Our interest in Game Development and Machine learning inspired us to work on Game analytics.

The web application is used to predict how likely a player is willing to spend upon downloading apps.

Python was the primary language used in this project. We used various Python libraries such as Scikit-learn, Pandas, Numpy, Matplotlib, etc.. for data analysis, classification and visualization.

Conda was used to create the virtual environment.

1. understanding the interesting game concepts,
2. finding the relevant features for the classification model
3. resolving the skewness in data

1. Data Visualization and successful implementation of different classification models to distinguish between spending player over non-spending players with an accuracy of 90.6%.
2. Followed PEP8 standard
3. Infrastructure setup

Python, HTML, bootstrap, javascript

STEP 1: Create Conda environment conda env create -f environment.yml

STEP 2: Activate the conda environment source activate mistplay

STEP 3: Get inside the mist_play/data directory and run the data pre-processing module python data_preprocessing.py

STEP 4: Get to the mist_play/model directory and run the machine learning model python model.py