Curriculum vitae

NIKHIL RAJPOOT

Address: H.NO. *45 SARASWATI VIHAR LONI GHAZIABAD

Phone: 956*******

Email: admonm@r.postjobfree.com

GitHub Link: https://github.com/NIKS717

LinkedIn Profile: https://www.linkedin.com/in/nikhil-rajpoot-8a8637210 PROFFESIONAL

SUMMARY

• Good knowledge in Data Analytics, Data Sciences – Machine Learning and Statistics.

• Proficient in working on various data sets and related models using programming languages R, Python.

• Adept in handling large data sets of various types for data processing, analysis, interpretation and eventually prediction using various machine learning techniques WORK

EXPERIENCE

JANUARY 2021 – FEBRURAY

2021 DELHI

DATA SCIENTIST INTERN

(WIDHYA )

• Data science Internship at Widhya Platform

• Tools/Language used –

Machine Learning algorithms such as Regression, Classification, Clustering, Market Basket

Analysis for Supervised/Unsupervised learning, Exploratory Data analysis, Visualization

Pandas, Scikit Learn, Matplotlib, Seaborn.

• Worked on 3 projects in the Data Science and ML domain related to market reach prediction and stock market analysis.

APRIL 2021 TO MAY 2021

DATA SCIENCE & BUSINESS ANALYTICS INTERN

(THE SPARK FOUNDATION)

Tools/Language used –, Exploratory Data analysis, Visualization, Pandas, Scikit Learn, Matplotlib, Seaborn, correlation analysis, Area plot, Bar plot, Globe

Project – The Global Terrorism (Level – Intermediate) May 2021

In this project I had performed Data Analysis on this project. I tried and find out hot zone of terrorism and security issue and insight by use of Exploratory data analysis tools and technique. MAY 2021 TO PRESENT.

DATA SCIENCE INTERNSHIP PROGRAM USING R,

PHYTHON, SQL AND TABLEAU.

(StepUp Analytics)

• Currently doing Data Science internship at StepUpAnalytics May 2021 – Present

DATA SCIENTIST INTERN

(CUREYA)

• Currently doing Data Science internship at Cureya. DATA Analyst INTERN

(Kpmg Virtual internship program)

• Currently doing Data Science internship at KPMG virtual Internship program.

September 2017 - February 2018 DELHI

PROSTHETIST & ORTHOTIST

During six-month tenure, I was involved in day-to-day activities in the respective companies, institute and hospital mainly concerned with clinical prosthetics and orthotics (Assessment and analysis, prescription). EDUCATION

Bachelor of prosthetics and orthotics (Bio Medical professional) PASSED FEB 2018

Pt. Deendayal Upadhyaya Institute for The Physically Handicapped, Delhi University, DIVISION - Second

Delhi, Vishnu Digambar Marg New Delhi

10+2, PASSED 2012

NEW ADARSH SENIOR SECONDARY SCHOOL

CBSE, DIVISION- First

Loni, Ghaziabad Uttar Pradesh.

X, PASSED 2010

GOVT BOYS SSS

CBSE, DIVISION – FIRST

DELHI, Nand nagri Delhi.

SKILLS

• Software skills: Python, R, Ansi SQL, Minitab, Tableau, MS Excel, basics of OpenCV.

• Data Science - Machine Learning algorithms such as Regression, Classification, Clustering, Market Basket Analysis for Supervised/Unsupervised learning

• Exploratory Data analysis, Visualization and Inferential Statistics including Test of Hypothesis

• Python with Data Analysis - Pandas, Scikit Learn, Matplotlib, Seaborn

• Neural language processing.

• Basics of Artificial Intelligence

TRAINING &

CERTIFICATONS

PERSONAL

INFORMATION

PROJECTS

• Successfully completed training and certification in Master in Data Science from Top Mentor.

• Data Science Bootcamp 2021(Video Based learning course) – from Udemy completed March 2021

• Data science machine learning deep science learning using phyton from Think Next Technologies.

• Pursuing Feature Engineering for Machine Learning from Udemy.

• Pursuing Feature Selection for Machine Learning from Udemy.

• Pursuing Google Data Analytics from coursera.

Birthday September 14, 1993

Gender Male

Marital Status Single

Father’s Name Ram Gopal

Nationality Indian

Project 1 Online news popularity (TOP MENTOR)

Aim -prediction of the popularity of the news article published online and recommending what changes to make in the article to become popular. What maximizes the number of shares?

Dataset

Database published by Mashable in a period of two year. Database contain 39797 record and 61 attributes

Keyword use -worst, average, best.

Data preprocessing and Exploratory Data Analysis

• Removed outlier and checked for NA values.

• Plotted graphs to visualize the relation between variables.

• Feature selection method used and also log transformation on the data to make it normally distributed.

Models used

• Linear regression

• Logistic regression

• Random forest

• Support vector machine

• Decision tree

• XgBoost method

Result and Analysis

Highest Accuracy is achieved by support vector machine and Removing attributes, changing number of trees does not improve accuracy Articles published on Wednesday and Monday were more popular. Avoid publishing during weekends.

Publish about entertainment and technology avoid social media, Project 2 HOTEL PREDICTION PROJECT (THINK NEXT

TECHNOLOGIES)

Problem Statement: This model predicts the probability of a customer will cancel a booking before checking in the hotel. It would be nice for the hotels to have a model to predict if a guest will actually come. This can help a hotel to plan things like personnel and food requirements. Maybe some hotels also use such a model to offer more rooms than they have to make more money.

DECLARATION

Dataset: This data set contains booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things.

Data Analysis Steps:

* Data Import

* Exploratory Data Analysis (EDA)

* Model Building

* Prediction part

Machine Learning Models Used:

* Random Forest Classifier

* Logistic Regression

Result : In this model I applied confusion matrix through this I calculate accuracy. array ([[14917, 17], [ 26, 8918]], dtype=int64)

Accuracy can be calculated as:

14917 + 8932 (Total number of correct predictions) / 14917 + 8932 + 17 + 12 (Total number of predictions)

= 23849 / 23878 * 100

= 99.87%

Project 3 wheat export (Top Mentor)

AIM: In this project i want to calculate export of wheat on the basis of quantity and also improvement of the given export’s countries

Tools used : Principal component Analysis(PCA),Clustering. Data preprocessing and Exploratory Data Analysis And involved steps:

• Removed outlier and checked for NA values.

• Apply PCA (Principal component analysis)

• Plotted elbow graph to visualize the relation between variables.

• Apply K means, Hierarchical clustering

• Use group by function to see the final result.

Done project with 95 Accuracy and finalize the complete wheat export By Creating of successful clustering model.

I do hereby declare that the above information is true to the best of my knowledge & assure you, my maximum dedication for the development of my career.

NIKHIL RAJPOOT

LONI, GHAZIABAD

APRIL 4, 2021

Contact this candidate