Financial Data Analyst and ML Specialist
Budget: $30 – $250 USD
I'm seeking a highly-skilled data scientist with an expertise in financial industry-related projects. The perfect candidate should be proficient in carrying out complex data analysis and applying machine learning techniques to dissect and interpret financial data.
Key Requirements:
- Proficient in Python for data analysis and machine learning tasks.
- Proficient in data analysis and machine learning.
Expectations:
I have a csv base and I need prescriptive analytical models to be built in Python with the following deliverables:
1) Initial step:
▪ Description of the situation.
▪ Show the Exploratory Analysis of the data.
2) Step for each technique:
▪ In each model option, include the requested instructions and the chosen options.
▪ Comment on the model output.
▪ Metric validation.
c) Final stage:
▪ Simulate results obtained with different inputs for each model.
Analysis suggestions:
a) Customers who came in the last month (churn)
b) Services each customer signed up for (telephone, multi-line, internet, online security, online backup, device protection, technical support, and TV and movie streaming)
c) Customer contact information (how long they have been a customer, contract, payment method, paperless billing, monetary charges, and total charges)
d) Demographic information about customers (gender, age group and whether they have partners and dependents)
Key Requirements:
- Proficient in Python for data analysis and machine learning tasks.
- Proficient in data analysis and machine learning.
Expectations:
I have a csv base and I need prescriptive analytical models to be built in Python with the following deliverables:
1) Initial step:
▪ Description of the situation.
▪ Show the Exploratory Analysis of the data.
2) Step for each technique:
▪ In each model option, include the requested instructions and the chosen options.
▪ Comment on the model output.
▪ Metric validation.
c) Final stage:
▪ Simulate results obtained with different inputs for each model.
Analysis suggestions:
a) Customers who came in the last month (churn)
b) Services each customer signed up for (telephone, multi-line, internet, online security, online backup, device protection, technical support, and TV and movie streaming)
c) Customer contact information (how long they have been a customer, contract, payment method, paperless billing, monetary charges, and total charges)
d) Demographic information about customers (gender, age group and whether they have partners and dependents)