https://github.com/hassanislam463/data-cleaning-and-modelling-top-5-categories-analysis-forage

This project involves cleaning, merging, and analyzing datasets to identify the top 5 performing categories based on aggregate popularity scores. It includes cleaned datasets, a final merged dataset, visualizations, and a presentation summarizing the tasks and results. Tools used: Microsoft Excel, Python, and PowerPoint.
https://github.com/hassanislam463/data-cleaning-and-modelling-top-5-categories-analysis-forage

data-analysis data-visualization microsoft-excel

Last synced: 5 days ago
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This project involves cleaning, merging, and analyzing datasets to identify the top 5 performing categories based on aggregate popularity scores. It includes cleaned datasets, a final merged dataset, visualizations, and a presentation summarizing the tasks and results. Tools used: Microsoft Excel, Python, and PowerPoint.

• Host: GitHub
• URL: https://github.com/hassanislam463/data-cleaning-and-modelling-top-5-categories-analysis-forage
• Owner: Hassanislam463
• Created: 2024-12-02T16:15:12.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-02T17:04:19.000Z (2 months ago)
• Last Synced: 2024-12-02T18:22:15.454Z (2 months ago)
• Topics: data-analysis, data-visualization, microsoft-excel
• Homepage:
• Size: 1.95 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# **Data Cleaning and Modelling: Top 5 Categories Analysis**

This repository contains a data analysis project focused on cleaning, merging, and analyzing data to identify the top 5 performing categories based on popularity scores. The project was completed in two main phases: **data cleaning** and **data modelling**.

---

## **Project Overview**

The goal of this project was to:

1. **Clean datasets** to ensure data consistency and usability.

2. **Merge datasets** to create a comprehensive dataset for analysis.

3. **Identify and analyze the top 5 categories** based on aggregate popularity scores.

### **Key Deliverables**

- A cleaned dataset.

- A final merged dataset.

- Insights into the top 5 categories by aggregate popularity scores.

- Visualizations (bar chart and pie chart) for better data understanding.

- A presentation summarizing the tasks and results.

---

## **Tasks and Methodology**

### **Task 1: Data Cleaning**

The first phase of the project involved cleaning three datasets:

1. Removing duplicates and irrelevant columns.

2. Ensuring data consistency (e.g., handling missing values).

3. Preparing the datasets for merging.

The cleaned datasets are available in the `data/cleaned_data.xlsx` file.

---

### **Task 2: Data Modelling**

This phase involved merging datasets and calculating aggregate popularity scores for each category.

**Steps:**

1. **Merging Datasets**:

   - Used the `Reaction` table as the base table.

   - Joined relevant columns from the `Content` and `Reaction Types` datasets using a **VLOOKUP** formula.

2. **Calculating Scores**:

   - Used the **SUMIF** formula to aggregate popularity scores for each category.

3. **Identifying the Top 5 Categories**:

   - Ranked categories by their total scores to identify the top 5 performers.

The final dataset can be found in `data/final_data.xlsx`.

---

### **Results**

#### **Top 5 Categories**

The analysis revealed the following top-performing categories:

1. **Animals**

2. **Science**

3. **Healthy Eating**

4. **Technology**

5. **Food**

#### **Visualizations**

- A **bar chart** visualizing the aggregate popularity scores for the top 5 categories.

- A **pie chart** showing the percentage share of each category in the top 5.