This repository contains a collection of machine learning tasks implemented using Python, focusing on data preprocessing, model building, evaluation, and practical problem-solving. Each task addresses a different real-world machine learning application, ranging from regression analysis to recommendation systems.
The goal of this project set is to demonstrate a strong understanding of:
- Data handling and preprocessing
- Model implementation using Scikit-learn
- Evaluation and interpretation of results
- Practical ML workflows using Jupyter Notebooks
Objective: To model and analyze relationships between input features and a continuous target variable using Linear Regression.
Key Steps:
- Data loading and preprocessing
- Feature scaling and preparation
- Model training using Linear Regression
- Evaluation using regression metrics
- Interpretation of model coefficients
Outcome: This task establishes a baseline regression model and provides insights into feature influence and predictive performance.
Objective: To build a content-based recommendation system that suggests similar restaurants based on user ratings and feature similarity.
Key Steps:
- Data preprocessing and encoding
- Feature vector construction
- Similarity computation using cosine similarity
- Recommendation function implementation
- Ranking and returning top-N recommendations
Outcome: The system successfully recommends restaurants similar to a given input, demonstrating practical use of similarity-based machine learning techniques.
Objective: To perform exploratory data analysis (EDA) and visualize patterns within the dataset.
Key Steps:
- Data loading using Pandas
- Statistical exploration of features
- Visualization using Matplotlib
- Insight extraction from plotted trends
Outcome: This task focuses on understanding the data before modeling, emphasizing the importance of visualization and analysis in machine learning pipelines.
- Python
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
- Jupyter Notebook
- Practical application of regression models
- Understanding similarity-based recommendation systems
- Importance of data preprocessing and feature encoding
- Model evaluation and result interpretation
- End-to-end ML workflow execution
.
├── Task1.ipynb # Regression analysis
├── Task2.ipynb # Recommendation system
├── Task3.ipynb # Data analysis and visualization
└── README.md
This project set demonstrates the application of machine learning techniques across different problem domains. Each task highlights a critical aspect of machine learning, from prediction and recommendation to exploratory analysis, forming a strong foundation for more advanced ML projects.
Aadithya K L