EDA-Agricultural Raw Material

Content This dataset comprises of prices and price % change for coarse wool, copra, cotton, fine wool, hardlog, hard sawnwood, hide, plywood, rubber, softlog, soft sawnwood and wood pulp.

• Load the dataset into a data frame using Pandas

• Explore the number of rows & columns, ranges of values etc.

• Handle missing, incorrect and invalid data

• Perform any additional steps (parsing dates, creating additional columns)

• Compute the mean, sum, range and other interesting statistics for numeric columns

• Explore distributions of numeric columns using histograms etc.

• Make a note of interesting insights from the exploratory analysis

Given Data :-Coarse wool PriceCoarse wool price % ChangeCopra PriceCopra price % ChangeCotton PriceCotton price % ChangeFine wool PriceFine wool price % ChangeHard log PriceHard log price % Change...Plywood PricePlywood price % ChangeRubber PriceRubber price % ChangeSoftlog PriceSoftlog price % ChangeSoft sawnwood PriceSoft sawnwood price % ChangeWood pulp PriceWood pulp price % Change and Month

Conclusion and Insigts :-

1 Suppose we are planning some bussines which requrired my own raw material than this information will help

• The given information includes the prices of various raw materials such as Coarse wool, Copra, Cotton, Fine wool, Hard log, Hard sawnwood, Hide, Plywood, Rubber, Softlog, Soft sawnwood, and Wood pulp.

• The respective prices of these raw materials are 320, 471, 1.29, 469, 275, 880, 76, 650, 0.89, 158, 230, and 880 (rounded figures).

• Analyzing the trends in the prices of these raw materials can help us understand which crops are profitable to grow in the future.

• Based on this information, we can forecast the future prices of these raw materials and make a budget accordingly.

2 Costing accoding to time period

• For instance, if a business requires a particular raw material during a specific month, analyzing the historical trends in the prices of that material can help in forecasting its future prices. Based on the forecasted prices and the budget allocated for that particular period, the business can plan for the procurement of the raw material.

3 Overlapping Observation

Overlapping of raw materials in the price chart does not necessarily mean that the requirement for all materials is the same throughout the year. Each raw material has its unique demand and supply factors that affect its price, and the demand for different materials can vary depending on various factors such as seasonal changes, industry trends, and market conditions.

For example, the demand for cotton may be higher during the summer months when clothing production increases, while the demand for rubber may be higher during the rainy season when there is an increased need for tires and other rubber products. Similarly, the demand for wood products may be higher during the construction season, while the demand for animal hides may be higher during the winter months when the demand for leather products increases.

Therefore, it is important to analyze the demand and supply factors for each raw material separately to understand its unique requirements and forecast its future prices accordingly. This information can help businesses make informed decisions about their procurement and production strategies and ensure that they have sufficient raw materials to meet their production needs.

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