The prediction task is to predict the number of rings on an abalone, a kind of shellfish. Full information of the dataset is provided here: https://archive.ics.uci.edu/ml/datasets/Abalone

I have followed all the steps as listed below :

1. Data Understanding
2. Data Retreival
3. Exploratory Data Analysis
4. Missing Value Analysis
5. Outlier Analysis
6. Feature Engineering
7. Feature Scaling
8. Modeling and Evaluation
9. Dimensionality Reduction
10. Modeling with Hyperparameters for tuning

Dataset provided (Abalone.data) had features which was highly correlated with each other, hence i had to use PCA to get higher dimensional data features which got me good accuracy as well as prediction power.

I have shown every graph in the jupyter notebook attached as well in R program.

Every code is properly commented for your better understanding.

I have successfully achieved the best accuracy (~99%) with less RMSE and R^2 as high as 0.99.