swastikkdas / movie-recommendation-system---sritesh-suranjan

Movie Recommendation System

Movie-Recommendation-System---Sritesh-Suranjan

Movie Recommendation System

Overview

This project implements a movie recommendation system using content-based filtering. It leverages natural language processing (NLP) techniques and cosine similarity to recommend movies based on their textual content, specifically the combination of movie overviews and genres.

Table of Contents

• Introduction
• Data
• Exploratory Data Analysis
• Data Preprocessing
• Text Vectorization
• Similarity Calculation
• Movie Recommendations Function
• Example Recommendations
• Model Saving and Loading
• Dependencies
• Usage
• Contributing
• License

Introduction

The Movie Recommendation System uses content-based filtering to recommend movies based on their textual information, combining movie overviews and genres. It employs natural language processing (NLP) techniques and cosine similarity to determine movie similarity.

Data

The dataset (top10K-TMDB-movies.csv) contains information about movies, including id, title, overview, and genre.

Exploratory Data Analysis

The initial exploration involves displaying the first 10 rows, generating summary statistics, and checking for null values.

Data Preprocessing

Data preprocessing includes selecting relevant columns (id, title, overview, genre), creating a new column tags by concatenating overview and genre, and dropping irrelevant columns (overview, genre).

Text Vectorization

Text vectorization is performed using CountVectorizer from sklearn.feature_extraction.text to convert text data into numerical vectors.

Similarity Calculation

Cosine similarity is calculated based on the vectorized tags, creating a similarity matrix.

Movie Recommendations Function

A function named recommends is defined to recommend movies based on user input.

Example Recommendations

An example demonstrates recommending movies for a specific input movie title, such as "Iron Man."

Model Saving and Loading

The processed data (new_data) and the similarity matrix are saved using the pickle module. Loading the model back into the program is also demonstrated.

Dependencies

• Python 3.x
• scikit-learn
• pandas
• numpy
• matplotlib
• seaborn

Usage

1. Install the required dependencies: pip install -r requirements.txt
2. Run the Python script or Jupyter notebook.

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.