This project aims to build a recommender system that suggests movies to users based on their preferences and ratings. The MovieLens dataset, which consists of user ratings and demographics, is utilized to train and evaluate the recommender system. Singular Value Decomposition (SVD) technique is employed to extract latent features from the user-movie rating matrix. Cosine similarity is then calculated between movies to identify similar movies. The system recommends movies to users by finding movies that are similar to those they have rated highly.
- Implementation of SVD to decompose the user-movie rating matrix into three matrices (U, S, and V).
- Extraction of top 50 components from the singular value matrix to represent latent features of movies.
- Calculation of cosine similarity between movies to identify similar movies.
- Generation of recommendations by finding movies that are similar to those the user has rated highly.
- Evaluation of the recommender system using standard metrics.
This project focuses on analyzing and modeling life expectancy data from various countries. The goal is to identify the key factors that contribute to life expectancy and develop a model to predict life expectancy based on these factors. Various statistical techniques, such as correlation analysis and linear regression, are employed to analyze the relationships between the variables. Data preprocessing techniques, including outlier detection and imputation, are performed to improve the quality of the data. The results of the analysis and modeling provide valuable insights into the factors that affect life expectancy and allow for predictions of life expectancy based on these factors.
- Analysis of life expectancy data across different countries.
- Identification of key factors that influence life expectancy using statistical techniques.
- Implementation of linear regression models to predict life expectancy based on various factors.
- Evaluation of the linear regression models using standard metrics.
- Interpretation of the results to understand the relationships between life expectancy and various factors.
This project aims to understand and model the factors that influence house prices. A dataset containing various property attributes and sales prices is analyzed. The objective is to identify the key features that contribute to housing prices and develop models to predict sales prices based on these features. Extensive data exploration, including visualization and statistical analysis, is performed to gain insights into the relationships between the variables. Multiple regression models, including linear regression, polynomial regression, and random forest regression, are employed to model the relationship between the features and the sales prices. The results of the analysis and modeling provide valuable insights into the factors that drive house prices and allow for predictions of sales prices based on these factors.
- Analysis of house price data to identify key features that influence prices.
- Implementation of multiple regression models, including linear regression, polynomial regression, and random forest regression.
- Evaluation of the regression models using standard metrics.
- Interpretation of the results to understand the relationships between house prices and various features.
- Development of models to predict house prices based on various features.