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Machine Learning Analysis Report

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.

📁 Problem 1: Decision Tree Classification

Dataset Details

  • 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.

🚀 Steps

  1. Data Exploration and Preprocessing

    • Handle missing values and outliers.
    • Feature selection based on correlation and importance scores.
    • Data normalization or standardization if needed.

  2. 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.).

  3. Visualizations and Insights

    • Confusion Matrix for performance evaluation.
    • Feature importance visualization for the Decision Tree.

📁 Problem 2: K-Means Clustering

Dataset Details

🚀 Steps

  1. Data Exploration and Preprocessing

    • Handle missing values and outliers.
    • Normalize or standardize the dataset.

  2. Clustering Analysis

    • Determine the optimal number of clusters (k) using:
      • Elbow Method
      • Silhouette Score
    • Apply the K-Means Clustering algorithm.

  3. 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.

📁 Problem 3: Linear Regression Analysis

Dataset Details

🚀 Steps

  1. Data Exploration and Preprocessing

    • Handle missing values and outliers.
    • Normalize or standardize features if necessary.
    • Perform feature engineering or selection.

  2. Regression Models

    • Train a Linear Regression model as the baseline.
    • Train advanced regression models, such as:
      • Ridge Regression
      • Lasso Regression
      • Random Forest Regression

  3. Model Evaluation

    • Compare models using:
      • Mean Squared Error (MSE)
      • R² Score
    • Discuss the impact of regularization and model complexity on performance.

  4. Visualizations and Insights

    • Visualize the predicted vs. actual values.
    • Analyze the influence of features using feature importance plots.

🛠️ Tools & Environment

  • Development Environment: Google Colab / Local Python Environment
  • Programming Language: Python
  • Key Libraries:
    • pandas
    • numpy
    • scikit-learn
    • matplotlib
    • seaborn

🧑‍💻 How to Use

  1. Clone the Repository

    git clone https://github.com/YourUsername/ML-Analysis-Projects.git
cd ML-Analysis-Projects

  2. Run the Notebooks

    • Follow the instructions in each notebook for preprocessing, model training, and evaluation.

📂 Repository Structure

├── datasets/               # Folder for datasets
├── notebooks/              # Jupyter notebooks for each problem
├── results/                # Folder for results and visualizations
├── README.md               # Project documentation (this file)

---

## 📝 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.