Machine Learning Portfolio

This repository is a collection of notebooks and scripts showcasing my skills in machine learning and data analysis. Here, you'll find projects ranging from exploratory data analysis (EDA) to complex machine learning models.

Projects

EDA on US Accidents

A comprehensive analysis of US traffic accident data to uncover insights and patterns.

• Objective: Understand the main causes of traffic accidents in the US.
• Tools Used: Pandas, Matplotlib, Seaborn
• Key Findings: [Add a brief summary of your findings here]

Linear Models

Implementation and comparison of linear models on a dataset.

• Objective: Predict [outcome] using linear regression techniques.
• Models Used: Simple Linear Regression, Multiple Linear Regression
• Results: [Add a brief summary of the model performance]

Nearest Neighbor Algorithm

An application of the k-nearest neighbors algorithm to a classification problem.

• Objective: Classify [items] based on their feature similarities.
• Algorithm Used: KNN
• Performance: [Add a brief summary of algorithm performance]

Convolutional Neural Networks (CNN)

Building and training a CNN to recognize and classify images.

• Objective: Image classification using CNNs.
• Dataset: [Name of the dataset]
• Accuracy: [Add the achieved accuracy]

Additional Projects

• Implementation of ML Models: A variety of machine learning models applied to different datasets.
• Minimum Edit Distance Algorithm: An exploration of string similarity and applications in NLP.
• Neural Network on Rock & Paper Dataset: Training a neural network to classify images of rock, paper, and scissors.

Installation

To run these notebooks, you will need to install the required Python packages. You can do this by running:

pip install -r requirements.txt