abhiwalia15
Technical Writer@Litmus | Data Scientist @Loblaws | GSoD'22 @OpenMined | Open-Source Contributor | Technical Writer | Master's in Applied Computing (AI)
LitmusCanada
Pinned Repositories
AI-for-Finance-Stocks-real-time-analysis-
1. First we fetch data of stocks in realtime from nse India website, perform basis data visualizations using python to analyze the stock. 2. Then we use machine learning LSTM technique to predict the future stock price and at last create an interactive web-app using Streamlit in python.
COVID-19
Performing Exploratory Data Analysis on COVID-19 Dataset and display the results by plotting of various types such as the plot of Worldwide Cases- confirmed, deaths, recovered and analysis by country. • After performing EDA, the cleaning and preprocessing of the dataset to make predictions using the ML technique(LightGBM, which is a gradient boosting framework that uses tree-based learning algorithms).
Deep-Learning-with-OpenCV
Deep Learning is a fast growing domain of Machine Learning and if you’re working in the field of computer vision/image processing already (or getting up to speed), it’s a crucial area to explore. With OpenCV 3.3, we can utilize pre-trained networks with popular deep learning frameworks. The fact that they are pre-trained implies that we don’t need to spend many hours training the network — rather we can complete a forward pass and utilize the output to make a decision within our application. OpenCV does not (and does not intend to be) to be a tool for training networks — there are already great frameworks available for that purpose. Since a network (such as a CNN) can be used as a classifier, it makes logical sense that OpenCV has a Deep Learning module that we can leverage easily within the OpenCV ecosystem. Popular network architectures compatible with OpenCV 3.3 include: GoogleLeNet (used in this blog post) AlexNet SqueezeNet VGGNet (and associated flavors) ResNet
Document-Scanner-Using-OpenCV-Python
• In this project, you will learn how to extract email and phone number from a business card or any document and save the output in a JSON file. • Initially we need to resize the images so OpenCV can handle it and then the following steps are applied-detecting the edges, finding contours, applying perspective transform to get top-down view, using pytesseract to extract text and then finally using regex expressions to identify only email and phone number.
Face-Classification-into-Blood-No-Blood
Competition
Facial-Expression-Recognition
• First we recognize the emotion of the person using Opencv and Keras by training our model on Data provided from Kaggle, program is trained for 30 epochs and we have got an accuracy of 71%. • After detecting the emotion out of 7 labels, we as user for his favourite artist and then recommend songs from Spotify using its API and movies from IMDB depending upon its mood.
fuel-consumption-and-Carbon-dioxide-emission-of-cars
In this notebook, we learn how to use scikit-learn to implement simple linear regression. We download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, evaluate your model using test set, and finally use model to predict unknown value.
Python-for-Data-Science-and-Machine-Learning-Bootcamp
program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning: Programming with Python NumPy with Python Using pandas Data Frames to solve complex tasks Use pandas to handle Excel Files Web scraping with python Connect Python to SQL Use matplotlib and seaborn for data visualizations Use plotly for interactive visualizations Machine Learning with SciKit Learn, including: Linear Regression K Nearest Neighbors K Means Clustering Decision Trees Random Forests Natural Language Processing Neural Nets and Deep Learning Support Vector Machines and much, much more!
Web-Scraping-in-Python
Implementing Web Scraping in Python with BeautifulSoup There are mainly two ways to extract data from a website: Use the API of the website (if it exists). For example, Facebook has the Facebook Graph API which allows retrieval of data posted on Facebook. Access the HTML of the webpage and extract useful information/data from it. This technique is called web scraping or web harvesting or web data extraction. This article discusses the steps involved in web scraping using implementation of Web Scraping in Python with Beautiful Soup Steps involved in web scraping: Send a HTTP request to the URL of the webpage you want to access. The server responds to the request by returning the HTML content of the webpage. For this task, we will use a third-party HTTP library for python requests. Once we have accessed the HTML content, we are left with the task of parsing the data. Since most of the HTML data is nested, we cannot extract data simply through string processing. One needs a parser which can create a nested/tree structure of the HTML data. There are many HTML parser libraries available but the most advanced one is html5lib. Now, all we need to do is navigating and searching the parse tree that we created, i.e. tree traversal. For this task, we will be using another third-party python library, Beautiful Soup. It is a Python library for pulling data out of HTML and XML files.
