Trending March 2024 # Buy Movie Tickets With Siri # Suggested April 2024 # Top 9 Popular

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You can now buy movie tickets with the help of Siri, and use nothing but your iPhone (or iPad if you want to bring it with you) when you get to the theater to stroll in and enjoy the show. This is thanks to a new Siri feature that arrived in iOS 6.1, and it’s super easy to use.

Before you can get movie tickets just by speaking to Siri, be sure you have the following:

iOS 6.1 (or later) installed on your iPhone or iPad

Fandango app installed on your iPhone/iPad (free download from the App Store)

Note: because this feature relies on Fandango, it is currently limited to the USA. Not all movie theaters apply either, read on to learn more.

You also may want to register with Fandango for an account and put a credit card on file, the account is free and the credit card is obviously so that tickets can be billed to you once they are purchased. That isn’t required because there is a Guest Checkout mode, but it makes the process easier if you plan on using this service often to buy tickets. That’ll be up to you.

Believe it or not, the hard part is already over. Now you just have to summon Siri as usual by holding the home button and use natural language to ask for a movie at a theater.

The two general methods are as follows;

“Buy/get movie ticket to [movie name] at [theater or location]”

or

“[Movie name] at [theater name] for [showtime]”

For some practical examples:

“Buy movie ticket to The Hobbit at AMC in San Francisco” – specify a specific movie theater

“Get a movie ticket to Django Unchained in San Francisco” – specify a movie for any theaters in a general region

“Zero Dark Thirty, AMC Bay Street at 7:00” – specify movie, theater, and show time

Once you see which showtime you want, tap on the “Buy Tickets” button to open up Fandango and complete the purchase.

Specifying a show time will jump you ahead one step further in the ticket purchase process, though you will still need to tap on the “Buy Tickets” button to launch Fandango and complete the transaction.

A hugely important thing to look out for is the tiny little ticket stub icon next to general theater listings, this is how you can instantly tell if a theater is going to support Fandango mobile tickets.

After the ticket has been purchased it will be stored in Fandango (or Passbook), so go to the theater as usual and show the usher the barcode within the Fandango app. That gets scanned, and away you go. You shouldn’t have to wait in the normal line for this, but I think that also depends on the individual theaters and some may require you to go to the front cashier anyway.

This is undeniably convenient, so much so that Fandango may add a “convenience fee” to the ticket price for some theaters, which is more convenient for their bottom line than it is for you. Whether or not that extra $2 is worth it is up to you, and whether or not you’ll be charged for it seems to depend on the theater itself. Officially and according to Fandango, mobile ticket purchases are not supposed to get that fee tacked on, but in trying this out I had the fee added to my purchase… so YMMV and things may change as the rollout continues.

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Apple Releases Ios 8.3 With New Siri Languages, Diverse Emojis And More

In addition to releasing OS X 10.10.3 to the public this morning, Apple has also rolled out iOS 8.3. The update, which developers and members of Apple’s public beta program have been testing since February, includes a number of new features and improvements.

Among those new features is support for Wireless CarPlay, which allows folks with vehicles or head units that support the feature to use CarPlay without dealing with cables. There is also new, more diverse emojis, additional language support in Siri, Safari fixes and more.

Interestingly enough, the new firmware comes just a few days before the Apple Watch is slated to become available for pre-order, but there doesn’t appear to be any new Watch-specific features or changes. There was plenty of that in 8.2, though, which landed in March,

iOS 8.3 is compatible with the iPhone 6, 6 Plus, 5s, 5c, 5, and 4s; the fifth-gen iPod touch; the iPad Air 2, iPad mini 3, original Air, mini 2, fourth-gen iPad, original mini, third-gen iPad and iPad 2. Jailbreakers should stay away until we learn of a successful 8.3 jailbreak.

