You will get AI Recommendation System for Movies, Products & Services
Project details
AI-Powered Movie Recommendation System built using Artificial Intelligence and Machine Learning techniques. The system aims to enhance the user experience by providing personalized movie suggestions based on the user’s previous preferences or the currently selected movie.
Key Tasks & Techniques Used:
Movie Data Analysis:
Analyze datasets containing movie information such as title, genre, rating, synopsis, cast, and more.
Recommendation Algorithms:
Content-Based Filtering: Suggest movies similar to the selected movie based on description and keywords.
Natural Language Processing (NLP):
Utilize TF-IDF and Cosine Similarity to measure similarity between movie descriptions.
Interactive Interface with Streamlit:
User-friendly interface for selecting a movie and viewing recommendations instantly.
Users can rate movies to improve personalization.
Deployment:
Deploy the model online using Hugging Face Spaces for easy access without requiring a local setup.
Technologies & Tools Used:
Python, Pandas, Scikit-learn, NLTK / SpaCy, Streamlit, Hugging Face, Pickle / joblib, TMDb API (optional for images and movie descriptions).
Key Tasks & Techniques Used:
Movie Data Analysis:
Analyze datasets containing movie information such as title, genre, rating, synopsis, cast, and more.
Recommendation Algorithms:
Content-Based Filtering: Suggest movies similar to the selected movie based on description and keywords.
Natural Language Processing (NLP):
Utilize TF-IDF and Cosine Similarity to measure similarity between movie descriptions.
Interactive Interface with Streamlit:
User-friendly interface for selecting a movie and viewing recommendations instantly.
Users can rate movies to improve personalization.
Deployment:
Deploy the model online using Hugging Face Spaces for easy access without requiring a local setup.
Technologies & Tools Used:
Python, Pandas, Scikit-learn, NLTK / SpaCy, Streamlit, Hugging Face, Pickle / joblib, TMDb API (optional for images and movie descriptions).
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-LearnWhat's included
| Service Tiers |
Starter
$99
|
Standard
$199
|
Advanced
$289
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 0 | 3 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10About Yossef
Data Science & AI specialist
Giza, Egypt - 8:00 pm local time
I specialize in:
- Data Science
- Data Analysis
- Microsoft Power Bi Expert
- ML & DL Models
- AI Agent - N8N | Workflow
- LLMs
- Python, TensorFlow, Scikit-learn, OpenCV
- NLP tools (spaCy, NLTK)
- Data visualization (Python, Power BI, IBM SPSS)
-Model deployment via Streamlit & Flask
I'm passionate about making AI accessible and applicable in domains like healthcare, computer vision, and natural language processing.
Open to freelance projects, AI collaborations, and mentorship opportunities.
Let’s connect and build something impactful together.
Steps for completing your project
After purchasing the project, send requirements so Yossef can start the project.
Delivery time starts when Yossef receives requirements from you.
Yossef works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Data Collection
I will review the dataset (check quality, missing values, and formats), confirm the project goals and KPIs, and request any necessary access credentials or sample records. This ensures clear scope and a smooth start to modeling and preprocessing.