Shreyas Kulkarni

Data Scientist familiar with gathering, cleaning and organizing data for use by technical and non-technical personnel. Advanced understanding of statistical and analytical techniques. With Numpy and Pandas as core base, Strong Exploratory Data Analysis i.e Data Mining, Building,Cleansing and Handling and Advance Tableau Presentations, with Story Telling skill can surely help benefit the company to identify and solve key factors.

Machine Learning Predictions along with the surplus advantage of front-end and acquainted knowledge of Time Series Analysis.

Hands-on Experience on 30+ Datasets and Multiple Projects

Regression Machine Learning Project- Insurance Price Predictor

• Prediction of Insurance Price based on the parameters mentioned and Calculating the same using KNN Regression, Linear Regression and Random Forest Regressor, along with STREAMLIT PYTHON FRONTEND which uses the model created and gives the predicted Insurance Price.

20+ TABLEAU PROJECTS and Analysis using multiple calculated field and INTERACTIVE DASHBOARDS ,  with Business Conclusions, along with GeoPandas and  Story Telling Presentations

Classification Machine Learning Project- House Grade Prediction

• Prediction of the Grade of the House based on all the number of floors, rooms, bathrooms, air quality index, using Classification Algorithms such as Gaussian NB- Naive Bayes and Logistic Regression.

Clustering Machine Learning Project- Topic - Absenteeism At Work Data

• Prediction of Absenteeism of employees at work with the following reasons mentioned by performing analysis and Clustering Algorithms like KMeans Clustering, Agglomerative Clustering , Density Based Clustering and Hierarchical Clustering

Python Exploratory Analysis Project- Topic- Housing Data 

• Univariate , Bivariate and Multivariate Analysis of data, to help the customer to choose the correct House in a correct city and correct size according to the needs of the customer.

Topic- Repaying Loans

• The following project was completed to project the customers who will repay the loans and who are most likely to default and not pay the loans.
• Based on the analysis, The bank can easily conclude on the status of the cutomers and can take the measures accordingly.

Project Management and Leadership

Strong Decision and Solution Seeker

Time Discipline and Management

Integrity and Adaptability 

Verbal And Wrriten Communication

Accepting Responsibilities

Active Listening and Group Discussions

● GENERAL SECRETARY SSJCOE COUNCIL 2021-22

● Mr SSJCOE Fresher 2019-2020

● Creative Head ACES Committee

● Publicity Relationship Manager CSI Committee

● Mr SHJP Fresher 2018-2019

● Youtube Channel Manager