-
Stars1
-
LanguageJupyter Notebook
-
Created over 4 years ago
-
Updated over 4 years ago
Reviews
There are no reviews yet. Be the first to send feedback to the community and the maintainers!
Repository Details
Used LDA (supervised technique) to perform dimensionality reduction on datasets. Modeled Logistic Regression on selected features to classify the wine customers. Model accuracy 100.00%
More Repositories
1
Data-Structures-and-Algorithms
Solution to 500+ popular data structure and algorithm problems in Java, C++ and Python programming languages.Java
140
2
FCM-Satellite-Images-in-Python
Fuzzy C-Means clustering on Satellite Images.Jupyter Notebook
2
3
Auto-Rickshaw-Tracking-System
Auto-Rickshaw Tracking System is flask based application to track auto rickshaw within college campus with the help of RFIDs .HTML
2
4
fuzzy-clustering-satellite-image
A web application to perform Fuzzy clustering on a satellite images using Expectation-Maximum AlgorithmPython
1
5
Wine-Customers-Classification-with-PCA-in-Python
Used PCA (an unsupervised technique) to perform dimensionality reduction on given dataset. Modeled Logistic Regression on selected features to classify the wine customers. Model accuracy 97.22%.Jupyter Notebook
1
6
Flight-Delay-Prediction-by-Logistic-Regression-in-Python
Modeled Logistic regression from scratch to predict the delay of flights.Jupyter Notebook
1
7
covid-detection-ct-scan-images
Detect whether a person is COVID-19 positive by CT Scan images of Transverse Section of Chest.Jupyter Notebook
1
8
Flight-Delay-Prediction-in-Python
Jupyter Notebook
1
9
SUV-Buyers-Classification-with-kPCA-in-Python
Used Kernel PCA to extract the principle components of non-linearly separable dataset of SUV Buyers. Modeled Logistic Regression to classify whether a person will buy a SUV or not. Model Accuracy is 91.25%Jupyter Notebook
1
10
PCA-from-Scratch-in-Python
Modeled Principal component analysis from scratch in PythonJupyter Notebook
1
11
SUV-Buyers-Classification-in-Python
Performed Classification on non-linearly separable datasets of SUV Buyers. Modeled all the classification techniques available to find the best algorithm that classify whether a person will buy a SUV or not. Used k-Fold Validation for all the techniques. Model Accuracy on test set are: Logistic Regression-89.00% KNN- 93.00% SVM-90.00% Kernel SVM-93.00%, Naive Bayer's-90.00%, Decision Tree-91.00%, Random Forest-91.00%Jupyter Notebook
1