Regression Using Deep Learning Project - 020MLRES4 - Machine Learning

Link to the dataset :

https://archive.ics.uci.edu/dataset/9/auto+mpg

Description

This project utilizes TensorFlow and Keras Tuner to perform regression analysis on the Auto MPG dataset from the UCI Machine Learning Repository. The goal is to predict the miles per gallon (MPG) of vehicles based on various attributes like Cylinders, Displacement, Horsepower, etc.

Installation

To set up the project environment, run the following commands:

pip install tensorflow pip install keras-tuner pip install pandas pip install numpy

Usage

To run the notebook, navigate to the project directory and start Jupyter Notebook:

jupyter notebook

Open projetregressionversion2.1.ipynb and execute the cells sequentially to see the results of the regression analysis.

Project Sections

• Data Preprocessing: Handling missing values and preparing data for analysis.
• One Hot Encoding: Encoding categorical variables as binary vectors.
• Relationship Scatters: Visualizing relationships between different variables.
• Correlation Heatmap: Analyzing the correlation between different features.
• Data Splitting: Dividing the dataset into training and test sets.
• Linear Regression: Implementing a linear regression model to predict MPG.
• R2_score Values: Evaluating the performance of the linear regression model.
• DNN Using One Single Input: Developing a deep neural network with a single input feature.
• DNN Using Multiple Inputs: Developing a deep neural network with multiple input features.

Data

The dataset used is the Auto MPG dataset, which can be automatically loaded via the notebook. It includes the following attributes:

• MPG (Miles per gallon)
• Cylinders
• Displacement
• Horsepower
• Weight
• Acceleration
• Model Year
• Origin

Collaborators :

-Malih Assaad

-Hussein Dakroub

-Mona El Hajj Chehade

Instructor:

-Professor Youssef Bakouny