Concrete Strength Prediction and Gender Classification

Gender Classification Project

This project focuses on predicting gender using machine learning and deep learning techniques. The dataset contains labeled images categorized as male and female. The primary objective was to develop and compare different models for accurate gender prediction.

Key Highlights:

• Conducted exploratory data analysis (EDA) for better understanding, including count plots and image visualization.
• Implemented a baseline SVM (LinearSVC) model for initial gender classification.
• Developed a Convolutional Neural Network (CNN) model with multiple Conv2D and Maxpooling2D layers to improve accuracy.
• Achieved a test accuracy of over 95% using the CNN model.
• Leveraged libraries such as Keras, scikit-learn, and matplotlib for model development and visualization.

Cement Strength Regression Project

In this project, we tackle cement strength prediction using regression techniques and neural networks. The dataset contains various features related to cement components and environmental conditions. The primary goal was to build accurate regression models for predicting cement strength.

Key Highlights:

• Conducted exploratory data analysis (EDA) to gain insights into the dataset's features and their distributions.

• Utilized Linear, Ridge, and Lasso Regression algorithms to predict cement strength.

• Developed a custom neural network architecture tailored to the regression problem, achieving a lower test mean squared error compared to traditional regression models.

• Made use of libraries like scikit-learn, TensorFlow, and Keras for regression modeling and neural network development.

• Datasets

• Gender Classification Project Dataset: Link to Kaggle Dataset

• Cement Strength Regression Project Dataset: Link to Kaggle Dataset