This repository contains the implementation and analysis of three machine learning problems using publicly available datasets. Each problem explores a different area of machine learning: classification, clustering, and regression.
- Dataset: Diabetes Dataset on Kaggle
- Objective: Predict the target variable using a Decision Tree Classifier and compare its performance with at least one other classifier.
-
Data Exploration and Preprocessing
- Handle missing values and outliers.
- Feature selection based on correlation and importance scores.
- Data normalization or standardization if needed.
-
Model Training and Evaluation
- Split data into training, validation (if necessary), and test sets.
- Train a Decision Tree Classifier and evaluate its performance using:
- Accuracy
- Precision
- Recall
- F1 Score
- Compare with another classifier (e.g., Random Forest, SVM, etc.).
-
Visualizations and Insights
- Confusion Matrix for performance evaluation.
- Feature importance visualization for the Decision Tree.
- Dataset: Wholesale Customers Dataset on UCI Machine Learning Repository
- Objective: Group data into distinct clusters using the K-Means algorithm and analyze the clustering results.
-
Data Exploration and Preprocessing
- Handle missing values and outliers.
- Normalize or standardize the dataset.
-
Clustering Analysis
- Determine the optimal number of clusters (
k) using:- Elbow Method
- Silhouette Score
- Apply the K-Means Clustering algorithm.
- Determine the optimal number of clusters (
-
Evaluation and Insights
- Evaluate clustering quality using suitable metrics (e.g., Silhouette Score).
- Interpret the clusters and their significance.
- Visualize the clusters using scatter plots or heatmaps.
- Dataset: Real Estate Valuation Dataset on UCI Machine Learning Repository
- Objective: Predict a continuous target variable using Linear Regression and compare its performance with advanced regression techniques.
-
Data Exploration and Preprocessing
- Handle missing values and outliers.
- Normalize or standardize features if necessary.
- Perform feature engineering or selection.
-
Regression Models
- Train a Linear Regression model as the baseline.
- Train advanced regression models, such as:
- Ridge Regression
- Lasso Regression
- Random Forest Regression
-
Model Evaluation
- Compare models using:
- Mean Squared Error (MSE)
- R² Score
- Discuss the impact of regularization and model complexity on performance.
- Compare models using:
-
Visualizations and Insights
- Visualize the predicted vs. actual values.
- Analyze the influence of features using feature importance plots.
- Development Environment: Google Colab / Local Python Environment
- Programming Language: Python
- Key Libraries:
pandasnumpyscikit-learnmatplotlibseaborn
-
Clone the Repository
git clone https://github.com/YourUsername/ML-Analysis-Projects.git cd ML-Analysis-Projects -
Run the Notebooks
- Follow the instructions in each notebook for preprocessing, model training, and evaluation.
├── datasets/ # Folder for datasets
├── notebooks/ # Jupyter notebooks for each problem
├── results/ # Folder for results and visualizations
├── README.md # Project documentation (this file)
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## 📝 Results
### Problem 1: Decision Tree Classification
- Decision Tree achieved an accuracy of **XX%**, precision of **YY%**, and recall of **ZZ%**.
- Compared to **Random Forest**, the Decision Tree performed **better/worse** in terms of F1 Score.
### Problem 2: K-Means Clustering
- The optimal number of clusters was determined to be **k=X** based on the Elbow Method and Silhouette Score.
- Clustering revealed distinct groups with meaningful patterns.
### Problem 3: Linear Regression Analysis
- Linear Regression achieved an MSE of **XX** and R² Score of **YY**.
- Compared to **Ridge Regression** or **Random Forest Regression**, **Model A** outperformed due to better handling of feature interactions or regularization.