Aspiring Data Scientist

Ashutosh Shinde

*** ***** ******, ****** ****, NJ 07307 551-***-**** ********@*******.***

https://www.linkedin.com/in/ashutosh-shinde-b4498b160/ https://github.com/ashindecodes EDUCATION

Stevens Institute of Technology, New Jersey Aug 2019-May 2021 Master’s in Data Science GPA: 3.802/4

Relevant Coursework: Machine Learning, Statistical Methods, Big Data Technologies, Multivariate Data Analysis, Applied Analytics, Introduction to Financial Applications in Python College of Engineering Pune, Maharashtra, India May 2017 Bachelor of Technology in Computer Science GPA:6.03/10 SKILLS

Programming Languages: Python, SQL, R, C

Machine Learning Packages: NumPy, Matplotlib, Scikit-learn, TensorFlow, Pandas, PySpark Tools: RStudio, Tableau, Excel

Certifications: Extreme Gradient Boosting with XGBoost, Machine Learning with Tree-Based Models in Python, Unsupervised Learning in Python, Supervised Learning using scikit-learn, Natural Language Processing in Python, Machine Learning for Finance in Python, Machine Learning in R, Machine Learning with PySpark PROJECTS

Credit Rating Prediction (Python Random Forest Support Vector Machine (SVM) Neural Network)

Developed efficient machine learning classification models Support Vector Machines, K Nearest Neighbors, Neural Network and Random Forest to predict the corporate credit ratings

Performed 7-fold Cross-Validation to optimize the training of each of the models

Improved the models by performing Dimension Reduction on the 98 features using Principle Component Analysis (PCA), to convert a set of linearly correlated features into uncorrelated features

Performed Grid Search technique to find the best possible parameters for all the models

Obtained prediction accuracy of 92% for K Nearest Neighbors Classifier and 89% for Neural Networks Kaggle: Titanic Survival Challenge (Python Decision Tree Classifier Logistic Regression)

Created classification models to predict the survival of a passenger using the algorithms K Nearest Neighbors, Decision tree, Logistic Regression, Naive Bayes and Linear Support Vector Machine (SVM)

Improved the models by performing Feature Engineering on the categorical variables along with visualizing the dataset using the libraries matplotlib and seaborn

Determined the significant features of the dataset by deploying the feature importance method of RF

Analyzed all the classification algorithms and observed the accuracy for Linear Support Vector Machine

(SVM) to be 80%

Kaggle: ASHRAE – Great Energy Predictor (Python Decision Tree Regressor Data Visualization)

Developed a Linear Regression and Decision Tree machine learning model to predict the energy consumption of the building

Deployed the Linear Interpolation technique to impute the missing data of the temperature variable

Performed Feature Engineering by adding new features using the timestamp feature

Cross Validated the dataset using k fold cross validation with k = 4

Performed Label Encoding to encode the categorical variables into numerical variables EXPERIENCE

Parth Knowledge Network Private Limited Mumbai, India Associate Software Engineer July 2017 - Dec 2018

Developed and automated code to calculate total business working hours, improved billing accuracy, reduced costs by 12% and reduced time thus increasing team efficiency

Increased productivity by 20% by replacing manual processes with automated and scheduled processes which minimized chances of manual error

Analyzed sales reports by developing data visualization dashboards using Tableau

Involved in all the phases of Software Development Life Cycle (SDLC) and identified and fixed bugs in the existing python application

Maintained the technical documentation related to Program development, Coding and Testing ACTIVITIES

Indian Graduate Students Association, Stevens Institute of Technology 2019-Present Member

Spandan Blood Donation Camp, College of Engineering Pune 2013-2017 Social Worker

Inter-Department Cricket Competition, College of Engineering Pune 2016-2017 Core Committee Member

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