📊 Correlation Analysis with Python This project demonstrates how to perform correlation analysis on a dataset using Python. It explores relationships between variables and visualizes the results to gain insights.

🧪 Project Overview The analysis focuses on identifying and quantifying relationships between numerical variables in the dataset. Key steps include:

Data Loading: Importing the dataset into a Pandas DataFrame.

Data Cleaning: Handling missing values and ensuring data consistency.

Correlation Calculation: Computing Pearson correlation coefficients between variables.

Visualization: Creating heatmaps and scatter plots to visualize correlations.

🔧 Tools & Libraries Python 3.x

Pandas: Data manipulation and analysis.

NumPy: Numerical computations.

Matplotlib: Data visualization.

Seaborn: Statistical data visualization.📊 Correlation Analysis with Python This project demonstrates how to perform correlation analysis on a dataset using Python. It explores relationships between variables and visualizes the results to gain insights.

🧪 Project Overview The analysis focuses on identifying and quantifying relationships between numerical variables in the dataset. Key steps include:

Data Loading: Importing the dataset into a Pandas DataFrame.

Data Cleaning: Handling missing values and ensuring data consistency.

Correlation Calculation: Computing Pearson correlation coefficients between variables.

Visualization: Creating heatmaps and scatter plots to visualize correlations.

🔧 Tools & Libraries Python 3.x

Pandas: Data manipulation and analysis.

NumPy: Numerical computations.

Matplotlib: Data visualization.

Seaborn: Statistical data visualization.

📈 Key Findings

🎯 Votes and budgets show the strongest positive correlation with gross earnings, indicating that higher budgets and more votes are typically associated with higher box office performance.

🏢 The production company has a low correlation with gross earnings, suggesting that the company name alone is not a strong predictor of financial success.