About Me
Hi, I'm Nikhil Vinod
I am a Data Science enthusiast with expertise in data analysis, machine learning, and statistical modeling. Skilled in SQL, Python (Pandas, NumPy, Scikit-learn), and data visualization, I enjoy transforming raw data into actionable insights.
Beyond technical skills, I am passionate about communicating insights effectively through technical blogging and project documentation. I am seeking an entry-level Data Scientist role to apply my knowledge, collaborate with a dynamic team, and contribute to data-driven solutions.
Area of Interest
Data Analysis
I can help you inspect, clean, and analyze your data to uncover valuable insights that will enable you to make informed and strategic decisions for your business.
Data Visualization
I specialize in creating clear and visually engaging dashboards and charts, making it easier for you and your team to understand complex data and communicate findings effectively.
Data Engineering
I can design and maintain efficient data pipelines and systems, ensuring that your data is clean, accessible, and ready for analysis whenever needed.
Machine Learning
I can build predictive models and algorithms tailored to your needs, such as customer behavior prediction, sales forecasting, or automating key decision-making processes.
Generative AI
I can build advanced generative AI models tailored to your needs, whether it's generating human-like text, automating content creation, enhancing chatbots, or optimizing decision-making with AI-driven insights.
Exploratory Data Analysis
I can thoroughly explore and analyze your data to uncover hidden patterns, anomalies, and trends, providing you with actionable insights and preparing your data for deeper analysis.
My Skills
Languages
Python, SQL
Key Skills
Machine Learning, Data Science, Exploratory Data Analysis, MS Word, MS Excel, Generative AI, Natural Language Processing (NLP),Statistical Analysis
Python Libraries
Seaborn, scikit-learn, Pandas, Numpy, Matplotlib, Statsmodel
IDEs
Jupyter Notebook, Google Collaboratory, PyCharm
MS Office
MS Word, MS Excel, MS PowerPoint
Portfolio
Glassdoor Company Reviews Sentiment Analysis
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Description:
This is a web application built with Streamlit that performs web scraping and sentiment analysis on Glassdoor reviews of a specified company. The app uses Selenium to extract reviews, VaderSentiment for sentiment analysis, and Google Gemini 1.5 Flash via LangChain to generate insights and recommendations.
Stock Price Prediction Using LSTM with Attention
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Description:
This project implements a deep learning model using Long Short-Term Memory (LSTM) with an Attention Mechanism to predict stock prices. The attention layer helps the model focus on relevant time steps, improving accuracy in forecasting stock movements.
Movie Recommender System
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Description:
This is an AI-powered Movie Recommender System that uses Google's Gemini API to provide personalized movie recommendations based on user preferences. The model suggests movies based on genres, actors, ratings, and other user inputs.
Image-to-Text System using Gemini
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Description:
This is an AI-powered Image-to-Text System built with Streamlit and Google Gemini API. It allows users to upload an image, provide a custom prompt, and receive AI-generated responses based on the image content.
Prioritizing Development Needs: A Socio-Economic Analysis of Nations
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Description:
This case study uses PCA to reduce data complexity and KMeans clustering to group countries based on socio-economic and health factors. The analysis identifies countries most in need of aid, providing actionable insights for HELP International to strategically allocate $10 million for maximum impact.
Empowering Early Detection of Heart Attack Risks with Machine Learning
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Description:
Engineered an advanced cardiovascular risk prediction system leveraging machine learning algorithms (Logistic Regression, XGBoost) to deliver high-accuracy health assessments. This solution analyzes critical biometric indicators and patient data to identify potential cardiac events, enabling proactive medical intervention. Demonstrated expertise in healthcare analytics through robust data preprocessing and predictive modeling.
In Depth Analysis of IPL Performance Trends 2021-2023
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Description:
This project focuses on analyzing Indian Premier League (IPL) data from the last three years (2021, 2022, and 2023) to extract actionable insights for cricket fans, analysts, and teams.By using SQL queries to explore player performances, team strategies, and season trends, this analysis forms the basis of a special edition magazine for IPL 2024. The data includes detailed match summaries, player attributes, batting statistics, and bowling statistics, offering a comprehensive view of IPL performance metrics.
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