Here are the release notes:

Improved performance

App launch

App responsiveness

Messages

Wi-Fi

Control Center

Safari tabs

3rd-party keyboards

Keyboard shortcuts

Simplified Chinese keyboard

Wi-Fi and Bluetooth fixes

Fixes an issue where you could be continuously prompted for login credentials

Addresses an issue where some devices disconnect intermittently from Wi-Fi networks

Fixes an issue where hands-free phone calls could become disconnected

Fixes an issue where audio playback could stop working with some bluetooth speakers

Orientation and rotation fixes

Addresses an issue that sometimes prevented rotating back to portrait after having rotated to landscape

Improves performance and stability issues that occurred when rotating the device between portrait and landscape

Fixes an issue where device orientation appeared upside down after pulling the iPhone 6 Plus from your pocket

Resolves an issue that sometimes prevented apps from rotating to correct orientation after switching apps in multitasking

Messages

Addresses issues that caused group messages to sometimes split

Fixes an issue that sometimes removed the ability to forward or delete individual messages

Resolves an issue that sometimes prevented a preview from appearing when taking a photo in Messages

Adds the ability to report junk messages directly from the Messages app

Adds the ability to filter out iMessages that are not sent by your contacts

Family Sharing fixes

Fixes a bug where certain apps would not launch or update on family members’ devicesFixes a bug that prevented family members from downloading certain free apps

Increased reliability for Ask to Buy notifications

CarPlay fixes

Fixes an issue where Maps could come up as a black screen

Fixes an issue where the UI could be incorrectly rotated

Fixes an issue where the keyboard could appear on the CarPlay screen when it shouldn’t

Enterprise fixes

Improves reliability of installing and updating enterprise apps

Corrects the time zone of Calendar events created in IBM Notes

Fixes a problem that could cause web clip icons to become generic after restarting

Improves reliability of saving the password for a web proxy

Exchange out-of-office message can now be edited separately for external replies

Improves recovery of Exchange accounts from temporary connection problems

Improves compatibility of VPN and web proxy solutions

Allows use of physical keyboards to log into Safari web sheets, such as for joining a public Wi-Fi network

Fixes an issue that caused Exchange meetings with long notes to be truncated

Accessibility fixes

Fixes an issue where using the back button in Safari causes VoiceOver gestures to not respond

Fixes an issue where VoiceOver focus becomes unreliable in draft Mail messages

Fixes an issue where Braille Screen Input cannot be used to type text in forms on webpages

Fixes an issue where toggling Quick Nav on a Braille Display announces that Quick Nav is off

Fixes an issue keeping app icons from being moveable on home screen when VoiceOver is enabled

Fixes an issue in Speak Screen where speech will not start again after pausing

Other improvements and bug fixes

Introduces a redesigned Emoji keyboard with over 300 new characters

iCloud Photo Library has been optimized to work with the new Photos app on OS X 10.10.3 and is now out of beta

Improves the pronunciation of street names during turn-by-turn navigation in Maps

Includes support for Baum VarioUltra 20 and VarioUltra 40 braille displays

Improves the display of Spotlight results when Reduce Transparency is turned on

Adds Italic and Underline format options for iPhone 6 Plus landscape keyboard

Adds the ability to remove shipping and billing addresses used with Apple Pay

Additional language and country support for Siri: English (India, New Zealand), Danish (Denmark), Dutch (Netherlands), Portuguese (Brazil), Russian (Russia),

Swedish (Sweden), Thai (Thailand), Turkish (Turkey)

Additional dictation languages: Arabic (Saudi Arabia, United Arab Emirates) and Hebrew (Israel)

Improves stability for Phone, Mail, Bluetooth connectivity, Photos, Safari tabs, Settings, Weather and Genius Playlists in Music

Addresses an issue where Slide to Unlock could fail to work on certain devices

Addresses an issue that sometimes prevented swiping to answer a phone call on the Lock screen

Addresses an issue that prevented opening links in Safari PDFs

Fixes an issue where selecting Clear History and Website Data in Safari Settings did not clear all data

Fixes an issue that prevented autocorrecting “FYI”

Addresses an issue where contextual predictions did not appear in Quick Reply

Fixes an issue where Maps did not enter night mode from hybrid mode

Resolves an issue that prevented initiating FaceTime calls from a browser or 3rd-party app using FaceTime URLs

Fixes an issue that sometimes prevented photos from properly exporting to Digital Camera Image folders on Windows

Fixes an issue that sometimes prevented an iPad backup from completing with iTunes

Fixes an issue where remaining time on timer would sometimes incorrectly display as 00:00 on Lock screen

Fixes an issue that sometimes prevented adjusting call volume

Vudu Online Movie Service Acquired By Wal

Vudu online movie service acquired by Wal-Mart

The word’s out today from the NY Times that that Wal-Mart is set to acquire Vudu, the online movie streaming service in an acquisition set to be worth approximately $100 million.  Additional information and the full press release can be found after the break.

Vudu had been known recently for its Vudu Box and Vudu XL service, used to distribute movies over the web via a hybrid peer-to-peer technology, in which they were the first to stream video at 1080p high-def.   Wal-Mart is evidently betting that the majority of consumers will gradually move toward the online distribution of media content, leaving DVDs and physical media behind in the dust.

Because DVD sales have declined in recent years, retailers and studios have looked to digital distribution to slide back in line with profits.  Wal-Mart, who happens to be the largest DVD retailer in the country, may seem like it has shot itself in the foot with this move.  But with digital distribution being an increasingly more economical medium, like the iPad partnership between the NY Times and Apple, this move may very well determine and reestablish their business model for the future.

Walmart Announces Acquisition of Digital Entertainment Provider, VUDU

Company takes next step to enhance home entertainment and information delivery options for consumers

BENTONVILLE, Ark., Feb. 22, 2010 — Walmart announced today a definitive agreement to acquire VUDU, Inc., a leading provider of digital technologies and services that enable the delivery of entertainment content directly to broadband high-definition TVs and Blu-ray players. The deal is expected to close within the next few weeks.

VUDU is a revolutionary service, built into a growing number of broadband-ready TVs and Blu-ray players, that delivers instant access to thousands of movies and TV shows directly through the television. Customers with broadband Internet access and an Internet-ready TV or Blu-ray player can rent or purchase movies, typically in high-definition, without needing a connected computer or cable/satellite service. New movies and features will be added continually, enabling customers to enjoy a product that continues to become more robust long after they have left the store.

“The real winner here is the customer,” said Eduardo Castro-Wright, vice chairman for Walmart. “Combining VUDU’s unique digital technology and service with Walmart’s retail expertise and scale will provide customers with unprecedented access to home entertainment options as they migrate to a digital environment.”

VUDU has licensing agreements with almost every major movie studio and dozens of independent and international distributors to offer approximately 16,000 movies, including the largest 1080p library of video on-demand movies available anywhere. Via their broadband Internet connection, users have the ability to rent or buy titles and begin viewing them instantly.

“We are excited about the opportunity to take our company’s vision to the next level,” said Edward Lichty, VUDU executive vice president. “VUDU’s services and Apps platform will give Walmart a powerful new vehicle to offer customers the content they want in a way that expands the frontier of quality, value and convenience.”

VUDU, based in Santa Clara, Calif., will become a wholly-owned subsidiary of Walmart. The company is not disclosing financial terms of the agreement as the acquisition is not material to its first quarter earnings for fiscal year 2011.

[via New York Times]

Movie Recommendation And Rating Prediction Using K

Recommendation systems are becoming increasingly important in today’s hectic world. People are always in the lookout for products/services that are best suited for them. Therefore, the recommendation systems are important as they help them make the right choices, without having to expend their cognitive resources. In this blog, we will understand the basics of knn recommender system and learn how to build a Movie Recommendation System using collaborative filtering by implementing the K-Nearest Neighbors algorithm. We will also predict the rating of the given movie based on its neighbors and compare it with the actual rating.

Types of kNN Recommender System

Recommendation systems can be broadly classified into 3 types —

Collaborative Filtering

Content-Based Filtering

Hybrid Recommendation Systems

Collaborative Filtering

Further, there are several types of collaborative filtering algorithms —

User-User Collaborative Filtering: Try to search for lookalike customers and offer products based on what his/her lookalike has chosen.

Item-Item Collaborative Filtering: It is very similar to the previous algorithm, but instead of finding a customer lookalike, we try finding item lookalike. Once we have an item lookalike matrix, we can easily recommend alike items to a customer who has purchased an item from the store.

Other algorithms: There are other approaches like market basket analysis, which works by looking for combinations of items that occur together frequently in transactions.

