Movie Recommendation System Based on Netfilx dataset (from Kaggle)

1. Importing Dataset:

   • Reads the Netflix dataset from a CSV file into a Pandas DataFrame.

2. Data Preprocessing:

   • Rearranges columns for better readability.
   • Drops rows with missing values in the 'description' column.
   • Fills any remaining missing values in the 'description' column with an empty string.

3. Input and Output Setup:

   • Defines the input features (x) as the first four columns and the output (y) as the last two columns.

4. Train-Test Split:

   • Splits the data into training and testing sets using the train_test_split function from sklearn.model_selection.

5. Text Vectorization:

   • Utilizes TF-IDF vectorization on movie descriptions using TfidfVectorizer from sklearn.feature_extraction.text.

6. Cosine Similarity Calculation:

   • Calculates the cosine similarity between movies based on their descriptions using linear_kernel from sklearn.metrics.pairwise.

7. User Input:

   • Takes user preferences for country, genre, and movie duration.

8. Recommendation System:

   • Filters the dataset based on user preferences.
   • If no exact matches are found, it recommends similar movies based on genre using cosine similarity.
   • Outputs recommended movies along with brief descriptions.

9. Predication example:

10. Enter your preferred country: India Enter your preferred movie genre: TV shows Enter your preferred movie duration: seasons Enter your preferred release year: 2021 Recommended movies for Kota Factory:

• Little Things
• Hometown Cha-Cha-Cha
• I Need Romance
• Feels Like Ishq
• Generation 56k
• Let's Eat
• Mad for Each Other
• Use For My Talent
• Club Friday The Series 8
• Beauty and the Bitches Recommended movies for The Big Day:
• Too Hot To Handle: Latino
• Too Hot to Handle: Brazil
• Too Hot to Handle
• The Wedding Coach
• Marriage or Mortgage
• Motel Makeover
• The Parisian Agency: Exclusive Properties
• Don't be the First one
• Ainori Love Wagon: Asian Journey
• Blown Away