This repository contains every project developed in the Data Science and AI Bootcamp by U-Camp. In parentheses, for every project, there will be a description in terms of the business department that could use the data insights from the data set.
- Description: In this project, we analyzed two small mobile selling datasets to answer basic queries and then created visualizations to better explain them.
- Tools: Pandas, NumPy, Seaborn for data analytics, and Matplotlib for visualizations.
Project 1: Mobile dataset study (Marketing/Customer service)
- Description: In this project, we worked on a loan dataset, first performing an exploratory analysis of the data, dealing with missing values and outliers, transforming categorical values, scaling, and finally reducing dimensions for modeling.
- Tools: Sklearn Imputer, Labeling, Scaler, and PCA functions for data preprocessing.
Project 2: Loan dataset study (Project management/Risk analysis)
- Description: In this project, we worked on a Human Resources dataset to first predict the salary of an employee based on education, years of experience, work position, and number of children as a regression model. Second, we predicted whether or not an applicant would stay in a position based on the rest of the variables.
- Tools: Sklearn preprocessing, Linear and Non-linear regression models, and Classification models application. Metrics and Score usage for modeling analysis.
Project 3: Human Resources dataset study (Talent management)
- Description: In this project, we worked on a customer dataset that offered information such as salary, credit card limit, and type of credit possession to classify them into categories. These categories can be used to target them better when offering services and products.
- Tools: Sklearn preprocessing, clustering models and clustering tools.
Project 4: Type of clients classification (Marketing/Customer service)