Update README.md

Machine Learning and Data Science/Intermediate/To explore Business Analytics/README.md

@@ -16,27 +16,7 @@ This project aims to explore Business Analytics using the `superstore.csv` datas
 - The dataset :
 <a href="https://github.com/Kushal997-das/Project-Guidance/blob/main/Machine%20Learning%20and%20Data%20Science/Intermediate/To%20explore%20Business%20Analytics/superstore.csv">Dataset.csv</a><br>
 
-The `superstore.csv` dataset contains sales data from a fictitious superstore. It includes information on orders, products, customers, regions, and more. The dataset typically includes the following columns:
-- Order ID
-- Order Date
-- Ship Date
-- Ship Mode
-- Customer ID
-- Customer Name
-- Segment
-- Country
-- City
-- State
-- Postal Code
-- Region
-- Product ID
-- Category
-- Sub-Category
-- Product Name
-- Sales
-- Quantity
-- Discount
-- Profit
+The `superstore.csv` dataset contains sales data from a fictitious superstore. It includes information on orders, products, customers, regions, and more.
 
 ## Objectives
 
@@ -62,44 +42,11 @@ You can choose any of the following tools for the analysis:
 Here are some potential business problems that can be derived from the dataset:
 
 1. **Sales Performance Analysis**
-   - Identify top-performing and underperforming products.
-   - Analyze sales trends over time.
-  <!-- image of a sales trend graph here to illustrate the analysis -->
-   ![Screenshot (81)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/4523052b-b10e-4245-91ea-96d93ac19481)
-
 2. **Profitability Analysis**
-   - Determine the most and least profitable products and categories.
-   - Analyze profit margins across different regions.
-   <!-- image of a chart showing profitability by product category here -->
-  ![Screenshot (82)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/33653e23-86c7-46e6-a3c3-441cb0f78b3d)
-
 3. **Customer Analysis**
-   - Segment customers based on purchasing behavior.
-   - Identify high-value customers and their buying patterns.
-  <!-- image of a chart showing customer segments here -->
-![Screenshot (83)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/4d4a71bf-e5b5-4c7e-a862-91909cfb807a)
-
 4. **Geographical Analysis**
-   - Evaluate sales performance across different regions and states.
-   - Identify regions with high and low sales and profitability.
-  <!--image of showing sales distribution by region here -->
-![Screenshot (84)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/21c5fa32-02fe-4913-9c63-3d9d4b6fa61d)
-
-![Screenshot (85)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/2bd3e66d-4b8b-4a1b-8c4b-ff1c79145559)
-
-![Screenshot (86)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/dada118b-b802-4dc6-8476-9d801d5295ed)
-
 5. **Operational Efficiency**
-   - Analyze shipping modes and their impact on delivery times and costs.
-   - Identify delays in order processing and shipping.
-   <!-- image of a bar chart comparing shipping modes here -->
-![Screenshot (87)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/24af6798-067c-4fe4-ba6a-0a790e631d8b)
-
 6. **Discount and Pricing Strategy**
-   - Analyze the impact of discounts on sales and profitability.
-   - Determine optimal discount rates that maximize profit without significantly affecting sales volume.
-   <!-- image of a showing the relationship between discount rates and sales here -->
-![Screenshot (89)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/01a11fef-858d-47a5-8c21-93d2ffab1c31)
 
 ## Getting Started
 
@@ -111,31 +58,11 @@ Here are some potential business problems that can be derived from the dataset:
 ### Steps
 
 1. **Load the Dataset**
-   - Load the dataset into your chosen tool.
-   - Inspect the first few rows to understand the structure and content.
-   <!-- image of the first few rows of the dataset here -->
-![Screenshot (90)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/2688d0c3-b8dc-4af7-9fa2-39b6b37e9ae5)
-
 2. **Data Cleaning**
-   - Check for and handle missing values.
-   - Remove any duplicate records.
-   - Convert data types as necessary (e.g., date columns).
-   <!-- image of a data cleaning process screenshot here -->
-![Screenshot (91)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/5bd7fa66-a080-4768-8cac-ddd8aaadce16)
-
 3. **Exploratory Data Analysis (EDA)**
-   - Generate summary statistics (mean, median, mode, standard deviation).
-   - Create visualizations (bar charts, line graphs, histograms, heatmaps) to explore the data.
-   <!-- image of summary statistics and key visualizations here -->
-  ![Screenshot (92)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/77893801-dad9-422d-8916-eea70e924934)
-
 4. **Analysis and Insights**
-   - Perform detailed analysis to identify key business problems.
-   - Use visualizations to support your findings.
-   - Provide actionable insights and recommendations.
-   <!-- image of key findings and insights here -->
-  ![Screenshot (93)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/dbdadc8f-00fa-442d-b798-c80fa05bce53)
 
+
 ## Deliverables
 
 - A detailed report outlining the analysis process, findings, and insights.
@@ -145,8 +72,6 @@ Here are some potential business problems that can be derived from the dataset:
 ## Conclusion
 
 This project will provide a comprehensive analysis of the `superstore.csv` dataset, uncovering key business problems and generating valuable insights to drive business decisions. By using Business Analytics tools, we can transform data into actionable intelligence, helping the business to improve its operations, profitability, and customer satisfaction.
-<!-- image of a final summary visualization or dashboard here to conclude the readme -->
-![Screenshot (94)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/6c17999f-d8a2-43e8-b956-79cb3176262e)
 
 
 > Notebook: