Predicting breast cancer using logistic regression
Budget: $30 – $250 USD
Abstract
Briefly introduce your project: the purpose, task, models you use, and the result
Introduction
Introduce your project in more details, including
The problem you try to solve. I.e. predicting cancer, predicting some prices, predicting something?
The data – where did you get the data. Any difficulties in the data (too many missing values, too many class columns…) – note that this is still an introduction, so please keep it brief
Models you use, and why you choose those models
Brief discuss of the result – which model performs the best
Preliminary Analysis
Detailed analysis on the column in the data – both target and features.
You can/should include plots like histograms, bar charts… that show individual column distribution, and scatter plots of features and target if applicable.
Please note that if your data have too many columns (over 20 25) then please do not include every single plot. Instead, please try to summarize the result, and only show interesting columns/correlations.
Data Preprocessing
Your preprocessing steps. Please justify all the steps you use. For examples, why you impute, why log transformation, etc.
Modeling and Results
Please discuss all the models you use in detail – which hyperparameters you choose to finetune, which values of hyperparameters you use, etc.
Then include tables that compare all the models’ performances. Please also discuss the result.
Conclusion
Conclude the project and any interesting finding
Briefly introduce your project: the purpose, task, models you use, and the result
Introduction
Introduce your project in more details, including
The problem you try to solve. I.e. predicting cancer, predicting some prices, predicting something?
The data – where did you get the data. Any difficulties in the data (too many missing values, too many class columns…) – note that this is still an introduction, so please keep it brief
Models you use, and why you choose those models
Brief discuss of the result – which model performs the best
Preliminary Analysis
Detailed analysis on the column in the data – both target and features.
You can/should include plots like histograms, bar charts… that show individual column distribution, and scatter plots of features and target if applicable.
Please note that if your data have too many columns (over 20 25) then please do not include every single plot. Instead, please try to summarize the result, and only show interesting columns/correlations.
Data Preprocessing
Your preprocessing steps. Please justify all the steps you use. For examples, why you impute, why log transformation, etc.
Modeling and Results
Please discuss all the models you use in detail – which hyperparameters you choose to finetune, which values of hyperparameters you use, etc.
Then include tables that compare all the models’ performances. Please also discuss the result.
Conclusion
Conclude the project and any interesting finding