This repository is a compilation of some of the python projects I have undertaken to build my skills as a data professional. This ReadMe file is a rough breakdown of each project in this repository along with the tools used. Each project showcases different aspects of data science, ranging from classification and regression models to exploratory data analysis and statistical analysis.
- Objective: Predict the participation of certain bank customers in an upcoming marketing campaign.
- Techniques Used: Classification models, consumer behavior analysis.
- Tools: Python, scikit-learn, pandas, matplotlib.
- Objective: Explore and analyze trends in Netflix movies and TV series.
- Techniques Used: Exploratory data analysis, visual analytics.
- Tools: Python, pandas, seaborn, matplotlib.
- Objective: Analyze and visualize data from the New York Stock Exchange for four different stocks.
- Techniques Used: Exploratory data analysis, statistical analysis.
- Tools: Python, pandas, numpy, matplotlib.
- Objective: Predict fraud in bank credit transactions using a classification model.
- Techniques Used: Classification models, random forest, fraud detection.
- Tools: Python, scikit-learn, pandas.
- Objective: Predict house prices using a regression model.
- Techniques Used: Regression analysis, linear regression.
- Tools: Python, scikit-learn, pandas, matplotlib.
- Objective: Conduct statistical analysis of financial data based on a WBL course taught at Zurich University of Applied Sciences by Dr. Marcel Dettling.
- Techniques Used: Statistical analysis, financial data analysis.
- Tools: Python, pandas, numpy, scipy.
If you have any questions or suggestions, please don't hesitate to reach out.
~Srijan