Group 298 Analysis Using Exploratory Data Analysis Techniques, we select the most relevant features of the dataset and use it as a basis to train our predictive model.

Inspiration We're inspired to combine different predictive models such as clustering and regression in an attempt to better forecast the sales of various companies.

What it does Our model can take in various parameters and use it to predict global and domestic sales of a company.

How we built it We clean the data using R Programming and Excel, and we develop the predictive model using pytorch.

Challenges we ran into Choosing the right model was difficult since we had limited understanding of how each model work. In the end, we end up combining the strengths of each model to make our own model

What we learned We applied the data analysis techniques of cleaning, EDA, formulating hypothesis, and using the data to predict the model to an actual dataset