bvnarayanan Goto Github PK

applestore-dataanalytics_sql

Comprehensive SQL project identifying stake holders and analyzing App Stores to find best recommendations.

bci_music-retention

Using the Neurosky software/hardware and Open Vibe, we tested how music affects participant's retention rates by looking at their "meditation score". We used matplotlib to visualize our results and did a t-test to see if our results were statistically significant.

bnarayanan_receiptml

customer-churn-analysis_powerbi-python

I conducted a customer churn analysis on a telecom dataset from Kaggle, creating a Power BI report. Before building the report, I performed Exploratory Data Analysis to identify significant attributes.

music-genre-classification_ml_report

We built a music genre classification model using Random Forest and SVM. Accuracy: Random Forest ~54%, SVM ~56%.

music-genre-classifier_code-dataset

This repo contains the dataset we used from Kaggle to create our classier, and the coding behind our Random Forest and SVM classifiers.

reddit-weep_code-dataset

This file encompasses the code and datasets (.csv files) for the Reddit & Weep project. It includes the implementation code as well as the necessary datasets used in the project. Additionally, the code for accessing the Reddit API is included.

reddit-weep_sentiment-analysis_report

We analyzed sentiment towards US private and public universities using VADER Sentiment Analysis and Reddit data. Explored common words used by students to understand topics discussed. Compared sentiment changes pre-pandemic and during the pandemic, examining differences between public and private universities.