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.
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]
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]
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]
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]
- 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.
To run these notebooks, you will need to install the required Python packages. You can do this by running:
pip install -r requirements.txt