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Introduction\n2. Data Wrangling\n3. Exploratory Data Analysis\n4. Conclusions\n\n## Introduction\n\n### Dataset Description\nThis dataset contains data on several thousand movies, including plot, cast, crew, budget, and revenues. It aims to predict the success of a movie before its release. The dataset consists of two related tables: `tmdb_5000_movies` and `tmdb_5000_credits`.\n\n### Questions for Analysis\n1. Which factors contribute to higher ratings and revenues?\n2. Which release months get the highest ratings and revenues?\n\n## Data Wrangling\n- Loaded and merged datasets.\n- Handled missing data and optimized data types.\n\n## Exploratory Data Analysis\n- Investigated relationships between budget, revenue, ratings, and vote counts.\n- Analyzed the impact of release month on ratings and revenues.\n\n## Conclusions\n1. Most movie ratings lie between 5 and 7.\n2. Weak positive relation between vote count and average rating.\n3. Strong relation between budget and revenue.\n4. Movies released in May, June, and November generate the highest revenue.\n\n### Limitations\n- Missing data in several columns.\n- Correlation does not imply causation.\n\n### Additional Research\n- Analyze the impact of different genres on revenue and ratings.\n- Investigate how movie popularity affects ratings.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famid68%2Finvestigate_a_dataset","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famid68%2Finvestigate_a_dataset","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famid68%2Finvestigate_a_dataset/lists"}