Sumit (RID : 474kwbrsbmh)

designation   Data Scientist

location   Location : Jaipur

experience   Experience : 4 Year

rate   Rate: $20 / Hourly

Availability   Availability : 1 Week

Work From   Work From : Any

designation   Category : Information Technology & Services

Last Active: 30+ Days ago
Shortlisted : 1
Total Views : 9
Key Skills
Python Pandas NumPy Matplotlib Seaborn
Discription
Page 1 of 3 Sumit-Data Scientist-4+Years Resume Objective Data Scientist with 4+ years of experience executing data driven solutions to increase efficiency, accuracy and utility of internal data processing. Experienced at creating data regression models using predictive data modelling and analyzing data mining algorithms to deliver insights and implement action-oriented solutions to complex business problems. Skills Python, Pandas, NumPy, Matplotlib, Seaborn, Exploratory Data Analysis, Machine Learning, Linear Regression, Logistic Regression, Decision Tree, Random Forest, SQL, Data Management, Data Mining, Handling Pressure, Collaboration, Problem Solving, Leadership. Professional Expertise Role: Data Scientist Period: Aug 2021 – July 2022. Technology Used: Python, Pandas, NumPy, Matplotlib, Seaborn, SQL The Project Profile: Leading telecom players, understands that customizing offerings is very important for its business to stay competitive. Currently, it is seeking to leverage behavioural data from more than 60% of the 50 million mobile devices active daily in India. They are doing this to help their clients better understand and interact with their audiences. Contribution:  Fetched the data onto Jupyter notebook from CSV file and DB2 database  Conducted the Data Preparation:  Observed the challenges in the dataset i.e., outliers, missing values.  Verified for any null value in the ‘ID’ column.  Used folium package to plot latitudes and longitudes to see if any discrepancy in the position of points.  Conducted Data Visualization using Matplotlib/Seaborn. Page 2 of 3  Conducted Data Analysis using Pandas, Sklearn  Univariate Analysis  Bivariate Analysis using correlation and chi square.  Observed the user behavior which are going to directly impact the company’s offerings for more than 60% of the 50 million mobile devices active daily in India. Professional Expertise Client: Westpac Services Project: Westpac Services Domain: BFS Role: Data Scientist Period: Sept 2018 – June 2021. Technology Used: Python, Pandas, NumPy, Matplotlib, Seaborn, SQL, Sklearn, Machine learning The Project Profile The Hogan suite of products is used by a number of the top US banks, and it supports most of the core banking functions with a highly integrated suite of systems. It was the first integrated mainframe banking system developed using middleware and object-oriented development methodology. DXC's technologically advanced Hogan Systems is designed to meet the core banking needs of global financial services institutions. HOGAN is an integrated system built on single and solid architecture. Application Systems (CIS, IDS, ODS, PAS and CAMS), Umbrella and Financial Support System (FSS) are main subsystems of HOGAN product. Contribution:  For the process of automating the loan eligibility process based on customer detail provided while filling out application form, conducted machine learning algorithms Decision Tree and Random Forest in automating the facility  Conducted Data Transformation and Data Preparation using Pandas, NumPy, Sklearn involving missing value imputation, fix for inconsistencies, Transform/encode discrete variables using one-hot encoding or Label Encoder.  Conducted Data Visualization using Matplotlib/Seaborn.  Conducted Data Analysis using Pandas, Sklearn  Univariate Analysis  Bivariate Analysis using correlation and chi square.  Verified the data for any multi-collinearity among independent variables Page 3 of 3  Verified for any outliers in the independent variables.  Conducted Machine Learning Algorithms Decision Tree, Logistic Regression and Random Forest  Evaluated the model and picked the best-fit using classification report (F1 score), Accuracy using Sklearn  Applied the model on Test Data Set in order to evaluate the accuracy and classification report of the model. *********THANKS********
Copyright : 2022 – OnBenchMark All Right Reserved.
Loading…

stuff goes in here!