Collaborative v/s Content-based filtering illustration

Content-based Filtering

These filtering methods are based on the description of an item and a profile of the user’s preferred choices. In a content-based recommendation system, keywords are used to describe the items, besides, a user profile is built to state the type of item this user likes. In other words, the algorithms try to recommend products that are similar to the ones that a user has liked in the past.

Hybrid Recommendation Systems

Hybrid Recommendation block diagram

Recent research has demonstrated that a hybrid approach, combining collaborative filtering and content-based filtering could be more effective in some cases. Hybrid approaches can be implemented in several ways, by making content-based and collaborative-based predictions separately and then combining them, by adding content-based capabilities to a collaborative-based approach (and vice versa), or by unifying the approaches into one model.

Netflix is a good example of the use of hybrid recommender systems. The website makes recommendations by comparing the watching and searching habits of similar users (i.e. collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated highly (content-based filtering).

Now that we’ve got a basic intuition of Recommendation Systems, let’s start with building a simple Movie Recommendation System in Python.

Find the Python notebook with the entire code along with the dataset and all the illustrations here.

TMDb — The Movie Database

The Movie Database (TMDb) is a community built movie and TV database which has extensive data about movies and TV Shows. Here are the stats —

For simplicity and easy computation, I have used a subset of this huge dataset which is the TMDb 5000 dataset. It has information about 5000 movies, split into 2 CSV files.

tmdb_5000_movies.csv: Contains information like the score, title, date_of_release, genres, etc.

tmdb_5000_credits.csv: Contains information of the cast and crew for each movie.

The link to the Dataset is here.

Step 1 — Import the dataset

Import the required Python libraries like Pandas, Numpy, Seaborn, and Matplotlib. Then import the CSV files using read_csv() function predefined in Pandas.

movies = pd.read_csv('../input/tmdb-movie-metadata/tmdb_5000_movies.csv') credits = pd.read_csv('../input/tmdb-movie-metadata/tmdb_5000_credits.csv')

Step 2 — Data Exploration and Cleaning

We will initially use the head(), describe() function to view the values and structure of the dataset, and then move ahead with cleaning the data.

movies.head()

Python Code:



Similarly, we can get an intuition of the credits dataframe and get an output as follows —

credits.head()

Checking the dataset, we can see that genres, keywords, production_companies, production_countries, spoken_languages are in the JSON format. Similarly in the other CSV file, cast and crew are in the JSON format. Now let’s convert these columns into a format that can be easily read and interpreted. We will convert them into strings and later convert them into lists for easier interpretation.

The JSON format is like a dictionary (key: value) pair embedded in a string. Generally, parsing the data is computationally expensive and time-consuming. Luckily this dataset doesn’t have that complicated structure. A basic similarity between the columns is that they have a name key, which contains the values that we need to collect. The easiest way to do so parse through the JSON and check for the name key on each row. Once the name key is found, store the value of it into a list and replace the JSON with the list.

# changing the genres column from json to string for index,i in zip(movies.index,movies['genres']): list1 = [] for j in range(len(i)): list1.append((i[j]['name'])) # the key 'name' contains the name of the genre movies.loc[index,'genres'] = str(list1)

In a similar fashion, we will convert the JSON to a list of strings for the columns: keywords, production_companies, cast, and crew. We will check if all the required JSON columns have been converted to strings using movies.iloc[index]

Details of the movie at index 25

Step 3 — Merge the 2 CSV files

We will merge the movies and credits dataframes and select the columns which are required and have a unified movies dataframe to work on.

movies = movies.merge(credits, left_on='id', right_on='movie_id', how='left') movies = movies[['id', 'original_title', 'genres', 'cast', 'vote_average', 'director', 'keywords']]

We can check the size and attributes of movies like this —

  Step 4 — Working with the Genres column

We will clean the genre column to find the genre_list

movies['genres'] = movies['genres'].str.strip('[]').str.replace(' ','').str.replace("'",'') movies['genres'] = movies['genres'].str.split(',')

Let’s plot the genres in terms of their occurrence to get an insight into movie genres in terms of popularity.

plt.subplots(figsize=(12,10)) list1 = [] for i in movies['genres']: list1.extend(i) ax = pd.Series(list1).value_counts()[:10].sort_values(ascending=True).plot.barh(width=0.9,color=sns.color_palette('hls',10)) for i, v in enumerate(pd.Series(list1).value_counts()[:10].sort_values(ascending=True).values): ax.text(.8, i, v,fontsize=12,color='white',weight='bold') plt.title('Top Genres') plt.show()

