Engineer Data
Resume:
E: ************@*****.***
LinkedIn:
https://www.linkedin.com/in/kirti-tambe-742430a9/
EDUCATION
PGP Data Science 2018-2019
Praxis Business School
B.E in Instrumentation 2015
Technology
B.V.Bhommaraddi College 62.26%
Of Engineering and Technology, Hubli
XII 2011
New Model Junior 60%
College, Kolhapur
X 2009
Holy Cross Convent 85.38%
High School, Kolhapur
SKILLS
Python 3/5
SQL 3/5
EXPERIENCE
INFOSYS LIMITED Oct, 2015 – Jul, 2018 Senior System Engineer
Worked as a SAP ABAP developer for one of the top Agrochemical company in Switzerland. My roles and responsibilities were to develop technical solutions for the Business Rrequirement, code analysis, worked on Sap-Script, Smartforms, ALV reporting, ALE IDOCS, user-exits in order to achieve the custom changes required.
ANALYTICS PROJECTS
PREDICTING ATTRITION
Tools Used: Python
Problem Statement: Employee attrition is a concern for organizations, thus the objective of the study was to find out the major attributes that lead to attrition and predict the attrition of an employee.
Approach: Performed exploratory data analysis, converting the categorical variables into numeric, features engineering, dropped some variables as they had no impact on the attrition. These insights would help the company to retain its employees. The model was built using Decision Tree to predict the attrition.
HOUSE PRICE PREDICTION
Tools Used: Python
Problem Statement: Predict the price of a houses and analyzing the factors that impact the price of the house.
Approach: The dataset consisted information about the location of the house, bedroom, price and other aspects such as square feet etc. Performed hypothesis, data exploration, data visualizing using graphs to gain insights about the data, data cleaning, feature engineering. The model was built using Linear Regression to predict the price of the house.
AUTOMATED GRAPH GENERATION FOR EDA
Tools Used: Python
Problem Statement: Creation for a reusable function that would generate graphs for a dataframe.
Approach: Plot histogram and box-plots for all the quantitative variables in the dataframe and bar graph for all the qualitative variables in the dataframe. This would reduce the amount of time spent in plotting the graphs manually for each variable.
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