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Assessing your cultural and operational fit
Taxi-Demand-Prediction
October 16, 2019 – October 16, 2019
Built a model for predicting taxi demand in a New York City
View ProjectSelf-Driving-Car
July 30, 2019 – July 30, 2019
Built CNN model for autopilot Car for predicting steering wheel angle
View ProjectAmazon-Apparel-Recommendations
July 27, 2019 – July 27, 2019
Built model for Amazon Apparel Recommendation
View ProjectNetflix-Movies-Recommendation-System
July 23, 2019 – July 23, 2019
Built system of movie recommendation of Netflix
View ProjectLSTM-on-Amazon-food-dataset
May 29, 2019 – May 29, 2019
Applied LSTM algorithm on Amazon Fine Food Review Dataset
View ProjectTruncated-SVD-on-amazon-food
April 24, 2019 – April 24, 2019
Built a model using Truncated SVD on amazon fine food review dataset
View ProjectRF-and-GBDT-using-XGBOOST-on-amazon-food-dataset
April 4, 2019 – April 4, 2019
Built Random Forest and GBDT using XGBOOST model on Amazon fine food review dataset
View ProjectDT-on-Amzon-food
March 16, 2019 – March 16, 2019
Applied Decision Tree ML technique on Amazon find food review dataset and used Graphviz to visualize Decision Tree
View ProjectSGD-for-Linear-Regression-on-Boston-House-Price-dataset
March 6, 2019 – March 6, 2019
Created the model of SGD for Linear Regression on Boston House Price dataset
View ProjectKNN-on-Amazon-food-review
February 26, 2019 – February 26, 2019
Amazon Fine Food Review, Sentiment Analysis using Machine Learning technique KNN.
View ProjectCultural Fit Analysis
The candidate's projects are all personal and focused on machine learning applications, which aligns with a Data Scientist role. The diversity of project topics (recommendation systems, demand prediction, self-driving cars, sentiment analysis) indicates a broad interest in the field. However, the lack of team projects or professional experience makes it difficult to assess collaboration or broader cultural fit.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.