This repository contains the set of experiments performed as part of the Data Science course. The primary objective of these experiments is to understand and implement various data science techniques, ranging from data preprocessing to machine learning model development and evaluation.
Each experiment is organized in a separate folder, containing the corresponding Jupyter Notebook.
Experiment 1: To preprocess and prepare data using NumPy and Pandas in Python for effective analysis and modeling.
Experiment 2: To implement and evaluate Linear Regression for predictive modeling using Python, and analyze the relationship between independent and dependent variables.
Experiment 3: To apply Logistic Regression for binary classification problems using machine learning, and assess model performance through appropriate evaluation metrics.
Experiment 4: To implement Decision Tree classifier models to perform supervised classification and evaluate model performance.
Experiment 5: To develop and evaluate logistic regression models for multi-class classification tasks using machine learning.
Experiment 6: To apply the Naive Bayes machine learning algorithm for classification tasks and assess accuracy, precision, and recall.
(More experiments will be added as we proceed further during this course.)
- Programming Language: Python
- Development Environment: Google Colab
- Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
Chirag Wadkar
B.Tech in Electronics & Computer Science, Pillai College of Engineering
📌 This repository is maintained as part of the academic coursework for Data Science and demonstrates the practical application of fundamental concepts.