Machine Learning Business Analyst

CURRICULUM VITAE

Jyotsna Sunil Pol. E-mail: ************@*****.***.

Address: Lohagaon, Pune. Contact: 741*******.

Linkdin Profile: www.linkedin.com/in/jyotsna-pol-830780244

Github: https://github.com/Jyotsnapol

OBJECTIVE.

To work in an esteemed organization, where I could utilize my knowledge, professional skills,

experience and strive hard to come up to the Organization’s expectations. Looking to maximize my

technical skills in the field of data science, python programming & SQL.

PROFESSIONAL SYNOPSIS.

Proficient in performing Exploratory Data Analysis (EDA) and Data Visualization technique

using Python Libraries such as Pandas and Seaborn.

Skilled in handling missing data and outliers using various Data Processing Technique.

Develop predictive models using supervised and unsupervised machine learning and deep

learning.

Develop and implement machine learning models to solve business problems, including

classification, regression and recommendation systems.

Build and manage end-to-end data science pipelines, including data preprocessing, feature

engineering, model development, and deployment.

Enthusiastic and detail-oriented Data Analyst with Power BI, SQL, and Advanced Excel

expertise.

Strong analytical skills and a passion for transforming raw data into actionable insights.

Adept at cleaning and analyzing large datasets, creating dynamic dashboards, and presenting

data-driven solutions. Eager to contribute to organizational success by leveraging analytical skills

and problem-solving abilities in real-world scenarios.

Proficiently performing and excelling under demanding work conditions, well versed in

finalization of Data Analysis. Excellent analytical skills that help identify problems and seek

solutions promptly.

Power BI and Tableau expertise for building interactive and insightful dashboard.

TECHNICAL SKILLS.

Programming Languages - Python.

Database - MySQL.

Machine Learning Algorithms - Linear Regression, Logistic Regression, Decision Tree,

Random Forest, Support Vector Machine.

Model Evaluation - Cross-validation, Performance metrics.

Data Visualization - Power BI, Tableau, Matplotlib, Seaborn.

Reporting and Communication - Building Dashboards, Presenting insights.

Libraries - Pandas, Numpy, Matplotlib, Seaborn, Scikit-learn.

Data cleaning and Preprocessing - Handling missing data, outliers, Data transformation.

CERTIFICATIONS.

Completed Data Science with Python Programming and Data Analyst course from ETL

HIVE institute, Pune.

EDUCATION.

Bachelor of Engineering in Electronics & telecommunications with 71.46%

Diploma in Electronics & telecommunications with 63%

SSC passed with 74.50%

PROJECTS.

1) Exploratory Data Analysis and Modeling with Cars93 Dataset.

Skills and Tools Used - Programming Languages - Python.

Libraries/Frameworks - Scikit-learn, Pandas, NumPy.

Machine Learning Technique - Regression, Classification.

Tools - Jupyter Notebook.

Key Tasks -

Preprocessed data by handling missing values, normalizing numeric variables, and encoding

categorical features.

Built and evaluated machine learning models (e.g., Linear Regression, Logistic Regression) to

predict car prices and other key metrics.

Split data into training and testing sets (e.g., 80%-20%).

Evaluate models using metrics like RMSE, MAE, and R for regression tasks.

Achieved specific results, e.g., "an R score of 0.99 for price prediction.

2) Data Analysis of Cars93 Dataset Using Python.

Tool Used: Python, Pandas Libraries.

Key Tasks -

Leveraged Python and pandas to perform in-depth analysis of the Cars93 dataset, focusing on car

specifications, pricing, and fuel efficiency.

Utilized pandas’ operations for data manipulation, including grouping, filtering, and statistical

aggregation, to identify trends in the automobile market.

3) Ecommerce Sales Performance Analysis and Reporting.

Objective: Analyze Ecommerce Sales Data Created or Interactive Dashboard using Power BI.

Tools Used: Power BI Desktop.

Key Tasks -

Created interactive dashboard to track and analyze online sales data.

Used complex parameters to drill down in worksheet and customization using filters and slicers.

Created connections, join new tables, calculations to manipulate data and enable user driven

parameters for visualizations.

Used different types of customized visualization (bar chart, pie chart, donut chart, clustered bar

chart, scatter chart, line chart, area chart, map slicers, etc.)

DECLARATION:

The information furnished above is correct and true to the best of my knowledge.

Date: Yours Faithfully,

Place: Pune Jyotsna Pol.

Contact this candidate