The InternSavy_DataAnalysis project is a comprehensive exploration into the world of data science through three distinct Jupyter Notebooks. Each notebook is dedicated to a unique aspect of data analysis, providing insights and predictive models that can be leveraged for academic and commercial purposes.
Utilizes the Admission_Predict_Ver1.1.csv dataset to predict university admission probabilities based on student performance metrics. The analysis includes data preprocessing, exploratory data analysis, and the application of regression models to forecast outcomes.
Analyzes the Mall_Customers.csv dataset to segment customers based on their shopping behavior and demographics. This subproject employs clustering techniques to identify distinct customer groups, aiding in targeted marketing efforts.
Focuses on extracting actionable insights from sales data to drive business decisions. The notebook presents a thorough analysis of sales trends, patterns, and performance metrics.
- Python for programming.
- Jupyter Notebook for interactive development and sharing of live code.
- Pandas for data manipulation and analysis.
- NumPy for numerical operations.
- Matplotlib/Seaborn for data visualization.
- Scikit-learn for implementing machine learning algorithms.