Merge pull request #708 from arijitde92/main

CVD Dataset Analysis

CVD Dataset Analysis/Dataset/README.md

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

CVD Dataset Analysis/Model/README.md

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+**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

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+numpy
+pandas
+matplotlib
+scikit-learn
+xgboost
+seaborn