> Created & cleaned datasets and made associations among them to analyze the data using Python. Made interactive visualizations and then built dashboards on Power BI as well as Qlik Sense. It provides 2 solutions – [i] for end users (tells them which OTT Platform & its subscription plan would be suitable for them) and, [ii] for businesses (helps a production house to determine which OTT it should release its movie on) – based on the analysis.
> Cleaned and visualized the raw data (51717 unique values) using various Python libraries (like pandas, numpy, seaborn, matplotlib, plotly). Analyzed restaurant types, rates, best location, services, cost, etc. to understand the quality of the restaurant, it’s demand from customers and so on. Performed Regression Analysis for building a model using sklearn.
> Used a pre-trained model, ensemble duonet, to detect the type of emotion displayed by the image selected at random from a dataset consisting of 35,000 photographs. Provided a confusion matrix for error detection showing an accuracy of 74%.
> Designed a circuit using of Arduino board, bread board, jump wires, Temperature sensor, soil moisture sensor, DC motor (Water pump), buzzer, green and red LEDs, LCD screen. Wrote and implemented a simple C code to make the system work in any soil condition after taking input from the farmer.