The Movie Recommender System End-to-End Project involves building a machine learning model that recommends movies to users based on their viewing history and preferences. The project involves several steps such as data collection, data preprocessing, exploratory data analysis, feature engineering, model selection.
During the data collection phase, we need to collect the movie dataset and load it into a suitable data structure such as a Pandas dataframe. In the data preprocessing phase, we perform data cleaning, data normalization, and data transformation techniques. In the exploratory data analysis phase, we conduct exploratory data analysis to understand the characteristics of the data and identify patterns and trends.
In the feature engineering phase, we extract and select relevant features from the data that can be used as inputs to the machine learning model. In the model selection phase, we select an appropriate machine learning model such as collaborative filtering or content-based filtering that can generate movie recommendations based on user preferences.
Overall, building a movie recommender system end-to-end project involves several technical skills such as programming in Python, knowledge of machine learning algorithms, data preprocessing techniques, and data visualization. It is an exciting project that requires attention to detail and creativity in identifying the most suitable techniques for generating accurate and relevant movie recommendations for users..
To use the movie recommendation system, you will need to have Python 3 installed on your computer. You can download Python 3 from the official Python website. Additionally, you will need to install the following packages:
- pandas
- numpy
- scikit-learn You can install these packages using pip, which is the default package installer for Python. Open the terminal and type the following command: !pip install pandas numpy scikit-learn
To use the movie recommendation system, you will need to have a dataset of movies and their attributes such as genre, director, actor, storyline, etc. You can obtain a dataset from various sources such as IMDB, Netflix, or Kaggle. The dataset should be in CSV format, with each row representing a movie and each column representing an attribute.
Once you have the dataset, you can run the "Movie Recomender System (5).ipynb" file, which will generate a personalized list of movie recommendations based on the user's past movie ratings and watch history. The user's data should be in CSV format, with each row representing a movie and each column representing a rating.
Contributions to the movie recommendation system are welcome! If you find any issues or have any ideas for improvement, please open an issue or a pull request on GitHub.