Movies Cleaning Dataset

Comprehensive analysis of a large dataset containing over 525,000 movies from various countries and time periods (1970-2025), using Python (Pandas, NumPy, Matplotlib/Seaborn).

Work steps:

- Assessed data quality and identified missing values in the genre, country, director, budget, box office, and rating columns.

- Cleaned and standardized country names (merged duplicate variants such as U.S.A and United States into a single consistent value).

- Handled missing values and outliers in the budget and box office figures.

- Performed exploratory data analysis (EDA) to uncover insights into the relationship between budget, box office revenue, and ratings, as well as genre performance over time.

Key findings:

- A strong correlation (0.71) between a movie's budget and its box office revenue.

- Almost no correlation between budget size and IMDb rating, indicating that higher spending doesn't guarantee better audience reception.

- Animation and Comedy achieved the highest average return on investment (ROI) among all genres.

Deliverables included: fully documented analysis code, illustrative charts, and a findings-and-recommendations report.