Neural-Network-Regression

This project showcases a custom neural network implementation along with a TensorFlow-Keras model.

Dataset Overview

The dataset contains 700 entries with the following columns:

• cement: Amount of cement used in the mixture
• water: Amount of water used in the mixture
• superplasticizer: Amount of superplasticizer used in the mixture
• age: Age of the concrete (in days)
• concrete_compressive_strength: Target variable - Concrete compressive strength

Exploratory Data Analysis

• Checked for missing values (none found).
• Checked data types and general statistics of the dataset.
• Visualized data distribution and correlation using boxplots and a heatmap.

Data Preprocessing

• Performed feature scaling using standardization on input features.
• Implemented a manual train-test split function to split the dataset into training and testing sets.

Neural Network Implementation

Custom Neural Network (using NumPy)

• Created a custom neural network class with a configurable number of hidden layers.
• Implemented the sigmoid activation function for the hidden layer and linear activation for the output layer.
• Utilized mean squared error as the loss function.
• Trained the neural network on the training set, displaying the loss at regular intervals.

Neural Network using TensorFlow

• Implemented a feedforward neural network using TensorFlow and Keras.
• Defined a custom callback to print the loss at specified intervals during training.
• Utilized the Adam optimizer and mean squared error loss function.
• Trained the neural network on the training set and evaluated on the test set.

Model Evaluation

• Calculated and printed the training and testing loss for both custom and TensorFlow neural networks.
• Calculated the R-squared value for model performance assessment.

Example Prediction

• Provided an example prediction for a new data point using the trained custom neural network.