Vedant Parikh

Task: To Develope and Deploy an Unsupervised Learning Algorithm for Test Failure Prediction. Learning: K-means algorithm, Autoencoders, SQL, Linux, Classification, Data Mining, Data Cleanning, EDA, Data Analysis, Unsupervised Learning. Language & Tools: Python, SQL, Linux, AWS (S3, Sagemaker, EC2). Libraries & APIs: Tensorflow, Numpy, Oracle_cx, Pyspark, Pandas, Numpy, Scikit-Learn, Matplotlib, Altair, Seaborn.

Topic: Writing my Thesis on "Vehicle Velocity Forecasting using Historical Data and Machine Learning" Contribution: Developing a state of the art algorithm to efficiently predict velocities of the Vehicle Learning: RNN, CNN, GAN, Data Visualising, Time Series Analysis & Forecasting, Exploratory Data Analysis, Pyspark Language & Tools: Python, Jupyter, Colab Libraries & APIs: Tensorflow, Keras, Numpy, Scikit-Learn, Pandas, Matplotlib, Altair, folium, Pyspark

Contribution: Employed in evaluating different Machine Learning models i.e. HMM, GMM; Markov Chains for forecasting Learning: Bayesian Rule, HMM, GMM, Linear Regression, Statical Analysis Languages & Tools: Python, Jupyter, Spyder Libraries: Numpy, Scikit-Learn, Pandas, Matplotlib