🐝 Honey Production Analysis (1998 - 2021)

This repository contains a Python project on analysing honey production yield in various U.S. cities and their production values across different years.

📌 Introduction

Honey production is an integral part of the agricultural industry and plays a vital role in pollination, ecosystem balance, and economic growth. This analysis delves into the honey production trends in the United States from 1998 to 2021. By examining key metrics such as honey yield per colony, total production, and production value, we uncover valuable insights into the industry's evolution, including the impact of Colony Collapse Disorder (CCD).

🔍 Objectives

1. Analyze honey production yield trends from 1998 to 2021.
2. Explore state-wise honey production patterns over time.
3. Investigate trends in colony numbers and yield .
4. Examine relationships between total honey production and production value.
5. Assess the changes in the production value, which reflects market demand.

---

📋 Methodology

1. Data Loading and Preprocessing:

   • Imported the dataset and inspected for missing values, data types, and shape.
   • Converted the year column to datetime format for time-series analysis.

2. Exploratory Data Analysis (EDA):

   • Aggregated key metrics (numcol, yieldpercol, totalprod, and prodvalue) over years and states.
   • Created visualizations to understand trends and relationships between variables.

3. Visualization Techniques:

   • Used scatter plots, line charts, and bar charts for trend analysis.
   • Pivoted state-year production data for state-wise comparison.
   • Combined multi-series plots to show correlations.

4. Insights:

   • Derived insights from visual trends such as declining yield per colony, decreasing colony numbers, and a positive correlation between production and its value.

---

⚙️ Tools & Technologies

Programming Languages:

• Python: Primary language used for data manipulation and analysis.

Libraries:

1. Pandas: For data wrangling, group-by operations, and pivot table creation.
2. NumPy: For numerical computations.
3. Matplotlib: For creating customized visualizations such as scatter plots, line charts, and bar plots.
4. Seaborn: For advanced visualizations, adding hues and enhancing clarity.

Technological Process:

1. Data Aggregation:

   • Grouped data by year and state to analyze trends at granular and overall levels.
   • Created pivot tables to compare state-wise total production across years.

2. Time-Series Analysis:

   • Plotted year-on-year metrics such as honey yield, production value, and colony numbers.
   • Highlighted the trends to study the CCD impact.

3. Correlation Analysis:

   • Investigated relationships between production metrics using multi-series plots.

---

🔍 Insights and Conclusions

• Overall Decline: Honey production yield has steadily decreased over the years.
• State Leaders: North Dakota, South Dakota, Florida, and California remain key contributors to honey production.
• Trends: A marked decline in the number of colonies and yield per colony before the spread of CCD.
• Correlation: Total production and production value are strongly correlated, peaking in 2014 before declining.
• Demand Reflection: Production value trends reflect market demand fluctuations, influenced by environmental and economic factors.

---

🚀 Future Directions

1. Investigate additional factors influencing honey production, such as environmental and climatic conditions.
2. Analyze the impact of Colony Collapse Disorder.
3. Explore predictive models to forecast future honey production and value trends.