ML2020-Uber-Data-Analysis

ML2020-Uber-Data-Analysis

Requirements

1 - Anaconda
2 - Python 3.7+
3 - Jupyter notebook

Setting up the environment

1 - Run the command conda env create -f environment.yml to setup your conda environment.
2 - If everything is successful run activate conda ML_UBER_ENV
3 - If you encounter any error and wish to rerun the setup execute conda env update --file environment.yml after fixing the issue. (optional)
4 - Execute conda install -c anaconda ipykernel
5 - Run *python -m ipykernel install --user --name=ML_UBER_ENV
6 - Install the Geopands library as per the instructions below.

Installing Geopandas

If you encounter some issue while installing Geopandas follow this guide.

Navigate to the lib folder and install the following.

Python 3.7 (Recommended if you installed our env)

1 - pip install Fiona-1.8.18-cp37-cp37m-win_amd64.whl
2 - pip install GDAL-3.2.1-cp37-cp37m-win_amd64.whl
3 - pip install geopandas-0.6.2-py2.py3-none-any.whl

Python 3.9

1 - pip install Fiona-1.8.18-cp39-cp39-win_amd64.whl
2 - pip install GDAL-3.2.1-cp39-cp39-win_amd64.whl
3 - pip install geopandas-0.6.2-py2.py3-none-any.whl

Python 3.8

1 - pip install Fiona-1.8.18-cp38-cp38-win_amd64.whl
2 - pip install GDAL-3.1.4-cp38-cp38-win_amd64.whl
3 - pip install geopandas-0.6.2-py2.py3-none-any.whl

NOTE: Do not forget to select the appropriate kernel in your jupyter notebook

Tested on Python 3.7

The WHL files are obtained from https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal

rtree is required to support the Spatial join in Geopandas

pip install Rtree and restart your jupyter kernel or Python interpreter.

Data Analysis ipy notebook

Google drive link: https://drive.google.com/file/d/1cyPE5LuHWhujok1q2nEl6QvsJ0lSlkoE/view?usp=sharing Due to the size of the notebook it cannot be uploaded on the Git repository.

NOTE: If you run into any issues, feel free to request us for assistance either by creating issues or contacting us directly.