Satish Goud Musirigari

Voice based food ordering system

The goal of this hackathon was to create and deploy a food ordering application, using speech recognition AI techniques. It was exciting to train a model using features extracted from voice samples and to integrate a machine learning classifier that could identify the correct food items and servings. It provided an excellent hands-on experience in coding an end-to-end solution, starting from pre-processing the data, to training and testing the model and finally deploying the model. The final user interface accurately identified the food item from a voice command and confirmed the order as a response.

Alexa Chatbot

The goal of this hackathon was to build a conversational bot to interact with the user and achieve the desired outcome, such as booking a doctor’s appointment/ providing a movie recommendation/ assisting in the purchase of a suitable mobile phone, based on the user’s requirements. Two approaches were used in achieving the task - (i) Amazon Alexa API based bot and (ii) Python coding based bot. It was very interesting and helped to strengthen my understanding and skills learnt in Natural Language Processing (NLP) and Dialog Management. It provided me with an in-depth experience in utterance usage, intent classification, entity design and conversational session creation.

Anti Face Spoofing

In this Hackathon, we implemented AI based Face and Expression recognition to build an anti-face spoofing application. The goal was to unlock a mobile application based on the accurate identification of a user’s face and expression, as a safety feature. Firstly, a Siamese Network based representation was obtained from a large dataset of face images and used for detecting Face Similarity. Then we built a small dataset of our team members’ face images and expressions and applied a deep learning based CNN model to perform Face and Expression Recognition. It was an amazing experience to unlock the mobile app with our Face and Expression Recognition model, at the end of the project.

Literacy Rate Prediction

The goal of this Mini-Hackathon was to predict whether the literacy rate was high/ medium/ low in the different districts of India. A real world government released dataset consisting of district wise demographics, enrollments, and teacher indicator data was used for this project. Extensive data-preprocessing was required to clean the data, remove missing data and values, transform the data by normalization, select uncorrelated features and integrate the data into a single dataframe. After the data was pre-processed, a machine learning classifier was trained to obtain a model that could accurately predict the literacy rate (high/ medium/ low). Through this work, we were able to gain excellent experience in all aspects of data pre-processing, model building and prediction.

Author Identification

The goal of this Mini-Hackathon was to identify the unique writing styles of different authors using a machine learning based approach. A manual, stylometric analysis using hand-crafted features was also used for comparison. This project helped me to understand the usefulness of machine learning based text classification for automated author prediction tasks. A machine learning model was trained on textual data with corresponding author labels, from the Gutenburg database. Bag of Words and Word2Vec based approaches were used for feature representation and analysis. Text classification and author prediction were achieved with >90% accuracy.

Cost-effective text mining

The goal of this Mini-Hackathon was to evaluate and derive computationally efficient text mining algorithms compared to deep learning methods. A clustering based approach was evaluated and the learning algorithms were tuned within each cluster to achieve the text mining results in a reduced time frame.

The dataset consisted of posts from Stack Overflow (largest online community for programmers to learn, share their knowledge, and advance their careers). Firstly, semantically rich representations of the words in the text were obtained. The data was then modelled using clustering methods and the learners within each cluster were separately trained. Finally the models were evaluated in terms of their training time and performance. This project showed the importance of exploring faster methods for text mining from Stack Overflow posts.

Retail sales forecasting

The goal of this Mini-Hackathon was to obtain the sales forecast for a big retail store. Such a forecasting model would be very useful for the retailer in planning the budgets and investments and minimizing revenue losses. This was presented to us as a Kaggle competition. The data consisted of a real world dataset that included the monthly sales of the store from the past 5 years. We completed the competition successfully by building a time series based machine learning model that accurately forecasted the monthly and product wise sales for the retail store.