Marketing Campaign
Netflix x Vice Campaign
Diwali Campaign
Act Now | A mental health initiative
Gate campaign
TribeVibe x Red Chief
Performed data-scraping and data-cleaning to create a dataset of Consumer Price Index for Delhi in various category of goods and Services, which is to be utilized in machine learning model to analyse and predict next 19 months.
Decomposed the time series data using Additive Seasonal Decomposition into the following components: Trend, Seasonality & Residue. Conducted Augmented-Dickey Fuller
test to check for stationarity. Used first-order differencing to convert non-stationary time series into stationary.
Built a SARIMAX model using seasonal_order = (2, 0, 1, 12) to predict the consimer price index for food and beverages goods in Delhi for 2023. Forecast revealed CPI crossing the 400.00 mark which directly disrupts the F&B companies. Data analysis for predicted values suggest that there has been an astonishing 85% increase in growth rate for CPI of F&B goods in last 10 years with an annual growth rate of 6%.
Manipulated data from the given database of Walmart sales (2011 - 2014). Eliminated NaN values, substituted with mean/average of the respective fields using SQL queries on popSQL.
Utilised Microsoft Power BI visualisation tool to translate raw data information into visual context like tables, cards, tree-map, etc. creating a data-dashboard to pull valuable insights from. Discovered which is the most profitable state(Arizona) or category of goods(Office supplies), in what year or occassion whihch inturn is helpful in developing a cunning marketing strategy evolved around a certain state/goods/occassion.
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