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amazing-mart-sales-analysis-using-power-bi

amazon-food-review

This is a sentiment analysis project based on pretrained modules like TextBlob and VADER on Amazon_Food_Reviews dataset.

car-purchase-decision

This is a car purchase decision data set, indicating wheather or not a client bought a car.

e-commerce-customer-segmentation

In this project we have used data from E commerce website to seggregate customers based on their interest.

estimation-of-obesity-levels-based-on-eating-habits-and-physical-condition

This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records.

face-mask-detection

Masks can help reduce the spread of respiratory illnesses, such as COVID-19. . In this project Deep Learning is used along with OpenCV for Detecting Faces With and Without mask.

heart-disease-prediction

In this project Decision Tree and some other machine learning algorithms are used for heart disease prediction.

insurance-charges-prediction

The aim of this projct is to predict Insurance charges of beneficiary based on Independant variables like age, bmi, smoker, sex, region, children etc. This dataset includes total 7 variables and 1338 rows.

seoul-bike-sharing-demand

In this Machine Learning project Rented Bike Count is predicted based on different Independent features using regression models like Linear Regression, KNN, SVR, DT, RF and XGBRegressor. Time series model SARIMAX is also used to predict dependent variable.

taxi-fare-prediction

Aim of this project is to predict taxi fare based on available data. This dataset contains total 8 variables and 50000 indexes.

travel-review-segmentation

This data set contains ratings from Google reviews. Reviews on attractions from 24 categories across Europe are considered. Using this data clustering model is built.

vaccine-usage-prediction

This dataset contains 30 categorical variables and 26707 records used to predict how likely the respondent will take flu vaccine.