Predicts the price of the house based off on many following features using Artificial Neural Network.
bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition grade ... sqft_basement yr_built yr_renovated lat long sqft_living15 sqft_lot15 montly yearly monthly
- Data Cleaning and preprocessing.
- Feature Engineering.
- Data Visualization and Analysis.
- Data Normalization and Scaling.
- Training using Sequential Model with Dense Layers having "relu" activation as its a regression problem.
- Compiled using AdamOptmizer as optmizer and Mean squared error (mse) as the loss function.
- Solving Overfitting issues using EarlyStoppings Callbacks and Dropout Layers in Network.
- Hyperparameter Tuning the Algorithms yielding best results.
- Testing the model on custom reallife data.
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
- TensorFlow 2
- Keras
- Scikit-Learn
- Seaborn
- Matplotlib
- Pandas
- Numpy