Lohit Adari

Recent graduate with excellent research, technical and problem-solving skills. Detail-oriented and able to learn new concepts quickly. Highly-motivated employee with desire to take on new challenges. Strong worth ethic, adaptability and exceptional interpersonal skills. Proficient in SQL, R programming and Power Bi and willing to learn and adept to working effectively.

Proven expertise in data manipulation, visualization, and predictive modeling, demonstrated through practical projects. Adept at leveraging R's diverse packages to derive meaningful insights from complex datasets. Successful application of skills in real-world projects, emphasizing data cleaning, exploratory analysis, and implementation of advanced statistical techniques. Possesses excellent problem-solving and communication skills, translating data-driven insights for diverse stakeholders.

• Testing and Confidence Interval Estimation R programming – Coursera
• Visualization and Communication with Tableau – Coursera
• Machine Learning for All- Coursera
• Python 101 for Data Science - IBM
• Big data 101 - IBM
• HackerRank - SQL
• HackerRank - Python

• Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
• IDS calls for the need of integration of Deep Neural Networks (DNNs) In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS) A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-'99' dataset has been used for training and benchmarking the network.
• We will be using DNN and compare with other algorithms like Decision Tree, K-nearest neighbor, Linear regression and find which algorithm performed better in accuracy, precision and recall.
• Churn prediction — R programming It is highly required to identify and maintain customers who stay and maintain service and who may change their service provider.
• A predictive model based on the regression Model was used to highlight the influence of numerous variables and their importance in forecasting the outcome of the target variable. A logistic regression model is the best option. When predicting a binomial attribute.
• Stock Market Volatility Predictions - R programming Statistical models such as HMM and GARCH can be used to forecast volatility in the stock exchange, with effectiveness evaluated through performance metrics like MAE and RMSE.
• To find the volatility of European stocks over time and identify the RMSE values. To estimate how close the actual and predicted plots look using statistical tools.
• Packages Used: RHmm, ggplot2, reshape2, Rugarch.
• Programming Skills: Data structure, Transforming Data, For-loops.
• Portage APL Dashboard – Power Bi A non-profit animal organization is to create a comprehensive, data-driven platform that connects animal welfare organizations, veterinary clinics, and pet owners to create a more efficient and effective animal welfare system.
• The Portage APL maintains all animal data, like vaccinations, diseases, and tags and it helps to keep a record of animal welfare and the responsibility of pet ownership.
• Tools: Data Analysis,Power query, SQL (Primary key, foreign key),SQL server,Power map, Azure SQL database for to get analytics and reports that provide real-time insights