Data Mining Course Projects

P3: Customers Behavior Analysis in an E-commerce

• Data Cleaning

• Classification using Regression

• Dimensionality Reduction using PCA

• Over-Sampling using SMOTE

• Trained Classifers: SVM, KNN, Logistic Regression, Decision Tree as training classifiers

• Used Grid Search for each classifiers

• Used Cross Validation for Grid Search

• Used Bar Plot to show each classifiers Accuracy, Precision, F1 Score, and roc_auc Score

• Useed Confusion Matrix plot for clssification results

• Clustering using K-means and DBSCAN to identify groups of customers with similar characteristics

• Used Silhouette Score to measure clustering Cohesion

P2.1: House Price Prediction

• Preprocessed Pandas DataFrame
• Visualized Data Distribution using Histogram
• Visualized Data Correlation using Pair Plot and Heatmap
• Trained Models using Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Elastic Net Regression, and XGBoost Regression
• Evaluate Model Prediction using Mean Squared Error(MSE), and R2 Score
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn

P2.2: Market Basket Analysis

• Used MLXtend library
• Applied TransactionEncoder
• Generated Frequent Itemsets using Apriori algoirthm
• Generated Association Rules

P1: Preprocessing and Visualizing Dataset

• Libraries: NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn
• Preprocessed Dataset
• Visualized Dataset using Scatter Plot, Histogram, Box Plot, and Pair Plot
• Encoded and Normalized Dataset
• Implemented Principal Component Analysis(PCA) from scratch
• Visualized PCA-reduced Data