ANJALI PANCHAL

732-***-**** ad4wpf@r.postjobfree.com

https://www.linkedin.com/in/anjalipanchal96a1ba280/

EDUCATION

Rutgers University, New Brunswick Anticipated Graduation – May 2024

Major in Data Science and Minor in Computer Science.

TECHNICAL SUMMARY

Familiar with Python Data visualization tools such as Matplotlib, Seaborn, Plotly

Familiar with R Data visualization tools such as ggplot2, Leaflet, RColorBrewer, Plotly

Familiar with Machine Learning, Predictive Modeling, and Data Mining with a broad understanding of Supervised and Unsupervised learning techniques and algorithms (e.g., Linear & Logistic Regression, K-NN, SVM, Naïve Bayes, Decision trees, Random Forest, Clustering, Principle Component Analysis, etc.)

TECHNICAL SKILLS

Languages: Python, SQL, R, Java, C, C++, HTML

IDE: Jupyter Notebook, R Studio, Visual Studio code, Eclipse

Visualization: Pivot Table, R ggplot2, Python Plotly

Analytics: Microsoft Excel, Microsoft Office, Tableau, PyTorch, AWS, Spark

Python Packages: Seaborn, Matplotlib, Pandas, Numpy, Scipy, Plotly

Machine Learning: Numpy, Pandas, Scikit-learn, SVM, Linear & Logistic Regression, Random Forests, Decision Trees, Nearest Neighbors, K-Means, Clustering

WORK EXPERIENCE

1)THE SPARKS FOUNDATION Jan 2024 – current (Till April 2024)

Data Science & Business Analytics Internship

Tesla Stock Market Prediction – Machine Learning & NLP Project

Working on building a time-series machine learning model (LSTM model) in Python to predict the Tesla Stock prices using sentiment analysis of news headlines; Analyzing trends and patterns in the stock market data to gain insights about possible future trends.

Complete text analysis, which includes text cleaning, data preprocessing techniques such as Stemming, Lemmatization, and Removal of words that are manually prepared for domain purposes.

Product Sales Analysis – Tableau Project

Analyzing huge real-world data of the company products and sales and giving meaningful insights using Exploratory Data Analysis (EDA) and Predictive Analytics.

Demonstrated strong data analytics skills to gain insights from the data to maximize the retail store’s sales and profits; Presented findings and recommendations to improve the business using Tableau.

2) KPMG Forage August 2023-November 2023

Data Analytics Consulting Internship

Reviewed the customer and transaction data of Sprocket Central Pty LTD using Excel Pivot Tables, Filters, Functions, and the Standard Data Quality Dimensions, resulting in improved data quality and accuracy.

Designed and delivered a comprehensive PowerPoint presentation outlining the approaches to boost business by analyzing customer datasets, identifying customer trends and behaviors, and following the 3 phases of Data Exploration, Model Development, and Interpretation. This resulted in increased client satisfaction and business growth.

Utilized Tableau to demonstrate, visualize, and develop an interactive dashboard that presented the underlying data trends, identified the customer segments with the highest customer value, and recommended a marketing and growth strategy for the client. This resulted in improved client retention and increased revenue for the client.

PROJECT EXPERIENCE

NYC Airbnb Rental Price Prediction – Machine Learning Project (Python)

This project aims to develop a reliable price prediction model using data processing and machine learning techniques to help the owners of Airbnb and customers by providing the Airbnb price evaluation.

Complete exploratory data analysis to extract information about the features having the most impact on Airbnb prices using data visualization skills; complete data cleaning and feature engineering by performing outlier detection, feature scaling, and feature importance.

Performed predictive modeling using Decision Trees, Random Forest, Lightgbm regressor, Xgboost, and KNN; Used cross-validation to test the models with different batches of data to optimize the models.

Spam Message Detector – (Python)

Complete text analysis which includes text cleaning, data preprocessing techniques such as Stemming, Lemmatization, and Removal of words that are manually prepared for domain purposes.

Text-Preprocessing (Tokenization, stop words removal, punctuations removal), Stemming (Bag of Words), TF-IDF

Building a model based on Text-Similarity, Naïve Based for text classification.

Showcases pipeline capabilities to store a pipeline of workflow.

Covid-19 Impacts on Unemployment (R language)

Using data visualization tools and statistical observation techniques, performed an analysis of covid 19 and unemployment in the United States; Performed different methods of data acquisition and data extraction such as web scraping, APIs.

Performed different data cleanings processes such as merging, removing missing values, filtering, feature engineering, and interactive plotting visualizations.

NYC city bike project (R language)

Predict trip duration of each user in the New York city bike program using real data about user information and location details in R studio by hypothesis testing and regression methods.

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