Nba Analysis Project

This project harnesses the nba-api to fetch NBA Stats.
Documentation can be found here: https://pypi.org/project/nba-api/

This project is broken into four parts:
    1. All Time Stats
    2. Player Comparison
    3. Team Anaylsis
    4. Point Prediction (Machine Learning)

Frameworks Used: Pandas, NumPy, Sklearn, Matplotlib, Plotly

All Time Stats

Used Jupyter Widgets and Plotly to create interative lists as well as charts to depict all time NBA stats

Features:
    1. Interactive List of All Time Stats


    2. Interactive Chart of All Time Stats

Player Comparison

Used Jupyter Widgets and Plotly to create interative lists as well as charts to depict player stats and comparisons

Key Features:
    1. Query for Player Career Stats and View Interactive Chart


    2. Interactive Chart to Compare Players


    3. Other Features
        1. All Time Career Stats Data with Averages
        2. All Time Career Stats Data Comparison between Players

Team Anaylsis

Used Jupyter Widgets and Plotly to create interative lists as well as charts to depict team stats and comparisons

Key Features:
    1. View All Time Stats Per Team (Query For Team)


    2. Interactive Chart to view Win/Loss (Query for Regular,Pre and Post Seasons as well as Season Year and Team Data)

Point Prediction

Created a Linear Regression Model to predict the amount of points a player would score using 2018-2019 Regular Season NBA Data
Correlation Matrix:



Model Scores:
Mean Absolute Error: 67.57841065663386
Mean Squared Error: 8226.306123212107
Root Mean Squared Error: 90.6989863405987
Model Accuracy: 0.9683556506013027

Cloning the Repo

Required Packages:
    1. Pandas
    2. NumPy
    3. Jupyter Notebook
    4. Jupyter Widgets
    5. Matplotlib
    6. Plotly
    7.Sklearn
    8.nba-api

After Installing the required packages simply use the terminal to query into the folder you cloned the repo in and run the command jupyter notebook