Updated README.md

CVD Dataset Analysis/Model/README.md

@@ -1,4 +1,4 @@
-p**Cardio Vascular Dataset Analysis**
+**Cardio Vascular Dataset Analysis**
 
 **GOAL**
 
@@ -44,6 +44,57 @@ The main aim of the project is to use multiple algorithms to implement models an
 * scikit-learn
 * xgboost
 
+**EDA RESULTS**
+<h4>Column Information</h4>
+<pre>
+ #   Column                             Non-Null Count  Dtype
+---  ------                             --------------  -----
+ 0   sex (F=0,M=1)                      399 non-null    int64
+ 1   age                                399 non-null    int64
+ 2   Family history of heart disease    399 non-null    int64
+ 3   History of heart disease           399 non-null    int64
+ 4   echotype-mass                      399 non-null    int64
+ 5   echotype-myxoma                    399 non-null    int64
+ 6   echotype-thrombose                 399 non-null    int64
+ 7   echotype-fibroma                   399 non-null    int64
+ 8   echotype-vegetation                399 non-null    int64
+ 9   echotype-papillary fibroelastoma   399 non-null    int64
+ 10  echomasstype                       399 non-null    int64
+ 11  echoposition                       399 non-null    int64
+ 12  echomalignancy                     399 non-null    int64
+ 13  echosize                           399 non-null    int64
+ 14  echonumbers                        399 non-null    int64
+ 15  echoconsistency                    399 non-null    int64
+ 16  surgeryposition                    399 non-null    int64
+ 17  surgerysize                        399 non-null    int64
+ 18  surgerynumbers                     399 non-null    int64
+ 19  pathotype-myxoma                   399 non-null    int64
+ 20  pathotype-thrombose                399 non-null    int64
+ 21  pathotype-fibroma                  399 non-null    int64
+ 22  pathotype-sarcoma                  399 non-null    int64
+ 23  pathotype-carci0ma                 399 non-null    int64
+ 24  pathotype-lypoma                   399 non-null    int64
+ 25  pathotype-vegetation               399 non-null    int64
+ 26  pathotype-endocarditis             399 non-null    int64
+ 27  pathotype-NBTE                     399 non-null    int64
+ 28  pathotype-papillary fibroelastoma  399 non-null    int64
+ 29  pathomalignancy                    399 non-null    int64
+</pre>
+
+<!-- <h4>Statistical Information</h4>
+<pre>
+	sex (F=0,M=1)	age	Family history of heart disease	History of heart disease	echotype-mass	echotype-myxoma	echotype-thrombose	echotype-fibroma	echotype-vegetation	echotype-papillary fibroelastoma	...	pathotype-thrombose	pathotype-fibroma	pathotype-sarcoma	pathotype-carci0ma	pathotype-lypoma	pathotype-vegetation	pathotype-endocarditis	pathotype-NBTE	pathotype-papillary fibroelastoma	pathomalignancy
+count	399.000000	399.000000	399.000000	399.000000	399.000000	399.000000	399.000000	399.000000	399.000000	399.000000	...	399.000000	399.000000	399.000000	399.000000	399.000000	399.000000	399.000000	399.000000	399.000000	399.000000
+mean	0.448622	50.854637	0.170426	0.355890	0.546366	0.195489	0.125313	0.030075	0.022556	0.080201	...	0.095238	0.067669	0.185464	0.095238	0.017544	0.037594	0.025063	0.012531	0.075188	0.373434
+std	0.497978	23.437052	0.376479	0.479383	0.498471	0.397074	0.331489	0.171009	0.148671	0.271944	...	0.293912	0.251493	0.389161	0.293912	0.131451	0.190451	0.156512	0.111379	0.264025	0.484323
+min	0.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
+25%	0.000000	37.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
+50%	0.000000	51.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
+75%	1.000000	70.000000	0.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
+max	1.000000	91.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
+</pre>
+-->
+
 **PLOTS**
 
 <p>
@@ -68,6 +119,9 @@ Top 5 negatively correlated features<br>
 <img src="../Images/top5_neg_features.png" alt="Top 5 negative features" title="Top 5 negative features" style="width:45%" />
 </p>
 
+**RESULTS**
+<img src="../Images/results.png" alt="Results of the six models" title="Results of the six models" />
+
 **CONCLUSION**
 
 We investigated the dataset, checking for missing values, visualizing the features, and understanding the relationships between different features. We trained and evaluated multiple predictive models, including Logistic Regression (LR), SVM, Naive Bayes (NB), Random Forest, XGBoost and Multi Layer Perceptron (MLP). We found that LR, Random Forest, XGBoost and MLP all achieved the highest accuracy on the test dataset.