Hi, I'm Harshini Murali

Aspiring Data Scientist | Machine Learning Enthusiast

View My Work

About Me

I am a dedicated and results-driven professional with a robust background in machine learning and data science. My academic journey has equipped me with a solid foundation in programming, data analysis, and statistical modeling. I have successfully applied these skills across various projects, demonstrating my ability to derive actionable insights from complex datasets.

In my recent endeavors, I have focused on developing recommendation systems, performing exploratory data analysis, and implementing data preprocessing techniques such as label encoding. These experiences have honed my proficiency in tools and libraries including Pandas, NumPy, Matplotlib, and Scikit-learn.

I am passionate about leveraging data-driven approaches to solve real-world problems and am committed to continuous learning and professional growth in the ever-evolving field of machine learning.

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Education

MSc in Data Science

University of Greenwich

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Bachelor of Computer Application

SRM Arts and Science College

Class XII (CBSE)

NSN Memorial School

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My Skills

Python

Python

Expert in Python programming for data science and automation.

R

R

Proficient in R for statistical analysis, data manipulation, and visualization.

SQL

SQL

Skilled in SQL for querying and managing relational databases efficiently.

JavaScript

JavaScript

Proficient in JavaScript for web development and interactive interfaces.

Tableau

Tableau

Experienced in creating dynamic visualizations and dashboards.

Power BI

Power BI

Proficient in Power BI for business intelligence and data storytelling.

Data Storytelling

Data Storytelling

Crafting compelling narratives using data to drive business decisions.

Interactive Visualizations

Interactive Visualizations

Creating dynamic and interactive charts for impactful presentations.

Advanced Charting

Advanced Charting

Proficient in creating advanced visualizations such as heatmaps, scatter matrix plots, and multi-dimensional charts for better insights.

Supervised Learning

Supervised Learning

Developing classification and regression models using supervised techniques.

Unsupervised Learning

Unsupervised Learning

Expert in clustering techniques and dimensionality reduction methods.

Clustering

Clustering

Skilled in clustering algorithms like K-Means, DBSCAN, and hierarchical clustering for grouping datasets.

Deep Learning

Deep Learning

Proficient in deep learning using TensorFlow and PyTorch for building advanced neural networks.

Natural Language Processing (NLP)

Natural Language Processing (NLP)

Experienced in processing and analyzing text data using NLP techniques like tokenization, stemming, and sentiment analysis.

Model Optimization

Model Optimization

Expert in optimizing machine learning models using hyperparameter tuning and regularization techniques.

Model Deployment

Model Deployment

Deploying machine learning models using , Heroku, Flask, Docker, and cloud services.

Projects

Sentiment Analysis on Movie Reviews

This project leverages natural language processing (NLP) to classify sentiments in movie reviews as positive, neutral, or negative using machine learning techniques.

Sentiment Analysis Visualization

Key Features

  • Data Preprocessing (Tokenization, Stemming, Removal of Stopwords).
  • Supervised ML Training on Labeled Datasets.
  • Dynamic Visualizations for Sentiment Insights.
  • Deployment-Ready for Real-Time Analysis.

Technologies Used

  • Python
  • TextBlob
  • Scikit-learn
  • Matplotlib
  • Seaborn

Project Overview

Analyze sentiments of movie review data in real-time using advanced NLP techniques. This project showcases powerful data visualizations and easy-to-deploy pipelines.

View on GitHub

Product Classification Using Machine Learning

This project applies machine learning techniques to classify products into various categories based on their attributes. By analyzing product data, the system improves inventory management and customer experience.

Product Classification Visualization

Key Features

  • Data preprocessing to clean and normalize product data.
  • Feature engineering to derive meaningful insights.
  • Training models using supervised learning techniques.
  • Performance evaluation with precision, recall, and accuracy metrics.
  • Easy integration into e-commerce platforms.

Technologies Used

  • Python
  • Pandas
  • Scikit-Learn
  • NumPy
  • Matplotlib

Project Overview

This project demonstrates the application of machine learning in product classification to enhance e-commerce functionalities. It includes preprocessing, model training, and deployment-ready pipelines.

View on GitHub

Customer Churn Prediction

This project aims to predict customer churn by analyzing historical data patterns. By leveraging machine learning algorithms, businesses can identify at-risk customers and implement strategies to improve retention.

Customer Churn Visualization

Key Features

  • Data Preprocessing: Data cleaning and preparation for accurate analysis.
  • Exploratory Data Analysis: Uncover patterns and trends using visualizations.
  • Predictive Modeling: Classification models to predict churn probability.
  • Feature Importance: Identify key drivers of customer churn.

Technologies Used

  • Python
  • Power BI
  • XGBoost
  • Scikit-Learn
  • NumPy
  • Matplotlib
View on GitHub

Analyzing Chronic Kidney Disease using Clustering

This project focuses on exploring and analyzing Chronic Kidney Disease (CKD) data using unsupervised learning techniques. Clustering algorithms like K-Means and Hierarchical Clustering are applied to identify patterns and group similar cases, providing valuable insights for early diagnosis and targeted interventions.

Clustering Visualization

Key Features

  • Data Preprocessing: Handled missing values and cleaned the dataset for accurate analysis.
  • Exploratory Data Analysis: Visualized correlations and trends in the data to uncover patterns.
  • Clustering Algorithms: Applied K-Means and Hierarchical Clustering for patient grouping.
  • Cluster Insights: Identified high-risk groups based on cluster analysis.

Technologies Used

  • Python
  • Clustering
  • Scikit-Learn
  • NumPy
  • Matplotlib
View on GitHub

Services

Data Analysis

Data Analysis

Performing detailed exploratory data analysis to uncover trends and insights. Cleaning and preprocessing data to ensure its accuracy and reliability for analysis. Conducting statistical analysis to evaluate relationships between variables. Creating insightful visualizations like histograms, scatter plots, and heatmaps. Using feature engineering techniques to prepare datasets for advanced modeling. Helping clients make informed decisions through in-depth data understanding.

Machine Learning Models

Machine Learning Models

Designing and developing predictive models to extract actionable insights from complex datasets. Specializing in supervised and unsupervised learning techniques to solve classification, regression, and clustering problems. Expertise in libraries like TensorFlow, PyTorch, and Scikit-learn. Leveraging hyperparameter tuning and model optimization to improve performance. Implementing scalable ML pipelines for real-world applications in diverse domains. Delivering solutions tailored to meet client requirements and business goals..

Data Visualization

Data Visualization

Crafting visually compelling dashboards and charts to represent complex data effectively. Utilizing tools like Matplotlib, Seaborn, and Tableau to uncover insights. Developing interactive visuals for better data storytelling and decision-making. Simplifying trends and relationships in data through meaningful visual summaries. Focusing on creating user-friendly and intuitive designs tailored to audience needs. Delivering actionable insights for stakeholders by making data accessible and comprehensible.

AI Integration

Product Recommendation

Building recommendation engines to enhance user engagement and retention. Leveraging collaborative and content-based filtering techniques for accurate predictions. Applying matrix factorization methods for handling sparse datasets efficiently. Tailoring algorithms to personalize product, content, or service recommendations. Measuring success through precision, recall, and other evaluation metrics. Delivering scalable solutions to meet user preferences across industries.

Contact

Have a question or want to work together? Reach out to me!

Email: harshinihachu6@gmail.com