1. Data Analysis and manipulation using pandas
   
2. Data Visualization using matplotlib
   
3. Test Train split
   

1. Created movie embedding path
   
2. Created user embedding path
   
3. Concatenated movie and user vectors
   
4. Weight initialization using the random uniform technique
   
5. Added 5 fully connected layers with activation function as ReLu
   
6. Adam optimizer used
   
7. Model has complied
   

1. Model is run for 18 epochs with verbosity 1
   
2. Achieved loss of 0.6
   
3. As seen below graph of Training error vs epoch, there is a reduction in error continuously after epoch=5
   

4. Minimum RSE of 0.8 is at epoch 18
   

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