Project Objective's
Data Cleaning and Preparation: Clean the dataset by replacing missing or NaN values in both numerical and categorical columns, ensuring data integrity for further analysis.
Standardization and Normalization: Apply data standardization and normalization techniques to rescale features, reduce data redundancy, and improve the performance of machine learning models.
Data Visualization: Use various visualization techniques such as histograms, scatter plots, and box plots to explore data distribution, detect outliers, and understand relationships between variables.
Statistical Analysis and Hypothesis Testing: Perform hypothesis testing and correlation analysis to validate assumptions about the dataset and investigate the strength of relationships between key app features and user ratings.