A collection of end-to-end machine learning pipelines, classification algorithms, and data preprocessing workflows developed using Python and scikit-learn.
This repository serves as a centralized portfolio of machine learning projects focused on building robust data pipelines, applying feature engineering, and conducting rigorous model evaluations.
Each sub-directory contains an independent project complete with its own dataset handling, exploratory data analysis, algorithm selection, hyperparameter tuning, and visual performance reporting.
| Project Folder | Description | Tech Stack / Model | Key Metric / Result |
|---|---|---|---|
titanic-survival-prediction |
Binary classification pipeline predicting passenger survival using historical data, missing value imputation, and automated grid search tuning. | Python, Pandas, Scikit-Learn ( |
~80% Test Accuracy |
- Core Language: Python 3
- Data Wrangling & Analysis: Pandas, NumPy
- Machine Learning: Scikit-Learn (
KNeighborsClassifier,GridSearchCV,MinMaxScaler,train_test_split) - Data Visualization: Matplotlib, Seaborn
- Version Control: Git, GitHub
- Clone the repository:
git clone https://github.com/Khushi07tech/machine-learning-projects.git cd machine-learning-projects - Navigate to a specific project directory:
cd titanic-survival-prediction - Install dependencies and run:
pip install -r requirements.txt python main.py
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