✓ IMDb Movie project explores IMDb movie data through detailed exploratory data analysis (EDA). ✓ It involves cleaning, processing, and visualizing key movie attributes such as ratings, genres, and box office performance. ✓ The analysis uncovers trends in movie success, factors influencing audience ratings, and insights into the film industry. ✓ Using Python (Pandas, Matplotlib, and Seaborn), the project provides data-driven conclusions about the dynamics of popular movies.

✓ This credit approval analysis project employs Exploratory Data Analysis (EDA) to identify key factors influencing loan defaults for a consumer finance company. ✓ Through comprehensive data cleaning, outlier detection, and feature engineering, I uncovered critical risk indicators including income-credit mismatches, employment status anomalies, and external credit score thresholds. ✓ My analysis revealed that applicants with credit amounts exceeding 4x their annual income, unemployed individuals, and those with low external credit scores presented the highest default risks. ✓ The project delivers actionable insights that enable the company to optimize their loan approval process, minimize financial losses, and maintain a healthy portfolio while approving creditworthy applicants.

✓ The Titanic Dataset Analysis explores key factors influencing passenger survival using Exploratory Data Analysis (EDA) and machine learning preparation. ✓ Through data cleaning, feature engineering, and statistical tests, the project identifies Sex, Pclass, and Embarked as the most significant predictors of survival. ✓ Key insights reveal that females, first-class passengers, and those from Cherbourg had higher survival rates, while males in third class faced the highest fatalities. ✓ The dataset was successfully processed for predictive modeling, making it a solid foundation for classification tasks.

✓ This project explores global population trends using the World Bank's dataset covering 266 countries/regions from 1960 to 2023. ✓ The analysis identifies key growth patterns, demographic shifts, and regional variations while assessing the impact of historical events like pandemics and conflicts. ✓ Key insights include India surpassing China as the most populated country, Africa's rapid growth, and Europe's population decline due to aging demographics. ✓ Urbanization trends, density variations, and migration effects are also analyzed. ✓ The dataset was cleaned, processed, and scaled for better readability, making it a valuable resource for population forecasting and policy planning.