https://github.com/yernaz-togizbayev/microsoft_store_data-analysis
Microsoft Store
https://github.com/yernaz-togizbayev/microsoft_store_data-analysis
data data-analysis data-visualization jupyter-notebook python3
Last synced: about 2 months ago
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Microsoft Store
- Host: GitHub
- URL: https://github.com/yernaz-togizbayev/microsoft_store_data-analysis
- Owner: yernaz-togizbayev
- Created: 2025-10-21T18:37:25.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2026-02-26T10:20:10.000Z (4 months ago)
- Last Synced: 2026-05-06T11:49:09.540Z (2 months ago)
- Topics: data, data-analysis, data-visualization, jupyter-notebook, python3
- Language: Jupyter Notebook
- Homepage: https://jovian.com/yernaz-togizbayev/microsoft-store-project
- Size: 1.29 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📊 Microsoft Store Data Analysis
This project explores and analyzes a dataset of applications from the **Microsoft Windows Store**.
Using Python and data analysis libraries, the project focuses on **data cleaning, exploratory analysis, visualization, and answering business-related questions**.
The dataset includes app categories, prices, ratings, number of downloads (people rated), and release dates.
---
## 🧠 Project Goals
- Clean and preprocess raw dataset
- Convert string-based prices to numeric values
- Perform exploratory data analysis (EDA)
- Visualize trends in downloads and pricing
- Answer analytical questions about popularity and revenue patterns
---
## 📂 Files Included
microsoft-store-project.ipynb # Full Jupyter Notebook
microsoft_store_project.py # Script version of the notebook
msft.csv # Dataset (required to run the project)
---
## 🛠 Technologies Used
- Python 3
- pandas
- numpy
- matplotlib
- seaborn
- Jupyter Notebook
---
## 🔎 Data Preparation & Cleaning
Key preprocessing steps:
- Removed empty/NaN rows
- Converted "Free" prices to 0
- Converted price strings (INR) to numeric values (EUR conversion)
- Renamed columns for easier access
- Converted `Price` column to numeric type
- Extracted:
- year
- month
- day
- weekday
---
## 📈 Exploratory Data Analysis
### 📌 Most Popular Category
Bar chart analysis shows which category appears most frequently.
Result: **Music** is the most frequent category in the dataset.
---
### 💰 Most Expensive Category
Pie chart of total category prices.
Result: **Developer Tools** accounts for the highest total price share.
---
### 📆 Most Active Download Year
Line plot analysis of downloads per year.
Result: Downloads peaked in **2016**, followed by a decline.
---
### 📅 Most Active Download Day
Bar plot of downloads by weekday.
Result: **Monday** has the highest download activity.
---
## ❓ Questions Answered
### 1️⃣ Most Popular & Unpopular Apps
Sorted by:
- Rating (primary)
- Number of people rated (secondary)
---
### 2️⃣ Top 10 Most Expensive Apps
Identified using both:
- `sort_values()`
- `groupby()` + sorting
---
### 3️⃣ When Did the Most Downloads Occur?
Grouped by:
- Year
- Month
- Day
- Weekday
Highest single-day download: **30 January 2018**
---
### 4️⃣ Most Downloaded Category
Grouped by category and summed total ratings.
Music category dominates downloads.
---
### 5️⃣ Total & Average Downloads
- Total downloads: ~2.9 million
- Average downloads per day: ~731
---
## 🚀 How to Run
### Install dependencies:
```bash
pip install pandas numpy matplotlib seaborn
```
### Run notebook:
```bash
jupyter notebook microsoft-store-project.ipynb
```
or run script:
```bash
python microsoft_store_project.py
```
---
## 🎯 Learning Outcomes
- Data cleaning & preprocessing
- Handling mixed-type columns
- Exploratory Data Analysis (EDA)
- Grouping & aggregation with pandas
- Data visualization best practices
- Business-oriented data questioning
---
## 📚 Acknowledgements
This project was completed as part of the online course "Data Analysis with Python: Zero to Pandas" by Jovian.
- Dataset: Kaggle (Windows Store dataset)