🚀 Machine Learning Projects Portfolio

A curated portfolio of end-to-end Machine Learning projects focusing on data preprocessing, exploratory data analysis (EDA), predictive modeling, and model evaluation across various domains.

Each project demonstrates a practical application of data science workflows, from cleaning and merging raw datasets to training, tuning, and evaluating statistical and machine learning models.

---

📋 Project Directory

#	Project Name	Algorithm Used	Key Task	Project Folder
1	Uber & Lyft Fare Price Predictor	Random Forest Regressor	Regression	1_uber_lyft_fare_prediction_random_forest
2	E-Commerce Customer Churn Predictor	Logistic Regression	Binary Classification	2_ecommerce_churn_logistic_regression
3	Movie Recommendation System	K-Nearest Neighbors (KNN)	Collaborative Filtering	3_movie_recommendation_KNN
4	NASA Pulsar Star Classification	Non-Linear SVM (RBF Kernel)	Binary Classification	4_NASA_Pulsar_Star_Classification_Non-Linear_SVM
5	Smart Image Compressor	K-Means Clustering (k-means++)	Unsupervised Color Quantization & Size Reduction	5_image_compressor_K_means_clusturing

Click on a project folder to view the details, dataset schema, performance metrics, and implementation steps.

---

🛠️ General Setup & Requirements

To explore and run the Jupyter notebooks in this repository:

1. Install Dependencies: Make sure you have Python installed, then install the necessary scientific libraries:

   pip install numpy pandas scikit-learn matplotlib seaborn openpyxl

2. Datasets: Since the datasets are large, they are not committed to GitHub. Please check the individual project folder's README.md for Kaggle links to download them.

3. Run Notebooks: Launch Jupyter Notebook or JupyterLab and open the .ipynb files in the respective project directories:

   jupyter notebook