imdb movie assignment upgrad

IMDb Movie Assignment

You have the data for the 100 top-rated movies from the past decade along with various pieces of information about the movie, its actors, and the voters who have rated these movies online. In this assignment, you will try to find some interesting insights into these movies and their voters, using Python.

Task 1: Reading the data

Subtask 1.1: read the movies data..

Read the movies data file provided and store it in a dataframe movies .

IMDb Movie Assignment

You have the data for the 100 top-rated movies from the past decade along with various pieces of information about the movie, its actors, and the voters who have rated these movies online. In this assignment, you will try to find some interesting insights into these movies and their voters, using Python.

Task 1: Reading the data

IMDB Movie Assignment: Problem Statement with Basic Instructions

What is the problem statement.

You have multiple rows of data that contain the list of movies that were top 100 rated movies in the past. The Data contains various information regarding Actors, Genres, Voters, and other information about the Movies.

In this case study, we are going to see how we can analyze the data. Also, we will make out How our understanding of Python can help us understand the data so well.

Some answers to the questions related to this assignment:

1. from where can i get the data to work on this case study.

You can find the attached files of data at the bottom of this page. Download the files and, yes, you will have your data.

2. Where do I need to practice and write the code?

To practice for the code, you all can download  Jupyter Notebook . Also, a file named ‘Movie Assignment.ipynb’ is also attached at the bottom of the page. The file itself contains all the instructions and information you need to follow while working on the data. The only thing that you need to do is download the file and start working on that in Jupyter Notebook.

NOTE: .ipynb is an extension that represents that file belongs to a Jupyter Notebook.

3. What basics should I know before looking forward to this assignment?

You all must have a basic understanding of the Jupyter notebook. You should know how to run and implement the codes. Basic knowledge of different modules of Python is also necessary, with an understanding of the importance of analysis.

4. How to start with the assignment?

• First and the foremost task is to have a look at the data and understand it. Read the data and gain knowledge about what it is all about and what does it represents.
• Always follow the instructions given in the notebook to run your analysis. It will help you to get a clear picture of what operation you need to perform.
• It is good practice to check the dataset after every operation you perform. It investigates whether the applied command is working the same as you want it to be.
• Never forget to import the modules before running the command.

MovieAssignmentDataDictionary (3).xlsx MovieAssignmentData (3).csv IMDb+Movie+Assignment (2).ipynb

Click on the above three links and you will have the data required for the case study to be performed.

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• Last updated December 2, 2020
• In AI Mysteries

Guide to IMDb Movie Dataset With Python Implementation

• Published on November 18, 2020
• by Ankit Das

Internet Movie Database (IMDb) is an online information base committed to a wide range of data about a wide scope of film substance, for example, movies, TV and web-based streaming shows, etc. The data which is introduced on the IMDb portal incorporates cast, creation group, director crew, individual accounts, plot outlines, random data, evaluations, fan, and critics reviews. 

The IMDb dataset contains 50,000 surveys, permitting close to 30 audits for each film. It was developed in 2011 by the researchers: Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts of Stanford University. The dataset was evenly divided into training and test sets. The training set contains 25000 reviews so as the test set.

A negative review has a score of ≤ 4 out of 10, and a positive survey has a score of ≥ 7 out of 10. Neutral reviews were excluded from this dataset. 

Here, we will examine the information contained in this dataset, how it was gathered, and give some benchmark models that gave high accuracy on this dataset. Further, we will implement the IMDB dataset using Keras Library.

Data Collection

The raw data was collected by the researchers from the IMDb website. They searched the content information present in each of the reviews and discovered any highlights that were representative for judging whether the review was positive or negative. The reviews were then evenly divided into training and test sets uploaded to their website. In each of the directories contained in the sets, there are another two directories representing pos and neg tags, to partition the information through various marks. In every one of these folders, there are numerous TXT records containing the substance of the film survey, with each document containing one report.

Loading the dataset Using Pytorch

Define the parameters that need to be passed to the function. The list x defined below will contain reviews with its polarity .

Code Implementation using Keras Library

The dataset can be downloaded from the following link .

Load the information from the IMDb dataset and split it into a train and test set. Ensure that the maximum number of words is 5000.

Let’s define the maximum length of the review. If the length of the review is more than 500, shorten it to maximum length. Suppose a review has a length shorter than 500 pad_sequence will add “0” to the remaining length.

