Mining insights with Python

Python is awesome! The process for extracting minerals through a flotation process is fascinating! Mining and flotation processes are not my area of expertise but what better way to work on my Python skills than analyze the data!

Why THIS Project?

I chose this project to practice my analysis skills with Python in Deepnote. With a large, real world dataset containing loads of floating point columns and thousands of rows I was excited to dive in.

Key Takeaways

• Lower % Silica Concentrate is desired.
• Something appears to have happened around the maximum % Silica Concentrate
• Python is very powerful!

Dataset Overview

This analysis explores whether % Silica Concentrate, an impurity in flotation processing, can be predicted based on operational metrics. Through data cleaning, correlation analysis, and visualization, I uncovered surprising fluctuations that may indicate external process disruptions.

The dataset, sourced from Kaggle, consists of 737,453 rows and 24 columns, including a timestamp column. The key variables of interest are % Iron Concentrate (the desired output) and % Silica Concentrate (an impurity). Eight columns were identified by engineers as significant factors influencing % Silica Concentrate.

Data Preparation & Cleaning

Upon loading the dataset into Deepnote, I identified two key issues:

1. String-formatted numerical values:?The numbers contained commas instead of decimal points.
2. String-formatted Date column:?The timestamps needed conversion to datetime format.

Using Python’s?pandas?library, I successfully converted all columns into numeric or datetime formats, ensuring the dataset was clean and ready for analysis.

Exploratory Data Analysis (EDA)

To gain initial insights, I generated summary statistics using?data.describe(). However, 14 columns related to flotation air flow and level were irrelevant to this analysis, so they were removed for clarity. The refined summary table highlighted key patterns:

• % Silica Concentrate had a high standard deviation, suggesting significant variability.
• Starch and Amina Flow values were inconsistent, with Starch Flow showing a surprising minimum value of zero.

The resulting table brought to light some useful information. I exported the table to Excel to highlight the numbers that stood out to me.

Next, I calculated correlations between % Silica Concentrate and the key variables. The strongest correlations were:

• Amina Flow: 0.1567
• Ore Pulp pH: -0.1477

These correlations, while present, are relatively weak, suggesting additional factors influence % Silica Concentrate.

Deep Dive into Maximum % Silica Concentrate Event

To investigate further, I identified the entry with the highest % Silica Concentrate and examined other values at that timestamp. Key observations:

• Starch Flow was significantly higher than the mean and standard deviation combined, potentially indicating an engineering intervention.
• The snapshot in time was insufficient to draw conclusions, so I expanded the analysis to include data from the previous and following day.

With 9,540 rows of data surrounding the peak, I visualized histograms and line charts. The results were striking:

• % Silica Concentrate fluctuated wildly, while Starch and Amina Flows were skewed right.
• Amina and Starch Flow dropped sharply before the silica spike, then spiked afterward, followed by another drop, leading to a second silica peak.

This raised critical stakeholder questions:

• Was there a?power outage?on July 28-29, 2017?
• Did a?machine malfunction?disrupt the process?
• Are there?engineering logs?available for further validation?

Key Takeaways & Next Steps

• Python enables rapid and effective analysis of large datasets.
• Deepnote AI was helpful in streamlining data exploration.
• While statistical correlations were weak, unexpected operational patterns emerged.
• Stakeholder insights are crucial?to validating findings and uncovering root causes.

For future analysis, I would explore:

• Time series modeling?to predict future silica spikes.
• Anomaly detection?to flag unexpected fluctuations in real time.
• Integration with operational logs?to correlate fluctuations with external events.

Conclusion

This project highlights the power of Python in uncovering hidden patterns in complex industrial datasets. While % Silica Concentrate may not be easily predicted through simple correlations, time-based analysis revealed anomalies worth further investigation.

If you have insights or are interested in data-driven problem-solving, let's connect! Whether you have feedback on this analysis or are seeking a data analyst, I’d love to chat.