diff --git a/Machine Learning and Data Science/Intermediate/To explore Business Analytics/README.md b/Machine Learning and Data Science/Intermediate/To explore Business Analytics/README.md index 48671760c..6731e14e2 100644 --- a/Machine Learning and Data Science/Intermediate/To explore Business Analytics/README.md +++ b/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 : Dataset.csv
-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. - - ![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. - - ![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. - -![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. - -![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. - -![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. - -![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. - -![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). - -![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. - - ![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. - - ![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. - -![Screenshot (94)](https://github.com/Kushal997-das/Project-Guidance/assets/135348911/6c17999f-d8a2-43e8-b956-79cb3176262e) > Notebook: