evitanegaraputri4

capstone

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/capstone

evitanegaraputri4/cousera-data-science

evitanegaraputri4/evitanegaraputri4

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/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/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.

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/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.