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Italian, English, Hindi, Business Development, Business Strategy, Management, Market Research, Strategic Planning, Energy, Business Planning, Contract Negotiation, Renewable Energy, Negotiation, Start-ups, Project Management, Sales Management, Solar Energy, Marketing, Strategy, Sales
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  • Data Analytics InternBrookfield Asset ManagementNovember 2015 – May 2016 (7 months)Greater New York City AreaGraduate AssistantStevens Institute of TechnologyNovember 2014 – November 2015 (1 year 1 month)Junior EngineerAcidaes Solutions Pvt. Ltd.March 2013 – June 2014 (1 year 4 months)Noida Area, IndiaEducationStevens Institute of TechnologyMaster's degree, Business Intelligence & AnalyticsMaster's degree, Business Intelligence & Analytics2016Rajasthan Technical University, KotaB.Tech., Electronics and CommunicationsB.Tech., Electronics and Communications2013SkillsData AnalysisSQLPythonSASC++RMicrosoft OfficeMicrosoft SQL ServerTableauStatisticsBig DataPredictive AnalyticsMachine LearningHadoopDecision TreesSee 12+Neural NetworksCluster AnalysisText MiningMultivariate AnalysisANOVASparkPCAWeb AnalyticsSocial Network AnalysisResearchNLPWindows AzureSee lessHow's this translation?Great•Has errorsThanks for your help!ProjectsKaggle Competetion | Predict Satisfied Customers | Python 3.4Starting 20161) The goal of the project was to help a bank classify which customer are dissatisfied from 370anonymized independent variables given and one binary target variable2) Plotted feature importance using random forest algorithm, two variables were found to beexplaining almost 56% variance of the model hence a need for dimensionality reduction wasobserved and a variance threshold based feature selection algorithm was implemented3) Outliers like 9999999999, -999999 were assumed as place holders for missing values and werefactored in as such into the model. Cross validation function was used to randomly distribute thedata into training and test set to avoid overfitting arising due to data set bias if any4) Built the predictive model using ensemble technique of extreme gradient boosting classificationalgorithm. The xgboost boost algorithm apart from taking weighted average of the decision treesalso attempts to regularize the model to check for multicollinearity and overfitting5) Predicted probabilities for the customers as satisfied or dissatisfied and tested the model using ROC curve which plots the true positive rate against the false positive rater & achieved accuracy of 0.846Team members: Utkarsh AsthanaRestaurant Review Analysis | Python 3.4Starting 20151) The goal of the project was to analyze customer reviews of a restaurant and build a classification model to perform sentiment analysis.2) The data was collected through a Yelp API, and contained one independent review variable and one dependent binary variable depicting the review as good or bad based on the rating. 3) For data preparation removed prefixes and suffixes, matched lowercases with uppercases and tokenized the data into a more granular form, this was achieved by using stemmer and tokenize functions in NLTK Package4) The stop words were removed next by subtracting the stop words corpus from the data obtained 5) For feature extraction TfidfVectorizer function of Sklearn was used to convert the tokenized text data into numeric feature matrix based on frequency of occurrence of each token with the n-gram range defined as 1 to 26) Built three different classification models using Multinomial Naive Bayes classifier, Support Vector Machines classifier and Logistic Regression7) The accuracy of the models was determined using the ROC curve with the Naïve Bayes and Logistic Regression algorithm giving the highest accuracy of .802 and .814 respectively as the final outputTeam members: Utkarsh AsthanaCapstone Project | Dun & Bradstreet | Language Localization for Global Markets| Python 3.4Starting 20151) Worked with a Dun & Bradstreet co-ordinator for business understanding and requirement gathering and contributed in the development of a D&B product.2) Key responsibilities performed was formulating a strategy to identify reliable data sources and gathering data which will help in product development.3) Used Python to scrape data from web sites and to process data which can be fed into the next phase of development.4) Created a comprehensive report and gave a final presentation on key notes and findings.Team members: Utkarsh AsthanaPricing Test Analysis | Python 3.4Starting May 20161)Analyzed results of a pricing test of a product conducted by the company to increase its revenue2)Generated performance matrices to evaluate the product among different business segments 3)Developed high level visualizations to generate actionable insights and identify key business drivers 4)Made recommendations based on the findings and evaluated success of the pricing testTeam members: Utkarsh AsthanaCustomer Credit Risk Analysis | SAS 9.4Starting 20141) Analyzed historical data set of customers to recommend good & bad customers for credit approval2) Performed variable transformation of categorical variables and imputed missing values3) Used Factor Analysis for feature extraction and applied Logistic Regression to build a supervised learning model for prediction; Used confusion matrix for model evaluationTeam members: Utkarsh AsthanaCertificationsR Programming at Coursera Verified Certificates, License UV8X2JBAL8June 2015 – PresentSAS Base ProgrammerSAS, License BP051697v9August 2015 – PresentHonors & AwardsProvosts Masters Fellowship
Sales Management, Brand Management, Sales Operations, Men's Footwear, Women's footwear, Children's Footwear, Footwear, Athletic Footwear, Sportswear, Apparel, Luxury Goods, Inventory Management, Income Statement, Forecasting, Profit
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English, Marketing, Social Media Marketing, New Business Development, Strategic Planning, Project Management, Management, Project Planning, Business Strategy, Business Development, Energy, Team Leadership
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