The certification I recently earned required the successful completion of two timed exams and a practical component. For the practical exam, I was given a hypothetical scenario in which my manager (Head of Data Science) gave me an assignment that was requested by another business leader (Product Manager). I was provided with instructions and a dataset to analyze, and had to provide a written report including code and output. Additionally, I recorded a presentation to give a high-level overview of my analysis and findings.
This repo contains my written report (.ipynb) and the corresponding dataset (.csv) and instructions (.pdf) from the practice practical exam. I am sharing this as a portfolio project for the purpose of demonstrating my Python and communication skills.
- Editor: JupyterLab 4.0.11
- Python Version: 3.12.3
- Data Manipulation:
pandas2.2.2numpy1.26.4scipy1.13.0
- Data Visualization:
matplotlib3.8.4seaborn0.13.2
- Machine Learning and Statistics:
scikit-learn1.4.2statsmodels0.14.2
- instructions.pdf: instructions provided by the certification organization
- toyota.csv: dataset provided by the certification organization
- kucharczyk_predicting_car_prices.ipynb: my written report including code and output
I performed exploratory data analysis, data preparation, and regression using Python to predict used car prices. K-fold cross-validation and grid search were used for model selection and hyperparameter tuning. The random forest model achieved a root-mean-squared error of 1210.38 GBP, R2 value of 0.96, and predicted 78% of prices within 10% of their actual values. The written report (.ipynb) provides all details relating to data exploration, preparation, analysis, and results.
The certification organization that provided the dataset and instructions can be found in the instructions (.pdf).