{"id":27446312,"url":"https://github.com/ifigeneiatsiflidou/applied-statistics-project","last_synced_at":"2026-04-29T16:39:12.645Z","repository":{"id":287915699,"uuid":"966190181","full_name":"ifigeneiatsiflidou/applied-statistics-project","owner":"ifigeneiatsiflidou","description":"Project for an Applied Statistics course, involving exploratory data analysis and predictive modeling of movie revenue using engineered features and multiple linear regression.","archived":false,"fork":false,"pushed_at":"2025-04-14T17:10:21.000Z","size":7827,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-15T04:16:00.471Z","etag":null,"topics":["correlation-analysis","data-analysis","linear-regression","python","scikit-learn","visualization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ifigeneiatsiflidou.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-14T14:39:39.000Z","updated_at":"2025-04-14T17:10:25.000Z","dependencies_parsed_at":"2025-04-15T04:26:02.059Z","dependency_job_id":null,"html_url":"https://github.com/ifigeneiatsiflidou/applied-statistics-project","commit_stats":null,"previous_names":["ifigeneiatsiflidou/applied-statistics-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ifigeneiatsiflidou/applied-statistics-project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ifigeneiatsiflidou%2Fapplied-statistics-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ifigeneiatsiflidou%2Fapplied-statistics-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ifigeneiatsiflidou%2Fapplied-statistics-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ifigeneiatsiflidou%2Fapplied-statistics-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ifigeneiatsiflidou","download_url":"https://codeload.github.com/ifigeneiatsiflidou/applied-statistics-project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ifigeneiatsiflidou%2Fapplied-statistics-project/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269832896,"owners_count":24482333,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-11T02:00:10.019Z","response_time":75,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["correlation-analysis","data-analysis","linear-regression","python","scikit-learn","visualization"],"created_at":"2025-04-15T04:15:58.589Z","updated_at":"2026-04-29T16:39:12.606Z","avatar_url":"https://github.com/ifigeneiatsiflidou.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Applied Statistics Project \nThis project was created as part of an **Applied Statistics** course and focuses on analyzing and predicting the worldwide box office revenue of movies using statistical techniques and data analysis in Python.\n\n## Project Overview\nThe dataset includes 3,000 movies from **The Movie Database (TMDb)**, featuring details like: budget, popularity, language, cast, and production companies. The project is divided into 2 exercises:\n\n## Exercises\n### Exercise 1: Exploratory Analysis and Feature Selection Based on Correlation\n\nWe begin by analyzing the relationship between the revenue and four explanatory variables:\n-\tbudget\n-\tenglish (a binary variable indicating whether the original language is English)\n-\truntime\n-\tpopularity\n\nFor each numerical explanatory variable, we compute the correlation coefficient with revenue and visualize the relationships using scatter plots. \nBased on the correlation values and visual evidence, we determine which single variable is the most informative for predicting movie revenue.\n\n### Exercise 2: Multiple Linear Regression\n\nWe extend our analysis by exploring additional variables that may improve our ability to predict movie revenue.\nMost of the dataset’s remaining information is in text or JSON format. From this, we engineer two new numerical explanatory variables:\t\n-\tThe number of male cast members per movie (from the cast field)\n-\tThe number of production companies involved in each movie (from the production_companies field)\n\nWe then:\n-\tCalculate the correlation of these new variables with revenue\n-\tSelect the four most predictive features (budget, popularity, men, companies)\n-\tFit a multiple linear regression model using scikit-learn\n-\tEvaluate the model using the R² score and a histogram of residuals\n-\tInterpret the model coefficients and discuss variable importance\n\nThe goal is to understand which variables most strongly affect movie revenue and how well a linear model can explain the variability in the data.\n\n## Contents\n`movie_revenues.csv` : Dataset with 3,000 movies \n\n`Applied_statistics_project.ipynb` : Main Jupyter notebook with code and analysis \n\n`Applied-Statistics-Project.pdf`: Final report\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fifigeneiatsiflidou%2Fapplied-statistics-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fifigeneiatsiflidou%2Fapplied-statistics-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fifigeneiatsiflidou%2Fapplied-statistics-project/lists"}