Vishal Bansal

1. Built a Naïve Bayes classifier from labeled data to classify tweets into positive and negative class for two candidates; Obama and Romney.
2. Used Rapid miner to train an LSTM model with the idea to explore long-term dependencies.

1. Utilized the monthly information of airline on-time flight performance published by "The Bureau of Transportation Statistics (BTS)" for the time period of Sep 2016 - Aug 2017.

2. Analyzed and formulated visualizations of the on-time flight performance for numerous Airlines; South-West, Delta, American Airlines, Express Jet etc.

3. Developed Phase maps, Node XL graphs, and various interactive visualizations and dashboards to effectively present a large amount of information… Show more

1. Utilized the monthly information of airline on-time flight performance published by "The Bureau of Transportation Statistics (BTS)" for the time period of Sep 2016 - Aug 2017.

2. Analyzed and formulated visualizations of the on-time flight performance for numerous Airlines; South-West, Delta, American Airlines, Express Jet etc.

3. Developed Phase maps, Node XL graphs, and various interactive visualizations and dashboards to effectively present a large amount of information for Planning & Strategy team. Show less

Project included-
1. Developing a revenue prediction model to be used for the 100,000 regional locations using the provided data.
2. Assessing and choosing most suited learner fit for given data.
3. Training the learner using Random Forest method.

1. Applied MS Apriori Algorithm (a significant improvement over traditional Apriori Algorithm ) to find frequent patterns with multiple minimum supports.

2. Included "Cannot-be-together" and "Must-have" item constrains in MS Apriori Algorithm to refine the pattern discovery.

1. Applied Decision Trees and Support Vector Machines algorithms to accurately and economically classify the news stories based on their headline subject for easier content discovery and categorization.

2. Developed a model to selectively filter the news feed by performing sentiment and opinion analysis using Python library NLTK.

1. Performed data cleaning, data analysis, and data visualization of Chicago crime datasets using Python and H2O with the inspiration to forecast where or when a crime will be committed and find out how the crime has changed over the years.
2. Compared the performance of the SVM, Neural Nets, Deep Learning using TensorFlow etc. Also, examined the improvement in accuracy by implementing Random Forest boosting and other Ensemble techniques.

Project included-
1. Assessing the heating and cooling load requirements of buildings as a function of building parameters.
2. Building a predictive linear regression model using Python.
3. Alternatively treating it as a classification problem for increasing the business value of data set.

My role as an assigned engineer included-
1. Study of the technical bid from the client.
2. Identify and plan critical design activities.
3. Engineering Management.
4. Vendor Management.
5. Client Coordination.
6. Analysis of high-temp and high-pressure steam pipelines in compliance with ASME B31.3.
7. Analysis of highly critical rotating equipment like Turbines, Compressors and connected pipelines in compliance with NEMA-SM-23.

My role as an assigned engineer included-
1. Study of the technical bid from the client (Takreer, ADNOC).
2. Identify and plan critical design activities.
3. Onsite Coordination and Audit at Ruwais Refinery, Al Ruwais, UAE.
4. Vendor Management.
5. Client Coordination.
6. Analysis of high-temp and high-pressure steam pipelines in compliance with ASME B31.3.
7. Analysis of highly critical rotating equipment like Turbines, Compressors and connected pipelines in compliance… Show more

My role as an assigned engineer included-
1. Study of the technical bid from the client (Takreer, ADNOC).
2. Identify and plan critical design activities.
3. Onsite Coordination and Audit at Ruwais Refinery, Al Ruwais, UAE.
4. Vendor Management.
5. Client Coordination.
6. Analysis of high-temp and high-pressure steam pipelines in compliance with ASME B31.3.
7. Analysis of highly critical rotating equipment like Turbines, Compressors and connected pipelines in compliance with NEMA-SM-23. Show less