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List of Projects :-

1. Classification of Arrhythmia using ECG Data

Notebook File Complete Project Dataset
  • The goal of this project is to predict if a person is suffering from cardiac arrhythmia or not and if yes, classify it into one of 12 available groups.
  • The Dataset used in this project is available at the UCI machine learning Repository. It can be found Here.
  • The best Model was Kernelized SVM over PCA Data.
  • Accuracy achieved = 80.21%

2. Diabetes-prediction

Notebook File Complete Deployed Project Dataset Working Link
  • The objective of the project is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.
  • The data set that has used in this project has taken from the Kaggle. "This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases.
  • Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage".
  • The model best worked on this dataset is Random Forest Classifier.
  • Accuracy achieved = 98.75%

3. Getting Admission in College Prediction

Notebook File Dataset
  • The objective of the project is to predict the chances of getting admission to a reputed University based on parameters like GRE Score, TOEFL Score, University Rating, SOP, LOR, CGPA, and Research submission.
  • The data set that has used in this project has taken from the kaggle.
  • The model best worked on this dataset is Linear Regression Model.
  • Accuracy achieved = 81.08%

4. Heart Disease Prediction

Notebook File Dataset
  • The objective of the project is to diagnostically predict whether or not a patient has Cardiac/Heart diabetes, based on certain diagnostic measurements included in the dataset like cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, etc.
  • The data set that has used in this project is taken from the Kaggle.
  • The model best worked on this dataset is Random Forest Classifier with n_estimators=90.
  • Accuracy achieved = 83.82%

5. Iris Flower Classification

| Notebook File1(all models) | Notebook File2(KNN based)| Notebook File3(SVM Based) |Dataset| |----|----|----|

  • The aim is to classify iris flowers among three species (setosa, versicolor, or virginica) from measurements of sepals and petals' length and width.
  • The iris data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
  • The central goal here is to design a model that makes useful classifications for new flowers or, in other words, one which exhibits good generalization.
  • The model best worked on this dataset is Support Vector Classifier..
  • Accuracy achieved = 98%

6. Loan Repayment Prediction

Notebook File Dataset
  • Predicts whether the bank should approves the loan of an applicant based on his profit using Ensemble Learning Methods.
  • The data set that has used in this project is taken from the Kaggle.
  • The model best worked on this dataset is Random Forest Classifier with n_estimators=600.
  • Accuracy achieved = 84.75%

7. Predict Employee Turnover

Notebook File Dataset
  • It is a guided Project.
  • The Objective of this project is to predict Employee Churn using Decision Tree and Random Forest Classifiers.
  • The Dataset is taken From guided project Course available at Coursera named Predict-Employee-Turnover-with-scikit-learn.
  • Accuracy achieved = 97%

8. Wine Quality Prediction

Notebook File Dataset
  • The Objective of the project is to predict the quality of the Wine based on different features present in the dataset.
  • The data set that has used in this project is taken from the Kaggle.
  • The model best worked on this dataset is Random Forest Classifier with n_estimators=100.
  • Accuracy achieved = 90.31%