Tejaswini Tambe 's repositories
Cancer-Donation-Portal-Python-Flask-App
Flask App for Cancer Donation Portal using basic Python, SQLite3, HTML, CSS and Javascript
Deep-Surveillance-Monitor-Facial-Emotion-Age-Gender-Recognition-System
Computer Vision module for detecting emotion, age and gender of a person in any given image, video or real time webcam. A custom VGG16 model was developed and trained on open source facial datasets downloaded from Kaggle and IMDB. OpenCV,dlib & keras were used to aid facial detection and video processing. The final system can detect the emotion, age and gender of people in any given image, video or real time webcam
Implementaion-of-Private-Cloud-using-ownCloud
Implementation of Private Cloud using ownCloud. ownCloud is a suite of client–server software for creating and using file hosting services. This repository explains implementing ownCloud on an Ubuntu VM running on top of a Windows host for secure cloud storage
Live-Video-Sketching-through-webcam-using-OpenCv-Python
Computer Vision model creates a live video sketch of frames through real time web cam video. Source code is written in python and model is based on OpenCV. Keras and Numpy have been used to optimize the performance of the model and posterize frames
ModSecurityCRS
Implementation of ModSecurity, Core Rule Set (CRS) on Apache server. ModSecurity, sometimes called Modsec, is an open-source web application firewall. ModSecurity was installed and configured on an Ubuntu VM using Virtual Box
Movie-Recommendation-Chatbot
Movie Recommendation Chatbot provides information about a movie like plot, genre, revenue, budget, imdb rating, imdb links, etc. The model was trained with Kaggle’s movies metadata dataset. To give a recommendation of similar movies, Cosine Similarity and TFID vectorizer were used. Slack API was used to provide a Front End for the chatbot. IBM Watson was used to link the Python code for Natural Language Processing with the front end hosted on Slack API. Libraries like nltk, sklearn, pandas and nlp were used to perform Natural Language Processing and cater to user queries and responses.
Object-Detecion-via-Smartphone-Camera-using-Faster-R-CNN
Detecting objects captured in the frame of a Smartphone Camera using Faster R-CNN algorithm. TensorFlow Object Detection API has been used for back end & OpenCV has been used to process the frames of video captured from Smartphone Camera. IPWebcam app is used to link Smarthphone to Object Detection Code
Real-Time-Object-Detection-API-using-TensorFlow
A Transfer Learning based Object Detection API that detects all objects in an image, video or live webcam. An SSD model and a Faster R-CNN model was pretrained on Mobile net coco dataset along with a label map in Tensorflow. This model were used to detect objects captured in an image, video or real time webcam. Open CV was used for streaming objects and preprocessing.
Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis-1
Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall
COVID-19-Detection-Flask-App-based-on-Chest-X-rays-and-CT-Scans
COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. A Flask App was later developed wherein user can upload Chest X-rays or CT Scans and get the output of possibility of COVID infection.
Online-Food-Ordering-Web-App
Online Food Ordering System Website using basic PHP, SQL, HTML & CSS. You can use any one of XAMPP, WAMP or LAMP server to run the Web App
Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis
Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets **(API keys included in code)**. The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are given for three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices for the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall