phase and source code final deliverable

Category Data Science/Machine Learning

Problem:

There are thousands of movies available on the internet. Some time the user does not get to search the exact movie according to his taste. So we need to develop a model which can recommend the movies to the user based on his previous interests. First you need to know the about user’s age, region and taste in order to recommend the movie having higher chances of getting view from the user.

According to the model a user can receive recommendations for those items that he/she has not rated before, but that have been already positively rated by users in his/her neighborhood It is based on the core assumption that users who have expressed similar interests in the past will share common interests in the future.

In this model we shall be using collaborative filtering. This model shall be analyzing large amount of information depicting user behavior and preferences to be used to predict what the user is going to do w.r.t the similarity with other users. Collaborative filtering is divided into three steps

• Develop a user model
• Find the similarities l Generate recommendation

Functional Requirements:

There are eight major tasks you will typically perform when developing a system. Tasks (b-i) should be implemented iteratively while developing the system.

1. Define the problem and select the dataset(s) of movies
2. Movie Data Analysis and Pre-processing
3. Feature Extraction
4. User Model Development
5. Find similarities in users with with the help of Nearest Neighbours
6. Generate Recommendations
7. Build system
8. Test System
9. Tune System

The program should have the machine learning approach to execute model detection.

Dataset:

First we need to download the dateset of movies in order to build and train our model. Data

can be downloaded from: https://grouplens.org/datasets/movielens/ Use 100k rating files for this model

Tools

Python

Jupyter Notebook Anaconda

Supervisor

Name: Muhammad Kaleemullah