E-commerce Sales Prediction Dataset

This repository contains a comprehensive and clean dataset for predicting e-commerce sales, tailored for data scientists, machine learning enthusiasts, and researchers. The dataset is crafted to analyze sales trends, optimize pricing strategies, and develop predictive models for sales forecasting.

📂 Dataset Overview

The dataset includes 1,000 records across the following features:

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📊 Data Summary

General Properties

Date:

• Range: 01-01-2023 to 12-31-2023.
• Contains 1,000 unique values without missing data.

Product_Category:

• Categories: Electronics (21%), Sports (21%), Other (58%).
• Most common category: Electronics (21%).

Price:

• Range: From 244 to 999.
• Mean: 505, Standard Deviation: 290.
• Most common price range: 14.59 - 113.07.

Discount:

• Range: From 0.01% to 49.92%.
• Mean: 24.9%, Standard Deviation: 14.4%.
• Most common discount range: 0.01 - 5.00%.

Customer_Segment:

• Segments: Regular (35%), Occasional (34%), Other (31%).
• Most common segment: Regular.

Marketing_Spend:

• Range: From 2.41k to 10k.
• Mean: 4.91k, Standard Deviation: 2.84k.

Units_Sold:

• Range: From 5 to 57.
• Mean: 29.6, Standard Deviation: 7.26.
• Most common range: 24 - 34 units sold.

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📈 Data Visualizations

The dataset is suitable for creating the following visualizations:

• 1. Price Distribution: Histogram to show the spread of prices.
• 2. Discount Distribution: Histogram to analyze promotional offers.
• 3. Marketing Spend Distribution: Histogram to understand marketing investment patterns.
• 4. Customer Segment Distribution: Bar plot of customer segments.
• 5. Price vs Units Sold: Scatter plot to show pricing effects on sales.
• 6. Discount vs Units Sold: Scatter plot to explore the impact of discounts.
• 7. Marketing Spend vs Units Sold: Scatter plot for marketing effectiveness.
• 8. Correlation Heatmap: Identify relationships between features.
• 9. Pairplot: Visualize pairwise feature interactions.

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💡 How the Data Was Created

The dataset is synthetically generated to mimic realistic e-commerce sales trends. Below are the steps taken for data generation:

1. Feature Engineering:

   • Identified key attributes such as product category, price, discount, and marketing spend, typically observed in e-commerce data.
   • Generated dependent features like units sold based on logical relationships.

2. Data Simulation:

   • Python Libraries: Used NumPy and Pandas to generate and distribute values.
   • Statistical Modeling: Ensured feature distributions aligned with real-world sales data patterns.

3. Validation:

   • Verified data consistency with no missing or invalid values.
   • Ensured logical correlations (e.g., higher discounts → increased units sold).

Note: The dataset is synthetic and not sourced from any real-world e-commerce platform.

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🛠 Example Usage: Sales Prediction Model

Here’s an example of building a predictive model using Linear Regression:

Written in python

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Load the dataset
df = pd.read_csv('ecommerce_sales.csv')

# Feature selection
X = df[['Price', 'Discount', 'Marketing_Spend']]
y = df['Units_Sold']

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Model training
model = LinearRegression()
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluation
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f'Mean Squared Error: {mse:.2f}')
print(f'R-squared: {r2:.2f}')