Pinned Repositories
apoorvaKR12695
Config files for my GitHub profile.
AzureDeployment
Bike-Sharing-Demand-Prediction-
ML Supervised- Developed a regression model using algorithms such as Random Forest and XGBoost to predict the hourly demand of bikes.Performed hyperparameter tuning techniques and achieved R2 score of 92% using XGBoost model and reduced the public waiting time significantly.
Boston-House-Price-Prediction
Botson House Prediction - Build the linear regression model using scikit learn in boston data to predict 'Price' based on other dependent variable.
cloudrundemos
This repository is for Cloud Run Demos
Face-Emotion-Detection-
Deep Learning -Built an face emotion detection app that detects the sentiment of the online classroom using live video from the webcam and real-time aggregated feedback to the instructors about the class using CNN model and deployed on Heroku platform.
Hotel-booking-analysis
Exploratory data analysis-The data set contains booking information for a city hotel and a resort hotel and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things. All personally identifying information has from the data. performed exploratory data analysis to gain the insights from it to decide on which factors the booking demand is high
image-caption-generator
Deep learning-based image captioning with Flickr8k dataset. Code includes data prep, model training, and a Streamlit app.
Mobile-Price-Range-Prediction
Supervised ML- Built a Multi-Class classification model to find the relation between features of a mobile phone(RAM, Internal Memory etc) and its selling price. Model will predict the price range indicating how high the price is.
Netflix-Movies-and-TV-Shows-Clustering
Unsupervised Machine Learning-Netflix Recommender recommends Netflix movies and TV shows based on a user's favorite movie or TV show. It uses a a K-Means Clustering model to make these recommendations. These models use information about movies and TV shows such as their plot descriptions and genres to make suggestions.
apoorvaKR12695's Repositories
apoorvaKR12695/Mobile-Price-Range-Prediction
Supervised ML- Built a Multi-Class classification model to find the relation between features of a mobile phone(RAM, Internal Memory etc) and its selling price. Model will predict the price range indicating how high the price is.
apoorvaKR12695/Bike-Sharing-Demand-Prediction-
ML Supervised- Developed a regression model using algorithms such as Random Forest and XGBoost to predict the hourly demand of bikes.Performed hyperparameter tuning techniques and achieved R2 score of 92% using XGBoost model and reduced the public waiting time significantly.
apoorvaKR12695/Face-Emotion-Detection-
Deep Learning -Built an face emotion detection app that detects the sentiment of the online classroom using live video from the webcam and real-time aggregated feedback to the instructors about the class using CNN model and deployed on Heroku platform.
apoorvaKR12695/apoorvaKR12695
Config files for my GitHub profile.
apoorvaKR12695/AzureDeployment
apoorvaKR12695/Boston-House-Price-Prediction
Botson House Prediction - Build the linear regression model using scikit learn in boston data to predict 'Price' based on other dependent variable.
apoorvaKR12695/cloudrundemos
This repository is for Cloud Run Demos
apoorvaKR12695/Hotel-booking-analysis
Exploratory data analysis-The data set contains booking information for a city hotel and a resort hotel and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things. All personally identifying information has from the data. performed exploratory data analysis to gain the insights from it to decide on which factors the booking demand is high
apoorvaKR12695/image-caption-generator
Deep learning-based image captioning with Flickr8k dataset. Code includes data prep, model training, and a Streamlit app.
apoorvaKR12695/Netflix-Movies-and-TV-Shows-Clustering
Unsupervised Machine Learning-Netflix Recommender recommends Netflix movies and TV shows based on a user's favorite movie or TV show. It uses a a K-Means Clustering model to make these recommendations. These models use information about movies and TV shows such as their plot descriptions and genres to make suggestions.
apoorvaKR12695/Spam-Detector-sample-github
Developing a Python-based spam detector using the Naive Bayes approach.
apoorvaKR12695/speech_emotion_detection
DL+ML project