Data Mining and Analysis Project

Project Overview

This project focuses on analyzing, visualizing, and extracting insights from a dataset using various data mining and machine learning techniques. The work includes preprocessing data, exploring relationships, implementing clustering methods, and applying decision tree algorithms using libraries like sklearn. Advanced visualization techniques are employed to interpret results effectively.

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Key Features and Steps

1. Data Preprocessing and Exploration

• Null Values Check: Identified and handled missing values in the dataset.
• Normalization: Standardized the dataset to ensure uniform scales for better analysis.

2. Data Visualization

• Visualized the dataset to understand patterns, distributions, and outliers using:
  • Scatter plots
  • Heatmaps
  • Correlation matrices

3. Statistical Measures

• Calculated Euclidean Distance to measure similarity between data points.
• Computed Entropy to evaluate uncertainty or information gain in the dataset.

4. Correlation Matrix

• Implemented and visualized the correlation matrix to identify relationships between variables and explore multicollinearity.

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5. Decision Tree Implementation

Using the sklearn library, decision tree algorithms were applied for classification and decision-making tasks:

• CART Algorithm (Classification and Regression Trees)
• Hunt's Algorithm for structured and efficient tree generation.

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6. Clustering Techniques

K-Means Clustering

• Performed clustering on the dataset and visualized clusters using scatter plots.
• Determined the optimal number of clusters using the Elbow Method and visualized results with a SEE chart.

Hierarchical Clustering

• Implemented hierarchical clustering and visualized the dendrogram to understand cluster formation.

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Tools and Libraries Used

• Python (Jupyter Notebook - Google Colab)
• Pandas: Data manipulation and preprocessing
• NumPy: Mathematical computations
• Matplotlib/Seaborn: Data visualization
• Sklearn: Machine learning algorithms (Decision Trees, K-Means)
• Scipy: Hierarchical clustering support

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How to Run the Project

Clone the Repository:

git clone https://github.com/9twy/Data-mining-and-analysis.git