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https://github.com/patriciavalentine/phone-sales-data-analysis

This project involved a comprehensive analysis of a phone sales dataset, performed in Jupyter Notebooks using Python and libraries such as Pandas, NumPy, and Matplotlib. The analysis included data cleaning, handling missing values, and calculating KPIs. Necessary visualization techniques were also employed to illustrate trends and relationships.
https://github.com/patriciavalentine/phone-sales-data-analysis

phone-sales python sales-analysis

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This project involved a comprehensive analysis of a phone sales dataset, performed in Jupyter Notebooks using Python and libraries such as Pandas, NumPy, and Matplotlib. The analysis included data cleaning, handling missing values, and calculating KPIs. Necessary visualization techniques were also employed to illustrate trends and relationships.

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README 

        
# 📊 PHONE SALES DATA ANALYSIS

This project is a comprehensive analysis of phone sales data aimed at uncovering insights into sales patterns, pricing, and customer preferences.



>The analysis was conducted using `Python` in Jupyter Notebooks, leveraging powerful libraries such as `Pandas`, `NumPy`, `Seaborn`, and `Matplotlib` for data manipulation and visualization.



## Project Overview

The project focuses on analyzing various aspects of phone sales, including brand performance, model popularity, pricing trends, and the impact of discounts.



The data was cleaned and processed to handle missing values, and key metrics such as total sales revenue, average selling price, and market share by brand were calculated.



Visualizations were created to provide clear insights into the distribution of selling prices, the most popular phone colors, and the relationship between discounts and sales volumes.



## 🛠️ Tools and Libraries

- **Python**: The main programming language used for the analysis.

- **Jupyter Notebooks**: For running and documenting the analysis process.

- **Pandas**: For data cleaning, manipulation, and analysis.

- **NumPy**: For numerical operations.

- **Seaborn** & **Matplotlib**: For creating detailed visualizations.



### Data Source:

The [phones dataset](https://github.com/patriciavalentine/PHONE-SALES-DATA-ANALYSIS/blob/main/Phone_Sales.csv) used in this project was obtained from **kaggle**.



*It includes detailed information on various phone brands, models, their selling prices, discounts, customer ratings, and more.*



### 📂 Files:

1. [The Raw Data](https://github.com/patriciavalentine/PHONE-SALES-DATA-ANALYSIS/blob/main/Phone_Sales.csv).

2. [The Cleaned Data](https://github.com/patriciavalentine/PHONE-SALES-DATA-ANALYTICS/blob/main/Cleaned%20Phones%20Sales%20Data.csv).

3. [The Project Notebook File](https://github.com/patriciavalentine/PHONE-SALES-DATA-ANALYTICS/blob/main/Phone%20Sales%20Data%20Analysis.ipynb).



## 👩‍💻 Inquiry Questions

*In the analysis, I sought to answer the following questions:*

1. Which phone models have the highest sales volume?

2. How do average selling prices vary across different phone brands?

3. Does a phone's color affect its pricing?

4. What are the top-rated mobile models?

5. Does a Higher Discount Lead to Higher Sales?



## 💻 Key Insights

- The analysis identified **Samsung as the top-selling brand**, with 696 units sold, and **Apple iPhone 11 as the leading model**, selling 36 units. Despite Samsung's sales success, *Apple recorded the highest total sales revenue*, suggesting that it is positioned as a premium phone brand.

- Significant variations in average selling prices across brands were observed, with typical price points ranging from 0 to 75,000 for most brands, except for Apple, which exceeds 100,000. This highlights the wide range of pricing strategies employed by different brands.

- **Apple exhibited a broader price variation among its models**, indicating a diverse product line that caters to different segments. The **presence of many outliers in the Samsung brand** suggests a range of high-quality models contributing to its pricing strategy.

- The analysis revealed that **black is the most sold color**, indicating a strong consumer preference. **Space Grey, although expensive, had low sales counts**, suggesting it targets a niche market. **Gold, however, demonstrated both high sales and average price**, reinforcing its strong market position.

- Colors like Space Grey and Silver commanded higher average selling prices, while more common colors like black, white, and blue were associated with lower prices. This indicates that unique colors may have a premium positioning, appealing to higher-end consumers.

- **Apple achieved the highest customer ratings**, indicating strong customer satisfaction and perceived product quality.

- The analysis also found a very **weak negative correlation (-0.06) between discount percentages and selling prices**, suggesting that increasing discounts do not significantly affect sales. This implies that changes in discount strategies may not have a meaningful impact on sales performance in this dataset.



## 📑 Recommendations

1. Given the minimal correlation between discount percentages and sales volume, the **Brands should consider re-assessing their discount strategies**. Instead of relying heavily on discounts to boost sales, they should explore other promotional tactics that may have a more significant impact on consumer behavior.

2. Since colors like Gold, Rose Gold, and Space Grey commanded higher prices, the **Brands should consider diversifying their color options** to include more unique and premium colors that can be marketed as exclusive. This could enhance consumer perception and potentially increase pricing strategies.



## THE 📈 PROJECT STRUCTURE

![four-smartphones](https://github.com/user-attachments/assets/ee420fa7-ad30-41cd-8fb7-1a5f0c9656bb)



1. **Data Cleaning**: Addressed missing values, ensured consistency, and prepared the data for analysis.

2. **Exploratory Data Analysis (EDA)**: Analyzed the data to uncover key insights, trends, and patterns.

3. **KPI Calculation**: Measured key performance indicators such as total sales revenue, average selling price, and market share by brand.

4. **Visualization**: Created visual representations of the data to make findings more interpretable and actionable.



*NOTE: For a detailed step-by-step explanation of the whole process, check the Notebook File [here](https://github.com/patriciavalentine/PHONE-SALES-DATA-ANALYTICS/blob/main/Phone%20Sales%20Data%20Analysis.ipynb).*



## CONCLUSION

The analysis provided a comprehensive understanding of key factors influencing phone sales, including model popularity, pricing strategies, the impact of color, and the effectiveness of discounts. These insights can guide strategic decisions in inventory management, pricing, and marketing, ultimately driving better sales performance and customer satisfaction.



## THE END.

### Thank you!