I am an aspiring Data Analyst with a strong foundation in Python, SQL, statistics, data visualization, Power BI, and machine learning.

I enjoy transforming raw data into clean datasets, meaningful visualizations, analytical insights, and data-driven recommendations.

My core workflow is:

Business Problem → Data Extraction → Data Cleaning → EDA → Statistical Analysis → Visualization → Insights → Decision Support

I also have hands-on experience developing Django and MySQL-based applications, giving me practical exposure to relational databases, structured data, backend systems, and data-driven applications.

🎓 Currently pursuing a Post-Graduate Program in Data Science, Analytics & GenAI at Imarticus Learning.

📍 Chennai, India

🎯 Career Focus: Data Analytics | Business Intelligence | Data Science | Machine Learning

SELECT WHERE GROUP BY HAVING ORDER BY JOIN Subqueries CTEs Window Functions CASE Statements Aggregations Query Optimization

Neural Networks RNN NLP Tokenization Text Processing Generative AI LLM Fundamentals

Django Flask Django ORM HTML5 CSS3 Bootstrap JavaScript

Git GitHub Jupyter Notebook VS Code MySQL Workbench Power BI

An integrated cricket analytics and player-performance prediction platform for exploring historical IPL data, analyzing player performance, comparing players, and generating batting and bowling estimates.

Key capabilities

• Historical player profiles, search, and comparison
• Match exploration and match details
• Batting analytics, bowling analytics, and exploratory data analysis
• Separate ML and DL inference workflows
• Model comparison and prediction pages in a Django application

Machine learning artifacts

• Batting production inference uses saved Joblib artifacts.
• Bowling production inference uses a saved wickets model.
• The exact algorithm and feature definitions should be taken from the training code, not inferred from artifact filenames.

Deep learning targets

Ten saved TensorFlow/Keras target models are organized through a model registry, with preprocessing/scaling artifacts and inverse transformation during inference.

Architecture

Existing MySQL Database
          ↓
       Django ORM
          ↓
 Analytics & Data Processing
          ↓
  ML / DL Inference Services
          ↓
 Predictions & Model Comparison
          ↓
       Django UI

Evaluation concepts: MAE, RMSE, and R². Successful inference confirms that the workflow runs; it does not by itself establish predictive accuracy or generalization. Predictions are estimates, not guaranteed match outcomes.

Technologies: Python Django MySQL Pandas NumPy Scikit-Learn TensorFlow Keras Joblib

🔗 View repository

A data analytics and machine learning project focused on analyzing and predicting crowd density using event, attendance, movement, seating, and crowd-related data.

Analyze crowd-related data, identify relevant factors associated with crowd density, develop predictive models, and present operational insights through visualization.

• Integrated multiple structured datasets
• Performed data cleaning and preprocessing
• Conducted exploratory data analysis
• Analyzed feature distributions and relationships
• Performed correlation analysis
• Selected relevant predictive variables
• Applied feature scaling using StandardScaler
• Trained and compared multiple machine learning models
• Built a predictive workflow
• Developed a Power BI dashboard
• Analyzed operational crowd patterns

Raw Data
    ↓
Data Integration
    ↓
Data Cleaning
    ↓
Exploratory Data Analysis
    ↓
Statistical Analysis
    ↓
Feature Selection
    ↓
Feature Scaling
    ↓
Machine Learning
    ↓
Prediction
    ↓
Power BI Dashboard
    ↓
Operational Insights

Python Pandas NumPy Matplotlib Seaborn Scikit-Learn Power BI

A full-stack school management platform designed to centralize academic and administrative workflows.

The project provided practical experience in transforming transactional data into analytical reports, performance metrics, and dashboards.

• Student performance analysis
• Class-wise performance analysis
• Exam-wise performance analysis
• Student ranking
• Grade calculation
• Attendance analysis
• Marks analysis
• Interactive dashboards
• Chart-based visualization

• Role-based authentication
• Student admission workflow
• Automated admission number generation
• Student account management
• Parent account management
• Attendance management
• Homework management
• Examination management
• Marks management

Student / Academic Data
          ↓
        MySQL
          ↓
      Django ORM
          ↓
    Data Processing
          ↓
   Analytical Logic
          ↓
   Reports & Dashboards

Python Django MySQL HTML CSS Bootstrap Chart.js

A production-oriented educational platform developed for a cosmetic science training organization.

