🤖 Machine Learning With Python

Author: Shakil Hossan
Dept. of CSE, University of Rajshahi
Email: shakilhossan113@gmail.com

This repository contains Python notebooks and datasets covering various core topics in Machine Learning, organized by key learning modules.

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📂 Repository Structure

1. Linear and Logistic Regression

• Simple Linear Regression
• Mulitple-Linear-Regression
• Logistic Regression
• Datasets: ChurnData.csv, FuelConsumptionCo2.csv

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2. Building Supervised Learning Models

• Decision_trees
• KNN_Classification
• Multi-class Classification
• Random_ Forests _XGBoost
• Regression_Trees_Taxi_Tip
• decision_tree_svm_ccFraud
• Datasets: Obesity_level_prediction_dataset.csv, creditcard.csv, drug200.csv, teleCust1000t.csv, yellow_tripdata.csv

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3. Building_Unsupervised_Learing

• Clustering:
  • K-Means-Customer-Seg
  • Dataset: Cust_Segmentation.csv
• Dimension Reduction and Feature Engineering:
  • PCA.ipynb

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4. Evaluating Machine Learing Model

• Evaluating Classification Models
• Evaluating_k-means_clustering
• Evaluating_random_forest

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5. Model Generalizability

• ML_Pipelines_and_GridSearchCV
• Regularization_in_LinearRegression

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6. Project

• Practice Project
• Dataset: weatherAUS_2

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🧪 Topics Covered

• Regression (Simple, Multiple, Logistic)
• Supervised Learning (Decision Trees, KNN, Random Forests, XGBoost)
• Unsupervised Learning (Clustering, PCA)
• Model Evaluation & Validation Techniques
• Model Generalizability and Regularization
• Practical Projects with real datasets

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📄 License

This project is licensed under the MIT License.