Drama appears to be the most popular genre followed by Comedy

Now let’s generate a list ‘genreList’ with all possible unique genres mentioned in the dataset.

genreList = [] for index, row in movies.iterrows(): genres = row["genres"] for genre in genres: if genre not in genreList: genreList.append(genre) genreList[:10] #now we have a list with unique genres

Unique genres

One Hot Encoding for Multiple Labels

‘genreList’ will now hold all the genres. But how do we come to know about the genres each movie falls into. Now some movies will be ‘Action’, some will be ‘Action, Adventure’, etc. We need to classify the movies according to their genres.

Let’s create a new column in the dataframe that will hold the binary values whether a genre is present or not in it. First, let’s create a method that will return back a list of binary values for the genres of each movie. The ‘genreList’ will be useful now to compare against the values.

Let’s say for example we have 20 unique genres in the list. Thus the below function will return a list with 20 elements, which will be either 0 or 1. Now for example we have a Movie which has genre = ‘Action’, then the new column will hold [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0].

Similarly for ‘Action, Adventure’ we will have, [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]. Converting the genres into such a list of binary values will help in easily classifying the movies by their genres.

def binary(genre_list): binaryList = [] for genre in genreList: if genre in genre_list: binaryList.append(1) else: binaryList.append(0) return binaryList

Applying the binary() function to the ‘genres’ column to get ‘genre_list’

We will follow the same notations for other features like the cast, director, and the keywords.

movies['genres_bin'] = movies['genres'].apply(lambda x: binary(x)) movies['genres_bin'].head()

genre_list columns values

Step 5 — Working with the Cast column

Let’s plot a graph of Actors with Highest Appearances

plt.subplots(figsize=(12,10)) list1=[] for i in movies['cast']: list1.extend(i) ax=pd.Series(list1).value_counts()[:15].sort_values(ascending=True).plot.barh(width=0.9,color=sns.color_palette('muted',40)) for i, v in enumerate(pd.Series(list1).value_counts()[:15].sort_values(ascending=True).values): ax.text(.8, i, v,fontsize=10,color='white',weight='bold') plt.title('Actors with highest appearance') plt.show()

Samuel Jackson aka Nick Fury from Avengers has appeared in maximum movies. I initially thought that Morgan Freeman might be the actor with maximum movies, but Data wins over assumptions!

When I initially created the list of all the cast, it had around 50k unique values, as many movies have entries for about 15–20 actors. But do we need all of them? The answer is No. We just need the actors who have the highest contribution to the movie. For eg: The Dark Knight franchise has many actors involved in the movie. But we will select only the main actors like Christian Bale, Micheal Caine, Heath Ledger. I have selected the main 4 actors from each movie.

One question that may arise in your mind is how do you determine the importance of the actor in the movie. Luckily, the sequence of the actors in the JSON format is according to the actor’s contribution to the movie.

Let’s see how we do that and create a column ‘cast_bin’

for i,j in zip(movies['cast'],movies.index): list2 = [] list2 = i[:4] movies.loc[j,'cast'] = str(list2) movies['cast'] = movies['cast'].str.strip('[]').str.replace(' ','').str.replace("'",'') movies['cast'] = movies['cast'].str.split(',') for i,j in zip(movies['cast'],movies.index): list2 = [] list2 = i list2.sort() movies.loc[j,'cast'] = str(list2) movies['cast']=movies['cast'].str.strip('[]').str.replace(' ','').str.replace("'",'')

castList = [] for index, row in movies.iterrows(): cast = row["cast"] for i in cast: if i not in castList: castList.append(i)

movies[‘cast_bin’] = movies[‘cast’].apply(lambda x: binary(x)) movies[‘cast_bin’].head()

cast_bin column values

Step 6 — Working with the Directors column

Let’s plot Directors with maximum movies

def xstr(s): if s is None: return '' return str(s) movies['director'] = movies['director'].apply(xstr)

plt.subplots(figsize=(12,10)) ax = movies[movies['director']!=''].director.value_counts()[:10].sort_values(ascending=True).plot.barh(width=0.9,color=sns.color_palette('muted',40)) for i, v in enumerate(movies[movies['director']!=''].director.value_counts()[:10].sort_values(ascending=True).values): ax.text(.5, i, v,fontsize=12,color='white',weight='bold') plt.title('Directors with highest movies') plt.show()

