Merge pull request #708 from arijitde92/main

CVD Dataset Analysis

CVD Dataset Analysis/Dataset/README.md

@@ -0,0 +1,7 @@
+
+## DATASET
+
+Dataset can be found at below link.
+
+https://www.kaggle.com/datasets/sanazkhanjani/heart-diseases-dataset
+

CVD Dataset Analysis/Model/README.md

@@ -0,0 +1,136 @@
+**Cardio Vascular Dataset Analysis**
+
+**GOAL**
+
+Implementation of different algorithms like logistic regression, SVM, Naive Bayes, Random Forest, XGBoost, and Multi Layer Perceptron to see which gives better accuracy.
+
+**DATASET**
+
+The dataset used in this project is related to cardiovascular diseases and contains various features such as age, gender, cholesterol levels, and more.
+
+**DESCRIPTION**
+
+The main aim of the project is to use multiple algorithms to implement models and compare their performance to determine the best-fitted algorithm by checking the accuracy score.
+
+**WORK DONE**
+
+* Analyzed the data to find insights such as correlation and missing values.
+* Plotted different visualizations to understand the data distribution and relationships.
+* Trained models using the following algorithms:
+    * Logistic Regression
+    * SVM
+    * Naive Bayes
+    * Random Forest
+    * XGBoost
+    * Multi Layer Perceptron
+* Evaluated the models based on accuracy, precision, recall, F1 score, and confusion matrix.
+* Identified that XGBoost performed the best with high accuracy.
+
+**MODELS USED**
+
+1. **Logistic Regression:** A statistical model that in its basic form uses a logistic function to model a binary dependent variable.
+2. **SVM:** Support Vector Machine performs well on classification problems when the dataset is not too large. It can also be used for regression tasks.
+3. **Naive Bayes:** A probabilistic classifier based on Bayes' Theorem with strong independence assumptions between the features.
+4. **Random Forest:** Provides higher accuracy through cross-validation. It handles missing values and maintains accuracy with large datasets.
+5. **XGBoost:** A library for developing fast and high-performance gradient boosting tree models. It achieves the best performance on a range of difficult machine learning tasks.
+6. **Multi Layer Perceptron:** MLPClassifier used to train a Multi Layer Perceptron for classification tasks.
+
+**LIBRARIES NEEDED**
+
+* numpy
+* pandas
+* matplotlib
+* seaborn
+* 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>
+Correlation Matrix<br>
+<img src="../Images/correlation_matrix.png" alt="Correlation Matrix" title="Correlation Matrix" style="width:45%" />
+</p>
+<p>
+Univariate Analysis<br>
+<img src="../Images/univariate_analysis.png" alt="Univariate Analysis" title="Univariate Analysis" style="width:45%" />
+</p>
+
+<p>
+Target class counts<br>
+<img src="../Images/target_class_counts.png" alt="Target class counts" title="Target class counts" style="width:45%" />
+</p>
+
+<p>
+Top 5 positively correlated features<br>
+<img src="../Images/top5_pos_features.png" alt="Top 5 positive features" title="Top 5 positive features" style="width:45%" />
+<br>
+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.
+We also concluded that age and cholesterol levels are significant features in predicting cardiovascular diseases.
+
+**CONTRIBUTION BY**
+
+*Arijit De*
+
+
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/de-arijit/)
+[![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/arijitde92)

CVD Dataset Analysis/requirements.txt

@@ -0,0 +1,6 @@
+numpy
+pandas
+matplotlib
+scikit-learn
+xgboost
+seaborn