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https://github.com/sreejabethu/sales-data-analysis-forecasting

Welcome to the Sales Data Analysis & Forecasting project! 🚀 This repository showcases my data analysis skills through exploratory data analysis (EDA), data cleaning, and visualization of sales and customer feedback data. The goal is to extract actionable insights to drive business decisions.
https://github.com/sreejabethu/sales-data-analysis-forecasting

analysis barchart data-visualization datacleaning exploratory-data-analysis forecasting histogram matplotlib-pyplot numpy-library pandas-library pycharm-ide sales-analysis salesdata salesdataanalysis seaborn-plots transformation

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Welcome to the Sales Data Analysis & Forecasting project! 🚀 This repository showcases my data analysis skills through exploratory data analysis (EDA), data cleaning, and visualization of sales and customer feedback data. The goal is to extract actionable insights to drive business decisions.

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README 

        
# 📊 Sales Data Analysis & Forecasting



Welcome to the Sales Data Analysis & Forecasting project! 🚀 This repository showcases my data analysis skills through exploratory data analysis (EDA), data cleaning, and visualization of sales and customer feedback data. The goal is to extract actionable insights to drive business decisions.



# 📝 Project Highlights



### 🔍 Overview



This project demonstrates my expertise as a Data Analyst by focusing on:



  📈 **Exploratory Data Analysis (EDA):** Detecting patterns and trends in sales data.

  

  🛠 **Data Cleaning & Transformation:** Ensuring the quality and reliability of the dataset.

  

  📊 **Visualizations:** Creating engaging and informative charts for decision-makers.



## Table Of Contents



[Tools & technologies used](toosl&technologiesused)



[Dataset Overview](datasetoverview)



[key insights extracted](keyinsightsextracted)



[Visualizations](visualizations)



[How to run the project](howtoruntheproject)



[Conclusion](conclusion)



### 🛠 Tools & Technologies Used



####  🖥 Programming Language: Python

####  📚 Libraries:

  **🐼 Pandas: For data manipulation and cleaning.**

  

  **🧮 NumPy: For numerical computations.**

  

  **🎨 Matplotlib and Seaborn: For visualization.**  

  

  ** IDE: PyCharm**



### 📁 Dataset Overview



The dataset includes the following columns:



**🆔 product_id: Unique identifier for each product.**    

**🏷️ product_name: Name of the product.**    

**📦 category: Product category.**    

**💰 discounted_price and actual_price: Pricing details.**

**🔢 discount_percentage: Discount percentage offered.**  

**⭐ rating and rating_count: Product rating and number of ratings.**

**🗒️ about_product: Short description of the product.**



### 🔑 Key Insights Extracted



  🎯**Product Ratings Distribution:**

        Analyzed how customers rate products across various categories.

        Insight: Certain categories consistently outperform others in terms of average ratings.



  **📊 Category-Wise Discount Analysis:

        Average discount percentages by category to identify pricing strategies.

        Insight: Categories with optimal discounts tend to have better sales performance.**



  **💹 Sales and Ratings Trends:

        Identified correlations between ratings, rating counts, and sales trends to understand customer preferences**.



### 📊 Visualizations



**Histogram: Distribution of product ratings to identify customer satisfaction trends.**



![image](https://github.com/user-attachments/assets/78d112c1-ed3a-4dd7-b2f2-f6275fa74181)



**Bar Charts: Average discount percentage across categories.**

![image](https://github.com/user-attachments/assets/b05ced4d-2e10-436b-97b9-18fa9dfdb0d4)



**Average rating counts by product category.**    

![image](https://github.com/user-attachments/assets/d686caa2-fc53-484f-9ce1-e20d23ae7e29)



### 🛠️ How to Run This Project



  Clone the repository to your local machine: git clone https://github.com/your-repo/sales-data-analysis.git

  Install the required Python libraries: pip install -r requirements.txt

  Run the main.py file: python main.py

  View the generated visualizations and insights in your terminal or saved output files.



### 7. 🎉 Conclusion



  This project demonstrates how effective data analysis can uncover hidden trends and provide actionable business recommendations. As a Data Analyst, I utilized my skills in data cleaning, analysis, and visualization to draw insights from a real-world dataset.



✍️ Contact Me



Feel free to reach out if you’d like to know more or collaborate on data projects:

📧 Email: [email protected]

🌐 LinkedIn: https://www.linkedin.com/in/sreejabethu/