• Developed dynamic Django web pages
• Built database-driven functionality
• Implemented course and program management
• Worked with relational database structures
• Integrated student reviews and media galleries
• Designed responsive user interfaces
• Developed backend workflows
• Deployed the application to a cloud hosting environment

Python Django SQL HTML CSS Bootstrap

I follow a structured approach when solving analytical problems:

Business Problem
       ↓
Identify Data Sources
       ↓
Data Extraction
(SQL / Python)
       ↓
Data Quality Validation
       ↓
Data Cleaning & Transformation
       ↓
Exploratory Data Analysis
       ↓
Statistical Analysis
       ↓
Visualization
       ↓
Predictive Modeling
(when required)
       ↓
Business Insights
       ↓
Decision Support

Good analytics starts with a good business question and reliable data.

I focus on understanding:

• What problem are we solving?
• What data is available?
• Is the data reliable?
• What patterns and anomalies exist?
• Which analytical method is appropriate?
• What does the result mean?
• How can the insight support a decision?

• Descriptive Statistics
• Probability Fundamentals
• Probability Distributions
• Sampling
• Hypothesis Testing
• Correlation
• Regression
• Homoscedasticity
• Heteroscedasticity
• Outlier Detection

• Missing Value Handling
• Duplicate Detection
• Data Transformation
• Feature Engineering
• Univariate Analysis
• Bivariate Analysis
• Multivariate Analysis
• Correlation Analysis
• Exploratory Data Analysis

• Distribution Analysis
• Trend Analysis
• Comparative Analysis
• Correlation Visualization
• KPI Analysis
• Dashboard Development
• Data Storytelling

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Gradient Boosting
• Classification

• K-Means Clustering
• Hierarchical Clustering
• Distance Metrics
• Dendrograms
• Cluster Evaluation

Data
 ↓
Data Cleaning
 ↓
EDA
 ↓
Feature Engineering
 ↓
Train / Test Split
 ↓
Model Training
 ↓
Model Evaluation
 ↓
Model Comparison
 ↓
Model Selection

Python
   ↓
NumPy & Pandas
   ↓
Data Cleaning
   ↓
Exploratory Data Analysis
   ↓
Statistics
   ↓
SQL
   ↓
Power BI
   ↓
Machine Learning
   ↓
Deep Learning
   ↓
NLP
   ↓
Generative AI

• Advanced SQL
• Statistical Analysis
• Power BI
• Machine Learning
• Deep Learning
• Natural Language Processing
• Generative AI
• Data Engineering Fundamentals
• Cloud & Deployment

I focus on understanding patterns, relationships, trends, and anomalies in data, not simply producing visualizations.

Hands-on experience with:

Python · SQL · Pandas · NumPy · Power BI · Statistics · Scikit-Learn

I aim to translate technical analysis into clear and actionable insights that stakeholders can understand and use.

Experience working with relational databases and structured application data using MySQL and Django ORM.

Knowledge of predictive modeling and machine learning techniques that can be applied when analytical problems require more than descriptive analysis.

Imarticus Learning

2026 - Present

Guru Nanak College (Autonomous)

2023 - 2026

I am seeking an entry-level Data Analyst opportunity where I can apply my knowledge of:

Python
SQL
Statistics
Power BI
Data Visualization
Pandas
NumPy
Machine Learning

I am interested in solving real-world problems involving:

• Business performance
• Customer analytics
• Operational analytics
• Reporting
• KPI analysis
• Predictive analytics
• Data-driven decision-making

• Data Analyst
• Junior Data Analyst
• Analytics Associate
• BI Analyst
• Business Analyst
• Junior Data Scientist
• Data Science Associate

Data Analytics · Business Intelligence · Statistical Analysis · Predictive Analytics · Data Visualization · Machine Learning · Data Science · NLP · Generative AI · Data Engineering

I am interested in connecting with:

Data Analysts · Data Scientists · BI Professionals · Recruiters · Technology Professionals

I continuously build practical projects to strengthen my ability to transform:

Raw Data
    ↓
Reliable Data
    ↓
Clear Analysis
    ↓
Meaningful Insights
    ↓
Business Understanding
    ↓
Better Decisions