- Include These Are Suggestions You Can Do More Remove Outliers Change Range Of Data Change Type Of Data Deal With 1 (294.75 KiB) Viewed 40 times
- Include These Are Suggestions You Can Do More Remove Outliers Change Range Of Data Change Type Of Data Deal With 2 (294.75 KiB) Viewed 40 times
- Include These Are Suggestions You Can Do More Remove Outliers Change Range Of Data Change Type Of Data Deal With 3 (294.75 KiB) Viewed 40 times
can use datasets from kaggle
Include (these are suggestions, you can do more!) remove outliers, change range of data, change type of data, deal with missing data, removing features, find mean, variance, std on variables, etc. (2) At least two charts from exploratory data analysis. Include, for example: histograms (summarize features), scatter plots to view relationships between features, boxplots (3) An iPython notebook that includes all code and findings (even if it was not the final results, or you did not use it for the two charts!) and (1) and (2) above.) Due May 12: (1) A brief section addressing feedback from the first two submissions. (3) A summary of results from the modeling phase of the project. (2) At least two additional charts. Include: Which performance metric was used, why? Which model was used, why? Were results expected or unexpected? (4) An iPython notebook that includes your code (even if it was not the final results, even if you did not present that model in your final findings or you did not use it for the two charts in (2) above.) creens 3-4 of 4
Include (these are suggestions, you can do more!) remove outliers, change range of data, change type of data, deal with missing data, removing features, find mean, variance, std on variables, etc. (2) At least two charts from exploratory data analysis. Include, for example: histograms (summarize features), scatter plots to view relationships between features, boxplots (3) An iPython notebook that includes all code and findings (even if it was not the final results, or you did not use it for the two charts!) and (1) and (2) above.) Due May 12: (1) A brief section addressing feedback from the first two submissions. (3) A summary of results from the modeling phase of the project. (2) At least two additional charts. Include: Which performance metric was used, why? Which model was used, why? Were results expected or unexpected? (4) An iPython notebook that includes your code (even if it was not the final results, even if you did not present that model in your final findings or you did not use it for the two charts in (2) above.) creens 3-4 of 4
Include (these are suggestions, you can do more!) remove outliers, change range of data, change type of data, deal with missing data, removing features, find mean, variance, std on variables, etc. (2) At least two charts from exploratory data analysis. Include, for example: histograms (summarize features), scatter plots to view relationships between features, boxplots (3) An iPython notebook that includes all code and findings (even if it was not the final results, or you did not use it for the two charts!) and (1) and (2) above.) Due May 12: (1) A brief section addressing feedback from the first two submissions. (3) A summary of results from the modeling phase of the project. (2) At least two additional charts. Include: Which performance metric was used, why? Which model was used, why? Were results expected or unexpected? (4) An iPython notebook that includes your code (even if it was not the final results, even if you did not present that model in your final findings or you did not use it for the two charts in (2) above.) creens 3-4 of 4