Pinned Repositories
cousera-data-science
evitanegaraputri4
House-Price-Prediction-Using-Spark-ML
House price prediction for Pakistan’s real estate market using PySpark. This project applies linear regression to analyze factors like location, size, and amenities, supporting informed decision-making.
Text-Processing-Classify-Topic-Label
This project classifies BBC News articles into five topics—Sport, Business, Politics, Tech, and Entertainment—using Naïve Bayes, Random Forest, and SVM. Feature extraction with TF-IDF and Bag of Words improves topic classification efficiency for enhanced information management.
Vehicle-Classification
Efficient vehicle classification using machine learning and deep learning models for Intelligent Traffic Systems. Classifies five vehicle types with models like SVM, Random Forest, and CNN, utilizing HOG, LBP, and Gabor features for enhanced accuracy in smart city traffic management.
Weather-Prediction-Using-Timeseries-Data
This project enhances agricultural weather forecasting by predicting solar radiation (SRAD) using machine learning and deep learning models, including KNN, Random Forest, XGBoost, LSTM, and hybrid methods like Voting and Stacking Regressors.
Weed-Classification
Weed classification using image processing and machine learning to boost agricultural productivity. This project classifies Charlock and Cleves weeds with models like logistic regression, SVC, random forest, and CNN, leveraging HOG, color histograms, and LBP.
evitanegaraputri4's Repositories
evitanegaraputri4/cousera-data-science
evitanegaraputri4/evitanegaraputri4
evitanegaraputri4/Weather-Prediction-Using-Timeseries-Data
This project enhances agricultural weather forecasting by predicting solar radiation (SRAD) using machine learning and deep learning models, including KNN, Random Forest, XGBoost, LSTM, and hybrid methods like Voting and Stacking Regressors.
evitanegaraputri4/Text-Processing-Classify-Topic-Label
This project classifies BBC News articles into five topics—Sport, Business, Politics, Tech, and Entertainment—using Naïve Bayes, Random Forest, and SVM. Feature extraction with TF-IDF and Bag of Words improves topic classification efficiency for enhanced information management.
evitanegaraputri4/Weed-Classification
Weed classification using image processing and machine learning to boost agricultural productivity. This project classifies Charlock and Cleves weeds with models like logistic regression, SVC, random forest, and CNN, leveraging HOG, color histograms, and LBP.
evitanegaraputri4/House-Price-Prediction-Using-Spark-ML
House price prediction for Pakistan’s real estate market using PySpark. This project applies linear regression to analyze factors like location, size, and amenities, supporting informed decision-making.
evitanegaraputri4/Vehicle-Classification
Efficient vehicle classification using machine learning and deep learning models for Intelligent Traffic Systems. Classifies five vehicle types with models like SVM, Random Forest, and CNN, utilizing HOG, LBP, and Gabor features for enhanced accuracy in smart city traffic management.