Welcome to the Repository!

This repository contains 8 projects, each focusing on a unique machine learning task. These projects explore data analysis, regression, classification, dimensionality reduction, clustering, and deep learning using diverse datasets. Below is a detailed description of each project and its objectives.

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Project 1&2: Prediction

Objective:

Build a regression model to predict attributes such as GDP per capita, social support, freedom, and other socioeconomic indicators.

Key Tasks:

1. Data Analysis:
   • Summarize the dataset (size, feature types).
   • Visualize distributions and analyze correlations.
2. Feature Engineering:
   • Compute statistical values and identify features requiring special treatment.
3. Model Training:
   • Train and evaluate linear regression models using closed-form solutions and SGD.
   • Explore Ridge, Lasso, and Elastic Net regularization.
   • Compare performance with polynomial regression.
4. Evaluation:
   • Test the model using a separate test set and evaluate its performance.

Tools:

• Jupyter Notebook for analysis and visualization.
• Libraries: NumPy, Pandas, Matplotlib, Scikit-learn.

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Project 3&4: Rock Classification

Objective:

Classify rock types (Igneous, Metamorphic, Sedimentary) using a dataset with 11 features.

Key Tasks:

1. Data Preparation:
   • Process and split the dataset into training, validation, and testing sets.
   • Visualize feature distributions and analyze correlations.
2. Model Training:
   • Train classifiers such as Logistic Regression, SVM, and Random Forest.
   • Tune hyperparameters using grid search or manual exploration.
3. Ensemble Methods:
   • Combine individual classifiers into an ensemble for improved performance.
4. Comparison with Human Performance:
   • Compare model accuracy with human accuracy and analyze the results.

Tools:

• Jupyter Notebook for analysis and training.
• Libraries: Scikit-learn, Matplotlib, Seaborn.

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Project 5&6: PCA and Clustering on Rock Images

Objective:

Apply PCA and other dimensionality reduction techniques to analyze rock images and identify clusters.

Key Tasks:

1. PCA Analysis:
   • Determine the number of components required to retain 90% variance.
   • Reconstruct and visualize images with reduced dimensionality.
2. Visualization:
   • Use PCA, t-SNE, LLE, and MDS to create 2D scatter plots of image embeddings.
3. Comparison with Human Features:
   • Use Procrustes analysis to compare embeddings with human-labeled features.
4. Clustering:
   • Perform clustering using K-Means and EM.
   • Visualize and evaluate clustering performance.
5. Neural Networks:
   • Build a feedforward neural network (using dense and CNN layers).
   • Procrustes analysis with human data.

Tools:

• Image processing: OpenCV, Matplotlib.
• Dimensionality Reduction: Scikit-learn.
• Procrustes Analysis: SciPy.

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Project 7&8: Working with Large Language Models

Objective:

Evaluate the performance of a pre-trained models on dataset.

Key Tasks:

1. Model Description:
   • Analyze the architecture and parameter breakdown of the selected CLIP model.
2. Image Classification:
   • Evaluate model accuracy across five conditions (realistic, geons, silhouettes, blurred, features).
   • Compare results with human performance.
3. Dimensionality Reduction and Visualization:
   • Use t-SNE to visualize embeddings in 2D space.
   • Add object images to the embedding visualization.
4. Analysis:
   • Compare model embeddings with human-labeled features using Procrustes analysis.
   • Compute correlation coefficients and analyze significance.

Tools:

• Pre-trained models: Huggingface Transformers.
• Visualization: Matplotlib, t-SNE.
• Libraries: NumPy, SciPy, Scikit-learn.