https://github.com/mohamed15058/amazon-sales
amazon-sales
https://github.com/mohamed15058/amazon-sales
dashboard excel machine-learning-algorithms matplotlib numpy pandas powerbi report seaborn
Last synced: 11 months ago
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amazon-sales
- Host: GitHub
- URL: https://github.com/mohamed15058/amazon-sales
- Owner: Mohamed15058
- Created: 2024-06-23T12:41:07.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-01T23:24:04.000Z (12 months ago)
- Last Synced: 2025-03-02T00:23:22.396Z (12 months ago)
- Topics: dashboard, excel, machine-learning-algorithms, matplotlib, numpy, pandas, powerbi, report, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 1.34 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# amazon-sales
Objective:
Analyze the Amazon sales report dataset attached in the mail to extract meaningful insights,
preprocess the data, create visualizations using Python libraries (matplotlib and seaborn),
build predictive models, and develop a dashboard for comprehensive data presentation.
Detailed Task Breakdown
Step 1: Exploratory Data Analysis (EDA)
1. Data Inspection:
○ Loadthe dataset and inspect the first few rows to understand its structure.
○ Checkthe data types of each column and identify any potential issues.
2. Summary Statistics:
○ Generate summary statistics for numerical and categorical variables.
○ Visualize the distribution of key features to identify trends and patterns.
Step 2: Data Preprocessing
1. Handling Missing Values:
○ Identify columns with missing values and decide on appropriate strategies to
handle them (e.g., imputation, removal).
2. Data Type Conversion:
○ Convert relevant columns to appropriate data types (e.g., converting Date
column to datetime format).
3. Outlier Detection and Treatment:
○ Identify and treat outliers in numerical columns to ensure data quality.
Step 3: Data Visualization
1. Using Matplotlib and Seaborn:
○ Create visualizations to understand data distributions and relationships.
○ Examples include histograms, bar plots, line plots, and heatmaps.
2. Visual Analysis:
○ Visualize sales trends over time (e.g., monthly sales trends).
○ Identify top-selling products and categories using bar plots.
○ Analyze regional sales distributions using geographical visualizations.
Step 4: Predictive Modeling
1. Building Predictive Models:
○ Develop models to predict the order status (Shipped, Canceled, etc.).
○ Useclassification algorithms such as logistic regression, decision trees, or
random forests.
2. Model Evaluation:
○ Evaluate the models using appropriate metrics (e.g., accuracy, precision,
recall).
○ Perform cross-validation to ensure model robustness.
Step 5: Dashboard Development
1. Dashboard Design:
○ Create an interactive dashboard to present key insights and visualizations.
○ Ensure the dashboard is user-friendly and provides actionable insights at a
glance.
2. Tools:
○ UsePython libraries like Dash, Plotly, or Streamlite to build the dashboard.
○ Integrate visualizations created using matplotlib and seaborn into the
dashboard
12- Some analysis by excel
13 - Dashboard and report by power bi