Project Description
Farming and harvest efficiency play a critical role in ensuring global food security and optimizing agricultural production. In this project, we will evaluate agricultural production, harvest efficiency and market dynamics by analyzing various farm, crop yields, weather and market price data. Our goal is to improve agricultural production processes, increase harvest efficiency and provide farmers with better market strategies. This analysis will help agricultural managers and farmers optimize their production and make strategic decisions.
Project Usage Areas
This project has several uses for agricultural managers, farmers and data analysts:
Agricultural Production Analysis: Optimizing production processes by analyzing farm and product efficiency.
Relationship between Weather and Production: Determining the effects of weather conditions on agricultural production by analyzing weather data.
Market Dynamics: Offering better market strategies to farmers by analyzing market prices.
Harvest Efficiency: Identifying ways to increase productivity by analyzing crop efficiencies.
Strategic Decisions: Developing agricultural strategies and optimizing production processes using data analysis.
Dataset Description
The data set to be used in this project includes agricultural and harvest efficiency data. The dataset consists of four main files in total:
Farm Data (farm_data)
FarmID: Farm ID
FarmName: Farm name
Location: Location (North, South, East, West)
FarmSize: Farm size (hectares)
Owner: Farm owner
Crop Yields (crop_yields)
YieldID: Product yield ID
FarmID: Farm ID
CropType: Crop type (Wheat, Corn, Rice, Soybeans)
PlantingDate: Planting date
HarvestDate: Harvest date
Yield: Yield (kg/ha)
Weather Data (weather_data)
Date: Date
Location: Location (North, South, East, West)
Temperature: Temperature (°C)
Rainfall: Rainfall amount (mm)
Humidity: Humidity (%)
Market Prices (market_prices)
PriceID: Price ID
CropType: Crop type (Wheat, Corn, Rice, Soybeans)
Date: Date
MarketPrice: Market price (USD/ton)
There are various dirty data problems in this dataset, such as missing data, outlier data, and wrong data type. This is an ideal data set to experience data cleaning and processing processes commonly encountered in real life.
Student Benefits
This project provides many benefits for students:
Data Manipulation: Students develop skills in examining, cleaning, and analyzing data sets.
Using Pandas: They learn to use the data processing and analysis methods of the Pandas library effectively.
Data Cleaning: They gain skills in cleaning missing data, outliers and incorrect data types.
Business Intelligence: By analyzing data sets, they improve their ability to evaluate agricultural production and harvest efficiency and make strategic decisions.
Reporting: Provides skills to effectively report and present analysis results.
Real Life Applications: Provides practical information about data problems and analysis processes encountered in real life.
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