Farheen Bano (@FarheenB)
  • Stars
    star
    153
  • Global Rank 151,042 (Top 6 %)
  • Followers 65
  • Following 2
  • Registered almost 5 years ago
  • Most used languages
    Java
    8.3 %
    HTML
    8.3 %
    Python
    8.3 %
  • Location 🇮🇳 India
  • Country Total Rank 4,500
  • Country Ranking
    Java
    379

Top repositories

1

Data-Structures-and-Algorithms

Solution to 500+ popular data structure and algorithm problems in Java, C++ and Python programming languages.
Java
140
star
2

FCM-Satellite-Images-in-Python

Fuzzy C-Means clustering on Satellite Images.
Jupyter Notebook
2
star
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
star
4

fuzzy-clustering-satellite-image

A web application to perform Fuzzy clustering on a satellite images using Expectation-Maximum Algorithm
Python
1
star
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
star
6

Flight-Delay-Prediction-by-Logistic-Regression-in-Python

Modeled Logistic regression from scratch to predict the delay of flights.
Jupyter Notebook
1
star
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
star
8

Flight-Delay-Prediction-in-Python

Jupyter Notebook
1
star
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
star
10

PCA-from-Scratch-in-Python

Modeled Principal component analysis from scratch in Python
Jupyter Notebook
1
star
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
star
12

Wine-Customers-Classification-with-LDA-in-Python

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%
Jupyter Notebook
1
star