We create a new column ‘director_bin’ as we have done earlier

directorList=[] for i in movies['director']: if i not in directorList: directorList.append(i)

movies['director_bin'] = movies['director'].apply(lambda x: binary(x)) movies.head()

So finally, after all this work we get the movies dataset as follows

Movies dataframe after One Hot Encoding

Step 7 — Working with the Keywords column

The keywords or tags contain a lot of information about the movie, and it is a key feature in finding similar movies. For eg: Movies like “Avengers” and “Ant-man” may have common keywords like superheroes or Marvel.

For analyzing keywords, we will try something different and plot a word cloud to get a better intuition:

from wordcloud import WordCloud, STOPWORDS import nltk from nltk.corpus import stopwords

plt.subplots(figsize=(12,12)) stop_words = set(stopwords.words('english')) stop_words.update(',',';','!','?','.','(',')','

    Above is a word cloud showing the major keywords or tags used for describing the movies

We find ‘words_bin’ from Keywords as follows —

movies['keywords'] = movies['keywords'].str.strip('[]').str.replace(' ','').str.replace("'",'').str.replace('"','') movies['keywords'] = movies['keywords'].str.split(',') for i,j in zip(movies['keywords'],movies.index): list2 = [] list2 = i movies.loc[j,'keywords'] = str(list2) movies['keywords'] = movies['keywords'].str.strip('[]').str.replace(' ','').str.replace("'",'') movies['keywords'] = movies['keywords'].str.split(',') for i,j in zip(movies['keywords'],movies.index): list2 = [] list2 = i list2.sort() movies.loc[j,'keywords'] = str(list2) movies['keywords'] = movies['keywords'].str.strip('[]').str.replace(' ','').str.replace("'",'') movies['keywords'] = movies['keywords'].str.split(',')

words_list = [] for index, row in movies.iterrows(): genres = row["keywords"] for genre in genres: if genre not in words_list: words_list.append(genre)

movies['words_bin'] = movies['keywords'].apply(lambda x: binary(x)) movies = movies[(movies['vote_average']!=0)] #removing the movies with 0 score and without drector names movies = movies[movies['director']!='']

Step 8 — Similarity between movies

We will be using Cosine Similarity for finding the similarity between 2 movies. How does cosine similarity work?

Let’s say we have 2 vectors. If the vectors are close to parallel, i.e. angle between the vectors is 0, then we can say that both of them are “similar”, as cos(0)=1. Whereas if the vectors are orthogonal, then we can say that they are independent or NOT “similar”, as cos(90)=0.

Recommendation System using K-Nearest Neighbors – Cosine Similarity

For a more detailed study, follow this link.

Below I have defined a function Similarity, which will check the similarity between the movies.

from scipy import spatial

def Similarity(movieId1, movieId2): a = movies.iloc[movieId1] b = movies.iloc[movieId2] genresA = a['genres_bin'] genresB = b['genres_bin'] genreDistance = spatial.distance.cosine(genresA, genresB) scoreA = a['cast_bin'] scoreB = b['cast_bin'] scoreDistance = spatial.distance.cosine(scoreA, scoreB) directA = a['director_bin'] directB = b['director_bin'] directDistance = spatial.distance.cosine(directA, directB) wordsA = a['words_bin'] wordsB = b['words_bin'] wordsDistance = spatial.distance.cosine(directA, directB) return genreDistance + directDistance + scoreDistance + wordsDistance

Let’s check the Similarity between 2 random movies

We see that the distance is about 2.068, which is high. The more the distance, the less similar the movies are. Let’s see what these random movies actually were.

It is evident that The Dark Knight Rises and How to train your Dragon 2 are very different movies. Thus the distance is huge.

Step 9 — Score Predictor (the final step!)

