Machine Learning - Sales Forecast (Python)
Budget: $30 – $100 USD
Using Predictive Machine Learning with python to forecast a sales csv file.
Use predictive modeling with python to forecast sales
Load and process data on jupyter notebook
CSV period: 1 year
Forecast period: 1 year or less (365 DAYS)
Statistical descriptive analysis of the data to be used, (hypothesis testing, confidence interval, correlation between variables)
Data to be used: Sales and Holidays. I will provide the data.
Machine Learning: Forecast the sales revenue of each product and sale by store, for the next year.
Correlation between variables, insert graph.
The prediction must provide future sales in terms of quantity and value for each item.
Predictive data modeling, with machine learning algorithms, using three distinct machine learning models that best fit the context and assess which one had the best performance.
Use cross-validation in ML models and confusion matrix.
Check the accuracy of the 3 models, when forecasting the year 2020.
Processing of data > Analysis and exploration of data > Creation of three machine learning models > Interpretation of results
Use predictive modeling with python to forecast sales
Load and process data on jupyter notebook
CSV period: 1 year
Forecast period: 1 year or less (365 DAYS)
Statistical descriptive analysis of the data to be used, (hypothesis testing, confidence interval, correlation between variables)
Data to be used: Sales and Holidays. I will provide the data.
Machine Learning: Forecast the sales revenue of each product and sale by store, for the next year.
Correlation between variables, insert graph.
The prediction must provide future sales in terms of quantity and value for each item.
Predictive data modeling, with machine learning algorithms, using three distinct machine learning models that best fit the context and assess which one had the best performance.
Use cross-validation in ML models and confusion matrix.
Check the accuracy of the 3 models, when forecasting the year 2020.
Processing of data > Analysis and exploration of data > Creation of three machine learning models > Interpretation of results