For example “Bangalore 0 0 0 0” 

We are adding the model=Sequential() line so that the data will flow from input to output in a sequence way. The Embedding layer turns each of the words into vectors of 32 digits.

LSTM Layer decides which words in the reviews are important that will flow through them. We will add a Dense layer to the furthest limit of our model and utilize a sigmoid function capacity to deliver good results. The sigmoid function will choose if the data ought to be given a 1 (positive)or a – 1(negative). 

Next Step is to train the model with epoch=5 and batch size=64. Our model gave an accuracy of 92.88% on training data.

We finished with an accuracy of 87.25% on the test dataset.

State of the art

The present state of the art on IMDb dataset is NB-weighted-BON + dv-cosine . The model gave an exactness of 97.4%. Graph star and BERT large finetune UDA are near contenders with a precision of around 96%.

In this article, we have discussed the details and implementation of IMDb dataset using Keras Library. The model trained on the test data gave a decent accuracy of around 87%. Additionally, we can increase the accuracy by training the model with more number of epochs.

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The Assignment

After waking up and discovering that he has undergone gender reassignment surgery, an assassin seeks to find the doctor responsible. After waking up and discovering that he has undergone gender reassignment surgery, an assassin seeks to find the doctor responsible. After waking up and discovering that he has undergone gender reassignment surgery, an assassin seeks to find the doctor responsible.

• Walter Hill
• Denis Hamill
• Michelle Rodriguez
• Tony Shalhoub
• Anthony LaPaglia
• 140 User reviews
• 89 Critic reviews
• 34 Metascore
• 1 win & 1 nomination

• Frank Kitchen

• Dr. Ralph Galen

• Honest John

• Nurse Becker

• Doctor Rachel Jane

• Sebastian Jane
• Hotel Manager

• Earl Hawkins

• Office Nurse

• Vladimir Gorski

• Mexican Man

• Stenographer
• All cast & crew
• Production, box office & more at IMDbPro

More like this

Did you know

• Trivia The first draft of the screenplay was written in 1978.
• Goofs At around 48:06, when the main character is videotaping herself, she points a gun directly at the camera from a distance of probably less than a foot. This has the unintended consequence of revealing that the pistol is an Airsoft replica of an M1911 .45 ACP with a much-smaller inner muzzle than that of the real firearm.

[first lines]

Frank Kitchen : I killed a lot of guys. They were worthless pieces of shit, but I killed them, and you're not supposed to kill people. So what happened to me? I guess maybe in the end... it was a lot better than what I deserved. But it takes a long time to work that out. In the meantime, you just want to get get even.

• Soundtracks Blindfold Written by Joseph Hicks Performed by Halo Stereo

User reviews 140

• Apr 23, 2017
• How long is The Assignment? Powered by Alexa
• March 3, 2017 (United States)
• United States
• Official site (Japan)
• Vancouver, British Columbia, Canada
• See more company credits at IMDbPro
• $5,000,000 (estimated)

Technical specs

• Runtime 1 hour 35 minutes

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SQL queries performed on IMDb database to provide recommendations to RSVP Movies based on insights.

manaswikamila05/RSVP-Movies

Folders and files, repository files navigation, rsvp-movies, problem introduction.

RSVP Movies is an Indian film production company which has produced many super-hit movies. They have usually released movies for the Indian audience but for their next project, they are planning to release a movie for the global audience in 2022.

The production company wants to plan their every move analytically based on data and have approached you for help with this new project. You have been provided with the data of the movies that have been released in the past three years. You must analyze the data set and draw meaningful insights that can help them start their new project.

You are a data analyst and an SQL expert. You must use SQL to analyze the given data and give recommendations to RSVP Movies based on the insights. For your convenience, the entire analytics process has been divided into four segments, where each segment leads to significant insights from different combinations of tables. The questions in each segment with business objectives are written in the script.

Data Set and Database Creation

• Download the IMDb dataset.
• The first tab contains the ERD and the table details. Study that carefully and understand the relationships between the table.
• Inspect each table given in the subsequent tabs and understand the features associated with each of them.
• Open your MySQL Workbench and start writing the DDL and DML commands to create the database.

If you don't wish to perform the data loading part, you can directly download the SQL script file containing all the commands and data required for the database creation and start directly with the querying.

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