Data Analyst Logistic

NIDHI PANCHANI

401-***-**** *****.**********@*****.***

DATA ANALYST

Professional Summary:

●3 years of experience as a Data Analyst with Machine Learning, Deep Learning, Data Mining with large datasets of structured and unstructured data, Data Validation, Data acquisition, Data Visualization, Predictive Modeling and developed predictive models that help to provide intelligent solutions.

●Experience with statistical programming languages such as R and Python.

●Extensive experience in Text Analytics, developing different Statistical Machine Learning, Data Mining solutions to various business problems and generating Data Visualizations using R and Python.

●Hands on Experience on Customer Churn, Sales Forecasting, Market Mix Modeling, Customer Classification, Survival Analysis, Sentiment Analysis, Text Mining, Recommendation Systems.

●Experience in using Statistical procedures and Machine Learning algorithms such as ANOVA, Clustering, Regression and Time Series Analysis to analyze data for further Model Building.

●Strong mathematical knowledge and hands on experience in implementing Machine Learning algorithms like K-Nearest Neighbours, Logistic Regression, Linear regression, Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Gradient Boosted Decision Trees, Artificial Neural Networks.

●Actively participated in all phases of the project life cycle including data acquisition, data cleaning and preprocessing, feature engineering, exploratory data analysis, model building, testing and validation, data visualization and final presentation to the client.

●Extensive hands-on experience and high proficiency in writing complex SQL queries like stored procedures, triggers, joins and subqueries along with that used MongoDB for extraction data.

●Experience with data visualization using tools like GGplot, Matplotlib, Seaborn, Tableau and using Tableau software to publish and presenting dashboards, storyline on web and desktop platforms.

●Experienced in Time series analysis to create a forecasted portfolio using R.

●Experienced working with Excel to analyze the data based on business needs.

●Extensive experience working in Agile-Scrum Development.

●Knowledge and experience in SAS to analyze data based on business needs.

●Utilize NLP applications such as topic models and sentiment analysis to identify trends and patterns within massive data sets using orange tool kits.

●Excellent initiative, innovative thinking skills, and the ability to analyze details and adopt a big picture view and excellent organizational, project management and problem-solving skills.

Technical Skills:

Data Analysis and Machine Learning: Tableau, Python, R, MS-Excel, SAS, SQL, Weka, Orange

Text mining: Natural Language processing: Orange toolkits

Data Visualization: Tableau, Microsoft PowerBI, ggplot2, MatplotLib, Seaborn

Regression Methods: Linear, Multiple, Decision trees and Support vector, Logistic, K- NN, Naive Bayes, Random Forest, Extreme Gradient Boosting, K- means, Artificial Neural Networks, Independent & pairwise t- tests, one way and two-way factorial ANOVA, Pearson's correlation

Times Series Analysis methods: Average, Naive, Seasonal Naive method and Arima model

Business Intelligence and ETL Tools: Tableau, Excel & PowerPoint, Microsoft SQL

Methodologies: Complete Software Development Lifecycle, Waterfall, Agile, Scrum

Notable Projects During Master in Data Analytics (Johnson & Wales University 2020-2022)

Healthcare Insurance Fraudulent Claims Prediction

Used the Healthcare Insurance Fraud dataset to predict the fraudulent claims using machine learning models such as, Logistic regression, K-nearest Neighbor, Extreme Gradient Boosting, Random Forest and Neural Network. The main purpose of this project was to find the best classification model for predicting fraudulent claims and finding the best explaining variables for having fraudulent claims.

Tools and Technologies used: R studio, various R libraries

Time Series Analysis Using Stocks (Forecast)

Used five different stocks to forecast the stock value for next 12 months and created a forecasted portfolio. Performed exploratory data analysis, data transformation, smoothing and converted to stationary to build the arima model. Forecasted the next 12 months value using different methods such as, Average, Naive, Seasonal Naive method and Arima model.

Tools and Technologies used: R studio, various R libraries

Predicting Hotel Booking Cancellations

Used Hotel demand dataset to predict booking cancellations using data mining algorithms such as, Logistic Regression, XGBoost, Neural Network and Random Forest Model and found influencing variables for booking cancellations.

Tools and Technologies used: R Studio, various R libraries

Ten Year Risk Of Coronary Heart Disease Prediction

Used the Ten Year risk of coronary heart disease dataset for study and analysis to predict heart disease in the next 10 years using a logistic regression model and K-nearest neighbor to create confusion matrix and statistics. Performed data cleaning and transformation, evaluated dataset using different statistical measures like mean, median, standard deviation, and hypothesis/correlation tests like Pearson’s Chi-Squared test, Two Sample T-test, Pearson’s product-moment correlation test etc, Performed data visualization techniques like bar plot, box plot, scatterplot matrix, correlation plot etc.

Tools and Technologies used: R studio with different library packages

Data Visualization Project(Tableau)- Human Resource Dataset

Used Human Resources dataset to analyze different matrices like the growth of an organization, how to increase the workforce performance, check the employees satisfaction, skills needed by the company. Visulized all analysis using Barplot, PieChart, Box and Whiskerplot, BubbleChart, Geoplot, Texttable, Heatmaps, Highlightstable, Horizontal Bar, Stacked bar, Side by Side bar, Treemaps, CircleChart, AreaChart, LineChart, Histogram.

Created four different dashboards for data visualization and presentation. Below are the details,

Employee Information Dashboard contains employee’s personal information and other information regarding gender ratio, hiring trend over the years 2006 to 2018, ratio of employee citizenship in the company, ratio of race distribution by gender in the company, geographical distribution of employees, and details of employee directory.

The Executive Dashboard contains the number of employees by departments, gender ratio of staff as of now, employees work for each job role, gender ratio for each job role, different job roles and employees age ratio.

Relationship between Metrics Dashboard contains salary range, satisfaction rate, relationship between age and working time in the same job role, relationship between salary and job satisfaction, relationship between the current manager and job satisfaction, performance score by category wise, ratio of performance score by gender.

Employee Sources & Attrition Dashboard contains recruitment channels though employees hired, number of absent employees from 2006 to 2018, type of reason behind the attrition.

Tools and Technologies used: Tableau

Created An Electronic Application For A Loan To Purchase A House

Used Python to build electronic mortgage application form which includes user inputs like First Name, Last Name, Annual Income, Credit Score (integer between 500 and 800), Purchase Price of the House and based on that it will calculate the interest rate and maximum loan amount and exported that data to csv file. Also imported this csv file into R to calculate the principal Payment, Interest Payment, Property Taxes, Insurance and Monthly Payment.

Technologies and Tools: R- Studio & Python

Professional Experience

Data Analyst at Prabhat Diagnostic Laboratory (August 2017 to August 2018)

●Managed lab data based on different catalogs and performed a certain level of analysis using tools like Microsoft Excel, Microsoft Powerpoint and created dashboards using Tableau.

●Also performed data collection, data cleaning, data processing, data normalization and also used Tableau to prepare a dashboard to visualize and present the data analysis.

Education:

●Master of Science in Data Analytics (GPA - 3.94), Johnson & Wales University, Providence RI USA.

●Master of Science in Microbiology(2018, 6.83 CGPA, First Class), Veer Narmad South Gujarat University, India.

●Bachelor of Science in Microbiology(2016, 6.09 CGPA, First Class), Veer Narmad South Gujarat University, Gujarat, India.

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