{"id":21806741,"url":"https://github.com/jendives2000/regressions","last_synced_at":"2026-05-04T20:37:32.382Z","repository":{"id":255027852,"uuid":"847028650","full_name":"jendives2000/regressions","owner":"jendives2000","description":"Performing of a Linear Regression analysis to determine the strength of the relationship between the number of reviews and sales for a retail company. 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The management wants to: \n*   understand the relationship between:\n    - the number of online customer reviews for a product\n    - and the monthly sales figures for that product.\n\n**They believe that more reviews should correlate with higher sales** but want to quantify this  \nrelationship to guide marketing strategies.  \n\n### Objectives  \n1.   Determine the stength of the relationship between:  \n    - the number of customer reviews (independent variable 𝑋)  \n    - and monthly sales (dependent variable 𝑌).\n     \n2.   Present your conclusions and recommendations\n\n### Dataset:  \nThe dataset was **randomly created using pandas**, this ensures we do **not bring bias** into the set.  \nIt has 120 rows.  \n\n## Practice Exercise #1:  \nYou are a data scientist working for an e-commerce company.  \nThe marketing team wants to:  \n*   understand the relationship between:\n    - the amount of money spent on online advertising\n    - and the revenue generated from those ads\n\n**They believe that more spendings on ad should correlate with higher revenues** but want to quantify this  \nrelationship to guide marketing strategies.  \n\n### Objectives  \n1.   Determine the stength of the relationship between:  \n    - the amount of money spent on online advertising (independent variable 𝑋)  \n    - and the revenue generated from those ads (dependent variable 𝑌).\n     \n2.   Present your conclusions and recommendations\n\n### Dataset:  \nThe dataset was uploaded from an external source as part of a specialized course.\nIt has 10 rows.  \n\n# Methodology:\nFor relationship strength evaluation between 2 variables, the **Pearson correlation coefficient** is the way to go.  \nFor practice and memorization purposes, I added the **mathematical formula** whenever it is needed using the **LaTex** language.  \nFor calculations I used the **numpy library**.\n\nFor visualizations, I used **matplotlib and scipy** to add the function itself.\n\n\n# Contact me:\n*   LinkedIn: https://www.linkedin.com/in/jytran-datascience\n\nMy name is Jean-Yves TRAN, I bring 9 years of promotional video creation and project management into Data. My goal is to  \nfirst become an ML Engineer and leverage that experience to mature into a Data Scientist.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjendives2000%2Fregressions","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjendives2000%2Fregressions","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjendives2000%2Fregressions/lists"}