https://github.com/ianisdev/ca_ms_ai_data_analytics

Continuous Assessment MSCAI - Data Analytics
https://github.com/ianisdev/ca_ms_ai_data_analytics

jupyter-notebook matplotlib numpy pandas python3 sklearn

Last synced: 12 days ago
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Continuous Assessment MSCAI - Data Analytics

• Host: GitHub
• URL: https://github.com/ianisdev/ca_ms_ai_data_analytics
• Owner: iAnisdev
• License: mit
• Created: 2024-11-05T19:48:31.000Z (13 days ago)
• Default Branch: main
• Last Pushed: 2024-11-05T20:07:52.000Z (13 days ago)
• Last Synced: 2024-11-05T21:19:53.219Z (13 days ago)
• Topics: jupyter-notebook, matplotlib, numpy, pandas, python3, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 123 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Data Analysis Continuous Assessment

## Overview

This project involves a comprehensive analysis of a dataset, focusing on data characteristics, manipulation, preprocessing, visualization, and machine learning applications. The goal is to extract insights and build a predictive model based on the processed data.

## Data Characteristics

### 1.1 Data Population (3 points)

- **Total number of records**: [insert total]

- **Number of missing values**: [insert total]

- **Population distribution**: [insert summary]

### 1.2 Data Attributes (4 points)

- **Attribute 1**: [description]

- **Attribute 2**: [description]

- **Attribute 3**: [description]

- **Attribute 4**: [description]

### 1.3 Types of Attributes (4 points)

- **Numerical**: [insert attributes]

- **Categorical**: [insert attributes]

- **Ordinal**: [insert attributes]

- **Binary**: [insert attributes]

## Data Manipulation

### 2.1 Missing Values / Categorical Data (10 points)

- **Technique Applied**: [insert technique, e.g., imputation, encoding]

- **Summary**: [brief summary of results]

### 2.2 Detecting and Removing Outliers (10 points)

- **Technique Applied**: [insert technique, e.g., Z-score, IQR method]

- **Summary**: [brief summary of results]

## Data Preprocessing

### 3.1 Scaling / Normalization Techniques (10 points)

- **Technique Applied**: [insert technique, e.g., Min-Max scaling, Standardization]

- **Summary**: [brief summary of results]

### 3.2 Feature Selection (10 points)

- **Technique Applied**: [choose one from the list]

  - Univariate Feature Selection

  - Tree-based Feature Selection

  - Greedy Feature Selection

- **Summary**: [brief summary of results]

## Data Visualization

### 4.1 Graph 1: [Graph Type from Top Group] (10 points)

- **Description**: [insert description]

- **Summary**: [brief summary of findings]

### 4.2 Graph 2: [Graph Type from Middle Group] (10 points)

- **Description**: [insert description]

- **Summary**: [brief summary of findings]

### 4.3 Graph 3: [Graph Type from Bottom Group] (10 points)

- **Description**: [insert description]

- **Summary**: [brief summary of findings]

## Machine Learning Use Cases

### 5.1 ML Algorithm Proposal (5 points)

- **Proposed Algorithm**: [insert algorithm name]

- **Reason for Selection**: [brief explanation]

### 5.2 ML Algorithm Implementation (10 points)

- **Implementation Summary**: [insert details of the implementation process]

### 5.3 ML Model Performance Report (5 points)

- **Performance Metrics**: [insert metrics, e.g., accuracy, precision, recall]

- **Summary**: [brief summary of performance results]