avigangarde's Stars
rishabhnmishra/SQL_Music_Store_Analysis
Digital Music Store data analysis using SQL
ksdiwe/Book-Recommender-System
Collaborative Filtering Based Recommender System
Pranavla/Food_delivery_time_prediction_model
Food delivery services like zomato,swiggy will show accurate time it will take to deliver a order. This is done with the help of machine learning. Let's undestand and make a ml model on the same
VeeraDinesh/SQL-PROJECTS
anshudhanshu/Bank-Marketing-Effectiveness-Prediction
rohit-1026/Bike-Sharing-Demand-Prediction
sapanapawar/Health-Insurance-Cross-Sell-Prediction
Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.
sapanapawar/Online-Retail-Customer-Segmentation
Using a public online retailer dataset to segment the customers based on purchases and frequency using K-means Clustering.
sapanapawar/Play-Store-App-Reviews-Analysis
Analysis of Play Store App Review Data
saurabh423/Book-Recommendation-System
Build a recommender engine that reviews customer ratings and recommend items and improve sales.
Arkopradhan/HEALTH-INSURANCE-CROSS-SELL-PREDICTION
handetushar/Airline-Passenger-Referral-Prediction
NLP Project
handetushar/Bike-Sharing-Demand-Prediction
Supervised ML Project
hmarathe420/Kotak_Mahindra_Bank_Stock_Price_Prediction
As a Data Science Intern at Bharat Intern, I have been assigned the exciting task of developing a Stock Price Prediction model for Kotak Mahindra. This project aims to leverage machine learning techniques to analyze historical stock market data and build a predictive model that can forecast future stock prices for Kotak Mahindra.
hmarathe420/Titanic_Classification
As a Data Science Intern at Bharat Intern, I am thrilled to be working on the "Titanic Survival Classification" project. In collaboration with the esteemed team at Bharat Intern, I have been entrusted with the task of developing a robust classification model to predict the survival outcomes of passengers aboard the historic RMS Titanic.
jaolekar/Netflix-clustering
jaolekar/Online-retail
Navin321-alma/Mobile-Price-Range-Prediction-Classification
Rochakr4/Bike-Sharing-Demand-Prediction---Capstone-Project
Project Title : Seoul Bike Sharing Demand Prediction Problem Description Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes.
Rochakr4/CAPSTONE-PROJECT-3--Cardiovascular-Risk-Prediction
CAPSTONE PROJECT 3- Cardiovascular Risk Prediction The dataset is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The classification goal is to predict whether the patient has a 10-year risk of future coronary heart disease (CHD). The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes. Variables Each attribute is a potential risk factor. There are both demographic, behavioral, and medical risk factors. Data Description Demographic: • Sex: male or female("M" or "F") • Age: Age of the patient;(Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous) Behavioral • is_smoking: whether or not the patient is a current smoker ("YES" or "NO") • Cigs Per Day: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.) Medical( history) • BP Meds: whether or not the patient was on blood pressure medication (Nominal) • Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal) • Prevalent Hyp: whether or not the patient was hypertensive (Nominal) • Diabetes: whether or not the patient had diabetes (Nominal) Medical(current) • Tot Chol: total cholesterol level (Continuous) • Sys BP: systolic blood pressure (Continuous) • Dia BP: diastolic blood pressure (Continuous) • BMI: Body Mass Index (Continuous) • Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.) • Glucose: glucose level (Continuous) Predict variable (desired target) • 10-year risk of coronary heart disease CHD(binary: “1”, means “Yes”, “0” means “No”) -DV
Rochakr4/Online-Retail-Customer-Segmentation
Problem Description In this project, your task is to identify major customer segments on a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.
SAKSHIDHYANI/Bike-Sharing-Demand-Prediction
SAKSHIDHYANI/Cardiovascular-Risk-Prediction
SAKSHIDHYANI/Netflix-Movies-and-Tv-Shows-Clustering
saumyadash9/Saumya-Dash-Bank-Marketing-Effectiveness-Prediction
shiyasAli/Health-insurance-cross-sell-prediction
predicting whether a person would be interested or not in an insurance scheme.
shiyasAli/Netflix-movies-and-TV-shows-clustering
vaishnavitechcoder/TREUE-TECHNOLOGIES
I created projects and complete my Task
vivek16pawar/Tweet-Sentiment-Analysis
yagnik99/Covid-19-Tweets-Sentiment-Analysis
Building a classification model to predict the sentiment of COVID-19 tweets. The tweets have been pulled from Twitter and manual tagging has been done.