Digging Some Mines With Python
Introduction
Imagine joining a company where every second counts. This company transforms massive clumps of dirt into valuable minerals, extracting iron from dirt, sand, and other impurities. Purity is the goal, and your job is to determine the optimal moments to extract specific substances. ??
The challenge doesn't end there—the data is a mess. It's even messier than the clumps the company digs up. You need to use your analytical skills to make sense of this chaos and uncover insights that could help the company extract more iron to sell.
Through a connection with Avery Smith , I got the opportunity to analyze this dataset using Python. Python has always been my favorite programming language, standing out from others like C++ or Java. Its readability and ease of use make it particularly appealing, so I chose to tackle this project with Python to extract valuable insights from the mineral dataset.
What I learned..
The Data
The data for this project was sourced from Kaggle and covers the mining dataset from March to September 2017.
Features Include:
In this analysis, I used Deepnote for all Python-based analysis tasks. Additionally, I utilized Carbon to export beautifully formatted images of select portions of the Python code used in this analysis. ???
For the full code, please check out my Deepnote notebook here!
Analysis
Data Preparation
Here are some fundamental steps I took before analyzing the insights from this dataset.
First, I utilized common Python packages such as Pandas, Seaborn, and Matplotlib to assist with the analysis.
Since the data exported from Kaggle was in CSV format, I imported it using the read_csv function.
Next, I noticed that the values in the date column were imported as strings, which isn't suitable for time series analysis. To resolve this, I converted the date column to Timestamp format, ensuring the dates could be accurately handled in my analysis.
Finally, by retrieving the maximum and minimum date values, I confirmed that the data covered the period from March 2017 to September 2017. ??
The Strong Negative Impact Between Iron & Silica
Suppose I were asked by a stakeholder to determine if there's a relationship between the extracted amounts of Iron and Silica.
The first step I took was to extract values from a specific day, storing the data in df_july for July 1st.
In addition to the essential columns like 'date', '% Iron Concentrate', and '% Silica Concentrate', I included 'Ore Pulp pH' and 'Flotation Column 05 Level' to explore any additional relationships between these key elements and other attributes.
To examine the relationships among these values, I used correlation matrices. Initially, I created a pair plot to visualize any patterns, as visual representations like charts and graphs provide an intuitive understanding of relationships.
To ensure there were no biases in the patterns, I also generated a correlation heatmap to support the visual analysis with numerical data.
领英推荐
From the analysis, it is evident that there's a strong negative relationship between the Iron and Silica concentration percentages. The heatmap indicates a correlation of approximately -0.96 between them.
This finding suggests that in each pulp processed in the plant, a higher percentage of Iron extracted corresponds to a lower percentage of Silica, and vice versa.
Substance Change During a Week
To gain a more precise view of substance changes during the chemical process, I examined the changes over the course of a week.
Using the code below, I extracted data from the first seven days of August to observe any variations occurring each day. Along with the concentration percentages of Iron and Silica, I included the flow of Ore pulp, Amina, and Starch to inspect changes in other chemical quantities during the process.
First, it's apparent that all three flows (Ore pulp, Amina, and Starch) showed increased activity towards the end of the week, peaking on Saturday. This suggests heightened plant activity and processing efforts towards the end of the week, likely to meet specific production targets before the week ends.
Next, there are sharp drops around Sunday and Monday. These drops in flow rates and concentrate levels might indicate weekend adjustments, such as reduced operations and maintenance activities. This could also suggest that after meeting production targets on Saturday, the plant undergoes maintenance to prevent potential issues in future operations.
Lastly, the sharp increase in Silica concentrate on Friday and the corresponding drop in Iron concentrate highlight the significant inverse relationship observed in the correlation matrices.
These insights emphasize the importance of continuous monitoring and adjustments to maintain optimal performance throughout the week.
Analysis of pH Levels
In the final step of this analysis, I aimed to identify any adjustments in pH levels across different months.
Using the code above, I created histograms and line plots to visualize pH levels over time.
From the histograms and line graphs, a noticeable decline in average pH levels is observed, particularly towards the end of August. This indicates the addition of more low pH chemicals during the process.
Several factors could contribute to this lowering of pH levels:
Conclusion
This project demonstrates the significant role that data analysis plays in optimizing the operations of a mining company. By leveraging Python for data processing and visualization, we were able to uncover valuable insights into the flotation process.
Key findings from the analysis include:
The insights gained from this project underscore the importance of continuous monitoring and timely adjustments in the chemical process to maintain optimal performance and efficiency. By understanding the relationships between various process parameters and their impacts on output quality, the company can make informed decisions to enhance their operations.
Overall, this analysis highlights the power of data-driven approaches in industrial settings and the value of using tools like Python to unlock actionable insights from complex datasets!
Call to Action
I hope you enjoyed this deep dive into mining data analysis! Exploring the complexities of the flotation process and discovering actionable insights has been an exciting and enlightening experience. ????
If you have any questions or are interested in collaborating on future projects, please reach out to me at [email protected].
For more intriguing projects and analyses, visit my portfolio website here.
Have a fantastic day! ??
Order Management Analyst | Data Analytics | Excel | SQL | R | Tableau | Data Visualization
8 个月Such a cool analysis using Python! Can't wait to get there. Really well done, Andy! ??
Technical Storyteller, Simplifying the web and writing technical documentation | Tech Community Manager | Software Engineer | Technical Writer | DevRel ??
9 个月Great article Andy. Particularly loved how detailed and diagrams you included. Well done. Do you have a blog where you publish?
Charge Integrity Analyst with a background in Physical Therapy
9 个月Well done Andy Chang!
Data scientist with 17+ years of experience, seeking new and challenging opportunities to do well.
9 个月Nice. Can you share the data set please? I would like to have a go at it myself.
Project Coordinator | ?? Business Analyst | Excel, Google Sheets, SQL, R, Tableau, Power BI
9 个月This is a great project Andy! Really well written??