Python-moviecorrelation

Movie Industry Correlation Analysis Overview This project analyzes a dataset of ~5,000 films to uncover relationships between key financial and performance metrics — including budget, gross revenue, audience score, and vote count. The goal was to identify which factors are most strongly correlated with a movie's commercial success. Tools & Technologies Python, Pandas, NumPy, Matplotlib, Seaborn Process

Data Cleaning: Handled missing values, corrected data types (converting float columns to int64/nullable Int64), removed duplicate entries, and standardized inconsistent formatting across columns Feature Engineering: Extracted release year from raw date strings using regex, and cleaned categorical fields (director, genre, country) Exploratory Analysis: Computed descriptive statistics and identified data quality issues before modeling Correlation Analysis: Applied Pearson correlation to quantify relationships between numeric variables (budget, gross, score, votes, runtime) Visualization: Built a correlation heatmap to visually communicate relationships between variables