Merge pull request #66 from athy9193/main

Update usage section in README.md, minor change in figure label in re…

Makefile.dot

@@ -11,7 +11,7 @@ n16[label="reports/reports.md", color="green"];
 n18[label="reports/wine_refs.bib", color="green"];
 n14[label="results/best_Model.pkl", color="red"];
 n10[label="results/final_model_quality.png", color="red"];
-n3[label="results/wine_quality_rank_per_feature.png", color="red"];
+n3[label="results/wine_quality_rank_per_feature.svg", color="red"];
 n8[label="src/download_data.py", color="green"];
 n15[label="src/fit_wine_quality_predict_model.py", color="green"];
 n6[label="src/pre_processing_wine.py", color="green"];

README.md

@@ -26,6 +26,22 @@ The final report can be found [here](https://github.com/UBC-MDS/Wine_Quality_Pre
 
 ## Usage
 
+There are two suggested ways to run this analysis:
+
+
+#### 1\. Using Docker
+To run this analysis using Docker, clone/download this repository, use the command line to navigate to the root of this project on your computer, and then type the following (filling in PATH_ON_YOUR_COMPUTER with the absolute path to the root of this project on your computer).
+
+
+```bash
+docker run --rm -v PATH_ON_YOUR_COMPUTER:/home/data_analysis_eg ttimbers/data_analysis_pipeline_eg make -C '/home/data_analysis_eg' all
+```
+To clean up the analysis type:
+
+```bash
+docker run --rm -v PATH_ON_YOUR_COMPUTER:/home/data_analysis_eg ttimbers/data_analysis_pipeline_eg make -C '/home/data_analysis_eg' clean
+```
+#### 2\. Using Makefile
 To replicate the analysis, clone this GitHub repository, install the
 [dependencies](#dependencies) listed below, and run the following
 commands at the command line/terminal from the root directory of this

reports/reports.Rmd

@@ -56,7 +56,7 @@ In this project we are trying to predict the quality of a given wine sample usin
 
 We eventually decided to pick neutral network Multi-layer Perception (MLP) model as the model that yield the best results after running the various machine learning models through the train data set, comparing their performance based on f1-score and checking consistency across cross-validation runs. We noticed that random forest recorded high f1-validation score at 0.84, however, it also had a large gap between train and validation with a perfect train score of 1. This caused us to think the model has overfitted. Logistic regression also showed promising f1 validation score results in our case, yet this high results were not consistent across cross-validation splits. Hence, with most models struggled to get to the 0.8 f1-score mark without significantly overfitting on the train set, while MLP shows consistent results across all cross-validation splits, our final choice landed on MLP model because we think it would generalize better.
 
-```{r, fig.cap = "Table 1: Score results among different machine learning model we have explore", fig.align='center'}
+```{r, fig.cap = "Figure 3: Score results among different machine learning model we have explore", fig.align='center'}
 knitr::include_graphics("../results/f1_score_all_classifiers.svg")
 ```
 
@@ -68,22 +68,22 @@ The Python and R programming languages [@R; @Python] and the following Python an
 
 Looking at the distribution plot of the respective wine quality group interacting with each explanatory features, we can see that higher quality wine seems to be more associated with higher `alcohol` level and lower `density`. Lower `volatile acidity` also seems to be indicative of better wine. Better ranked wine also seem to have `higher free sulfur dioxide` level than poor wine though the relationship is not that clear based on the plot. The rest of the features do not seems be very distinguishable among different quality wine.
 
-```{r distribution plot, fig.cap = "Figure 3: Distribution plot between wine quality and various attributes from physicochemical test", fig.align='center'}
+```{r distribution plot, fig.cap = "Figure 4: Distribution plot between wine quality and various attributes from physicochemical test", fig.align='center'}
 
 knitr::include_graphics("../eda/wine_EDA_files/wine_quality_rank_per_feature.svg")
 
 ```
 
 Since this is a multi-class classification, our goal was to find a model that was consistent and able to recognize patterns from our data. We choose to use a neutral network Multi-layer Perception (MLP) model as it was consistent and showed promising results. If we take a look at the accuracy scores and f1 scores across cross validation splits, we can see that it is pretty consistent which was not the case with many models.
 
-```{r, echo=FALSE,out.width="50%", out.height="20%",fig.cap="Figure 4: Accuracy scores and f1 scores across cross validation splits for neutral network Multi-layer Perception (MLP) model",fig.show='hold',fig.align='center'}
+```{r, echo=FALSE,out.width="50%", out.height="20%",fig.cap="Figure 5: Accuracy scores and f1 scores across cross validation splits for neutral network Multi-layer Perception (MLP) model",fig.show='hold',fig.align='center'}
 knitr::include_graphics(c("../results/f1_score_random_forest.svg","../results/f1_score_mlp.svg"))
 ``` 
 
 
 Our model performed quite well on the test data as well. If we take a look at the confusion matrix below. As we discussed earlier, the prediction at the lower end of wine quality spectrum is acceptable. As we can see from the confusion matrix below, ~13% error rate for the lower end of spectrum and also very acceptable false classifications in the high end of spectrum.
 
-```{r, fig.cap = "Figure 5: Confusion Matrix", fig.align='center'}
+```{r, fig.cap = "Figure 6: Confusion Matrix", fig.align='center'}
 knitr::include_graphics("../results/final_model_quality.png")
 ```