- Built classification models to predict the edibility of mushrooms based on their physical characteristics.
- Provides an accurate and reliable model that can help identify poisonous mushrooms and prevent potential health hazards.
- Uses the Mushroom Data Set on UCI and employs various machine learning algorithms, such as logistic regression, SVM, and MLP to train and evaluate the models.
- The project also includes exploratory data analysis and data visualization to gain insights into the dataset.
- Used pandas, numpy, seaborn and matplotlib libraries.
- Machine learning project with pandas, sklearn, SQLite3, and Flask.
- Web application that provides an interface to allow users to upload CSV files containing similar real estate data to obtain precise predictions.
- Populating prediction results into a SQLite3 database then querying and displaying their results with Flask.
- Uses the Melbourne Housing Dataset
- Targets sale prices and uses a subset of columns in the set as features to predict housing prices.
- Splits training and validation data to fit three different regressors in which the mean absolute error will be calculated and stored for each of them: Decision tree, Decision tree with a maximum limit of 100 nodes and Random Forest.
- Web scraping Yahoo for Apple’s stock history with BeautifulSoup, creating and inserting the data into a SQLite3 database, and using Flask to display it.