Implements Neural Network from scratch with one hidden layer. Uses activation functions and optimizer to improve the performance of Neural Network for prediction of car prices
#Data Set: The data set of this project is taken from Kaggle Kaggle Machine Learning Repository. https://www.kaggle.com/datasets/nehalbirla/vehicle-dataset-from-cardekho The dataset contains 4340 data points collected form websites.
Dataset Information: The dataset contains 4340 data points collected form websites, Features consist variables Car Name (Car_Name), Year (Year), Present Price (Present_Price),Driven Kilometer (Kms_Driven), Fuel Type (Fuel_Type), Seller Type(Seller_Type) , Transmission(Transmission),Owner(Owner) and to predict Selling Price (Selling_Price).
Target Variable: Selling Price (Selling_Price)
#Activation functions used:
- Sigmoid Activation Function
- Tanh Activation Function
- ReLu Activation Function
#Code: https://colab.research.google.com/drive/1gbJzYszrV6y5JcsIy_dY3wBhVmnjsQBW