March 26, 2017
This repo contains my submission for the Azavea Junior Data Analyst code test.
My part-1 answers are contained in a markdown file, ConceptualQuestionsAnswers.md. My part-2 answers are contained in a Jupyter Notebook, Data-analyst-test_Part-2.ipynb. This file contains all of my code and analyses for part 2.
Additionally, this directory contains 4 subdirectories:
- RF
- LR
- DT
- GB
These four folders contain the results from four models: Random Forest, Logistic Regression, Decision Trees, and Gradient Boosting. Each folder contains three CSV's:
- features.csv
- models.csv
- splits.csv
features.csv contains the features importances for each model, models.csv contains performance metrics of each model averaged over three test/train splits, and splits.csv contains the performance for each individual split for each model. More information can be found inside of Data-analyst-test_Part-2.
The original data and instructions are contained in Cars_mileage.csv and orig_README.md, respectively. The original repo can be found here.
- Python 3.X
- Pandas
- Numpy
- sci-kit learn
- matplotlib
- seaborn
- Jupyter Notebook
The full dependency list is stored in environment.yml.
To install, first download Anaconda or Miniconda.
Then download the environment.yml file. You can install the environment with the following command:
conda env create -f environment.yml
After you have installed Anaconda, you can run the jupyter notebook with:
jupyter notebook
More documentation here.