data complexion python notebook in HTML format. Scoring guide…

Question Answered step-by-step data complexion python notebook in HTML format. Scoring guide… data complexion python notebook in HTML format. Scoring guide (Rubric) – Foundations for Data ScienceCriteria PointsQuestion 1Find the summary statistics for numerical columns and  observations. (use describe function)4Question 2r observations on the acceptance rate for each campaign4Question 3bservationYou need to a python notebook in HTML format.Happy Learning!Scoring guide (Rubric) – Foundations for Data ScienceCriteria PointsQuestion 1Find the summary statistics for numerical columns and observations. (use describe function)4Question 2 observations on the acceptance rate for each campaign4Question 3r observations on acceptance rate for each campaign according to the income level7Question 4Write the code and your observations on average amount spent on different products across all campaigns7You need to  a python notebook in HTML format.Happy Learning!Scoring guide (Rubric) – Foundations for Data ScienceCriteria PointsQuestion 1Find the summary statistics for numerical columns andr observations. (use describe function)4Question 2 observations on the acceptance rate for each campaign4Question 3 observations on acceptance rate for each campaign according to the income level7Question 4Write the code and your observations on average amount spent on different products across all campaigns7Question 5Write the code and your observations on average number of purchases from different channels across all campaigns7Question 6Write the code and your observations on percentage acceptance for different categorical variables across all campaigns7Question 7Write the code and your observations on the percentage amount spent on different products for each category of the mentioned categorical variables7Question 8servations on percentage purchases from different channels for different categories of the income_cat column4Question 9Find the correlation matrix for the columns mentioned below and visualize the same using heatmap3Question 10Based on your analysis, write the conclusions and recommendations for the CMO to help make the next marketing campaign strategy10Question 5Write the code and your observations on average number of purchases from different channels across all campaigns7Question 6Write the code and your observations on percentage acceptance for different categorical variables across all campaigns7Question 7Write the code and your observations on the percentage amount spent on different products for each category of the mentioned categorical variables7Question 8servations on percentage purchases from different channels for different categories of the income_cat column4Question 9Find the correlation matrix for the columns mentioned below and visualize the same using heatmap3Question 10Based on your analysis, write the conclusions and recommendations for the CMO to help make the next marketing campaign strategy10s on acceptance rate for each campaign according to the income level7Question 4Write the code and your observations on average amount spent on different products across all campaigns7Question 5Write the code and your observations on average number of purchases from different channels across all campaigns7Question 6Write the code and your observations on percentage acceptance for different categorical variables across all campaigns7Question 7Write the code and your observations on the percentage amount spent on different products for each category of the mentioned categorical variables7Question 8servations on percentage purchases from different channels for different categories of the income_cat column4Question 9Find the correlation matrix for the columns mentioned below and visualize the same using heatmap3Question 10Based on your analysis, write the conclusions and recommendations for the CMO to help make the next marketing campaign strategy10 python notebook in HTML format. Scoring guide (Rubric) – Foundations for Data ScienceCriteria PointsQuestion 1Find the summary statistics for numerical columns and  observations. (use describe function)4Question 2r observations on the acceptance rate for each campaign4Question 3bservationYou need to a python notebook in HTML format.Happy Learning!Scoring guide (Rubric) – Foundations for Data ScienceCriteria PointsQuestion 1Find the summary statistics for numerical columns and observations. (use describe function)4Question 2 observations on the acceptance rate for each campaign4Question 3r observations on acceptance rate for each campaign according to the income level7Question 4Write the code and your observations on average amount spent on different products across all campaigns7You need to  a python notebook in HTML format.Happy Learning!Scoring guide (Rubric) – Foundations for Data ScienceCriteria PointsQuestion 1Find the summary statistics for numerical columns andr observations. (use describe function)4Question 2 observations on the acceptance rate for each campaign4Question 3 observations on acceptance rate for each campaign according to the income level7Question 4Write the code and your observations on average amount spent on different products across all campaigns7Question 5Write the code and your observations on average number of purchases from different channels across all campaigns7Question 6Write the code and your observations on percentage acceptance for different categorical variables across all campaigns7Question 7Write the code and your observations on the percentage amount spent on different products for each category of the mentioned categorical variables7Question 8servations on percentage purchases from different channels for different categories of the income_cat column4Question 9Find the correlation matrix for the columns mentioned below and visualize the same using heatmap3Question 10Based on your analysis, write the conclusions and recommendations for the CMO to help make the next marketing campaign strategy10Question 5Write the code and your observations on average number of purchases from different channels across all campaigns7Question 6Write the code and your observations on percentage acceptance for different categorical variables across all campaigns7Question 7Write the code and your observations on the percentage amount spent on different products for each category of the mentioned categorical variables7Question 8servations on percentage purchases from different channels for different categories of the income_cat column4Question 9Find the correlation matrix for the columns mentioned below and visualize the same using heatmap3Question 10Based on your analysis, write the conclusions and recommendations for the CMO to help make the next marketing campaign strategy10s on acceptance rate for each campaign according to the income level7Question 4Write the code and your observations on average amount spent on different products across all campaigns7Question 5Write the code and your observations on average number of purchases from different channels across all campaigns7Question 6Write the code and your observations on percentage acceptance for different categorical variables across all campaigns7Question 7Write the code and your observations on the percentage amount spent on different products for each category of the mentioned categorical variables7Question 8servations on percentage purchases from different channels for different categories of the income_cat column4Question 9Find the correlation matrix for the columns mentioned below and visualize the same using heatmap3Question 10Based on your analysis, write the conclusions and recommendations for the CMO to help make the next marketing campaign strategy10Find the summary statistics for numerical columns and observations. (use describe function) Based on your analysis, write the conclusions and recommendations for the CMO to help make the next marketing campaign strategy Computer Science Engineering & Technology Networking Share QuestionEmailCopy link Comments (0)