Vinod Gujiri

Data analysis professional prepared to deliver impactful insights and drive business decisions. Adept in data visualization, statistical analysis, and database management, with strong focus on teamwork and adaptability. Known for reliability, analytical thinking, and results-driven approach. Skilled in Python, SQL, and Excel, ensuring robust data solutions and strategic recommendations.

UpGrad - Professional Certificate Program in AI and Data Science, 03/06/25, 01/01/26

Skills Used Skills Used

Lead Scoring Case Study (Capstone Project) 

• Developed an end-to-end Lead Scoring Model using Logistic Regression to identify high-conversion prospects.
• Performed data cleaning, missing value treatment, and feature engineering on raw marketing lead data using Pandas.
• Automated data preprocessing steps to make the model pipeline reusable.
• Demonstrated how data-driven targeting can reduce marketing cost and improve ROI.

Skills Demonstrated:

• Python (Pandas, NumPy, Scikit-learn), SQL, Excel, Data Visualization, Machine Learning, EDA

Credit Risk Analysis,

• Conducted Exploratory Data Analysis (EDA) to identify patterns and trends influencing loan default behavior.
• Performed risk profiling of customers based on income, credit history, loan amount, and repayment behavior.
• Cleaned and preprocessed financial datasets by handling missing values, outliers, and data inconsistencies using Python (Pandas, NumPy).
• Analyzed relationships between variables using correlation analysis and data visualization techniques.
• Identified key factors contributing to high-risk vs low-risk borrowers to support better lending decisions.
• Built customer segments based on risk level to help in credit approval strategy.

Skills Demonstrated:

• Risk Analysis, Financial Data Analysis, EDA, Data Cleaning, Business Insights, Data Visualization

Customer Segmentation using K-Means (Snapdeal Internship), 

• Applied K-Means Clustering to segment customers based on purchasing behavior and transaction patterns.
• Performed data preprocessing and feature scaling to prepare customer data for clustering analysis.
• Conducted Exploratory Data Analysis (EDA) to understand spending trends, frequency, and customer value.
• Identified high-value, mid-value, and low-engagement customer groups to support targeted marketing strategies.
• Translated clustering results into actionable business insights for personalized campaigns and retention strategies.

• Clustering • Unsupervised Learning • Customer Analytics • Python • Data Visualization • Business Strategy

Stock Price Analysis

• Analyzed historical stock market data to identify price trends, volatility patterns, and movement behavior.
• Performed time-series analysis to study stock performance over different periods.
• Cleaned and structured raw financial data using Python (Pandas, NumPy).
• Calculated key financial indicators such as moving averages, returns, and volatility.
• Used data visualization techniques to highlight price trends and trading patterns.
• Compared performance across multiple stocks to derive investment insights.
• Applied statistical techniques to understand correlation and risk patterns in stock movements.

• Time Series Analysis • Financial Data Analysis • Python • Statistics • Data Visualization • Trend Analysis

File Handling Project (Python)

• Implemented file read, write, and append operations using Python.
• Processed structured and unstructured data from text/CSV files for analysis.
• Automated data extraction and storage tasks to improve workflow efficiency.
• Applied exception handling to manage file errors and ensure reliable execution.
• Used Python scripts to clean and transform raw file data into usable formats.
• Demonstrated understanding of file I/O operations and data processing fundamentals.

Skills Used

• Python, File Handling, Data Processing, Automation, Error.

Handling