Data Analysis
Expertise in statistical analysis, data wrangling, and data visualization using Python/R for effective decision making.
Expertise in statistical analysis, data wrangling, and data visualization using Python/R for effective decision making.
Proficient in developing interactive dashboards, data visualization, and storytelling with tools like Tableau, Power BI, and Python libraries.
Expertise in developing predictive models using various algorithms, hyperparameter tuning, and cross-validation for solving real-world problems.
Skilled in implementing natural language processing techniques, including sentiment analysis, topic modeling, and named entity recognition using Python libraries.
Proficient in extracting, transforming, and loading (ETL) data from various sources using tools like Python, SQL, and Apache Spark.
Analyzes various aspects of Diabetes in the Pima Indian tribe, including blood pressure, BMI, and glucose level. Statistical analysis and visualization tools are used to identify correlations and trends in the data.
Concepts Used: Exploratory Data Analysis (EDA), Data Visualization, Statistics
Trained a convolutional neural network (CNN) model on a dataset of labeled facial images to classify human emotions accurately.
Concepts Used: Deep Learning, Convolutional Neural Network (CNN)
Build a classification model to predict clients who are likely to default on their loans. Give recommendations to the bank on important features to consider while approving a loan.
Concepts Used: Logistic Regression, Decision Trees, Random Forests, and Ensemble Methods
This project aims to visualize and analyze crime data in NYC, using heat maps, histograms & scatter plots. It'll incorporate demographic & socioeconomic data to identify correlations between crime & factors such as poverty & education.
Concepts Used: Exploratory Data Analysis (EDA), Data Visualization, Statistics
Build a recommendation system for Amazon products using collaborative filtering techniques in Python. It involved analyzing user-item interactions and implementing matrix factorization algorithms to provide personalized recommendations.
Concepts Used: Collaborative Filtering, Matrix Factorization
This project uses Python to predict credit card fraud by analyzing transaction data. A machine learning model is trained to detect fraud in real-time and is optimized using hyperparameters.
Concepts Used: Logistic Regression, Decision Trees, Random Forests
This project predicts grad school admission using linear regression and R Shiny. It analyzes academic records, GRE scores, and more to create a model that can predict admission probability.
Concepts Used: Linear Regressions, Multiple Linear Regressions, XGBoost
Explored the rise of luxury brand stocks during the COVID-19 pandemic using Python. It involved collecting and analyzing stock market data to identify trends and patterns. Machine learning models such as regression and time-series analysis are used to predict future stock prices.
Concepts Used: Linear Regression, Time-Series Analysis