This project shows:
- ability of writing solid,structured Python code
- ability of using existing utilities(libraries) for processing and analyzing data.
- ability of utilizing Machine Learning models
- statistical analysis application
- exploratory analysis application
- analytical and data pre-processing skills
Project is combined project that involves:
- Data pre-processing
- Exploratory Analysis
- Statistical Analysis
- Machine Learning
When the mined ore undergoes primary processing, a crushed mixture is obtained. It is sent for flotation (enrichment) and two-stage purification:
- Flotation - A mixture of gold-bearing ore is fed into the flotation plant. After beneficiation, a rough concentrate and "tailings", i.e. product leftovers with a low concentration of valuable metals, are obtained. The stability of this process is affected by the inconsistent and non-optimal physical and chemical state of the flotation slurry (mixture of solid particles and liquid).
- purification - The crude concentrate undergoes two purifications. The output is the final concentrate and new tailings.
- Rougher feed
- Rougher additions (or reagent additions):
- Xanthate
- Sulphate
- Depressant
- Rougher process - floatation
- Rougher tails
- Float banks
- Cleaner process
- Rougher Au — gold rough concentrate
- Final Au — gold final concentrate
- air amount
- fluid levels
- feed size — raw material granule size
- feed rate
[stage].[parameter_type].[parameter_name]. Пример: rougher.input.feed_ag
Possible values for block [stage]:
- rougher — floatation
- primary_cleaner
- secondary_cleaner
- final — final characteristics
Possible values for block [parameter_type]:
- input — raw material characteristics
- output — product characteristics
- state — parameters that characterize the current state of the stage
- calculation
| Column | Description |
|---|---|
| children | number of childern in family |
| days_employed | record of employment in days |
| dob_years | customer age in years |
| education | customer education level |
| education_id | customer education level id |
| family_status | family status |
| family_status_id | family status id |
| gender | gender |
| income_type | type of employment |
| debt | whether a customer was in debt |
| total_income | monthly total income in Rubles |
| purpose | purpose to get a loan |
The company "Zyfra" develops solutions for the efficient operation of industrial plants. The company needs a machine-learning model to help optimize production to avoid launching enterprises with unprofitable characteristics. The company needs a machine learning model to predict the recovery rate of gold from gold ore.
pandas numpy matplotlib seaborn scipy sklearn