Shivam Singh's repositories
Skin-Cancer-Detection-using-ResNet-50-deployed-on-Streamlit
Classify Skin cancer from the skin cancer pictures using ResNet 50. The dataset for the project is obtained from the Kaggle ISIC-Archive.
Credit-Card-Fraud-Detection-Model-Deployment-using-Flask
End to End Credit Card Fraud Detection with Python & Machine Learning using Flask
Perceptron-Model-from-Scracth
In this project, we have implemented the classic Perceptron for binary classification (0/1 class labels). Invented in 1957 by Frank Rosenblatt at the Cornell Aeronautical Laboratory, a perceptron is the simplest neural network possible: a computational model of a single neuron. A perceptron consists of one or more inputs, a processor, and a single output.
K-Means-Clustering-from-Scratch
Build the customer segmentation model using K-Means Clustering Algorithm
Detecting-Lane-Line-on-Highway
Detecting Lane Line is one of the most fundamental concepts for building self-driving cars. In this project I have used OpenCV, applying Canny edge detection and Hough Transformation to fine Lane Lines on the roads.
FIFA-2018-Data-Analysis-
In this project, we will be looking at the player data provided by FIFA which contains information such as personal details, wages, physical attributes, technical skills, potential and their positional strengths. This is primarily data of FIFA 2018.Through this project, you will get a glimpse of insights behind the beautiful game and the kind of information and decisions a football manager goes through.
Python-For-Beginners
Core Python day wise preparation
Customer-Segmentation
The Aim of the this repository is to build the predictive model with which we can identify or analyze the customer behavior and can also segment the customers in different categories with the technique called "RFM Analysis".
Emergency-vs-Non-Emergency-Vehicle-Classification
Fatalities due to traffic delays of emergency vehicles such as ambulance & fire brigade is a huge problem. In daily life, we often see that emergency vehicles face difficulty in passing through traffic. So differentiating a vehicle into an emergency and non emergency category can be an important component in traffic monitoring as well as self drive car systems as reaching on time to their destination is critical for these services. In this problem, you will be working on classifying vehicle images as either belonging to the emergency vehicle or non-emergency vehicle category. For the same, you are provided with the train and the test dataset. Emergency vehicles usually includes police cars, ambulance and fire brigades.
Building-a-Reccomendation-System-for-an-ecommerce-site
Recommender Systems aim to help a user or a group of users to select items from a crowded item or information space.
IPL-first-inning-score-prediction
Build a model to predict the first innings score of any IPL match (In terms of range).
Healthcare-Analytics-II---AV-Hackathon
The Competition is conducted by Analytics Vidhya named Janatahack - Healthcare Analytics II. The task is to accurately predict the Length of Stay for each patient on case by case basis so that the Hospitals can use this information for optimal resource allocation and better functioning. The length of stay is divided into 11 different classes ranging from 0-10 days to more than 100 days. I tried almost all classifier algorithms for this Multioutput problem to get better accuracy. I achieved accuracy of around 42.867% using LightGBM which is very powerful ensemble technique.
Gender-Prediction-Model
Gender Prediction for E-Commerce With the evolution of the information and communication technologies and the rapid growth of the Internet for the exchange and distribution of information, Electronic Commerce (e-commerce) has gained massive momentum globally, and attracted more and more worldwide users overcoming the time constraints and distance barriers. It is important to gain in-depth insights into e-commerce via data-driven analytics and identify the factors affecting product sales, the impact of characteristics of customers on their purchase habits. It is quite useful to understand the demand, habits, concern, perception, and interest of customers from the clue of genders for e-commerce companies. However, the genders of users are in general unavailable in e-commerce platforms. To address this gap the aim here is to predict the gender of e-commerce’s participants from their product viewing records.
Topic-Modelling-for-Research-Article
Researchers have access to large online archives of scientific articles. As a consequence, finding relevant articles has become more difficult. Tagging or topic modelling provides a way to give token of identification to research articles which facilitates recommendation and search process. Given the abstract and title for a set of research articles, predict the topics for each article included in the test set.
INeuron-Assignments
This repository contains all the assignments related to Python, Statistics, NLP and CV.
School-Data-Analysis
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.
Cardiovascular-Disease-Prediction
To build an application to classify the patients to be healthy or suffering from cardiovascular disease based on the given attributes.
Wine-Variety-Prediction---NLP-Technique
Multi class Classification using NLP techniques
Python-for-Data-Science
Everything you need to know about Python
Skewed-data-classification
This notebook will test different methods on skewed data. The idea is to compare if preprocessing techniques work better when there is an overwhelming majority class that can disrupt the efficiency of our predictive model
WNS-Analytics-Vidhya-Hackathon
This is my first notebook where I create ensemble model
Business_Case_Studies
Solving Business cases with Machine Learning and Deep Learning
Practice-Problem-Twitter_Sentiment_Analysis
The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.