So now when we have everything in place, we will now build the score predictor. The main function working under the hood will be the Similarity() function, which will calculate the similarity between movies, and will find 10 most similar movies. These 10 movies will help in predicting the score for our desired movie. We will take the average of the scores of similar movies and find the score for the desired movie.

Now the similarity between the movies will depend on our newly created columns containing binary lists. We know that features like the director or the cast will play a very important role in the movie’s success. We always assume that movies from David Fincher or Chris Nolan will fare very well. Also if they work with their favorite actors, who always fetch them success and also work on their favorite genres, then the chances of success are even higher. Using these phenomena, let’s try building our score predictor.

import operator

def predict_score(): name = input('Enter a movie title: ') new_movie = movies[movies['original_title'].str.contains(name)].iloc[0].to_frame().T print('Selected Movie: ',new_movie.original_title.values[0]) def getNeighbors(baseMovie, K): distances = [] for index, movie in movies.iterrows(): if movie['new_id'] != baseMovie['new_id'].values[0]: dist = Similarity(baseMovie['new_id'].values[0], movie['new_id']) distances.append((movie['new_id'], dist)) distances.sort(key=operator.itemgetter(1)) neighbors = [] for x in range(K): neighbors.append(distances[x]) return neighbors K = 10 avgRating = 0 neighbors = getNeighbors(new_movie, K)

print('nRecommended Movies: n') for neighbor in neighbors: avgRating = avgRating+movies.iloc[neighbor[0]][2] print('n') avgRating = avgRating/K print('The predicted rating for %s is: %f' %(new_movie['original_title'].values[0],avgRating)) print('The actual rating for %s is %f' %(new_movie['original_title'].values[0],new_movie['vote_average']))

Now simply just run the function as follows and enter the movie you like to find 10 similar movies and it’s predicted ratings

predict_score()

Recommendation System using K-Nearest Neighbors: Predict Scores

Thus we have completed the Movie Recommendation System implementation using the K-Nearest Neighbors algorithm.

Sidenote — K Value

In this project, we have arbitrarily chosen the value K=10.

But in other applications of KNN, finding the value of K is not easy. A small value of K means that noise will have a higher influence on the result and a large value make it computationally expensive. Data scientists usually choose as an odd number if the number of classes is 2 and another simple approach to select k is set K=sqrt(n).

Check out – Future of AI and ML in the Next 10 Years

Ready to Use kNN Recommender System?

kNN algorithm is a reliable and intuitive recommendation system that leverages user or item similarity to provide personalized recommendations. kNN recommender system is helpful in e-commerce, social media, and healthcare, and continues to be an important tool for generating accurate and personalized recommendations.

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Magix Movie Edit Pro Plus Review + Free Giveaway

If you are into video editing, you will probably heard of the Magix Movie Edit Pro software as it has been around for quite some long time. At its 18th version, the Movie Edit Pro software is now a mature, and more powerful movie editing tool than before. We have a chance to play around with the Movie Edit Pro 18 MX Plus and we are really impressed. The following is our review of the product and at the end, we are also going to give away 10 copies of Magix Movie Edit Pro Plus. Stay tuned and read on.

Usage

If you are a first-time user of Magix Movie Edit Pro software (or any other video editing software), then you have to be prepared for a steep learning curve. Much as I like to say that it is intuitive and easy to use, it is not. There are just too many buttons, effects, settings and options that make it too confusing for a first time user. On a positive side, it also means that there are just too many features waiting for you to explore.

To get started, you will have to create a new project and import in your video clip(s). You can add the clips either from your local hard disk or external SD cards. There is also an option to connect your AVHCD, HDV camera, DV Camera, and even TV video input to your computer and have Movie Edit Pro record the video from it. Yes, if you have a 3D video, you can import it in and edit it in Movie Edit Pro too.

Features and Effects

There are more than 1000 effects, music clips and title templates that you can use in this software. One thing that I like about the Preview pane is that everytime you apply an effect, it will show up real-time so you can see the changes and make the necessary adjustment accordingly. Other features also include the ability to split, trim, crop, divide and stabilize scenes. This version also allows you to insert more tracks (99 tracks for picture and sound), complete color correction, movie templates and professional dubbing. If you are into green screen and chroma-keys, they are available as well.

