Business problem - To predict the sales of a specific product by using the investment amount in advertisement
Data wrangling - filling missing values using numpy library
EDA - Data visualization done by matplotlib and seaborn library to
understand the relation between dependent and independent variables and use anova test for checking the stats difference between the features
feature selection - selection based on significance level or alpha
values using stats model
Predictive modelling - done using linear regression ML Algorithm
Sales increase to optimum level of the products model accuracy = 85%
r2 score = 0.6
root mean square error = 15
Model deployment using django & Heroku
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