Portfolio Details

Each project in this portfolio reflects my hands-on experience in solving real-world problems using data science, machine learning, and full-stack development. From predictive models to intelligent web applications, these solutions are built with a focus on scalability, functionality, and impact.

Project Information

  • Project Name: Crop Yield EDA
  • Category: Exploratory Data Analysis
  • Focus Area: Agricultural Analytics
  • GitHub Repo: Link

Technologies Used

The project was built using Python with libraries like Pandas, Matplotlib, and Seaborn for data manipulation and visualization. It involved cleaning and exploring a large crop dataset to understand yield patterns across regions, seasons, and crop types. The insights uncovered serve as the foundation for future modeling and predictive analysis in agriculture.

Details of Project

The Crop Yield EDA project is a comprehensive analysis of agricultural data to uncover patterns affecting crop production. Using statistical exploration and visual analytics, this project identifies trends in crop yields based on environmental conditions, seasons, crop types, and geographic regions.

It focuses on understanding the underlying structure of the data, detecting missing or inconsistent records, and generating meaningful insights for future use in machine learning or decision support tools. The results help stakeholders in agriculture make data-driven decisions and optimize farming strategies.

Project Features

  • Cleaned and preprocessed raw agricultural yield datasets.
  • Visualized crop yield trends using Seaborn and Matplotlib.
  • Explored relationships between yield, season, and region.
  • Detected outliers and handled missing data efficiently.
  • Delivered actionable insights for further predictive modeling.