NANDAN LAHURIKAR

Sterling Heights, MI 248-***-**** ac43mz@r.postjobfree.com

SUMMURY:

Self-Driven, energetic Data Scientist with experience in developing machine learning algorithms with python and R. Proficient at multitasking, possess good teamwork skills, work in a fast-paced startup environment, seeking new opportunities.

EDUCATION

M.S. Electrical Engineering, Cleveland State University Jan 2015 - Dec 2016 B.E. Electronics and Telecommunication, Pune University Aug 2007 - May 2013 Self-Driving Car Nanodegree, Udacity Jan 2017 - Sep 2017 TECHNICAL SKILLS

• Programming Languages: Python, R, C++, SQL.

• Machine Learning: Classification, Regression, Time Series Analysis, Statistics, Computer Vision, Naïve Bayes, Markov Chain, regex.

• Framework and Libraries: TensorFlow, Keras, OpenCV, Pandas, NumPy, Scikit-learn, Stats-model, NLTK, LightGMB, JSON, AREMA.

• Data Visualization: Plotly, Seaborn, Matplotlib, Tableau, Shiny.

• Deep Learning: CNN, RNN, FCN, VGG, LSTM.

PROFESSIONAL EXPERIENCE

Data Scientist - Soothsayer Analytics, Livonia MI Sep 2017 – Present

• Performed data cleaning, descriptive statistics, and data visualization on the scores of 27K students.

• Applied feature extraction and built regression models, predicted 75% improvement in students’ progress.

• Advised client to improve the program by fostering reading in the specific community within the state.

• Created statistical models and natural language processing methods to identify consumer shopping habits.

• Used latent Dirichlet allocation technique to extract sentiments and people’s emotion in test.

• Developed data visualization models and Tableau dashboards to present the client with business insights.

• Implemented and practiced Machine learning techniques on structured and unstructured data.

• Built analytical solutions and models by manipulating large datasets. PROJECTS

Vehicle Detection and Tracking (OpenCV, SVM, Classification)

• Created a vehicle detection and tracking pipeline with OpenCV, HOG, and support vector machines (SVM).

• Optimized and evaluated the model on identifying vehicles in a video from a front-facing camera on a car. Traffic Sign Classification (CNN, Feature Extraction)

• Built and trained a CNN to classify traffic signs, an optimized network using top 5 predictions.

• Successfully tested images around 93% validation accuracy by overcoming data and over fitting challenges. Self-driving car localization with particle filter (C++, Markov Assumption, Vehicle Dynamics)

• Predicted new position of the car using motion model.

• Successfully tested 2 D particle filter capable of localizing a vehicle with single-digit-level accuracy using LiDAR and maps.

Analysis and Prediction of stock data (Prediction)

• Researched behavior apple stock market data to predict future stock values using python.

• Used logistic regression algorithm, statistics analysis and successfully predicted next week’s stock value.

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