Alan-Szalkowski/African-Cichlid

African Cichlid Machine Learning Project Overview This project focuses on predicting care difficulty and analyzing relationships between various traits of African cichlids using multiple machine learning algorithms. It combines regression, classification, and KNN-based prediction models to understand how species traits such as aggression, activity level, size, and environmental preferences relate to overall care requirements. The dataset was manually created through detailed research on African cichlid species to ensure realistic behavioral and environmental attributes. This project showcases end-to-end ML development — from data creation and preprocessing to model training, visualization, and evaluation. Key Features Custom-built dataset of African cichlid species Models used: Linear Regression – to predict care difficulty and tank size Logistic Regression – for classifying care levels Random Forest Classifier K-Nearest Neighbors (KNN) – to compare classification accuracy Data preprocessing with: One-Hot Encoding StandardScaler Train/Test split Visualizations: Correlation heatmaps Residual plots Scatter plots showing predicted vs actual values Model Evaluation Each model was trained and evaluated using R² and Mean Squared Error (MSE) for regression, and accuracy for classification. Results showed that the regression models captured strong relationships between features and care difficulty, while KNN and Random Forest provided high classification accuracy when predicting care levels. Insights Aggression and activity levels strongly influenced overall care difficulty. Environmental preferences (temperature, pH, and hardness) were key predictors for tank requirements. Random Forest and KNN performed best in classification tasks. Tools & Libraries Python Pandas, NumPy Scikit-learn Matplotlib, Seaborn Future Improvements Add a user input system that suggests compatible species for a user’s tank. Expand dataset beyond 200 species for strong …