May 15, 2021

Spotify Stream Analysis

Summary: In Progress Resources Python Version: 3.7 Packages: pandas, json, requests Visualizations: matplotlib, seaborn, plotly express and graph objects Article: https://aditya-gaikwad3.medium.com/how-much-i-listen-to-music-is-my-music-diverse-analyzing-my-spotify-streaming-history-804afffc18b5 Full Code can be accessed at the Github repository

May 2, 2021

Customer Analytics

Summary: Data fragmented into 4 Segments using a combination of Principal Component Analysis (4 Components) and K-Means Clustering. Career-focused - Young group of high-income and well-qualified people. Well-off - Mid-Age wealthy group in a big city. Standard - Average Population. (Middle Class) Few opportunities - Customers from small cities with mediocre income and not highly qualified. A Descriptive Analysis of the Number of purchases, Brand choice, and Revenue generated per segment.

April 15, 2021

Toronto House Prices: A Hedonic Geospatial Analysis

Summary: Created a tool that estimates house prices (RMSLE ~ 0.1325) to help house buyers, sellers, or agent negotiate their house prices and external factors such as neighborhood income, distance to the nearest hospital, transit stop, park, and crimes nearby. Gathered data from multiple data sources, primary Zoocasa.ca, and external factors Toronto Open Data Portal, Airbnb.ca, Toronto Police Service Data Portal. Engineered 15 features from 8 different datasets using Python.

June 24, 2020

Help Dognition Improve Test Completion of the Users.

Summary: Used Tableau to Analyse the Data and recommended strategies to be implemented to increase the test completion rate by 15% over 3 months. Generated Insights based on Top 10 countries completing tests, narrowed it down to provinces, and recommended the location to advertise locally (Area Customed Advertising). Suggested company to send timely reminders based on analysis of data on a monthly, weekly, and daily basis. Most of the users are activer between 6-8 pm, set reminders accordingly.