Machine Learning - ARIMA Model & Time Series Forecasting - Python
Budget: $2 – $8 USD
I am looking for an experienced data scientist who can help me with a time series forecasting project.
Specific Goal:
- My specific goal for this project is to predict sales using time series forecasting techniques.
Data Availability:
- I have historical data available for the forecasting project, which will be provided to the selected candidate.
Machine Learning Model:
- The ideal machine learning model for this project would be Linear Regression.
Skills and Experience:
- Strong knowledge and experience in time series forecasting, specifically using the ARIMA model.
- Proficiency in Python and data manipulation libraries such as Pandas.
- Experience in building and evaluating linear regression models.
- Ability to analyze and interpret data to make accurate predictions.
- Familiarity with statistical concepts and techniques related to time series forecasting.
If you have the required skills and experience, please submit your proposal with relevant examples of your previous work in time series forecasting using Linear Regression.
Specific Goal:
- My specific goal for this project is to predict sales using time series forecasting techniques.
Data Availability:
- I have historical data available for the forecasting project, which will be provided to the selected candidate.
Machine Learning Model:
- The ideal machine learning model for this project would be Linear Regression.
Skills and Experience:
- Strong knowledge and experience in time series forecasting, specifically using the ARIMA model.
- Proficiency in Python and data manipulation libraries such as Pandas.
- Experience in building and evaluating linear regression models.
- Ability to analyze and interpret data to make accurate predictions.
- Familiarity with statistical concepts and techniques related to time series forecasting.
If you have the required skills and experience, please submit your proposal with relevant examples of your previous work in time series forecasting using Linear Regression.