Motivation : Data Scientists spends most of their time performing Data Wrangling and Analysis, having an interest in finance I opted for this task during my internship at The Sparks Foundation.
Libraries Used: Pandas, NumPy, Seaborn, Matplotlib
Language Used: Python
Explanation : In this project i performed Exploratory Data Analysis over the provided Sample Super Store Dataset.
Being a Financial data the key motive of my analysis was to analyse the ways to improve Profit so After loading the data in a dataframe,i checked for inconsitencies and plotted the profit and loss values to get an idea of variance of the data.
I then one by one analyse all the features of the dataset and plotted it against profit.
Conclusion:
1.In doing all that i figured out that people mostly use Standard Ship Mode for delivery and Technology Category provides the maximum returns.
2.I also figured out that Bookcases and Tables in the Furniture category are the ones that causes massive loses.
3.Texas's SuperStore has the worst performance among all the Stores.
