Objective of this Project: Utilizing Data Science applications to implement data-driven solutions for optimizing products, minimizing operational costs and generating greater returns on investments.

More detailed information regarding the problems addressed are explained in my Medium Article.

Lets look at some of the Machine learning and Deep Learning methods I have used:

1. Exploratory Data Analysis and Recommendation Engine: Want your revenue growth to be on the high? Then start delivering products that meets their requirements and satisfaction levels.

Exploratory Data Analysis is a crucial part of the solution as it allows us to understand the audience on a deeper level and generate major insights that answer some of the most critical questions regarding their preferences.

Recommendation Engine is a great tool that allows us to optimize marketing strategies to promote similar products based on user preferences. My recommendation engine was based on Collaborative Filtering that considers similar products and similar users to recommend products to a random user.

2. TimeSeries Analysis and Forecasting Revenue Growth: Predicting revenue growth is a critical tool in every business Today. It helps us make informed decisions about everything from staffing and inventory to new product lines and potential marketing efforts. If you know how your total sales are going to look like, you can strategize your business plans accordingly either to minimize the losses or understand the factors that are bringing in greater ROIs.

3. Sentiment Analysis: By utilizing NLP techniques we can analyze the hidden sentiments that our audience leaves behind in their reviews and comments. This allows us to build customer loyalty and create efficient customer life cycles.

4. CycleGAN: The objective of this model was to showcase the application of General Adversarial Networks also known as GANs. GAN’s are revolutionizing the animation industry. For eg. GANs can produce 3D models required in video games, animated movies, or cartoons by analyzing 2D photos in a short period of time significantly helping animators save time and utilize their time elsewhere for other important tasks.

Using CycleGAN, I generated new data with my preferences. My preference this time was ‘Monet’s Art Style’ and the new data generated was Monet-esque images.

Please Check out my Github Repository for all the details: Link to Github Repository