Create README.md

Recommendation Systems/Movie-Recommendation-System/README.md

@@ -0,0 +1,48 @@
+# Content-Based Movie Recommendation System
+
+## Overview
+
+This project implements a content-based recommendation system using plot summaries from movies. Given a movie title, the system recommends similar movies based on the similarity of their plot summaries.
+
+## Dataset
+
+The project uses two datasets:
+- `tmdb_5000_credits.csv`: Contains information about the cast and crew of the movies.
+- `tmdb_5000_movies.csv`: Contains metadata about the movies, including title, overview, genres, and other relevant details.
+
+## Features
+
+- Data cleaning and merging of credits and movies datasets.
+- Extraction of features from the movie overview using TF-IDF Vectorization.
+- Calculation of movie similarity using the sigmoid kernel.
+- Recommendation of similar movies based on user input.
+
+## Getting Started
+
+### Prerequisites
+
+Make sure you have the following libraries installed:
+
+- pandas
+- numpy
+- scikit-learn
+
+You can install them using pip:
+
+```bash
+pip install pandas numpy scikit-learn
+```
+
+## Usage
+- Load the Datasets: Load the tmdb_5000_credits.csv and tmdb_5000_movies.csv datasets.
+- Data Cleaning and Merging: Clean and merge the datasets to create a unified DataFrame.
+- Create Recommendations: Use the give_recommendations function to find similar movies based on a given title.
+
+## Function Definitions
+give_recommendations(title, sig=sig)
+- Parameters:
+    - title (str): The title of the movie for which recommendations are to be found.
+    - sig (array-like): The similarity matrix computed from the TF-IDF matrix.
+- Returns: A list of recommended movie titles based on the provided title.
+## Conclusion
+This content-based movie recommendation system provides a way to explore movies based on their plot summaries. It can be further enhanced by integrating additional features like user ratings or incorporating other recommendation techniques.