Python script for analyzing and predicting NBA team and player valuations based on various factors such as attendance, endorsements, salaries, and performance metrics and incoorporates R as well for advanced Visualizations.

• plot_team_cluster_R.R: R script for clustering NBA teams based on various performance metrics.
• Rstudio.R: General R scripts for data manipulation and preliminary data analysis.
• basketball_ref.ipynb: Jupyter notebook analyzing data scraped from basketball-reference.com.
• players_evaluation_nba.ipynb: Notebook for detailed player performance and metric analysis.
• team_evaluation_nba.ipynb: Notebook for assessing team performance across different seasons.

The script uses the following Python libraries:

• pandas: for data manipulation and analysis
• statsmodels: for statistical modeling
• matplotlib: for data visualization
• seaborn: for data visualization
• stathelper: for statistical Calculations

Packeges for R:

• install.packages("tidyverse")
• install.packages("dplyr")
• install.packages("ggplot2")
• install.packages("cluster")

• Player Evaluation: Analyze individual player performance using both basic and advanced metrics to assess contributions and efficiency such as USG_PCT,TS_PCT,OFF_RATING, W_PCT etc.
• Team Performance Assessment: Evaluate team success and strategic effectiveness using statistical performance comparisons.

• Team Clustering: K-means and hierarchical clustering to categorize NBA teams based on Elbow Method and various plots.
• Player Clustering: Meaningful groups based on performance metrics.

• Diverse Visualization Tools: Leverage histograms, PCA scatter plots, and dendrograms to provide clear and insightful visual representations of complex datasets.
• Interactive Visual Analysis: Employed interactive plots(ggplot,px) to dynamically explore correlations and distributions within the data.

• Dimensionality Reduction: Applied PCA to distill important information from numerous statistical metrics.
• Clustering Algorithms: Implement K-means clustering to uncover groupings in the data that reveal hidden patterns and validates the PCA results.

• Python and R Integration: Combine the statistical and graphical power of R with Python's robust data manipulation and machine learning capabilities to enhance analytical robustness.

To run the script, make sure you have the required Python and R libraries installed and the data files in the data/ directory. Then, simply run the script, and it will execute the analysis and prediction steps.

• Create a Script to keep the data scrapping from same sources anytime.
• Deploy all the Visualizations in Cloud.