Team-Predictions
Predicting an IPL ALL Stars team based on past performance of Players.
abhiwalia15's Repositories
abhiwalia15/fuel-consumption-and-Carbon-dioxide-emission-of-cars
In this notebook, we learn how to use scikit-learn to implement simple linear regression. We download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, evaluate your model using test set, and finally use model to predict unknown value.
abhiwalia15/Face-Recognition-with-OpenCV-in-Python
In this project we have discussed on how to perform face recoginition with openCV in python .
abhiwalia15/Breast-Cancer-Classification-in-TensorFlow
abhiwalia15/Weather-Station-Clustering-using-DBSCAN-scikit-learn
DBSCAN is specially very good for tasks like class identification on a spatial context. The wonderful attribute of DBSCAN algorithm is that it can find out any arbitrary shape cluster without getting affected by noise. For example, this following example cluster the location of weather stations in Canada. DBSCAN can be used here, for instance, to find the group of stations which show the same weather condition. As you can see, it not only finds different arbitrary shaped clusters, can find the denser part of data-centered samples by ignoring less-dense areas or noises.
abhiwalia15/Build-an-AI-Startup-with-PyTorch
test
abhiwalia15/caffe
Caffe: a fast open framework for deep learning.
abhiwalia15/China-GDP-2014-1960-
In this project, we fit a non-linear model to the datapoints corrensponding to China's GDP from 1960 to 2014.
abhiwalia15/Clustering-on-Vehicle-dataset
Imagine that an automobile manufacturer has developed prototypes for a new vehicle. Before introducing the new model into its range, the manufacturer wants to determine which existing vehicles on the market are most like the prototypes--that is, how vehicles can be grouped, which group is the most similar with the model, and therefore which models they will be competing against. Our objective here, is to use clustering methods, to find the most distinctive clusters of vehicles. It will summarize the existing vehicles and help manufacturers to make decision about the supply of new models.
abhiwalia15/CNN-from-Scratch
A scratch implementation of Convolutional Neural Network in Python using only numpy and validated over CIFAR-10 & MNIST Dataset
abhiwalia15/Content-based-recommendation-system
Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous, and can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library.
abhiwalia15/Customer-Segmentation-with-k_means-
Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data. Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retaining those customers. Another group might include customers from non-profit organizations. And so on. Lets download the dataset. To download the data, we will use !wget to download it from IBM Object Storage.
abhiwalia15/deep-learning-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
abhiwalia15/DFA-Automata-Theory-Assignment-Programs
DFA checking strings validity or non-validity .
abhiwalia15/Fashion-MNIST
Classifying Images of Clothing
abhiwalia15/FlutterWithFirebase
A Flutter app with firebase libraries implementation
abhiwalia15/Hand-Gesture-Recognition-Using-Background-Elllimination-and-Convolution-Neural-Network
Hand Gesture Recognition using Convolution Neural Network built using Tensorflow, OpenCV and python
abhiwalia15/Handwritten-Digits-Recognition-in-python
In this tutorial we will learn how to recognize handwritten digits in python using machine learning library called scikit learn.
abhiwalia15/Human-Cell-Records
In this project, you will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant. SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.
abhiwalia15/Medical-Researcher-drug-prediction-project
Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y. Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The feature sets of this dataset are Age, Sex, Blood Pressure, and Cholesterol of patients, and the target is the drug that each patient responded to. It is a sample of binary classifier, and you can use the training part of the dataset to build a decision tree, and then use it to predict the class of a unknown patient, or to prescribe it to a new patient
abhiwalia15/Object-detection-using-HSV-ColorSpace
Detecting objects using real time web cam using their colors properties
abhiwalia15/opencv-1
Open Source Computer Vision Library
abhiwalia15/Predict-Fuel-Efficiency-Regression-
In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where we aim to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture). This notebook uses the classic Auto MPG Dataset and builds a model to predict the fuel efficiency of late-1970s and early 1980s automobiles. To do this, we'll provide the model with a description of many automobiles from that time period. This description includes attributes like: cylinders, displacement, horsepower, and weight.
abhiwalia15/recommendation-systems-based-on-Collaborative-Filtering
Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore recommendation systems based on Collaborative Filtering and implement simple version of one using Python and the Pandas library.
abhiwalia15/Sign-Language-Interpreter-using-Deep-Learning
A sign language interpreter using live video feed from the camera.
abhiwalia15/Telecommunication-customer-base
Imagine a telecommunications provider has segmented its customer base by service usage patterns, categorizing the customers into four groups. If demographic data can be used to predict group membership, the company can customize offers for individual prospective customers. It is a classification problem. That is, given the dataset, with predefined labels, we need to build a model to be used to predict class of a new or unknown case. The example focuses on using demographic data, such as region, age, and marital, to predict usage patterns. The target field, called custcat, has four possible values that correspond to the four customer groups, as follows: 1- Basic Service 2- E-Service 3- Plus Service 4- Total Service Our objective is to build a classifier, to predict the class of unknown cases. We will use a specific type of classification called K nearest neighbour.
abhiwalia15/Telecommunications-Company-Analyst-Prediction-project
A telecommunications company is concerned about the number of customers leaving their land-line business for cable competitors. They need to understand who is leaving. Imagine that you are an analyst at this company and you have to find out who is leaving and why.
abhiwalia15/tesseract
Tesseract Open Source OCR Engine (main repository)
abhiwalia15/Text-Classification-with-Movies-Reviews
This project classifies movie reviews as positive or negative using the text of the review. This is an example of binary—or two-class—classification, an important and widely applicable kind of machine learning problem. We'll use the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are balanced, meaning they contain an equal number of positive and negative reviews.
abhiwalia15/tfjs-models
Pretrained models for TensorFlow.js
abhiwalia15/Youtube-Downloader-in-Python
Download youtube videos and select the quality and the destination where you want to save them.