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Geetha Pai

Passionate Data Scientist

+971 582363387 [email protected]

Resume

Hello, I am

Geetha Pai.

3

Machine Learning Projects

1

Deep Learning Project

About Me

Passionate Data Scientist Professional with a Master’s degree and a strong passion for transforming data into valuable insights.Driven by curiosity and a problem-solving mindset, I’m constantly looking for ways to turn complex data into actionable recommendations. I’m deeply committed to staying on the cutting edge of technology, which is why I regularly attend industry events, webinars, and conferences to keep up with the latest advancements in data science and technology.

Whether it’s applying machine learning models to solve real-world problems or simply learning new techniques, I’m always striving to expand my skill set. If you’re looking for a dedicated, results-driven Data Scientist who thrives on learning and collaboration, let’s connect and discuss how I can help bring your data to life.

Key Skills

Python,SQL,Math&Stats,Machine learning

My Projects

My Experience

Programming: Python, SQL
Data Analysis: Pandas, NumPy
Machine Learning: Scikit-learn, PyTorch
Data Visualization: Matplotlib, Seaborn.

Statistics & Probability: Understanding of key Statistical Concepts.
Tools & Software: Jupyter Notebook,Pycharm.

Awards & Certificate

Python: Beginner to Advanced For Data Professionals

SQL for Data Science

Math and Statistics For AI, Data Science

Master Machine Learning for Data Science & AI: Beginner to Advanced

AWS Certified Cloud Practitioner

AWS Certified Cloud Practitioner

Let's Connect

Feel free to get in touch with me. I am always open to discussing new projects, creative ideas or opportunities to be part of your visions.

[email protected]

https://www.linkedin.com/in/geetha-p-365038324/

+ 971 582363387

Machine Learning Project

Flight Delay Prediction Using Machine Learning Models

Domain/Function: Aviation

Developed a Flight Delay Prediction model using real flight data. Applied ML algorithms like Logistic Regression, Random Forest, XGBoost, and CatBoost. Built an interactive Streamlit app to predict delays and gained hands-on experience in data preprocessing and model evaluation.

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Engagement

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on GitHUB

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on Linkedin

Flight Delay Prediction Using Machine Learning Models

This Statistics gives alot of info about the flight journey.

Flight Delay Prediction Using Machine Learning Models

This is the Frontend built using Streamlit which takes in the input data from the user.

Project Details

This project focuses on predicting flight delays using machine learning techniques and real-world flight data. The process began with data cleaning and feature engineering to handle missing values and create meaningful variables such as departure time, distance, and airline codes. I explored multiple machine learning models—including Logistic Regression, Random Forest, XGBoost, and CatBoost—to classify flights as delayed or on time. Each model was evaluated using metrics like accuracy, precision, recall, and F1-score. To make the solution interactive and user-friendly, I built and deployed a web application using Streamlit, where users can input flight details and receive delay predictions instantly. This project helped strengthen my skills in end-to-end ML workflows, working with structured data, model selection, and deploying real-time applications.

Project video

Deep Learning Project

Deep Learning Project on Potato Plant Diseases 🌿

Domain/Function: Data Science

🌿 AI for Agriculture | Deep Learning Project 🚀 Built a CNN-based model to classify potato leaf diseases (Early Blight, Late Blight, Healthy) using VGG and ResNet architectures. ✅ Automates disease detection ✅ Reduces dependency on manual inspection ✅ Scalable for real-world farming use

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on Deep Learning Project on Potato Plant Disease Disease

Deep Learning Project on Potato Plant Diseases 🌿

Images of the potato plant leaves which are unhealthy.

Deep Learning Project on Potato Plant Diseases 🌿

Images of the potato plant leaves which are unhealthy and are classified accordingly with accuracy.

Project Details

The traditional methods of identifying potato leaf diseases rely heavily on expert knowledge and manual inspections, which can be time-consuming, inconsistent, and not always available, especially in remote or resource-limited areas. The ability to automatically and accurately classify potato leaf diseases from images can revolutionize how we monitor and manage potato crops. This project aims to develop a robust machine learning model that can distinguish between healthy leaves and various diseases such as Early Blight and Late Blight.
Note : Excuse for the bad throat in the video.

Project video

Machine Learning Project

Data Analysis in Hotel Domain.

Domain/Function: Data Analysis in Hotel Domain.

Analyzed insights like booking transactions, customer preferences, and cancellation rates]. Built visualizations to highlight patterns and trends. Used data cleaning and wrangling techniques for accuracy.

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Engagement

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on Linkedin

Data Analysis in Hotel Domain.

Different Statistics using Data Visualisation.

Data Analysis in Hotel Domain.

Data Insights shows average occupancy rate per city.

Project Details

Exciting data analysis project focusing on hotel bookings, where I explored trends, customer preferences, and booking behaviors. This project honed my skills in key tools, e.g., Python, Pandas, Matplotlib, etc.] while applying real-world data techniques.
Skills Applied:
Data Collection.
Data Exploration. Exploratory Data Analysis (EDA).
Data Cleaning.
Data Transformation.
Visualization with [tools like Matplotlib] to present Bar Graphs.

Project video

Credit Card Launch.

Domain/Function: Credit Card Launch Data Analysis!

Project for AtliqO Bank, where I had the opportunity to dive deep into customer,customer credit score and transactions data, solve different queries, and bring insights to life using Python libraries Pandas, Numpy, Matplotlib and Seaborn libraries.

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Engagement

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on Linkedin

Credit Card Launch.

Data Analysis to predict Target Users.

Credit Card Launch.

Data Key Insights after Data Analysis.

Project Details

Project for AtliqO Bank, where I had the opportunity to dive deep into customer,customer credit score and transactions data, solve different queries, and bring insights to life using Python libraries Pandas, Numpy, Matplotlib and Seaborn libraries. This hands-on project was an incredible way to apply my new skills while tackling real-world business problems.

Project video

Machine Learning Project

Insurance Premium Prediction!

Domain/Function: Premium Prediction

Successfully developed a predictive model aimed at estimating health insurance premiums for Shield Insurance. The project involved analyzing and predicting premiums based on key factors such as age, smoking habits, BMI, and medical history.

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Engagement

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on Linkedin

Insurance Premium Prediction!

The Screenshot shows description of steps involved.

Insurance Premium Prediction!

The Screenshot shows Data Analysis within Pandas Dataframe with the Jupyter Notebook.

Project Details

As part of my work, I successfully developed a predictive model aimed at estimating health insurance premiums for Shield Insurance. The project involved analyzing and predicting premiums based on key factors such as age, smoking habits, BMI, and medical history.
💡Key Highlights: 💡
Data Analysis & Preprocessing: Collected, cleaned, and transformed large datasets, including demographic information, lifestyle habits, and medical histories, to ensure accurate modeling.
Feature Engineering: Implemented feature engineering techniques to transform raw data into meaningful insights, including categorizing BMI, grouping age ranges, and encoding medical history.
Model Development : Built and trained several machine learning models, including Linear Regression,XGBOOST to predict health insurance premiums with high accuracy. Model Evaluation & Model FineTuning: Used multiple evaluation metrics (MAE, MSE, RMSE) to assess model performance and Fine Tunings.

Project video