Netflix Analysis

📌 Project Overview

This project analyzes the Netflix Titles Dataset, focusing on data cleaning, preprocessing, and visualizations to uncover insights about Netflix's content.

🚀 Features

• Data Cleaning & Handling Missing Values
• Exploratory Data Analysis (EDA) with Visualizations
• Trend Analysis on Netflix's Content Catalog
• Insights on Movie & TV Show Releases Over Time
• Genre and Country-based Analysis
• Recommendation Engine for Personalized Content Suggestions

📂 Dataset

• Source: Netflix Titles Dataset (netflix_titles.csv)
• Columns: Title, Director, Cast, Country, Release Year, Rating, Duration, etc.

🛠️ Installation & Setup

1. Clone the repository:

   git clone https://github.com/your-username/netflix-analysis.git
   cd netflix-analysis

2. Run the Jupyter Notebook:

   jupyter notebook netflix_titles.ipynb

🤖 Recommendation Engine

The project includes a content-based recommendation engine that suggests similar movies and TV shows based on genres, cast, and other metadata.

Sample Code for Recommendations:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def get_recommendations(title, df):
    tfidf = TfidfVectorizer(stop_words='english')
    tfidf_matrix = tfidf.fit_transform(df['description'].fillna(''))
    cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)
    indices = pd.Series(df.index, index=df['title']).drop_duplicates()
    idx = indices[title]
    sim_scores = list(enumerate(cosine_sim[idx]))
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
    sim_scores = sim_scores[1:6]
    return df['title'].iloc[[i[0] for i in sim_scores]]

📊 Screenshots

Here are some visual insights from the analysis:

📜 License

This project is licensed under the MIT License.

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Author: Anas Waleed Tahir

GitHub: anasengence