Another useful feature is the multiCam editing (with 2 cameras) that allows you to shoot the same video with two different cameras and edit them in a single project. This is usually used by the pros to combine same scene of the video take from different prespective.

Supported Input and Output Formats

Magix Movie Edit Pro supports a whole wide range of video format, including AVI, DV-AVI, MPEG-1, MPEG-2, MPEG-4, MTS, M2TS, MXV, MJPEG, QuickTime, WMV(HD) and MKV, and it can supports full HD up to 1080p as well as recording taken at 50fps.

As for the output, you can either upload your finished project to Youtube, Facebook or Vimeo, or even burn it into DVD (with custom menu design and professional templates) and Blu-ray Discs.

Thanks to the Magix Group for sponsoring this great movie making software.

Magix Movie Edit Pro Plus is available for $99.99.

Damien

Damien Oh started writing tech articles since 2007 and has over 10 years of experience in the tech industry. He is proficient in Windows, Linux, Mac, Android and iOS, and worked as a part time WordPress Developer. He is currently the owner and Editor-in-Chief of Make Tech Easier.

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Siri Not Working On Iphone Or Ipad? How To Fix Siri & Troubleshoot Problems

Siri usually works great on iPhone and iPad, but sometimes Siri stops working or Siri may not work as intended. If you experience problems with Siri, this guide will walk you through troubleshooting Siri so that you can fix Siri to work on your iPhone or iPad again.

We’ll cover several effective troubleshooting tricks to fix common problems with Siri.

How to Fix Siri Not Working on iPhone, iPad

The first things you should do if Siri is not working is to check the following:

Be sure the iPhone or iPad has an active internet connection through wi-fi and/or a cellular connection

Make sure nothing is obstructing the microphone on the iPhone or iPad (for example, some cases may cover the microphone)

Be sure that Siri is enabled in Settings

Be sure you are speaking clearly and concisely in a language Siri recognizes

Most Siri problems come down to an issue with the devices wi-fi or internet connection, which is why that should be the first thing to check, along with making sure the microphone is not covered and that the service is actually enabled. If you still experience trouble with Siri, or if Siri continues to not work, the following tips should be followed to troubleshoot.

Fix Siri by Rebooting the iPhone, iPad, iPod touch

Forcibly restarting the iPhone or iPad is often enough to fix an inexplicable Siri problem.

For most iPhone and iPad models, hold down the Home button and Power button as described here

For iPhone 7 and newer, hold down Down Volume and Power button as described here

You can also issue a soft restart by turning the iOS device off and back on again.

When the iPhone or iPad boots back up again, try to use Siri as usual, it should work.

Fix Siri Problems by Toggling Siri Off & On Again

Here is how you can toggle Siri off and on again, which resolves many of the simple issues with the service:

Open the “Settings” app on iPhone or iPad and go to “Siri”

Turn the setting next to “Siri” OFF by hitting the toggle switch

Confirm that you want to turn Siri off by tapping “Turn Off Siri”

Wait a few moments, then toggle the Siri switch back ON to re-enable Siri in iOS

Hold down the Home button to activate Siri and ask a question to confirm the feature is working as intended

Often just toggling the feature back on is sufficient to get Siri working again. Sometimes users find the feature was turned off too, which is a bit unusual but obviously if Siri is disabled then Siri can’t be used.

If Siri says “Siri Not Available” or “I’m sorry, I can’t complete your request right now” or similar…

Siri Not Available and similar error messages usually indicate that Siri has a problem with the internet connection. Be sure you are connected to wi-fi or an active cellular data plan, and then try again later.

Very rarely, Siri may not be working because of a problem with an Apple Siri Server that is unrelated to the iPhone or iPad itself, but that is uncommon. If that is the case, Siri should start working again shortly on its own.

Other Siri Troubleshooting Basics

Does the iPhone support Siri? Obviously this guide only applies to iPhone and iPad devices that support Siri, if you have an ancient model device it will not have the Siri feature at all

Siri requires internet access to use, the iPhone or iPad must be on wi-fi or have an active cellular connection to use Siri

Siri must be enabled for the feature to be usable

Siri must have a functioning Home button for the feature to be activated (aside from Hey Siri, which is voice activated)

If Siri is working but Hey Siri is not working, be sure to enable Hey Siri separately in Siri settings

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