Data Machine Learning

OBJECTIVE WORK EXPERIENCE

Dedicated and motivated

Employee seeking for a

position where I can apply

my abilities and knowledge

including my creativity,

honesty and dedication

towards work.

ANALYTICS SKILLS

Data Cleaning

Exploratory Data

Analysis

Data Visualization

Data Manipulation

Predictive Analysis

CERTIFICATION

Completed a classroom

course in Data

Analytics using Python

from NIVT, Kolkata.

Completed an Online

course in EXL Services

Certified Associate in

Data Analytics.

PwC AC Kolkata – I worked here for 6 months as a Contractual Employee then I was converted a Regular Employee based on my performance. Currently, I am working here and have 1.5 years of experience. I use SQL, Python, Excel, Alteryx, Monarch, Tableau tools to perform my daily ETL, Data Analysis work.

ACADEMIC QUALIFICATIONS

MCA • 2020 • HERITAGE INSTITUTE OF TECHNOLOGY,

KOLKATA completed under MAKAUT with 6.12 (DGPA) marks. BCA • 2016 • SEACOM MANAGEMENT COLLEGE, HOWRAH

completed under MAKAUT with 79.2% or 7.92 (DGPA) marks. HIGHER SECONDARY • 2013 • SANTRAGACHI KEDARNATH

INSTITUTION, HOWRAH completed the 12th standard examination under WBCHSE with 47% marks.

SECONDARY • 2011 • SANTRAGACHI KEDARNATH INSTITUTION, HOWRAH completed the 10th standard examination under WBBSE with 60% marks.

PROGRAMMING SKILLS

Python Programming: General Data Structure, Data

Manipulation (using Numpy, Pandas and Pandas-Profiling), Data Visualization (using Matplotlib, Seaborn, Lux), Machine Learning (using Scikit-Learn).

Advanced Excel: Excellent analytical skills using Advanced Excel (with knowledge of pivot tables, sorting, filtering, dynamic Vlookup, logical function, text functions, Sum if, Count if, etc.). I have done Statistical Analysis using Excel Data Analysis tool. R Programming: Data Visualization, Data Manipulation using data frame, import and export, Joining Concept, Paste function, rbind and cbind etc.

Tableau, MySQL, Workbench, Alteryx, Monarch

ARNAB BANERJEE

KEEN INTEREST IN ANALYTICS

EMAIL - **********@*****.***

MOBILE NO. - +91-705*******

LinkedIn - https://www.linkedin.com/in/arnab-banerjee-94218a9a/ GitHub - https://github.com/arnabBan

PROJECT

Report Oriented

High-Profit & Loss Making Categories - 1. I found the top three profitable Product Sub-Categories in each region using the raw data and PIVOT Tables. 2. I found the two most loss - making Product Sub-Categories in each region using PIVOT Tables.

Prediction Oriented

Stroke Prediction – Here, I have done Exploratory Data Analysis using Numpy, Pandas-Profiling library of python. I have used Lux, Matplotlib, Seaborn for visualizing the data. My target feature was not balanced that’s why I made it balanced using Random Over Sampler. I applied different types of encoders on categorical features after that I have used Standard Scaler for scaling the data. After that I applied here four machine learning models which are Logistic Regression, K-Nearest Neighbors Classifier, Random Forest Classifier, Decision Tree Classifier. From that models I have chosen my model for predicting whether a patient is likely to get stroke based on higher accuracy value and f1- score value. The model is K-Nearest Neighbors Classifier.

E-commerce Data Prediction – Here, I have done Exploratory Data Analysis using Numpy, Pandas library of python. I have used Matplotlib, Seaborn for visualizing the data. I applied different types of encoders on categorical features after that I have used Standard Scaler for scaling the data. After that I applied here four machine learning models which are Logistic Regression, Random Forest Classifier. From that models I have chosen my model for predicting the product is reaching to the customer on time or not, based on higher accuracy value and f1-score value. The model is Random Forest Classifier.

Contact this candidate