• The purpose of my project was to analyze a real estate property dataset from Washington, USA and provide valuable insights for the real estate market.
• Downloaded real estate property dataset from Kaggle for Washington, USA.
• Conducted comprehensive data preprocessing, including handling missing values, outliers, and encoding categorical variables.
• Performed feature engineering to create new meaningful features.
• Conducted exploratory data analysis (EDA) to gain insights into data distribution and relationships.
• Utilized GridSearchCV for thorough model optimization.
• Achieved 67% accuracy using Lasso as the best-performing model.
• Developed a client-facing API using FastAPI for real estate price predictions.
• Created a responsive website using HTML, CSS, Bootstrap, and JavaScript to enhance user experience and interact with the API.
• Deployed the model, API, and website to AWS for accessibility and scalability.
Technologies Used: Python, Numpy, Pandas Matplotlib, Seaborn, scikit-learn, Joblib, JSON, FastAPI, AWS, HTML,CSS,Bootstrap and JavaScript.
• The purpose of this personal data science project was to develop an image classification system capable of accurately identifying and categorizing facial features.
• Collected a diverse dataset of facial images through web scraping from Google
• Performed efficient data preprocessing using OpenCV, including face and eyes detection for extracting and cropping facial regions from the images.
• Used Wavelet transform for feature engineering, capturing important image details.
• Conducted thorough model optimization using GridSearchCV to find the best performing classifier.
• Achieved 95.4% accuracy using Support Vector Machine (SVM) as the best-performing classifier.
• Effectively communicated findings through comprehensive reports, including precision, recall, and F1-score metrics.
Technologies used: Python, Numpy, Pandas Matplotlib, Seaborn, scikit-learn, OpenCV, PyWavelets, Joblib, JSON, Selenium
• Developed a dynamic dashboard in Tableau to unlock road accident insights that are not visible.
• Utilized SQL to analyze accident data and extract valuable insights for data-driven decision-making.
• Created interactive charts, graphs, and maps within the Tableau dashboard to visualize accident data effectively.
• Empowered users to explore accident trends by various dimensions, including severity, location, vehicle type, road type, and weather condition.
• Implemented the dashboard as a decision support tool to aid in reducing casualties and improving road safety.
• Demonstrated expertise in data visualization and analysis, utilizing Tableau and SQL to extract actionable insights from complex accident data.
Technologies Used: SQL, Tableau
