{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30587,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 3. Data Mining and Applied Machine Learning\n\nWelcome to [**\"Data Mining and Applied Machine Learning\"!**](https://dayche.com/courses/data-mining-machine-learning-python)  \n\nStructured by: [@zahrazolghadr](https://www.kaggle.com/zahrazolghadr)\n\nThis course is your gateway to mastering data-driven solutions using the **CRISP-DM methodology**. Our journey begins with **translating real-world business problems** into the language of data science. From there, we dive deep into the world of **data preparation**, exploring techniques and algorithms for cleaning, normalizing, and engineering features, recognizing that in data science, **\"garbage in, garbage out\"** holds true.\n\nNext, we turn our attention to **machine learning** algorithms, covering a spectrum from **decision trees** and **statistical models** to **neural networks**, **SVM**, and **ensemble approaches** for predictive models. We also delve into **clustering** and **association rule** algorithms. The course ensures a strong foundation in **evaluating these models**, providing insights into various techniques tailored for different data science tasks.\n\nWhat makes this course stand out is the **hands-on** approach. Throughout the journey, you'll find practical **Python** code in **Kaggle notebooks**, including **mini projects** and a **final project** focused on **\"Customer Behavior Analysis\"**. Get ready to dive into the world of data mining, problem-solving, and practical application, where you'll emerge with the skills needed for real-world data science challenges.   \n\n[**Join us**](https://dayche.com/courses/data-mining-machine-learning-python) on this exciting adventure!  \n\n> All videos in this course are **Farsi/Persian** language!","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"markdown","source":"## Content Of Notebooks:    \n\n**Weekly Excercise Project:**  [Carvana Project](https://www.kaggle.com/code/zahrazolghadr/dm-excercise-week01-eda)\n\n### **1. Data Understanding and Preparation**   \n**week 1: Exploratory Data Analysis (EDA)**  \n    1.1. [EDA in Data Mining](https://www.kaggle.com/code/zahrazolghadr/eda-in-data-mining)      \n    \n**week 2: Data Quality**  \n    1.2. [Data Cleaning in DM-FeatureScreening & Consistency](https://www.kaggle.com/code/zahrazolghadr/data-cleaning-in-dm-featurescreening-consistency)  \n    1.3. [Data Cleaning in DM-Outliers & Missing Values](https://www.kaggle.com/code/zahrazolghadr/data-cleaning-in-dm-outliers-missing-values)      \n    \n**week 3: Data Transformation**  \n    1.4. [Data Transformation (Noramlization & Scaling, Encoding, Discretization and Construction) ](https://www.kaggle.com/code/zahrazolghadr/data-transformation)    \n    \n**week 4: Feature Selection**   \n    1.5. [House Price Project: Data Preparation](https://www.kaggle.com/code/zahrazolghadr/house-price-preparation)  \n    1.6. [Dimension Reduction-Feature Selection](https://www.kaggle.com/code/zahrazolghadr/dimension-reduction-feature-selection)      \n    \n**week 5:Feature Extraction and more**   \n    1.7. [Dimension Reduction-Feature Extraction](https://www.kaggle.com/code/zahrazolghadr/dimension-reduction-feature-extraction)  \n    1.8. [Dimension Reduction-Sampling](https://www.kaggle.com/code/zahrazolghadr/dimension-reduction-sampling)   \n    1.9. [Data Aggregation: RFM Analysis](https://www.kaggle.com/code/zahrazolghadr/data-aggregation-rfm-analysis)  \n    1.10. [Data Integration](https://www.kaggle.com/code/zahrazolghadr/data-integration)\n    \n    \n### **2. Predictive Modeling and Evaluation**   \n**week 6: Data science Projects Pipeline**  \n    2.1. [Bankloan Project Pipeline](https://www.kaggle.com/code/zahrazolghadr/bankloan-pipeline)  \n    2.2. [HousePrice Competition Pipeline](https://www.kaggle.com/code/zahrazolghadr/houseprice-competition-pipeline)  \n    2.3. [Test Design Strategies for Model Evaluation ](https://www.kaggle.com/code/zahrazolghadr/test-design-strategies-for-model-evaluation)     \n    \n**week 7&8: Decision Tree Models**   \n    2.4. [Classification Decision Tree ](https://www.kaggle.com/code/zahrazolghadr/classification-decision-tree)  \n    2.5. [Decision Tree Regression](https://www.kaggle.com/code/zahrazolghadr/decision-tree-regression)    \n    2.6. [Overcoming Imbalanced Data Challenges](https://www.kaggle.com/code/zahrazolghadr/overcoming-imbalanced-data-challenges)    \n    \n**week 9:Statistical Models**    \n    2.7. [Naive Bayes Models](https://www.kaggle.com/code/zahrazolghadr/naive-bayes-models)  \n    2.8. [Linear, Ridge and Lasso Regression](https://www.kaggle.com/code/zahrazolghadr/linear-ridge-and-lasso-regression)  \n    2.9. [Ridge,Lasso and ElasticNet Logistic Regression](https://www.kaggle.com/code/zahrazolghadr/ridge-lasso-and-elasticnet-logistic-regression)    \n    \n**week 10: Black-Box Models**   \n    2.10. [KNN for Classification](https://www.kaggle.com/code/zahrazolghadr/knn-for-classification)   \n    2.11. [KNN for Regression](https://www.kaggle.com/code/zahrazolghadr/knn-for-regression)  \n    2.12. [KNN to Find Neighbors](https://www.kaggle.com/code/zahrazolghadr/knn-to-find-neighbors)  \n    2.13. [Neural Network Model for Classification](https://www.kaggle.com/code/zahrazolghadr/neural-network-model-for-classification)  \n    2.14. [Neural Network Model for Regression](https://www.kaggle.com/code/zahrazolghadr/neural-network-model-for-regression)    \n\n**week 11: SVM & Ensemble Models**      \n    2.10. [SVM for Classification (SVC)](https://www.kaggle.com/code/zahrazolghadr/svm-for-classification-svc)   \n    2.11. [SVM for Regression (SVR)](https://www.kaggle.com/code/zahrazolghadr/svm-for-regression-svr)  \n    2.12. [Ensemble Models for Classification](https://www.kaggle.com/code/zahrazolghadr/ensemble-models-for-classification)  \n    2.13. [Ensemble Models for Regression](https://www.kaggle.com/code/zahrazolghadr/ensemble-models-for-regression)   \n    \n    \n### **3. Clustering and Evaluation**  \n**week 12: Distance & Density Based Clustering**  \n    3.1. [Clustering in Unsupervised Learning](https://www.kaggle.com/code/zahrazolghadr/clustering-in-unsupervised-learning)   \n    \n**Final Project: [Customer Behavioral Segmentation and Profiling](https://www.kaggle.com/code/zahrazolghadr/final-project-dm)**  \n\n","metadata":{}},{"cell_type":"markdown","source":"#### Next Notebook:   \n###### [EDA in Data Mining](https://www.kaggle.com/code/zahrazolghadr/eda-in-data-mining) \n\n#### Previous Courses:   \n###### [1. Dive into Data Science -->](https://www.kaggle.com/code/rouzbeh/1-dive-into-ds-content) \n###### [2. Statistics for Data Science -->](https://www.kaggle.com/code/rouzbeh/2-statistics-for-ds-content) \n","metadata":{}}]}